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Sensitivity Analysis of Idle Times and Project Durations in Construction Scheduling

Permanent Link: http://ufdc.ufl.edu/UFE0021841/00001

Material Information

Title: Sensitivity Analysis of Idle Times and Project Durations in Construction Scheduling
Physical Description: 1 online resource (35 p.)
Language: english
Creator: Wisniewski, Tomasz And
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: construction, delays, idle, project, sensativity
Building Construction -- Dissertations, Academic -- UF
Genre: Building Construction thesis, M.S.B.C.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: In residential construction, 80 to 90% of every project is completed by specialty subcontractors. Depending on the size of a project and the volume of work, it is a constant challenge to match work with the available resources. In many cases, constraints on resources play an important role in planning or undertaking new projects. On the other hand, the lack of sufficient flow of new projects creates unproductive resources and increased costs. My study focused on building a model to test the extent of the effect of activity delays, resource management and idle time on the project duration. The case study used was that of an independent painting subcontractor working in Gainesville, Florida provided data over the past 10 years.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Tomasz And Wisniewski.
Thesis: Thesis (M.S.B.C.)--University of Florida, 2007.
Local: Adviser: Flood, Ian.
Local: Co-adviser: Issa, R. Raymond.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021841:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021841/00001

Material Information

Title: Sensitivity Analysis of Idle Times and Project Durations in Construction Scheduling
Physical Description: 1 online resource (35 p.)
Language: english
Creator: Wisniewski, Tomasz And
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: construction, delays, idle, project, sensativity
Building Construction -- Dissertations, Academic -- UF
Genre: Building Construction thesis, M.S.B.C.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: In residential construction, 80 to 90% of every project is completed by specialty subcontractors. Depending on the size of a project and the volume of work, it is a constant challenge to match work with the available resources. In many cases, constraints on resources play an important role in planning or undertaking new projects. On the other hand, the lack of sufficient flow of new projects creates unproductive resources and increased costs. My study focused on building a model to test the extent of the effect of activity delays, resource management and idle time on the project duration. The case study used was that of an independent painting subcontractor working in Gainesville, Florida provided data over the past 10 years.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Tomasz And Wisniewski.
Thesis: Thesis (M.S.B.C.)--University of Florida, 2007.
Local: Adviser: Flood, Ian.
Local: Co-adviser: Issa, R. Raymond.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021841:00001


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fc5c3abc34bb5bd6c13ad5439eb8c50b
d2caf063ffbda34fa606d2a17896da67f2d1b5fe







SENSITIVITY ANALYSIS OF IDLE TIMES AND PROJECT DURATIONS IN
CONSTRUCTION SCHEDULING






















By

TOMASZ ANDRZEJ WISNIEWSKI


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE INT BUILDING CONSTRUCTION

UNIVERSITY OF FLORIDA

2007

































O 2007 Tomasz Andrzej Wisniewski




























To my parents Krystyna and Andrzej; to my siblings Maciej and Izabela; to my children:
Tomasz, Shanti and Nila; and to Diala for their love and support.












TABLE OF CONTENTS


page


LIST OF TABLES .........__... ......._. ...............5....


LIST OF FIGURES .............. ...............6.....


AB S TRAC T ......_ ................. ............_........7


CHAPTER


1 INTRODUCTION ................. ...............8.......... ......


Challenges.................... .............
Pre construction Phase............... ...............9..

Bid Shopping ............. ..... ._ ...............9.....
Construction Phase ................. ...............10........_ ......

Stacking and Overcrowding ................ ........_ ...............10 .....
W weather Conditions ................._ .... ........._ ...............11......

Interference by the Owner and Change Orders ......... ................. ................ 12
Planning, Communication, and Out of Sequence Scheduling .............. .................12
Closeout ................. .. .......... ...............13......

Aim, Obj ectives, and Scope of Study ........._..... ...._... ...............13..
M ethodology ........._..... ...._... ...............13.....


2 LITERATURE REVIEW ........._.._.. ...._... ...............14....


3 MODEL ........._.._.. ...._... ...............19....


Des cripti on ................. ................. 19........_..
Lim stations ................. ...............2. 1........_.....


4 ANALY SI S............... ...............2


5 CONCLUSION AND RECOMMENDATIONS .............. ...............31....


Conclusion ..............._ ...............3. 1........_.....
Recommendations............... ............3


LIST OF REFERENCES ................. ...............33........_......


BIOGRAPHICAL SKETCH .............. ...............35....











LIST OF TABLES

Table page

3-1. Matrix of tasks and respective delays occurrences ................. ...............24.............

3-2. Matrix of tasks and durations ................. ...............26..............

3-3. Matrix of tasks and delays ................. ...............26........... ..

3-4. Table of tasks, average, variance and randomized durations .............. .....................2

3-5. Table of tasks, average, variance and randomized delays ........._...... ....__._ ...............27











LIST OF FIGURES


Figure page

3-1. Precedence diagram on a single proj ect for interior painting ................. .......................22

3-2. Resource constraints in a sequence of proj ects ................. ...............23........... .

4-1. Average idle time versus K .............. ...............28....

4-2. Variance idle time versus K............... ...............29...

4-3. Average proj ect duration versus K ................ ...............29..............

4-4. Variance proj ect duration versus K ................. ...............30.......... ...









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science in Building Construction

SENSITIVITY ANALYSIS OF IDLE TIMES AND PROJECT DURATIONS IN
CONSTRUCTION SCHEDULING

By

Tomasz Andrzej Wisniewski

December 2007

Chair: lan Flood
Cochair: R. Raymond Issa
Major: Building Construction

In residential construction, 80 to 90% of every proj ect is completed by specialty

subcontractors. Depending on the size of a project and the volume of work, it is a constant

challenge to match work with the available resources. In many cases, constraints on resources

play an important role in planning or undertaking new proj ects. On the other hand, the lack of

sufficient flow of new proj ects creates unproductive resources and increased costs. My study

focused on building a model to test the extent of the effect of activity delays, resource

management and idle time on the proj ect duration. The case study used was that of an

independent painting subcontractor working in Gainesville, Florida provided data over the past

10 years.









CHAPTER 1
INTTRODUCTION

Subcontractors play a vital role in the construction industry. They are specialty

contractors, hired to perform specific tasks on the project. On many proj ects, particularly

residential projects, it is common for 80-90% of the work to be performed by subcontractors

(Hinze and Tracey 1994).

In August 2000, TW Painting, a sole proprietary painting business was started. Initial

investments included a van, several hand tools, ladders and paint sprayer. By the end of 2000,

several small proj ects were completed for a local contractor from Newberry, Florida. In 2001,

after an intense marketing effort, TW Painting was awarded several contracts with a maj or

General Contractor firm, Shannon Homes Inc. At this point, with a workload ranging form four

to six parallel proj ects, the Owner in order to fulfill his obligation had to increase number of

painters, which resulted in an increase in payroll, insurance and workmen compensation fees. By

the end of the same year, due to a decrease in the work load and decrease in profitability, the

painting crew was reduced to three people. In 2002, in response to a new State Government

regulation regarding workmen compensation, TW Painting was incorporated with the State of

Florida and its name was changed to TNT Painting Inc. In that same year, the company got into a

partnership with another painting subcontractor in order to fulfill their obligations under

contracts with several local contractors. For the following two years, TNT performed work for

several local contractors with work ranging from 2,000 SF homes to 7,000 SF residences. In

2005, TNT started a long term business relation with Tommy Waters Custom Homes Inc.,

Gainesville's prime custom home builder. From that point on, TNT Painting was constantly

resource by a crew of three painters working full time.










Challenges

For the last seven years, TNT Painting Inc has come across many challenges attempting

in all circumstances to remain both a competitive and profitable business at the same time. Just

like in any business venture, it took many years to build up its name and client base, facing many

obstacles on the way. Challenges were faced in every phase of the construction proj ect:

preconstruction, construction, and closeout. Following is a list of a several maj or problems

specialty subcontractors are facing on daily basis while managing their businesses.

Preconstruction Phase

The first challenge that every subcontractor has to face is to secure a contract. The

struggle begins in the early stages of the bidding process where they are exposed to unethical

contractors' behavior.

Bid Shopping

Every project starts with the bidding process. It is very common for a contractor to seek

several bids from different subcontractors for the same proj ect. Generally, in residential

construction, the lowest bidder wins a contract. In Gainesville specifically it seems that every

General Contractor works with several subcontracting businesses providing the same type of

services in order to secure the lowest bids and also to Einish the proj ect on time without costly

time and budget overruns. There are many scenarios where bid shopping occurs. Many times

contractors will put in a higher bid in order to justify their cost to a client. However, after the

contract is signed between the contractor and the owner, they will go back to the lower bidder

and grant them the subcontract, letting them provide the service. This leads to a large difference

between what they will charge an owner and how much they will actually pay to a subcontractor.

In some cases, it was obvious that the contractor was using estimates provided by TNT to add the

amounts to their bids to the owner, but during the construction phase using another, cheaper









subcontractor to paint on particular proj ect in order to maximize the contractor' s profit margin

without informing an owner that a different subcontractor was used. In some other cases,

contractors themselves estimate allowance for a paint j ob and after signing a contract with an

owner, they try to Eind a subcontractor who would provide the service for less then it was

stipulated in the original contract. In any of those different scenarios, the main intent of bid

shopping is to maximize the contractor' s profit without sharing the cost savings with the owner.

