Faculty of Engineering

Optimization of Managing Construction Projects

Activities via the Application of Genetic Algorithm

Prepared by:

Suhaib Ziad Abu-Eisheh

Supervised by:

Prof. Dr. Anas Al Rabadi

Assoc. Prof. Dr. Ibrahim A.Mohammed

A Thesis

Submitted to Faculty of Engineering as a Partial Fulfillment of

Requirements for Master Degree in Engineering Project

Management

August 2020

DEDICATION

To my Family, especially my beloved Father

and great Mother….

To my Professors, Staff and Friends ….

To Everyone who has helped me to accomplish my work

To my lovely country Jordan…..

To all Citizens in Jordan….

To all of you I feel honor to dedicate this work.

Suhaib Ziad Abu-Eisheh

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ACKNOWLEDGMENTS

In the name of Allah, and peace and blessings be upon Prophet Mohammad. First, all

praise is due to Allah, the Lord of the Worlds, who has helped me to accomplish this

modest work. Secondly, I’m indebted to my advisor Prof. Anas Al-Rabedi who has given

freely of his time and experience, provided me with information and given support during

this work. Furthermore, I would like to express my deep thanks to the distinguished co-

advisor Dr. Ibrahim Abed for his endless support, patience, understanding, and guidance

throughout the last two years.

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Table of Contents

Dedication…………………………………………………………………....................... i

Acknowledgement………………………………………………………..........................ii

Table of contents……………………………………………………………..………….iii

List of figures…………………………………………………………………………… v

List of tables…………………………………………………………………………… vii

List of appendices……………………………………………………….......................viii

List of abbreviations………………………………………………………………….... ix

Abstract…………………………………………………………………………………. x

1 Chapter One: Introduction…………………………………………………………1

1.1 Background…………………………………………………………………….1

1.1.1 Definition………………………………………………………………….……1

1.1.2 Optimization Algorithms in Civil Engineering…………………………....……4

1.1.3 GA for Construction Project Management………………………………….5

1.2 Research Methodology ……………………………………..……………..…...7

1.3 Problem Statement ……………………………..……………………….…...…8

1.4 Significance of the Study………………………………..……………….…..…9

1.5 Objectives of the Study …………………………………………………………...11

1.6 Thesis Structure ………………..………………………………..………………...12

2 Chapter Two: Literature Review…………………………………………….….…13

2.1 Overview of Global PM Development………………………………….……..13

2.2 Overview of PM Development in Jordan………………………………….…..15

2.3 Overview of Optimization in PM…………………………………………….........19

2.4 Challenges of PM………………………………………………………….…..18

2.5 Summary………………………………………………………………..……...21

3 Chapter Three: Genetic Algorithm……………………………………….…....….22

3.1 Genetic Algorithm Development Overview…………………...………...…....…..22

3.2 Limitations of GA…………………………………………………….……….45

4 Chapter Four: Research Methodology…………………………………………...44

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4.1 Study Methodology……………………………………………………….…..44

4.2 Project’s Resources and Design Constraints……………………….…………….45

4.3 Case Study ……………………………………………..…………..…………….45

4.4 Application of Genetic Algorithm to Case Study………………….………....48

4.5 Data and Constraints of Case Study…………….…………………….………….50

5 Chapter Five: Results and Discussions………………………..………………….54

5.1 Results……………………………………………………………….…………...54

5.2 Discussions………………………………………………………….……………63

6 Chapter Six: Conclusions and Recommendations……………………….………67

6.1 Conclusions…………………………………………………………..…………...67

6.2 Recommendations……………………………………………………..………….68

6.3 Future Studies…………………………………………………………………69

References……………………………………………………………………………….70

Thesis Abstract in Arabic………………………………………………………………83

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List of Figures

Figure 1-1: Configuration of optimization algorithms categorization……………..………….3

Figure 1-2: Flowchart of genetic algorithm methodology…………………………..………...6

Figure 1-3: Research Methodology ……………………………….…………….………..8

Figure 1-4: Major construction project types…………………………………..……...….9

Figure 2-1: Senior aspects of PM approach……………………………………….…......17

Figure 2-2: Opportunities and challenges of PM with its macro and micro factors……..20

Figure 3-1: Configuration of Goldberg GA model………………………………………23

Figure 3-2: Crossover approach applied to GA strategy ……………..…………..……...25

Figure 3-3: Several layouts for efficient delivering of construction materials…………..38

Figure 3-4: Invalid situations of trusses design………………………………………….39

Figure 3-5: Chromosomes defined for GA process for trusses optimized design……….40

