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研究生: Celine Kurniajaya
Celine Kurniajaya
論文名稱: Applying Genetic Algorithm in a Multi-Objective Design Process Optimization Problem
Applying Genetic Algorithm in a Multi-Objective Design Process Optimization Problem
指導教授: 歐陽超
Chao Ou-Yang
口試委員: 楊朝龍
Chao-Lung Yang
郭人介
Ren-Jieh Kuo
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業管理系
Department of Industrial Management
論文出版年: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 51
中文關鍵詞: pareto-optimal solutionspareto-frontpareto solutionmulti objective genetic algorithmworker allocationtotal time delay penaltytotal working cost
外文關鍵詞: pareto-optimal solutions, pareto-front, pareto solution, multi objective genetic algorithm, worker allocation, total time delay penalty, total working cost
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Design process scheduling is conducted by worker allocation to several tasks in project to achieve two objectives, minimizing the time delay penalty and minimizing total working cost. By minimizing these, the company can provide cheaper product. It can also make product launching faster. Thus, the company will have a competitive advantage.
Because there were two objectives that need to reach, this study used Multi-Objective Genetic Algorithm and pareto-optimality principle to solve multi-objective problem in a wheels’ design process in a Taiwanese UAV company. The solutions for this problem was called pareto-optimal solutions. From MOGA, we got the minimum time delay penalty, minimum working cost, and the combination of which worker finishes the task. Time differences was the differences between expected working time and actual completion time. This research’s goal is to provide pareto-optimal solutions that will be given to management for decision making. It is also expected to show the possible minimum working hours and additional hours to finish tasks in a wheels’ design process.


Design process scheduling is conducted by worker allocation to several tasks in project to achieve two objectives, minimizing the time delay penalty and minimizing total working cost. By minimizing these, the company can provide cheaper product. It can also make product launching faster. Thus, the company will have a competitive advantage.
Because there were two objectives that need to reach, this study used Multi-Objective Genetic Algorithm and pareto-optimality principle to solve multi-objective problem in a wheels’ design process in a Taiwanese UAV company. The solutions for this problem was called pareto-optimal solutions. From MOGA, we got the minimum time delay penalty, minimum working cost, and the combination of which worker finishes the task. Time differences was the differences between expected working time and actual completion time. This research’s goal is to provide pareto-optimal solutions that will be given to management for decision making. It is also expected to show the possible minimum working hours and additional hours to finish tasks in a wheels’ design process.

ABSTRACT i ACKNOWLEDGEMENT ii TABLE OF CONTENTS iii LIST OF FIGURES vi LIST OF TABLES vii CHAPTER I INTRODUCTION 1 1.1 Background 1 1.2 Objective 2 1.3 Research Structure 2 CHAPTER II LITERATURE REVIEW 3 2.1 Genetic Algorithm (GA) 3 2.2 Multi-objective Problems (MOP) 4 2.2.1 Evolutionary-based optimization algorithms (MOEAs) 4 2.2.2 Swarm intelligence-based optimization algorithms (SI-based algorithms) 7 2.2.3 Pareto-Optimal Solution 8 CHAPTER III RESEARCH METHODOLOGY 11 3.1 Data Collection 11 3.1.1 Task flow 11 3.1.2 Working time 13 3.2 Worker Allocation 14 3.2.1 Worker allocation assumptions 14 3.2.2 The Mathematical Models 15 3.3 Multi-Objective Genetic Algorithm 20 3.3.1 Determining multi-objective function 20 3.3.2 Procedure for multi-objective genetic algorithm 20 Step 1: Initialization 21 Step 2: Initializing population 22 Step 3: Evaluation step: Calculating fitness function f(x). 23 Step 4: Updating a tentative set of Pareto solutions 23 Step 5: Selection 23 Step 6: Crossover 23 6.1 Parent Selection 24 6.2. Crossover Point 24 6.2.1 COWGC 24 Step 7: Mutation 26 Step 8: Elite preserve strategy 27 Step 9: Terminating conditions 28 Step 10: User selection 28 3.3.3 Pareto Solution Set 28 3.4 MOP: Pareto Optimality 29 3.4.1 Pareto Front 29 CHAPTER IV RESULT AND ANALYSIS 30 4.1 Data Collection 30 4.1.1 Task Flow 30 4.1.2 Worker Working Time 32 4.2 Multi-objective Genetic Algorithm 36 4.2.1 ANOVA Analysis 37 4.2.2 Pareto Solution Set 37 4.2.3 Pareto Optimality and Pareto Front 38 4.3 Worker Allocation 39 4.3.1 Worker Allocation Result 39 4.3.2 Time Delay Penalty Result 41 4.3.3 Total Working Cost Result 42 CHAPTER V CONCLUSION AND FUTURE RESEARCH 44 5.1 Conclusion 44 5.2 Contribution 44 5.3 Future Research 45 References 46

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