As long as subcontractor was able to cover his costs and make a profit, the main party bearing

the cost of bid shopping was the unaware owner. Many ways exist that prevent contractors from

bid shopping, but unfortunately in the Gainesville' s residential market, most of the contracts

between the General Contractor(GC) and Subcontractors were based on initial Eixed-price bid

and verbal agreement to proceed. It was more convenient for a GC to not have too much paper

trace in order to manipulate real costs of proj ects.

Construction Phase

During the construction phase there are several factors influencing TNT's productivity

and profitability on the proj ect or simply its efficiency as a business entity: stacking and

overcrowding, weather, overtime, interference by the owner, change orders, disruption of the

sequence of works, idle time, change of scope of work, to mention just a few.

Stacking and Overcrowding

Probably due to the size of proj ects or maybe the relatively small volume of work, none of

the contractors used any scheduling tools in order to organize the sequence of work and attribute

sufficient time to each activity. With lack of basic information, it was very difficult to schedule a

painting crew to have enough time buffers between other trades working on the same proj ect.

One of the maj or problems was stacking and overcrowding trades due to lack of planning or

simply because the contractor was planning to close on the house earlier then expected,










decreasing the time available to finish a proj ect. In the case of TNT, the reason for stacking and

overcrowding was simply due to a competing of interests. In most of the cases, contractors have

their own carpenter crews which were laborers paid by the hour. No matter how hard those

people will work at the end of the week they will get the same amount of money, so their

productivity was TNT's maj or concern. On the other hand, TNT as an independent

subcontractor, worked based on a fixed price for a proj ect; so the faster the work was done, more

profit could be generated by minimizing variable cost in particular labor cost. Competing

interests did not matter as long as there were enough time buffers between painters and

carpenters, but as soon as the painting crews caught up with the carpenters' delays, idle time and

lost of productivity were a result. At other times, overcrowding was an issue where, due to a lack

of proper scheduling, another trade crew would try to execute their task at the same place and

time. In this case, it was necessary to adjust plans and shift the painting crew to do other tasks,

send them to another proj ect or in extreme cases send them home.

Weather Conditions

During the hurricane season, it was important to account in advance for changing weather

conditions and plan in a way where it would be possible to shift priorities from exterior to

interior painting in case of rain or a storm. In the winter time, which lasts only several weeks

during December and January, the temperature was a maj or consideration. Most of the paint

products were not recommended to be used in temperature under 500F to prevent a loss in their

quality and durability. Usually during cold days, paint work was limited to interior painting

including use of portable heaters in order to provide steady temperature. Unfortunately at many

times, it was impossible to account for changing weather conditions and the lack of a proper

work environment created delays and unproductive time for the painting crew.









Interference by the Owner and Change Orders

Another important factor influencing productivity and profitability is the owner' s

interference which in many cases leads to change orders. As much as subcontractors try to shield

themselves from having to answer to many bosses, from time to time they will come across

overzealous owners who feel that they have to be involved in the everyday management of the

proj ect. There is nothing more disruptive than an owner with general knowledge based on

reading popular design magazines or watching home improvement TV shows. Such an owner

decides to change the scope or direction of on-going work in the midst of the construction

process. Under normal circumstances, they should follow the proper procedure of creating a

change order, getting an estimate from a particular subcontractor and receiving (or not) approval

from the contractor. In general, the schedule of works needs to be adjusted accordingly with the

change order. In the reality of residential construction, the general contractors usually in order to

please the owners try to incorporate changes into the original fix price bid in a way that the

subcontractors have to bear the financial cost of those changes.

Planning, Communication, and Out of Sequence Scheduling

The lack of communication between parties was the maj or factor for poor planning and

occasionally out of sequence scheduling. Inefficient communication lead to a decrease in

productivity, additional idle time, and increase in the subcontractor's cost. Many times general

contractor would shift the work priority from one proj ect to another without communicating with

the trades, creating situations where if a subcontractor at that particular time has only one

proj ect, they would loose money because their crew would not have enough work to do. The

subcontractor has to bear the cost of unproductive and idle time by paying his crew for a full day,

or days, of work in the case where there was no work. On the other hand, if a contractor decided









to Einish proj ect earlier, then the subcontractor has to bear the acceleration cost by paying his

crew overtime.

Closeout

Similarly to the different phases of work described above, the final phase of a proj ect a

closeout; carries unexpected or unaccounted Einancial risks: extensive punch-out, extending a

closing date or simply rework are just a few items which can increase the subcontractor cost at

the end of proj ect.

Aim, Objectives, and Scope of Study

This research aims at gaining better insight into the significance of different uncertainties

in terms of adding to risk for streams of proj ects (as opposed to a single proj ect).

What are the potential risk factors in the case of a sequence of multiple proj ects.

How delays and task durations are sensitive to the different levels of uncertainty.

Similarly, what is the interference between uncertainties and idle time of resources.

My study analyzed schedule risks affecting delays and durations of tasks, and overall project

duration. The type of construction work considered is that of a small size painting subcontractor

in North Central Florida undertaking painting proj ects between 2000 and 2006.

Methodology

This research will base its model on a precedence diagram of multiple duration-driven

tasks using the Critical path Method (CPM). In addition, constraints on the resources will be

introduced to incorporate for the availability of those resources. Microsoft excel will be used to

include these two approaches of duration-driven and resource-driven into the model.









CHAPTER 2
LITERATURE REVIEW

Risks are a main factor in the success or failure of a proj ect and a proj ect manager

influencing cost, time and quality. It remains that risk analysis and management depend on

intuition, judgment and experience and that formal risk analysis and management techniques are

rarely used due to doubts on their suitability of use for construction. The uniqueness of risk

management in construction lies in it being an on-going activity in proj ect development starting

at the conceptualization of the project to its delivery (Akintoye and Macleod 1997).

The construction industry involves a lot of the humanistic features that makes the use of

probabilistic analysis inappropriate for risk analysis. Fuzzy logic is one alternative that was

introduced requiring the user to express the risk linguistically. The results and outcomes of the

analysis are descriptive in the same, more humanistic manner (Mak 1995).

As reported in a worldwide survey, the maj ority of construction proj ects have failed to

meet their proj ected deadlines. Uncertainty in the internal and external environment of a proj ect

is very important in determining delays in a schedule. The traditional assumption that an activity

has only one possible outcome is no longer valid in risk assessment of the progress of work. An

effective schedule should account for all possible outcomes of an activity with an assessment of

proj ect uncertainty (Mulholland and Christian 1999).

Network scheduling techniques such as PERT has proved very reliable for scheduling on

a precedence based on activity start time. The Critical Path Method (CPM) is also another

duration-driven technique inputting proj ect activities, durations and dependence relationships.

Minimized proj ect duration is always the object of scheduling an activity in a construction

proj ect. However, in the event that not enough resources are available or a resource conflict









occurs between the competing activities, resource conflict resolution decisions will occur

resulting in delay and rescheduling of under-resourced activities (Patterson 1984).

The resource management techniques schedule activities to meet certain resource

availability limitations. Resource leveling is done at the process level (micro level) as opposed to

the proj ect level (macro level) where CPM can be used. At the process level, concentration is

made on production rates, resource sharing and availability allocation of different types of

resource is a complicated management issue since errors may lead to higher costs and idleness

on many proj ects. Simulation is commonly used for analysis at this level. An example of

handling construction proj ects at the process level is SIRBUS/CYCLONE simulation (Ammar

and Mohieldin 2002).

Quantifying and minimizing risk has been studied in the literature and recent studies have

shown reliable cost and schedule estimates are one way to control it. Focus was brought to range

estimating and stochastic scheduling as probabilistic estimating and scheduling methods in

comparison to the deterministic, more risky methods. In addition, it was found that combining

those two probabilistic techniques of costing and scheduling will quantify risk in an attempt to

minimize it. Multiple-simulation analysis techniques (MSAT) were developed and tested and

were found to be a reliable means of integrating range estimating and probabilistic scheduling

(Isidore and Back 2002).

It is argued that if risks can be predicted they can also be prevented. Accurate and

informed proj ect risk analysis along with a proj ect risk action management is very beneficial as

to the results of proj ect management. Monitoring progress of tasks can reduce the risks in a very

obvious way. Another conceptual procedure is through negotiation with the Owner or other

stakeholders (Berkeley et al. 1991).









Risk in the construction industry cannot be removed entirely. However, the parties

involved tend to minimize it, share it or completely shift to the other parties involved. Transfer of

risk is guided by whether the receiving party is experienced enough to assess, minimize or

control it. The risk allocation between the Owner and the GC has been researched and it was

found that many allocations were well defined. Quality of work for example is the GC's

responsibility whereas Defective design and related risks are the Owner's responsibility. It

remains that risks like Third party delays are undecided or variable (Kangari 1995).

