臺灣博碩士論文加值系統

分层工厂选址问题（HFLP）是一个扩展版本的设施布局问题的目的是确定在更高层次的设施能有效地服务于下级的方式接收设施的位置。粒子群优化（PSO）算法已被应用到了许多古典和现实世界的问题。然而，由于PSO算法是一种随机搜索算法，它很可能会失败在运行结束时将达到全球价值时，这个问题太复杂了。本研究提出的PSO和TS的解决所谓的HPSO-TS的分层工厂选址问题（HFLP）的组合。此外，这项研究还提出确切的方法分支定界，遗传算法和经典的粒子群算法进行比较，以该算法。以符合现实世界中，本研究认为流量在网络或其他期限的车辆通行能力，并在结果车辆任务的最佳数目也需要考虑。在这项研究中，对于HFLP一个混合整数规划模型与流量限制开发。作出的决定是定位了一些设施上的一组潜在的位点，确定所述网络分配，以及对网络流量的分配的数目。我们的目标是尽量减少整体需求加权长途旅行，打开设备成本，以及流量分配成本。随机密钥为基础的解决方案，代表和解码方法，提出了实现HPSO-TS。解码方法开始通过将颗粒引入一个优先列表，然后构造为分配网络。数值计算结果表明，该算法是有效的，因为在解决此类问题的精确解相比，给人最优或接近最优的解决方案。最后，该算法用于解决XYZ公司的研究情况。

Hierarchical facility location problem (HFLP) is an extended version of facility layout problem which aims to determine the location of facilities in a way that facilities in higher level can serve lower level efficiently. Particle swarm optimization (PSO) algorithms have been applied to a number of classical and real-world problems. However, since PSO is a stochastic search algorithm, it is likely to be failed to reach global value at the end of a run when the problem is too complicated. This study proposes a combination of PSO and TS for solving the hierarchical facility location problem (HFLP) called HPSO-TS. Moreover, this study also proposes the exact method branch and bound, genetic algorithm, and classical PSO to be compared to the proposed algorithm. To conform to the real world, this study considers the flow capacity in the networks or in other term the vehicle capacity, and in the results the optimal number of vehicle assignments also need to be considered. In this study, a mixed integer programming model for HFLP with flow capacity constraint is developed. The decisions to be made are locating a number of facilities on a set of potential sites, determining the network assignments, and number of flow assignments on the networks. The objective is to minimize the overall demand weighted distance travel, opening facilities cost, and flow assignments cost. A random key-based solution representation and decoding method is proposed for implementing HPSO-TS. The decoding method starts by transforming the particles into a priority list and then constructed as the assignment networks. Numerical results show that the proposed algorithm is effective and gives optimal or close to optimal solutions as compared with exact solutions in solving this kind of problems. Finally, the proposed algorithm is used to solve the study case of XYZ Company.

ABSTRACT i
ACKNOWLEDGEMENT ii
List of Figures v
List of Tables vi
Chapter 1 Introduction 1
1.1. Background 1
1.2. Research Issues and Objective 5
1.3. Research Purposes 6
1.4. Research Scope 7
1.5. Organization of the Thesis 7
Chapter 2 Literature Review 9
2.1 Facility Location Problem 9
2.2 Hierarchical Facility Location Problem 10
2.3 Metaheuristic Approaches 10
2.4 Genetic Algorithms 11
2.5 Particle Swarm Optimization 12
2.6 Tabu Search 16
2.7 Published Literatures in Hierarchical Facility Location Problems 17
Chapter 3 Methodology 23
3.1 Research Framework 23
3.2 Problem Statement 23
3.3 Assumptions 24
3.4 Influence Diagram and Unit Conversion 26
3.5 Mathematical Formulation 28
3.6 Hybrid Particle Swarm Optimization – Tabu Search Framework 31
3.7 Solution Representation and Decoding Method 32
3.8 Classical PSO 35
3.9 Tabu search procedure for PSO 37
3.10 Genetic Algorithm for HFLP 38
Chapter 4 Computational Experiments 41
4.1 Parameters Setting 42
4.2 Computational Result 43
4.3 Hypothesis Test 50
4.4 Case Study of XYZ Company 51
4.5 Sensitivity Analysis 63
Chapter 5 Conclusion and Future Research 66
5.1 Conclusion 66
5.2 Future Research 67
REFERENCES 69
Appendix.1 Baseline parameters for each problem size. 73
Appendix.2 Small size problem 26 factorial design experiments. 74
Appendix.3 Medium size problem 26 factorial design experiments. 75
Appendix.4 Large size problem 26 factorial design experiments. 76
Appendix.5 Three – dimensional graph of demand against other parameters. 77

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