Figure 3-6: History of: (1) GA fitness function (2) trusses weight evolution..………….40

Figure 3-7: Project’s network of the original problem with 18 activities ..…………………..42

Figure 4-1: Layout of a road construction scheme in Amman…………………………..44

Figure 4-2: The location of the capital of Jordan, Amman, and Madounah ……….…...46

Figure 4-3: Configuration of the road cross section in Amman via AutoCAD ..…...…..47

Figure 4-4: Sketch of the road project in AutoCAD ………………………...………….47

Figure 4-5: Configuration of GA applied to road construction project in Amman ……..49

Figure 4-6: Illustration of capital’s weight percentage of project items............................51

Figure 4-7 Crossover Configuration for genes…………………………………………..52

Figure 4-8 Mutation utilized in GA ……………………………………………………..52

Figure 5-1: Values of MATLAB results of time and budget and fitness function .……..54

Figure 5-2: Results of optimal choice selected from MATLAB ………………………..55

Figure 5-3: Layout of project’s cost corresponding to the ten generations. …………….56

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Figure 5-4: Illustration of estimated project’s duration with 10 generations ..…….…....57

Figure 5-5: MATLAB outputs of project’s generations with duration and cost ..………58

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List of Tables

Table 1-1: Most globally recognized optimization algorithms……………………...……2

Table 3-1: GA parameters defined for the optimization process…………… .................. 36

Table 4-1: Data Analysis of Road Construction Activities…………………………..… 50

Table 5-1: GA Solution Methodology MATLAB followed throughout the definition of

Population – Generation No. 1 ...........................................................................................................59

Table 5-2: GA Solution Methodology MATLAB followed throughout the definition of

Population – Generation No. 6………………………………………………………….…...60

Table 5-3: GA Solution Methodology MATLAB followed throughout the definition of

Population – Generation No. 10……………………………………………………….…….60

Table 5-4: GA Solution Methodology MATLAB followed throughout the definition of

duration, cost and daily cost – Generation No. 1………………………………...……...61

Table 5-5: GA Solution Methodology MATLAB followed throughout the definition of

duration, cost and daily cost – Generation No. 6………………………………………...62

Table 5-6: GA Solution Methodology MATLAB followed throughout the definition of

duration, cost and daily cost – Generation No. 10……………………………………….62

Table 5-7: Outputs of GA optimization via MATLAB, concerning the optimal cost

estimated for the project………………………………………………………………..…….63

Table 5-8: The most appropriate options obtained after optimization was performed for

activities via GA…………………………………………………………………………64

Table 5-9: Comparison between conventional Selection (via Primavera P6 Software)

with GA results………………………………………………………………………......65

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List of Appendices

A: Primavera P6 Configuration – Project’s activities with their estimated durations…...79

B: The MATLAB Code utilized in the Work……………………………………………81

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List of Abbreviations

Abbreviation Description

GA Genetic Algorithm

COCOM Constructive Cost Model

CA Cultural Algorithm

ACO Ant colony Optimization

PM Project Management

EU European Union

NN Neural Network

PSO Particle Swarm Optimization

ABC Artificial Bee Colony

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Optimization of Managing Construction Projects Activities via the

Application of Genetic Algorithm

By

Suhaib Ziad Abu-Eisheh

Supervisor

Prof. Anas Al-Rabadi

Co. Supervisor

Assoc. Prof. Dr. Ibrahim A.Mohammed

ABSTRACT

Project management prior almost three decades have many challenges and constraints

including laborious tasks and hand operated project activities scheduling, besides long

manual project calculations and time-consuming cost analysis. Genetic algorithm (GA)

has simplified for project managers diverse large-scale projects monitoring and

administrating and allows them control quality of the projects through unique solution

methodologies which take decision through multiple selection steps in order to find the

optimized. In this work, GA is applied as a case study for constructing a road project near

Amman, the capital of Jordan, with total length of 1.16 km as well as a total width of 16

meters. After conducting this work, it was found that GA has chosen the one option

between three ones provided in order to completely achieved the twelve activities of road

construction project using the least amount of both cost as well as time. It was found that

the costliest activities were bituminous first- and second-layers execution. Additionally,

G6 was the best generation that corresponded to a cost of 1,085,128.5 JD, i.e. 1,530,524.9

US$. The fitness value of cost and duration is 43.43, and duration needed to execute the

activities of the road construction project is 365 days. This work recommends many

points to enhance the outputs from GA process.

Keywords: Construction Project, Project Management, Genetic Algorithm,

Optimization, Daily Cost, time planning.

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