Insurance covering risks is becoming very popular. However, many risks especially from

the contractor' s side are not practically insured for instance scope changes. Contingencies are

sometimes used to account for a certain risk that is assumed as a percentage of the contract

amount: the contractors remain unaware of the risk modeling techniques. For small and medium

size contractors, a separate contingency line item affects their competitiveness in the market and

usually to win a bid, a contractor is forced to assume more risk than they should or could

effectively handle (Smith and Bohn 1999).

Subcontracts have a very important role in the construction industry. Despite their

necessity and importance, the subcontract award remains a very subj ective issue varying among

general contractor and subcontractors. The subcontractors seem to be the weakest party in the

deal which makes them vulnerable to unfair practices especially by the GC's. Generally, the

subcontract is bound by the same terms of contract between the Client and the GC without being

given the opportunity to review or negotiate. However, the subs willingly accept this

disadvantage because failure to do so will prevent them from being awarded any work.

Awareness of the contract terms are necessary since lack of awareness of the clauses of the









contract will create a lot of risk unaccounted for in the bid and possibly financial loss that many

small size subcontractors cannot handle (Hinze and Tracey 1994).

An economical conflict exists between the contractor' s proj ect managers managing the

subcontractors to meet budget and cost on one specific proj ect and the subcontractors trying to

manage their resources on multiple proj ects in a manner that is more profitable and productive to

them. This conflict was modeled reflecting the adversarial nature of the relationship. It was

found that partnering in an attempt to align long term interests will improve performance of the

model as well as sharing production planning information which is an approach studied by lean

construction techniques (Sacks and Harel 2006).

A productivity model providing insight how subcontracts allocate resources suggests that in case

of unforeseen delay or need, a contractor should consider paying a subcontractor to hold their

idle workers on site (O'Brien 2000).

Techniques such as Data Envelopment Analysis DEA attempt to model productivity of

subcontractor helping them identify efficient practices and management policies (El-Mashaleh et

al. 2001).

The construction site was compared to a manufactured site to describe the applicability of

lean construction and its implementation in the complex one-of-a-kind operations. In both cases,

a low-cost, fast and smooth process is required for success of the proj ect. However, lean

construction, in opposition to lean manufacturing, requires a quick response proj ect handling any

exceptions (Paez et al. 2005).

Lean construction suggests a reduced cost in the construction proj ect. However, a

prerequisite for such a technique is mutual cooperation between contractors and their

subcontractors. Success of the construction process in general is related to the interfaces between









interdependent subcontractors. This approach will maximize the value added and minimize the

cost for all the parties involved. It is suggested that small size subcontractors will help reduce

transaction costs, information flow and quality (Miller et al. 2002).

Further detailed studies show another application of the lean principles in the

construction business is achieving better labor and cost performance by improving the reliability

of the process: it was determined that more reliable materials, information and equipment

availability contribute to better performance. However, labor resources were found to be

deficient (Thomas et al. 2003).









CHAPTER 3
MODEL

Description

To examine the significance of different uncertainties in terms of adding risk for a stream

of proj ects, a computer model was created using MS-Excel to mimic the real-life construction

process and the Monte Carlo simulation was created for a sequence of proj ects.

At first, a template for a single painting proj ect was created with 1 1 tasks, logically

connected by using a precedence diagram. Durations were attributed to every task based on the

author' s professional experience. The Total Float (TF) was calculated for each activity in order

to find the Critical Path for a proj ect. Delays between tasks were introduced in order to replicate

the interference with other trades as shown in Figure 3.1.

Table 3-1 summarizes the tasks included in the model, the causes and durations of their

respective delays and the course of actions for the available resources in case of delay

occurrence. Consequently, tasks were divided into 4 maj or activity groups to create resource

dependencies between sequences of proj ects: an activity on a proj ect could not be started before

resources from the previous proj ect would be available. If resources from the previous proj ect

were available but the activity on the proj ect was not ready to start yet, this would create an idle

time for the available resources. Delays between proj ects were introduced by connecting

dummies in front of every proj ect with a delay value randomly chosen from 0 to 120 days.

Resource dependencies are schematically shown on Figure 3-2.

For every task, different values of delays and durations were used as shown in Table 3-2

and Table 3-3.Consequently using random numbers, the average and the variance of task

durations and delays, a randomized duration was calculated. The following formula was used for

randomization:













where
R: is a random number
n=6
o: standard deviation of the durations
X : mean of the durations

However, in order to incorporate uncertainty into the model, a factor K was introduced and

multiplied by the variances (of task durations and delays). The initial value of K was 0 (no

variation of task durations and delays from the respective mean) and was gradually increased by

0.25 increments to its maximum value of 3.0 (significant variance). This variation of K was

introduced to measure and control the model's sensitivity to increased uncertainty. Table 3-4 and

Table 3-5 show the different tasks and the randomized task durations and delays respectively.

The precedence diagram with its logical dependencies was replicated to create a stream of 129

sub-sequential proj ects. For statistical accuracy, the first and last 10 proj ects were excluded from

the data collection.

With the aim of measuring the sensitivity of idle time to the uncertainty level, K was

initially set to 0 and measurements of idle time for a stream of 109 proj ects were taken 30 times.

The average and variance were measured and plotted onto a graph. Then K value was set for

0.25 and measurements of idle time for a stream of 109 proj ects were taken 30 times again. The

average and variance were also measured and plotted onto a graph. This process was repeated till

K reaches a maximum value of 3.

The same procedure was followed in order to measure the sensitivity the proj ect duration

to the level of uncertainty.









Limitations

The foremost limitation of the model is the lack of historic data on tasks durations,

delays, and project delays. In order to test the model, limits had to be set based on the author's

professional experience regarding task durations and project delays. Since the aim of this

research was to improve the decision process for short and medium term resource planning, the

model was replicating processes and factors witnessed during a one-year business cycle for a

painting subcontractor. Tasks durations represent the durations needed to undertake projects of

3,000 SF to 5,000 SF custom, residential homes. These durations vary from 1 to 10 days. To

avoid guessing unpredictable factors, for instance weather, only the interior part of the projects

was included in the model in order to gain control in manipulating variables. The delays between

tasks were caused by other trades and their tasks' durations. In order to avoid building a

complicated model, limits were set for those delays between 0 and 10 days. Limits of project

delays were set for a maximum of 120 days. Another limitation was an assumption of no

correlation between delays of other trades leading to an underestimation of those delays. Every

delay was treated independently and the size of the painting crew was set to be a fixed number of

3 people. Therefore, adjustments in the crew size (increasing or decreasing) were not considered

in the scenarios of the model.















B. Int. trim prep.
C. Int. trim prime
D. Int. trim sand
E. Int. trim 1st coat
F. Int. trim 2nd coat
G. Latex cut-in



9 0 11 11 0 16 16 0 21 21 26
9 2 11 11 5 16 16 5 21 21 26

26 I 0 ; 28








H. Punch-out


Figure 3-1. Precedence diagram on a single proj ect for interior painting


A: Wall Painting


















II II II




















TF IF
001


Figure 3-2. Resource constraints in a sequence of proj ects












Table 3-1. Matrix of tasks and respective delays occurrences


Delays
When


Tasks


1. Due to late start of plaster crew.
2. Due to plaster drying time.

1. Due to late start of carpenter' s
crew
2. Decrease in carpenter's
productivity
3. Absence of carpenters
1. Due to late start of carpenter' s
crew
2. Decrease in carpenter's
productivity
3. Absence of carpenters
4. Other Trades interference
1. Due to late start of carpenter' s
crew
2. Decrease in carpenter's
productivity
3. Absence of carpenters
4. Other Trades interference
1. Other Trades
2. Due to late start of carpenter' s
crew
3. Decrease in carpenter's
productivity
4. Absence of carpenters
1. Other Trades
2. Decrease in carpenter's
productivity
3. Absence of carpenters


Durations Choices

1. Wait
0-7 Days 2. Go to other Project
3. Find another Project
1. Wait
2. Do other Tasks
1-7 Days
3. Go to other Project
4. Find another Project


1. Wait
0-4 Days 2. Do other Tasks
3. Go to other Project



1. Wait
0-3 Days 2. Do other Tasks
3. Go to other Project




1. Do other Tasks
0-5 Days
2. Wait




1. Do other Tasks
0-4 Days Wi


Action


Best: Go to other Project
Worst: Wait


Best: Do other Tasks
Worst: Wait




Best: Do other Tasks
Worst: Wait





Best: Do other Tasks
Worst: Wait





Best: Do other Tasks
Worst: Wait




Best: Do other Tasks
Worst: Wait


Dummy


Before start of a
task


Before and during
task




Before and during
task





Before and during
task





Before and during
task




Before and during
task


Int. Wall Paint




Int. Tnim Prep.





