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研究生： Moh. Taufiq Budi Hanyata
Moh. Taufiq - Budi Hanyata
論文名稱： 整合自適速度粒子群優化法與庫伯演算法於一雅加達地區之倉儲指派問題
Hybrid Self Adaptive Particle Swarm Optimization (SAVPSO) – Cooper Heuristic for Facility Location Allocation Problem in Jakarta Area
指導教授： 歐陽超
Chao Ou-Yang
口試委員: 郭人介
Ren-Jieh Kuo
楊朝龍
Chao-Lung Yang
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： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 91
中文關鍵詞： 工业和仓储区 、禁止区 、选址 、SAVPSO 、库珀启发式
外文關鍵詞： Industrial and Warehousing Area, Forbidden Region, Location Allocation, SAVPSO, Cooper Heuristic
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    • 本研究为X物流公司的案例研究，有意向在雅加达建立一个新车厂。该库必须设在工业和仓储区，别人都认为是禁止的区域。获得本研究的目的有关仓库的数量信息应在工业和仓储区的位置，客户可以从车厂担任数间被打开。连续无容量限制的位置分配模型，提出了研究解决，​​其中有许多有界多边形区域的限制问题。本研究采用随机数据均匀分布在三个方案的要求，以测试该算法的性能。在案例研究，本研究探讨了当前位置集合，在城市地区部署后勤服务的所有车厂位置（X）物流，印尼雅加达。自混合速度自适应粒子群优化（SAVPSO）和库珀启发式被提议作为启发式方法。显示计算结果，所提出的方法可以处理有界多边形区域的限制，并产生最佳的解决方案。在X中物流公司的真实案例，应打开新的设施的数量，有两个仓库。本研究得到的坐标位置的新站，以及哪些客户将服务和总成本与车厂的数量分析的比较的形式进行。

    This research is a case study of X Logistic Company which has an intention to build a new depot in Jakarta. The depot must be located in the industrial and warehousing area, others are considered as forbidden regions. The objective of this research is obtained information about the number of depots should be opened between the location in industrial and warehousing area, and the number of customers that can serve from the depot. Continuous uncapacitated location allocation model is proposed in this research to solve the problem which has many bounded polygonal zone as the constraints. This research used random data uniform distribution as a demand in three scenarios to test the performance of the proposed algorithm. In the case study, this research examines the current location set of all depot location X logistic that deploy logistic service in urban area, Jakarta, Indonesia. Hybrid Self Adaptive Velocity Particle Swarm Optimization (SAVPSO) and Cooper Heuristic is proposed as metaheuristic approach. Numerical results shown that the proposed methodology can handle the bounded polygonal zone constraints, and generate optimal solutions. In the real case study of X Logistic Company, the number of new facilities should be opened, is two depots. This research obtain the coordinate location new depots, and which customers will be served, and a comparison will be conducted of analyzing the total cost with number of depot.

    Abstract i Acknowledgments ii Table of Content iii List of Figures v List of Tables vii List of Appendix viii Chapter 1 Introduction 11 1.1. Background 11 1.2. Motivation 13 1.3. Objectives 14 1.4. Scope Limitation 14 1.5. Organization of Thesis 15 Chapter 2Literature Review 16 2.1. Location Allocation Problem 16 2.1.1. Classifications of Facilities 17 2.1.2. Classified on the Physical Space or Locations 18 2.1.3. Classifications of the Demand 19 2.2. Particle Swarm Optimization 19 2.3. The Published Papers in Location Allocation Problem 21 Chapter 3 Research Methodology 26 3.1. Conceptual Thinking 26 3.2. Research Framework 27 3.3. Existing Condition 30 3.4. Model Development 30 3.5. Proposed SAVPSO – Cooper Heuristic 34 3.5.1. Particle Swarm Optimization 34 3.5.2. Self Adaptive Velocity PSO (SAVPSO) 36 3.5.3. Cooper Heuristic 38 3.6. Hybrid SAVPSO Algorithm 42 Chapter 4 Computational Result 44 4.1. Parameter Setting with DOE 44 4.2. Computational Test 50 4.3. Hypothesis Test 56 4.4. Result of Case Study 57 4.5. Sensitivity of Variable 60 Chapter 5 Conclusion and Future Research 64 5.1. Conclusion 64 5.2. Future Research 65 References 66 Appendices 70

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