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研究生： Merciawati Hutomo
Merciawati - Hutomo
論文名稱： Multi-Objective Dynamic Guiding Chaotic Search Particle Swarm Optimization (MO-DCPSO) for Optimal Labor Shifts Utilization
Multi-Objective Dynamic Guiding Chaotic Search Particle Swarm Optimization (MO-DCPSO) for Optimal Labor Shifts Utilization
指導教授： 鄭明淵
Min-Yuan Cheng
口試委員: 姚乃嘉
Nie-Jia Yau
周瑞生
Jui-Sheng Chou
晁立中
Li-Chun Chao
學位類別： 碩士
Master
系所名稱： 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年： 2013
畢業學年度： 101
語文別： 英文
論文頁數： 100
中文關鍵詞： multiple labor shifts 、multi-objective optimization 、particle swarm optimization 、dynamic guiding 、chaotic search
外文關鍵詞： multiple labor shifts, multi-objective optimization, particle swarm optimization, dynamic guiding, chaotic search
相關次數： 點閱：223  下載：4 
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    • Multiple labor shifts system is commonly used in construction project to achieve the desired deadline. However, evening and night shifts may bring negative impacts and the application must be as low as possible. In this study, a new multi-objective optimization algorithm has been proposed and applied to solve multiple labor shifts problem. The proposed approach, MO-DCPSO, presents a new multi-objective optimization algorithm based on DCPSO which hybridizing dynamic guiding and chaotic search concepts with PSO. The multiple labor shifts problem has three objectives to minimize: project duration, project cost and total labor hours in evening and night shifts, while also maintaining all scheduling constraints, such as job logic and daily resource limit. An application example is used to illustrate the performance of the proposed approach. This study also builds NSGA-II and MOPSO model for comparison methods. The obtained Pareto front may provide solutions for construction practitioners to facilitate the decision making for construction trade-off problems.

    Multiple labor shifts system is commonly used in construction project to achieve the desired deadline. However, evening and night shifts may bring negative impacts and the application must be as low as possible. In this study, a new multi-objective optimization algorithm has been proposed and applied to solve multiple labor shifts problem. The proposed approach, MO-DCPSO, presents a new multi-objective optimization algorithm based on DCPSO which hybridizing dynamic guiding and chaotic search concepts with PSO. The multiple labor shifts problem has three objectives to minimize: project duration, project cost and total labor hours in evening and night shifts, while also maintaining all scheduling constraints, such as job logic and daily resource limit. An application example is used to illustrate the performance of the proposed approach. This study also builds NSGA-II and MOPSO model for comparison methods. The obtained Pareto front may provide solutions for construction practitioners to facilitate the decision making for construction trade-off problems.

    ABSTRACT i ACKNOWLEDGEMENT ii TABLE OF CONTENTS iv ABBREVIATION vii LIST OF FIGURES viii LIST OF TABLES ix 1 INTRODUCTION 1 1.1 Research Motivation 1 1.2 Research Objective 4 1.3 Scope Definition 4 1.4 Research Methodology 5 1.4.2 Literature Review 7 1.4.3 Newly Proposed Model 8 1.4.4 Problem Formulation 8 1.4.5 Case Study 9 1.4.6 Conclusion and Recommendations 9 1.5 Study Outline 10 2 LITERATURE REVIEW 12 2.1 Particle Swarm Optimization (PSO) 12 2.2 Dynamic Guiding Particle Swarm Optimization with Embedded Chaotic Search (DCPSO) 16 2.2.1 Dynamic Guiding Approach 16 2.2.2 Chaotic Search Approach 17 2.2.3 DCPSO Algorithm Procedures 21 2.3 Multi-objective Optimization 23 3 MULTI-OBJECTIVE DYNAMIC GUIDING CHAOTIC SEARCH PARTICLE SWARM OPTIMIZATION (MO-DCPSO) 27 3.1 Model Overview 27 3.2 Procedure Details of MO-DCPSO Algorithm 29 3.2.1 Initialize using chaotic sequences 29 3.2.2 Update velocity and location of particles 29 3.2.3 Evaluate fitness values of the particles 30 3.2.4 Update local best (pBest) 30 3.2.5 Update archive 30 3.2.6 Update global best (gBest) 30 3.2.7 Dynamic guiding approach 31 3.2.8 Chaotic search approach 31 3.2.9 Check stopping criteria 31 4 PROBLEM FORMULATION 32 4.1 Problem Statements 32 4.2 Objective Functions 32 4.2.1 Minimize project duration 33 4.2.2 Minimize project cost 33 4.2.3 Minimize labor hours in evening and night shifts 33 4.3 Decision Variables and Constraints 34 4.3.1 Shift option 34 4.3.2 Labor constraints 35 4.3.3 Priority value 36 4.4 Performance Evaluation 36 4.4.1 Number of solutions in Pareto-optimal front found 36 4.4.2 The spread of solutions found 36 4.4.3 Hypervolume value of Pareto-optimal front found 37 5 CASE STUDY 38 5.1 Case study data 38 5.2 Parameter Configuration 42 5.3 MO-DCPSO Model Performance 42 5.4 Comparison with Other Methods 49 6 CONCLUSIONS AND RECOMMENDATIONS 53 6.1 Conclusions 53 6.2 Recommendations 54 References 55 Appendix 59

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