Int. Tnim Prime






Int. Tnim Sand





Int. Trim 1st
Coat




Int. Trim 2nd
Coat










Table 3-1. Continued


Before and during
task

Before start of a
task

Before and during
task
Before start of a
task
Before start of a
task


1. Do other Tasks
0-5 Days
2. Wait
1. Wait
0-5 Days 2. Go to other Project
3. Find another Project
1. Do other Tasks
1-7 Days Wi
1. Do other Tasks
0-2 days
2. Wait
1. Do other Tasks
0-2 days
2. Wait


Best: Do other Tasks
Worst: Wait

Best: Go to another Project
Worst: Wait

Best: Do other Tasks
Worst: Wait
Best: Do other Tasks
Worst: Wait
Best: Do other Tasks
Worst: Wait


Cut-in Latex


Punch-out


Doors Pnimer

Doors Sand

Doors 1st Coat


1. Other Trades


1. Other Trades


1. Other Trades

1. Other Trades

1. Other Trades





Tasks 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th

X Dummy 0 1 2 3 4 5 6 7

A Int. wall paint 1 2 3 4 5 6 7 8 10

B Int. trim prep. O 1 2 3 4

C Int. trim prime 0 1 2 3

D Int. trim sand 0 1 2 3 4 5

E Int. trim 1st coat 0 1 2 3 4

F Int. trim 2nd coat 0 1 2 3 4 5
G Latex cut-in 0 1 2 3 4 5

H Punch-out 1 2 3 4 5 7

I Doors primer 0 1 2
J Doors sand 0 1 2

K Doors 1st coat 0 1 2 3 4 5 6 7


Table 3-2. Matrix of tasks and durations
Tasks 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th


X Dummy

A Int. wall paint 1

B Int. trim prep. 1

C Int. trim prime 1
D Int. trim sand 1

E Int. trim 1st coat

F Int. trim 2nd coat 1

G Latex cut-in

H Punch-out 1

I Doors primer 1
J Doors sand 1

K Doors 1st coat 1


6 7


8 9 10


8 9 10


Table 3-3. Matrix of tasks


and delays












Table 3-4. Table of tasks, average, variance and randomized durations

Tasks Mean Variance Random Duration


Table 3-5. Table of tasks, average, variance and randomized delays

Tasks Mean Variance Random Duration

X Dummy

A Int. wall paint 3.5 24.0 2.8 3

B Int. trim prep. 5.1 34.4 -1.2 1

C Int. trim prime 2.0 10.0 2.4 2

D Int. trim sand 1.5 6.7 -1.8 1

E Int. trim 1st coat 2.5 14.0 1.9 2

F Int. trim 2nd coat 2.0 10.0 -0.4 1

G Latex cut-in 2.5 14.0 3.5 4

H Punch-out 2.5 14.0 -2.1 1

I Doors primer 3.7 18.7 3.9 4

J Doors sand 1.0 4.0 0.4 1

K Doors 1st coat 1.0 0.3 1.9 2


Dummy

Int. wall paint

Int. trim prep.

Int. trim prime

Int. trim sand

Int. trim 1st coat

Int. trim 2nd coat

Latex cut-in

Punch-out

Doors primer

Doors sand

Doors 1st coat


3.0

14.0

7.5

3.0

22.5

14.0

22.8

7.5

1.5

1.5

1.5










CHAPTER 4
ANALYSIS

The main obj ective of the analysis was to observe the sensitivity of the proj ect duration in

the model and the idle time to different levels of uncertainty. Resource's idle time was defined as

the difference between time when the activity on the next project was available and the time

when resources had to Einish the previous activity in order to start the next one. Lack of idle time

was represented by a null value, and a positive number was a measure of the idle time. The same

procedure was repeated for project durations, and the averages and variances were plugged into

two separate graphs.

After plotting the data into Scatter XY graph polynomial, a trend line was added and

value R2 was calculated to find if there is a strong or weak correlation between the data. In the

first case, where sensitivity was measured between both the uncertainties and the idle time, there

was no proof that such a correlation exists. Results did not have any distinctive pattern and the

correlation constant R2 was less than 0.1 indicating a weak correlation between the two. Figure

4-1 and Figure 4-2 represent these Eindings.


"Average Idle time Versus K"
140
120-

S100-




40-
20-


-20

-+ Idle time Versus K -m- Minimum Limit Maximum Ilmit


Figure 4-1. Average idle time versus K












"Variance Idle time Versus K"


1600
1400 *1
g!1200.
S 1000


m 6: 00 R= 0 027 3
E 400
a, 200

0 05 1 15 2 25 3 35




Figure 4-2. Variance idle time versus K



In the case of proj ect durations, the data was plotted into scatter XY and a trend line was


added showing some patterns of increasing project durations with the increasing values of K. In


addition, the correlation constant R2 had a value of 0.5 indicating some correlation between the


proj ect duration and the uncertainties. Figure 4-3 and Figure 4-4 represent these findings.


"Average project duration versus K"
200
180-
S160-
~140-
S120-
S100-
S80-


S40 ---- ~-

20

0 0.5 1 1 .5 2 2.5 3 3.5

-oAverage project duration versus K -c-Minimum Limit Maximum Limit



Figure 4-3. Average proj ect duration versus K














"Variance project duration versus K"
1200

1000 -

oa

8 00 -







0 05 1 15 2 25 3 3







Figure 4-4. Variance proj ect duration versus K









CHAPTER 5
CONCLUSION AND RECOMMENDATIONS

Conclusion

Results showed no correlation among uncertainties and idle time for the available

resources. This suggests that the idle time is not directly sensitive to the uncertainty in tasks'

durations and delays and could be attributed to other factors affecting the proj ect. It might be that

our logical dependency between resource allocations should be constructed in a different way, or

simply idle time in the stream of proj ects becoming less sensitive due to some risk factors we did

not accounted for.

On the other hand my study shows that some correlation exists between the uncertainty in

tasks' durations and delays and the overall project duration. This suggests that the project

duration is directly sensitive to the variability of delays in a construction project. Although the

uncertainty could also lead to a reduction of task duration and one might expect increasing

uncertainty not to affect overall project duration since we win some and lose some, but in actual

fact uncertainty does tend to increase project duration because collective performance of

concurrent tasks is the worst performing task not the average performing task. Results of this

study are consistence with this concept.

Recommendations

This study may serve for future inquiry into similar sequencing, scheduling and resource

management problems. Collecting enough historical data about resource use would improve the

quality of future models and would serve as a reference point to find the most efficient resource

allocation. This could be achieved by creating a better model describing the real life scenario of a

residential construction project by including more subcontractors and their inter-relations and

that accounts for other causes for delays and idle times on a construction proj ect, such as weather









and close-out. Other considerations for the model are to include alternative strategies for

optimizing resource usage: for instance, the use of multiple crews and allowing for flexible

allocation of crew members within different proj ects according to the need. Other factors to be

included are flexible crew size and the effect of learning and forgetting curves and their

influence on idle times, productivity of crews, proj ect durations and therefore profit.










LIST OF REFERENCES


Akintoye, A., and Macleod, M. (1997). "Risk Analysis and Management in Construction."
International Journal of Project Ma'~nageement 15(1), 3 1-3 8.

Ammar, M., and Mohieldin, Y. A. (2002). "Resource Constrained Project Scheduling Using
Simulation." Journal of Construction Management and' Economics, 20(1i), 323-3 30.

Berkeley, D., Humphreys, P.C., and Thomas, R.D. (1991). "Proj ect Risk Action Management."
Journal of Construction Management and Economics, 9(1), 3-17.

El-Mashaleh, M., O'brien, W.J., and London, K. (2001). "Envelopment Methodology to
Measure and Compare Subcontractor Productivity at the Firm Level." international Journal
ofProductivity and Performance Management, 56(4), 358-368.

Hinze, J., and Tracey, A. (1994). "The Contractor- Subcontractor Relationship: The
Subcontractor' s View." Journal of Construction Engmneering and Management, ASCE,
120(2), 274-287.

Isidore, L., and Back, W. E. (2002). "Multiple Simulation Analysis for Probabilistic Cost and
Schedule Integration." Journal of Construction Engineering and Management, ASCE,
128(3), 211-219.

Kangari, R. (1995). "Risk Management Perceptions and Trends of U. S. Construction." Journal
of Construction Engineering and Management, ASCE, 1 21(4), 422-429.

Mak, S. (1995). "Risk Analysis in Construction: A Paradigm Shift from a Hard to Soft
Approach." Journal of Construction management andEconomics, 13(1), 385-392.

Miller, C., Packham, G., and Thomas, B. (2002). "Harmonization between Main Contractors and
Subcontractors: A Prerequisite for Lean Construction." Journal of Construction Research,
3(1), 67-82.

Mulholland, B., and Christian, J. (1999). "Risk Assessment in Construction Schedules." Journal
of Construction Engineering and Management, ASCE, 125(1), 8-15.

O'Brien, W. (2000). "Multi-project resource allocation: parametric models and managerial
implications." Proceedings, Eighth Annual Conference of the IGLC, University of Sussex,
Brighton, UK, 17-19.

Paez, O., Salem, S., Solomon, J., and Genaldy, A. (2005). "Moving from Lean Manufacturing to
Lean Construction: Toward a Common Sociotechnological Framework." Journal ofI~uman
Factors and Ergonomics in Manufacturing, 15(2), 233-245.

Patterson, J. (1984). "A Comparison of Exact Approaches for Solving the Multiple Constrained
Resource, Proj ect Scheduling Problem." Journal of2anagement Science, 30(7), 854-867.










