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研究生： 陳奕叡
Yi-ruei Chen
論文名稱： 應用粒子群最佳化演算法為基礎之分群演算法於同步揀貨區訂單揀貨系統之品項分配
Application of Particle Swarm Optimization Algorithm-Based Clustering Algorithm to Item Assignment in A Synchronized Zone Order Picking System
指導教授： 郭人介
Ren-jieh Kuo
郭伯勳
Po-hsun Kuo
口試委員: 歐陽超
Chao Ou-Yang
駱至中
Chih-chung Lo
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： 2013
畢業學年度： 101
語文別： 英文
論文頁數： 80
中文關鍵詞： 分群分析 、存放指派 、倉儲 、粒子群最佳化演算法 、基因演算法
外文關鍵詞： Cluster analysis, Storage assignment, Warehousing, Particle swarm optimization, Genetic algorithm
相關次數： 點閱：258  下載：2 
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    • 訂單揀貨作業在倉儲中扮演著非常重要的角色。目前有許多研究已經討論如何在揀貨作業中節省揀貨時間與減少閒置時間。在同步揀貨區訂單揀貨系統中，將客戶訂單中愈常兩個同時出現的物品分開擺放，愈能減少揀貨時間。因此物品相似度的計算即為計算兩產品同時在同一訂單中出現的次數。本研究應用兩種萬用演算法依據物品之相似度來指派物品至存放區域，以便縮短揀貨時間。本研究使用三種不同規模的問題，來測試各個演算法之參數以及效能。結果比較顯示，粒子群演算法相較於基因演算法與已知演算法有較好之求解效果，對於縮減揀貨時間能達到更好之效能。

    Order picking operation plays a very important role in warehousing. There have been some researches discussing how to save the picking time and reduce the idle time in picking operation. If two items appears in the same order simultaneously, then assigning them to two different picking zones can reduce the picking time for a synchronized zone order picking system. Thus, a simple way to measure the similarity between two items is to measure the co-appearance of two items in the same order. Therefore, this study attempts to apply genetic algorithm and particle swarm optimization algorithm to assign items to storage zones based on items’ similarities for shortening the picking time. Three different size problems are employed to testify algorithms’ performance. Computational results indicate that PSO algorithm outperforms GA and existing algorithm in terms of picking time.

    摘要 I ABSTRACT II 致謝 III CONTENTS IV LIST OF FIGURES VI LIST OF TABLES VII CHAPTER 1 INTRODUCTION 1 1.1 Research Motivation 1 1.2 Research Objectives 2 1.3 Research Scope 2 1.4 Research Framework 3 CHAPTER 2 LITERATURE REVIEW 6 2.1 Warehouse Design 6 2.1.1 Warehouse Design Steps 8 2.1.2 Layout Design 8 2.2 Warehouse Operation 9 2.2.1 Storage Assignment 10 2.2.2 Zoning Policy 12 2.2.3 Clustering 14 2.3 Meta-Heuristics Methods 15 2.3.1 Genetic Algorithm 15 2.3.2 Particle Swarm Optimization Algorithm 16 CHAPTER 3 METHODOLOGY 19 3.1 Assumptions and Notation 20 3.2 The Clustering Model 21 3.3 The Existing Meta-heuristic Algorithm 22 3.4 The Algorithms for Order Picking System 23 3.4.1 Particle Swarm Optimization Algorithm 25 3.4.2 Genetic Algorithm 28 CHAPTER 4 MODEL EVALUATIONAL RESULTS AND ANALYSIS 31 4.1 Experimental Setup 31 4.2 Experiment Results 32 4.3 Statistic Test 39 4.3.1 Comparison between PSO and GA 39 4.3.2 Comparison among PSO, GA and existing algorithm 41 4.3.3 Comparison among PSO, GA for real case data 43 CHAPTER 5 CONCLUSION AND FUTURE RESEARCH 44 5.1 Conclusion 44 5.2 Contributions 44 5.3 Future Research 45 REFERENCE 46 APPENDIX I Pseudocode 49 APPENDIX II General Factorial Design of Determining Tuning Parameters for Solving Order Picking Problem 58 APPENDIX III Computational Result 79 APPENDIX IV Statistical Test Results 81

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