Sacks, R., and Harel, M. (2006). "An Economic Game Theory Model of Subcontractor Resource
Allocation Behavior." Journal of Construction Management and Economics, 24(1), 869-
881.

Smith, G., and Bohn, C. (1999). "Small to Medium Contractor Contingency and Assumption of
Risk." Journal of Construction Engmneering and Management, ASCE, 125(2), 101-108.

Thomas, H. R., Horman, M., Minchin, R.E. and Chen, D. (2003). "Improving Labor Flow
Reliability for Better Productivity as Lean Construction Principle." Journal of Construction
Engmneering and Management, ASCE, 129(3), 251-261









BIOGRAPHICAL SKETCH

Tomasz Wisniewski was born in Torun, Poland. He spent his early years in Poland and

immigrated to the United States in 1997. After graduating in 2002 from Santa Fe Community

College with an AA degree, he transferred to the University of Florida where he started his

studies at the Warrington College of Business Administration and received his Bachelor of

Science degree in business management in 2005. In the same year, he started graduate studies at

the Rinker School of Building Construction at the University of Florida. He was awarded the

Master of Science in Building Construction in December 2007.





PAGE 1

SENSITIVITY ANALYSIS OF IDLE TIMES AND PROJECT DURATIONS IN CONSTRUCTION SCHEDULING By TOMASZ ANDRZEJ WISNIEWSKI A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN BUILDING CONSTRUCTION UNIVERSITY OF FLORIDA 2007 1

PAGE 2

2007 Tomasz Andrzej Wisniewski 2

PAGE 3

To my parents Krystyna and Andrzej; to my siblings Maciej and Izabela; to my children: Tomasz, Shanti and Nila; and to Di ala for their love and support. 3

PAGE 4

TABLE OF CONTENTS page LIST OF TABLES................................................................................................................. ..........5 LIST OF FIGURES.........................................................................................................................6 ABSTRACT.....................................................................................................................................7 CHAPTER 1 INTRODUCTION................................................................................................................. ...8 Challenges..................................................................................................................... ............9 Preconstruction Phase........................................................................................................9 Bid Shopping..............................................................................................................9 Construction Phase..........................................................................................................10 Stacking and Overcrowding.....................................................................................10 Weather Conditions..................................................................................................11 Interference by the Owner and Change Orders........................................................12 Planning, Communication, and Out of Sequence Scheduling.................................12 Closeout...........................................................................................................................13 Aim, Objectives, and Scope of Study.....................................................................................13 Methodology...........................................................................................................................13 2 LITERATURE REVIEW.......................................................................................................14 3 MODEL........................................................................................................................ ..........19 Description..............................................................................................................................19 Limitations.................................................................................................................... ...21 4 ANALYSIS..................................................................................................................... ........28 5 CONCLUSION AND RECOMMENDATIONS...................................................................31 Conclusion..............................................................................................................................31 Recommendations................................................................................................................ ...31 LIST OF REFERENCES...............................................................................................................33 BIOGRAPHICAL SKETCH.........................................................................................................35 4

PAGE 5

LIST OF TABLES Table page 3-1. Matrix of tasks and re spective delays occurrences.................................................................24 3-2. Matrix of tasks and durations............................................................................................. ....26 3-3. Matrix of tasks and delays................................................................................................ ......26 3-4. Table of tasks, average, variance and randomized durations.................................................27 3-5. Table of tasks, average, variance and randomized delays......................................................27 5

PAGE 6

LIST OF FIGURES Figure page 3-1. Precedence diagram on a single pr oject for interior painting.................................................22 3-2. Resource constraints in a sequence of projects......................................................................23 4-1. Average idle time versus K................................................................................................ ....28 4-2. Variance idle time versus K.............................................................................................. .....29 4-3. Average project duration versus K......................................................................................... 29 4-4. Variance project duration versus K........................................................................................ 30 6

PAGE 7

Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science in Building Construction SENSITIVITY ANALYSIS OF IDLE TIMES AND PROJECT DURATIONS IN CONSTRUCTION SCHEDULING By Tomasz Andrzej Wisniewski December 2007 Chair: Ian Flood Cochair: R. Raymond Issa Major: Building Construction In residential construction, 80 to 90% of every project is completed by specialty subcontractors. Depending on the size of a proj ect and the volume of wo rk, it is a constant challenge to match work with the available re sources. In many cases, constraints on resources play an important role in planning or undertakin g new projects. On the other hand, the lack of sufficient flow of new projects creates unproduc tive resources and increased costs. My study focused on building a model to test the extent of the effect of ac tivity delays, resource management and idle time on the project duration. The case study used was that of an independent painting subcontractor working in Gainesville, Florida provided data over the past 10 years. 7

PAGE 8

CHAPTER 1 INTRODUCTION Subcontractors play a vital role in the construction i ndustry. They are specialty contractors, hired to perform specific tasks on the project. On many projects, particularly residential projects, it is co mmon for 80-90% of the work to be performed by subcontractors (Hinze and Tracey 1994). In August 2000, TW Painting, a sole proprietary painting bus iness was started. Initial investments included a van, severa l hand tools, ladders and pain t sprayer. By the end of 2000, several small projects were completed for a lo cal contractor from Newb erry, Florida. In 2001, after an intense marketing effort, TW Painti ng was awarded several co ntracts with a major General Contractor firm, Shannon Homes Inc. At this point, with a workload ranging form four to six parallel projects, the Owner in order to fulfill his obligation had to increase number of painters, which resulted in an increase in payroll, insurance and workmen compensation fees. By the end of the same year, due to a decrease in the work load and decrease in profitability, the painting crew was reduced to three people. In 2002, in response to a new State Government regulation regarding workmen co mpensation, TW Painting was inco rporated with the State of Florida and its name was changed to TNT Painting In c. In that same year, the company got into a partnership with another painting subcontractor in order to fulfill th eir obligations under contracts with several local co ntractors. For the following two years, TNT performed work for several local contractors with work ranging from 2,000 SF homes to 7,000 SF residences. In 2005, TNT started a long term business relati on with Tommy Waters Custom Homes Inc., Gainesvilles prime custom home builder. From that point on, TNT Painting was constantly resourced by a crew of three painters working full time. 8

PAGE 9

Challenges For the last seven years, TNT Painting In c has come across many challenges attempting in all circumstances to remain both a competitive and profitable business at the same time. Just like in any business venture, it took many years to build up its name and client base, facing many obstacles on the way. Challenges were faced in every phase of the construction project: preconstruction, construction, and closeout. Following is a list of a several major problems specialty subcontractors are facing on daily basis while managing their businesses. Preconstruction Phase The first challenge that every subcontractor has to face is to secure a contract. The struggle begins in the early stages of the bidd ing process where they are exposed to unethical contractors behavior. Bid Shopping Every project starts with the bi dding process. It is very co mmon for a contractor to seek several bids from different subcontractors fo r the same project. Gene rally, in residential construction, the lowest bidder wi ns a contract. In Gainesville specifically it seems that every General Contractor works with several subcontr acting businesses providing the same type of services in order to secure the lowest bids and also to finish the project on time without costly time and budget overruns. There are many scenar ios where bid shopping occurs. Many times contractors will put in a higher bid in order to ju stify their cost to a cl ient. However, after the contract is signed between the contractor and th e owner, they will go b ack to the lower bidder and grant them the subcontract, letting them provide the service. This leads to a large difference between what they will charge an owner and how much they will actually pay to a subcontractor. In some cases, it was obvious that the contractor was using estimates provided by TNT to add the amounts to their bids to the owner, but during the construction phase using another, cheaper 9

PAGE 10

subcontractor to paint on particular project in order to maximize the contractors profit margin without informing an owner that a different subcontractor was used. In some other cases, contractors themselves estimate allowance for a pa int job and after signi ng a contract with an owner, they try to find a subcontractor who would provide the service for less then it was stipulated in the original contra ct. In any of those different scen arios, the main intent of bid shopping is to maximize the contract ors profit without sharing the cost savings with the owner. As long as subcontractor was able to cover his costs and make a profit, the main party bearing the cost of bid shopping was the unaware owner. Many ways exist that prevent contractors from bid shopping, but unfortunately in the Gainesvilles residential market, most of the contracts between the General Contractor(GC) and Subcont ractors were based on in itial fixed-price bid and verbal agreement to proceed. It was more c onvenient for a GC to not have too much paper trace in order to manipulate real costs of projects. Construction Phase During the construction phase there are several factors influencing TNTs productivity and profitability on the project or simply its efficiency as a business entity: stacking and overcrowding, weather, overtime, interference by the owner, cha nge orders, disruption of the sequence of works, idle time, change of scope of work, to mention just a few. Stacking and Overcrowding Probably due to the size of projects or maybe the relatively small volume of work, none of the contractors used any scheduling tools in orde r to organize the sequence of work and attribute sufficient time to each activity. With lack of basi c information, it was very difficult to schedule a painting crew to have enough time buffers between other trades working on the same project. One of the major problems was stacking and overcr owding trades due to lack of planning or simply because the contractor was planning to close on the house earlier then expected, 10

PAGE 11

decreasing the time available to finish a project. In the case of TNT, the reason for stacking and overcrowding was simply due to a competing of inte rests. In most of the cases, contractors have their own carpenter crews which were laborers paid by the ho ur. No matter how hard those people will work at the end of the week they will get the same amount of money, so their productivity was TNTs major concern. On the other hand, TNT as an independent subcontractor, worked based on a fixed price for a project; so the faster the work was done, more profit could be generated by minimizing variable cost in particular labor cost. Competing interests did not matter as long as there we re enough time buffers between painters and carpenters, but as soon as the painting crews caugh t up with the carpenters delays, idle time and lost of productivity were a result At other times, overcrowding was an issue where, due to a lack of proper scheduling, another trad e crew would try to execute th eir task at the same place and time. In this case, it was necessary to adjust pl ans and shift the painting crew to do other tasks, send them to another project or in extreme cases send them home. Weather Conditions During the hurricane season, it was important to account in advance for changing weather conditions and plan in a way where it would be possible to shift priorities from exterior to interior painting in case of rain or a storm. In the winter tim e, which lasts only several weeks during December and January, the temperature wa s a major consideration. Most of the paint products were not recommended to be used in te mperature under 50F to prevent a loss in their quality and durability. Usually during cold days paint work was limited to interior painting including use of portable heaters in order to pr ovide steady temperature. Unfortunately at many times, it was impossible to account for changing weather conditions and the lack of a proper work environment created delays an d unproductive time for the painting crew. 11

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Interference by the Owner and Change Orders Another important factor influencing produc tivity and profitabili ty is the owners interference which in many cases leads to change or ders. As much as subcontractors try to shield themselves from having to answer to many bosses, from time to time they will come across overzealous owners who feel that they have to be involved in the everyday management of the project. There is nothing more disruptive than an owner w ith general knowledge based on reading popular design magazine s or watching home improvement TV shows. Such an owner decides to change the scope or direction of on-going work in the midst of the construction process. Under normal circumstances, they s hould follow the proper procedure of creating a change order, getting an estimate from a particular subcontractor and rece iving (or not) approval from the contractor. In general, the schedule of works needs to be adjusted accordingly with the change order. In the reality of residential construction, the general contractors usually in order to please the owners try to incorporate changes into the original fix price bid in a way that the subcontractors have to bear the financial cost of those changes. Planning, Communication, and Out of Sequence Scheduling The lack of communication between parties was the major factor for poor planning and occasionally out of sequence scheduling. Ineffi cient communication lead to a decrease in productivity, additional idle time and increase in the subcontr actors cost. Many times general contractor would shift the work priority from one project to another without communicating with the trades, creating situations where if a subc ontractor at that part icular time has only one project, they would loose money because thei r crew would not have enough work to do. The subcontractor has to bear the cost of unproductive and idle time by paying his crew for a full day, or days, of work in the case where there was no wo rk. On the other hand, if a contractor decided 12

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13 to finish project earlier, then the subcontractor has to bear the acceler ation cost by paying his crew overtime. Closeout Similarly to the different phases of work desc ribed above, the final phase of a project a closeout; carries unexpected or unaccounted fi nancial risks: extens ive punch-out, extending a closing date or simply rework are just a few items which can increase the subcontractor cost at the end of project. Aim, Objectives, and Scope of Study This research aims at gaining better insight into the significance of different uncertainties in terms of adding to risk for streams of projects (as opposed to a single project). What are the potential risk factors in th e case of a sequence of multiple projects. How delays and task durations are sensitive to the different levels of uncertainty. Similarly, what is the interference between uncertainties and idle time of resources. My study analyzed schedule risks affecting delays and durations of tasks, and overall project duration. The type of construction work considered is that of a small size painting subcontractor in North Central Florida undertakin g painting projects between 2000 and 2006. Methodology This research will base its model on a precedence diagram of multiple duration-driven tasks using the Critical path Method (CPM). In addition, constraints on the resources will be introduced to incorporate for the availability of those resources. Microsoft excel will be used to include these two approaches of duration-driven and resour ce-driven into the model.

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CHAPTER 2 LITERATURE REVIEW Risks are a main factor in the success or failure of a project and a project manager influencing cost, time and quali ty. It remains that risk analysis and management depend on intuition, judgment and experience and that formal risk analysis and management techniques are rarely used due to doubts on their suitability of use for cons truction. The uniqueness of risk management in construction lies in it being an on-going activity in proj ect development starting at the conceptualization of the project to its delivery (Akintoye and Macleod 1997). The construction industry involves a lot of the humanistic features th at makes the use of probabilistic analysis ina ppropriate for risk analysis. Fuzzy logic is one alternative that was introduced requiring the user to express the risk linguistically. The results and outcomes of the analysis are descriptiv e in the same, more humanistic manner (Mak 1995). As reported in a worldwide survey, the major ity of construction proj ects have failed to meet their projected deadlines. Un certainty in the internal and ex ternal environment of a project is very important in determining delays in a sc hedule. The traditional assumption that an activity has only one possible outcome is no longer valid in risk assessment of the progress of work. An effective schedule should account for all possible outcomes of an activity with an assessment of project uncertainty (Mulho lland and Christian 1999). Network scheduling techniques such as PERT has proved very reliable for scheduling on a precedence based on activity start time. The Critical Path Method (CPM) is also another duration-driven technique input ting project activities durations and dependence relationships. Minimized project duration is always the object of scheduling an activ ity in a construction project. However, in the event that not enough resources are available or a resource conflict 14

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occurs between the competing activities, res ource conflict resoluti on decisions will occur resulting in delay and rescheduling of under-resourced activities (Patterson 1984). The resource management techniques schedul e activities to meet certain resource availability limitations. Resource le veling is done at the process level (micro level) as opposed to the project level (macro level) where CPM can be used. At the process le vel, concentration is made on production rates, resour ce sharing and availability allo cation of different types of resource is a complicated manage ment issue since errors may lead to higher costs and idleness on many projects. Simulation is commonly used fo r analysis at this level. An example of handling construction projects at the process level is SIRBUS/CYCLONE simulation (Ammar and Mohieldin 2002). Quantifying and minimizing risk has been studie d in the literature and recent studies have shown reliable cost and schedule estimates are one way to control it. Focus was brought to range estimating and stochastic scheduling as probabi listic estimating and scheduling methods in comparison to the deterministic, more risky me thods. In addition, it was found that combining those two probabilistic techniques of costing and scheduling will quantify risk in an attempt to minimize it. Multiple-simulation analysis techni ques (MSAT) were developed and tested and were found to be a reliable means of integrati ng range estimating and probabilistic scheduling (Isidore and Back 2002). It is argued that if risks ca n be predicted they can also be prevented. Accurate and informed project risk analysis al ong with a project risk action mana gement is very beneficial as to the results of project management. Monitoring progress of tasks can reduc e the risks in a very obvious way. Another conceptual procedure is through negotiation with the Owner or other stakeholders (Berkeley et al. 1991). 15

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Risk in the construction i ndustry cannot be removed entirely. However, the parties involved tend to minimize it, share it or completely shift to the other part ies involved. Transfer of risk is guided by whether the receiving party is experienced enough to assess, minimize or control it. The risk allocation between the Owner and the GC has been researched and it was found that many allocations were well defined. Quality of work for example is the GCs responsibility whereas Defectiv e design and related risks are the Owners responsibility. It remains that risks like Third party delays are undecided or variable (Kangari 1995). Insurance covering risks is becoming very popul ar. However, many risks especially from the contractors side are not pr actically insured for instance sc ope changes. Contingencies are sometimes used to account for a certain risk that is assumed as a percentage of the contract amount: the contractors remain unaware of the ri sk modeling techniques. For small and medium size contractors, a separate contingency line item affects their competitiveness in the market and usually to win a bid, a contractor is forced to assume more ri sk than they should or could effectively handle (Smith and Bohn 1999). Subcontracts have a very important role in the construction i ndustry. Despite their necessity and importance, the subcontract awar d remains a very subjective issue varying among general contractor and subcontractors. The subcon tractors seem to be the weakest party in the deal which makes them vulnerable to unfair pr actices especially by the GCs. Generally, the subcontract is bound by the same terms of contract between the Client and the GC without being given the opportunity to review or negotiate. However, the subs willingly accept this disadvantage because failure to do so will prevent them from being awarded any work. Awareness of the contract terms are necessary since lack of aw areness of the clauses of the 16

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contract will create a lo t of risk unaccounted for in the bid and possibly financial loss that many small size subcontractors cannot handle (Hinze and Tracey 1994). An economical conflict exists between the contractors project managers managing the subcontractors to meet budget and cost on one sp ecific project and the su bcontractors trying to manage their resources on multiple projects in a manner that is more profitable and productive to them. This conflict was modeled reflecting the ad versarial nature of th e relationship. It was found that partnering in an attempt to align long term interests will improve performance of the model as well as sharing producti on planning information which is an approach studied by lean construction techniques (Sacks and Harel 2006). A productivity model providing insight how subcontra cts allocate resources suggests that in case of unforeseen delay or need, a c ontractor should consider paying a subcontractor to hold their idle workers on site (OBrien 2000). Techniques such as Data Envelopment Analys is DEA attempt to model productivity of subcontractor helping them identif y efficient practices and manage ment policies (El-Mashaleh et al. 2001). The construction site was compared to a manufact ured site to describe the applicability of lean construction and its implementation in the complex one-of-a-kind oper ations. In both cases, a low-cost, fast and smooth process is require d for success of the project. However, lean construction, in opposition to lean manufacturing, requires a quick response project handling any exceptions (Paez et al. 2005). Lean construction suggests a reduced cost in the construction project. However, a prerequisite for such a technique is mutual cooperation between contractors and their subcontractors. Success of the construction process in general is related to the interfaces between 17

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18 interdependent subcontractors. This approach will maximize the value added and minimize the cost for all the parties involved. It is suggested that small size subcont ractors will help reduce transaction costs, information flow and quality (Miller et al. 2002). Further detailed studies show another appl ication of the lean principles in the construction business is achieving better labor an d cost performance by improving the reliability of the process: it was determined that more reliable materials, information and equipment availability contribute to bette r performance. However, labo r resources were found to be deficient (Thomas et al. 2003).

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CHAPTER 3 MODEL Description To examine the significance of different uncerta inties in terms of adding risk for a stream of projects, a computer model was created using MS-Excel to mimic th e real-life construction process and the Monte Carlo simulation wa s created for a sequence of projects. At first, a template for a single painting pr oject was created with 11 tasks, logically connected by using a precedence diagram. Durations were attributed to every task based on the authors professional experience. The Total Float (TF) was calculated for each activity in order to find the Critical Path for a project. Delays be tween tasks were introduced in order to replicate the interference with other tr ades as shown in Figure 3.1. Table 3-1 summarizes the tasks included in th e model, the causes and durations of their respective delays and the course of actions for the available resour ces in case of delay occurrence. Consequently, tasks were divided into 4 major activity groups to create resource dependencies between sequences of projects: an activity on a projec t could not be started before resources from the previous projec t would be available. If resources from the previous project were available but the activity on th e project was not ready to start ye t, this would create an idle time for the available resources. Delays betw een projects were introduced by connecting dummies in front of every project with a delay value randomly chosen from 0 to 120 days. Resource dependencies are sche matically shown on Figure 3-2. For every task, different values of delays and durations were used as shown in Table 3-2 and Table 3-3.Consequently using random number s, the average and the variance of task durations and delays, a randomized duration was calculated. The following formula was used for randomization: 19

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X n Ractual n i i 2 )(1 where R: is a random number n = 6 : standard deviation of the durations X : mean of the durations However, in order to incorporate uncertainty into the model, a factor K was introduced and multiplied by the variances (of task durations and delays). The initial value of K was 0 (no variation of task durations and delays from the respective mean) and was gradually increased by 0.25 increments to its maximum value of 3.0 (si gnificant variance). This variation of K was introduced to measure and control the models sensitivity to increased uncertainty. Table 3-4 and Table 3-5 show the different tasks and the rando mized task durations and delays respectively. The precedence diagram with its logical dependencies was replicated to create a stream of 129 sub-sequential projects. For statistical accuracy, th e first and last 10 projects were excluded from the data collection. With the aim of measuring the sensitivity of idle time to the uncertainty level, K was initially set to 0 and measurements of idle tim e for a stream of 109 projects were taken 30 times. The average and variance were measured and pl otted onto a graph. Then K value was set for 0.25 and measurements of idle time for a stream of 109 projects were taken 30 times again. The average and variance were also m easured and plotted onto a graph. This process was repeated till K reaches a maximum value of 3. The same procedure was followed in order to measure the sensitivity the project duration to the level of uncertainty. 20

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Limitations The foremost limitation of the model is the lack of historic data on tasks durations, delays, and project delays. In orde r to test the model, limits had to be set based on the authors professional experience regardi ng task durations and project de lays. Since the aim of this research was to improve the decision process fo r short and medium term resource planning, the model was replicating processe s and factors witnessed during a one-year business cycle for a painting subcontractor. Tasks durations represent the durations needed to undertake projects of 3,000 SF to 5,000 SF custom, residential homes. Thes e durations vary from 1 to 10 days. To avoid guessing unpredictable factors, for instance weather, only the interior part of the projects was included in the model in order to gain contro l in manipulating variables. The delays between tasks were caused by other trades and their tasks durations. In orde r to avoid building a complicated model, limits were set for those de lays between 0 and 10 days. Limits of project delays were set for a maximum of 120 days. Another limitation was an assumption of no correlation between delays of othe r trades leading to an underest imation of those delays. Every delay was treated independently a nd the size of the painting crew wa s set to be a fixed number of 3 people. Therefore, adjustments in the crew size (increasing or decreasin g) were not considered in the scenarios of the model. 21

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B. Int. trim prep. C. Int. trim prime D. Int. trim sand E. Int. trim 1st coat F. Int. trim 2nd coat G. Latex cut-in 1066099011110161602121026 0B00C00D00E00F0-26G-26 1566399211115161652121526 FFTFIF 0001 26028 D UMMY0A0 0H0 0011 26228 122222233224 0I00J-2222K0 231242412525126 I. Doors primer J. Doors sand K. Doors 1st coat H. Punch-out A: Wall Painting Figure 3-1. Precedence diagram on a si ngle project for interior painting 22

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23 Figure 3-2. Resource constraints in a sequence of projects 1066099011110161602121026 0B00C00D00E00F0-26G-26 1566399211115161652121526 FFTFIF 0001 26028 D UMMY0A0 0H0 0011 26228 122222233224 0I0 0J-2222K0 231242412525126 1066099011110161602121026 0B00C00D00E00F0-26G-26 1566399211115161652121526 FFTFIF 0001 26028 D UMMY0A0 0H0 0011 26228 122222233224 0I0 0J-2222K0 231242412525126

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Table 3-1. Matrix of tasks and respective delays occurrences Delays Tasks When Why Durations Choices Action Dummy Int. Wall Paint Before start of a task 1. Due to late start of plaster crew. 2. Due to plaster drying time. 0-7 Days 1. Wait 2. Go to other Project 3. Find another Project Best: Go to other Project Worst: Wait Int. Trim Prep. Before and during task 1. Due to late start of carpenters crew 2. Decrease in carpenters productivity 3. Absence of carpenters 1-7 Days 1. Wait 2. Do other Tasks 3. Go to other Project 4. Find another Project Best: Do other Tasks Worst: Wait Int. Trim Prime Before and during task 1. Due to late start of carpenters crew 2. Decrease in carpenters productivity 3. Absence of carpenters 4. Other Trades interference 0-4 Days 1. Wait 2. Do other Tasks 3. Go to other Project Best: Do other Tasks Worst: Wait Int. Trim Sand Before and during task 1. Due to late start of carpenters crew 2. Decrease in carpenters productivity 3. Absence of carpenters 4. Other Trades interference 0-3 Days 1. Wait 2. Do other Tasks 3. Go to other Project Best: Do other Tasks Worst: Wait Int. Trim 1st Coat Before and during task 1. Other Trades 2. Due to late start of carpenters crew 3. Decrease in carpenters productivity 4. Absence of carpenters 0-5 Days 1. Do other Tasks 2. Wait Best: Do other Tasks Worst: Wait Int. Trim 2nd Coat Before and during task 1. Other Trades 2. Decrease in carpenters productivity 3. Absence of carpenters 0-4 Days 1. Do other Tasks 2. Wait Best: Do other Tasks Worst: Wait 24

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25Table 3-1. Continued Cut-in Latex Before and during task 1. Other Trades 0-5 Days 1. Do other Tasks 2. Wait Best: Do other Tasks Worst: Wait Punch-out Before start of a task 1. Other Trades 0-5 Days 1. Wait 2. Go to other Project 3. Find another Project Best: Go to another Project Worst: Wait Doors Primer Before and during task 1. Other Trades 1-7 Days 1. Do other Tasks 2. Wait Best: Do other Tasks Worst: Wait Doors Sand Before start of a task 1. Other Trades 0-2 days 1. Do other Tasks 2. Wait Best: Do other Tasks Worst: Wait Doors 1st Coat Before start of a task 1. Other Trades 0-2 days 1. Do other Tasks 2. Wait Best: Do other Tasks Worst: Wait

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Table 3-2. Matrix of tasks and durations Tasks 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th X Dummy A Int. wall paint 1 2 3 B Int. trim prep. 1 2 3 4 5 6 7 C Int. trim prime 1 2 3 4 5 D Int. trim sand 1 2 3 E Int. trim 1st coat 2 3 4 5 6 7 8 9 10 F Int. trim 2nd coat 1 2 3 4 5 6 7 G Latex cut-in 2 3 4 5 5 7 8 9 10 H Punch-out 1 2 3 4 5 I Doors primer 1 2 J Doors sand 1 2 K Doors 1st coat 1 2 Table 3-3. Matrix of tasks and delays Tasks 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th X Dummy 0 1 2 3 4 5 6 7 A Int. wall paint 1 2 3 4 5 6 7 8 10 B Int. trim prep. 0 1 2 3 4 C Int. trim prime 0 1 2 3 D Int. trim sand 0 1 2 3 4 5 E Int. trim 1st coat 0 1 2 3 4 F Int. trim 2nd coat 0 1 2 3 4 5 G Latex cut-in 0 1 2 3 4 5 H Punch-out 1 2 3 4 5 7 I Doors primer 0 1 2 J Doors sand 0 1 2 K Doors 1st coat 0 1 2 3 4 5 6 7 26

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Table 3-4. Table of tasks, average, variance and randomized durations Tasks Mean Variance Random Duration X Dummy A Int. wall paint 2.0 3.0 5.2 5 B Int. trim prep. 4.0 14.0 4.0 4 C Int. trim prime 3.0 7.5 3.1 3 D Int. trim sand 2.0 3.0 0.5 1 E Int. trim 1st coat 6.0 22.5 1.7 2 F Int. trim 2nd coat 4.0 14.0 3.0 3 G Latex cut-in 5.9 22.8 -2.1 1 H Punch-out 3.0 7.5 1.7 2 I Doors primer 1.5 1.5 2.6 3 J Doors sand 1.5 1.5 2.2 2 K Doors 1st coat 1.5 1.5 2.9 3 Table 3-5. Table of tasks, average, variance and randomized delays Tasks Mean Variance Random Duration X Dummy A Int. wall paint 3.5 24.0 2.8 3 B Int. trim prep. 5.1 34.4 -1.2 1 C Int. trim prime 2.0 10.0 2.4 2 D Int. trim sand 1.5 6.7 -1.8 1 E Int. trim 1st coat 2.5 14.0 1.9 2 F Int. trim 2nd coat 2.0 10.0 -0.4 1 G Latex cut-in 2.5 14.0 3.5 4 H Punch-out 2.5 14.0 -2.1 1 I Doors primer 3.7 18.7 3.9 4 J Doors sand 1.0 4.0 0.4 1 K Doors 1st coat 1.0 0.3 1.9 2 27

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CHAPTER 4 ANALYSIS The main objective of the analysis was to obser ve the sensitivity of the project duration in the model and the idle time to different levels of uncertainty. Resources idle time was defined as the difference between time when the activity on the next project was available and the time when resources had to finish the previous activity in order to start the next one. Lack of idle time was represented by a null value, and a positive number was a measure of the idle time. The same procedure was repeated for project durations, an d the averages and variances were plugged into two separate graphs. After plotting the data into Scatter XY gr aph polynomial, a trend line was added and value R2 was calculated to find if there is a strong or weak correlation betw een the data. In the first case, where sensitivity was measured between both the uncertainties and the idle time, there was no proof that such a correlation exists. Results did not have any distinctive pattern and the correlation constant R2 was less than 0.1 indicating a weak correlation between the two. Figure 4-1 and Figure 4-2 represent these findings. "Average Idle time Versus K" y = 0.6793x2 1.4446x + 58.582 R2 = 0.0354-20 0 20 40 60 80 100 120 140 00 511 522 533KIdle time (crew days. 5 ) Idle time Versus K Minimum Limit Maximum limit Figure 4-1. Average idle time versus K 28

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"Variance Idle time Versus K" y = -24.511x2 + 80.326x + 1167.3 R2 = 0.02730 200 400 600 800 1000 1200 1400 160000 511 522 533KIdle time Variance (Crew day. 5 s squarred) Figure 4-2. Variance idle time versus K In the case of project durations the data was plotted into scatter XY and a trend line was added showing some patterns of increasing project durations with the increasing values of K. In addition, the correlation constant R2 had a value of 0.5 indicating some correlation between the project duration and the uncerta inties. Figure 4-3 and Figure 44 represent these findings. "Average project duration versus K" y = 0.1669x2 + 4.1675x + 62.155 R2 = 0.5833 0 20 40 60 80 100 120 140 160 180 20000 511 522 533KAverage project duration (days. 5 ) Average project duration versus K Minimum Limit Maximum Limit Figure 4-3. Average project duration versus K 29

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30 "Variance project duration versus K" y = -32.037x2 + 213.04x + 308.51 R2 = 0.19460 200 400 600 800 1000 120000 511 522 53KVariance project duration (day3 5 s squarred) Figure 4-4. Variance project duration versus K

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CHAPTER 5 CONCLUSION AND RECOMMENDATIONS Conclusion Results showed no correlation among uncertain ties and idle time for the available resources. This suggests that the idle time is not directly sensitive to th e uncertainty in tasks durations and delays and could be attributed to other factors affecting the proj ect. It might be that our logical dependency between re source allocations should be cons tructed in a different way, or simply idle time in the stream of projects becoming less sensitive due to some risk factors we did not accounted for. On the other hand my study shows that some co rrelation exists between the uncertainty in tasks durations and delays and the overall pr oject duration. This sugge sts that the project duration is directly sensitive to the variability of delays in a construction project. Although the uncertainty could also lead to a reduction of task duration and one mi ght expect increasing uncertainty not to affect overall project duration since we win some and lose some, but in actual fact uncertainty does tend to increase projec t duration because collective performance of concurrent tasks is the worst performing task not the average performing task. Results of this study are consistence wi th this concept. Recommendations This study may serve for future inquiry into similar sequencing, scheduling and resource management problems. Collecting enough historical data about resource use would improve the quality of future models and would serve as a re ference point to find the most efficient resource allocation. This could be achieved by creating a bette r model describing the real life scenario of a residential construction project by including more subcontractors and their inter-relations and that accounts for other causes for delays and idle times on a construction pr oject, such as weather 31

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and close-out. Other considerations for the m odel are to include alternative strategies for optimizing resource usage: for instance, the use of multiple crews and allowing for flexible allocation of crew members within different projec ts according to the nee d. Other factors to be included are flexible crew size and the effect of learning and forgetting curves and their influence on idle times, productivity of crew s, project durations and therefore profit. 32

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LIST OF REFERENCES Akintoye, A., and Macleod, M. (1997). Risk Analysis and Management in Construction. International Journal of Project Management, 15(1), 31. Ammar, M., and Mohieldin, Y. A. (2002). Res ource Constrained Proj ect Scheduling Using Simulation. Journal of Construction Management and Economics 20(1), 323. Berkeley, D., Humphreys, P.C., and Thomas, R. D. (1991). Project Risk Action Management. Journal of Constructi on Management and Economics 9(1), 3. El-Mashaleh, M., Obrien, W.J., and L ondon, K. (2001). Envelopment Methodology to Measure and Compare Subcontractor Productivity at the Firm Level. International Journal of Productivity and Performance Management, 56(4), 358. Hinze, J., and Tracey, A. (1994). The C ontractor-Subcontractor Relationship: The Subcontractors View. Journal of Construction Engineering and Management ASCE, 120(2), 274. Isidore, L., and Back, W. E. (2002). Multiple Simulation Analysis for Probabilistic Cost and Schedule Integration. Journal of Construction Engineering and Management, ASCE, 128(3), 211. Kangari, R. (1995). Risk Management Percepti ons and Trends of U.S. Construction. Journal of Construction Engineering and Management ASCE, 121(4), 422. Mak, S. (1995). Risk Analysis in Constructio n: A Paradigm Shift from a Hard to Soft Approach. Journal of Constructi on Management and Economics 13(1), 385. Miller, C., Packham, G., and Thomas, B. (2002). Harmonization between Main Contractors and Subcontractors: A Prerequisi te for Lean Construction. Journal of Construction Research 3(1), 67. Mulholland, B., and Christian, J. (1999). R isk Assessment in Construction Schedules. Journal of Construction Engineering and Management ASCE, 125(1), 8. OBrien, W. (2000). Multi-proj ect resource allocation: parame tric models and managerial implications. Proceedings, Eighth Annual Conf erence of the IGLC, Un iversity of Sussex, Brighton, UK, 17-19. Paez, O., Salem, S., Solomon, J., and Genaldy, A. (2005). Moving from Lean Manufacturing to Lean Construction: Toward a Common Sociotechnological Framework. Journal of Human Factors and Ergonomics in Manufacturing 15(2), 233. Patterson, J. (1984). A Comparison of Exact Approaches for Solving the Multiple Constrained Resource, Project Scheduling Problem. Journal of Management Science, 30(7), 854. 33

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34 Sacks, R., and Harel, M. (2006). An Economic Ga me Theory Model of Subcontractor Resource Allocation Behavior. Journal of Constructi on Management and Economics 24(1), 869 881. Smith, G., and Bohn, C. (1999). Small to Medium Contractor Contingency and Assumption of Risk. Journal of Construction Engineering and Management ASCE, 125(2), 101. Thomas, H. R., Horman, M., Minchin, R.E. and Chen, D. (2003). Improving Labor Flow Reliability for Better Productivity as Lean Construc tion Principle. Journal of Construction Engineering and Management, ASCE, 129(3), 251

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BIOGRAPHICAL SKETCH Tomasz Wisniewski was born in Torun, Pola nd. He spent his early years in Poland and immigrated to the United States in 1997. After graduating in 2002 from Santa Fe Community College with an AA degree, he transferred to th e University of Florida where he started his studies at the Warrington College of Business Administration and received his Bachelor of Science degree in business management in 2005. In the same year, he started graduate studies at the Rinker School of Building Construction at the University of Florida. He was awarded the Master of Science in Building Construction in December 2007. 35