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研究生： Firdaus Fanny Putera Perdana
Firdaus - Fanny Putera Perdana
論文名稱： 以離散型粒子群最佳化演算法求解具越庫作業之車輛途程問題
A Discrete Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Cross-Docking
指導教授： 喻奉天
Vincent F. Yu
口試委員: 郭伯勳
Po-Hsun 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： 2013
畢業學年度： 101
語文別： 英文
論文頁數： 68
中文關鍵詞： 越庫作業 、粒子群演算法 、車輛途程問題
外文關鍵詞： cross-docking, particle swarm optimization, vehicle routing problem
相關次數： 點閱：352  下載：10 
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    • 越庫作業使公司有機會在不增加資本投入或重大基礎建設的情形下，提高他們對顧客的的服務水準。而易腐產品是越庫作業的一種常見的應用。因為維持產品質量和按時交貨對這些公司是非常重要的。為了成功地採用這種方法，關鍵是投入時間和精力，為企業改善流程。我們的目標是盡量減少總運輸成本，且不違反車輛容量限制。我們提出了一個數學模型問題，並開發一個離散粒子群優化演算法。結果呈現我們所提出的解決方法可以有效地解決這個問題。

    Cross-docking is a method that gives companies opportunities to improve their service level to their customers without high monetary investment or major changes in the infrastructure. One of the popular employments of cross-docking is in perishable product companies because maintaining product quality and on time delivery is highly important for these companies. In order to successfully employ this method, it is critical for the companies to invest time and effort in process improvement. The objective is to minimize the total transportation cost without violating vehicle capacity constraints. We propose a mathematical model and develop a discrete particle swarm optimization algorithm for the problem. Computational results indicate that the proposed solution method can effectively solve the problem.

    Table of Contents MASTER’S THESIS RECOMMENDATION FORM ii QUALIFICATION FORM iii ABSTRACT iv ACKNOWLEDGEMENT v CHAPTER 1: INTRODUCTION 1 1.1. Background 1 1.2. Objectives 4 1.3. Organizations 4 CHAPTER 2: LITERATURE REVIEW 7 2.1. Cross-Docking in General 7 2.2. Vehicle Routing Problem 10 2.3. Vehicle Routing Problem with Cross-Docking 11 2.4. Particle Swarm Optimization 15 2.5. Discrete Particle Swarm Optimization 17 2.6. Related Literatures Discussing Vehicle Routing Problem with Cross-Docking 20 CHAPTER 3: MODEL DEVELOPMENT 26 3.1. Problem Statement 26 3.2. Assumptions 26 3.3. Mathematical Formulations 28 CHAPTER 4: METHODOLOGY 33 4.1. Encoding Method 33 4.2. Generating Initial Solution 33 4.3. Position Updating Rule 35 4.4. Local Search 40 CHAPTER 5: NUMERICAL COMPUTATIONS 44 5.1. Instances 44 5.2. Desktop Properties & Parameter Settings 44 5.3. Numerical Examples for Small Instances 44 5.4. Numerical Examples for Large Instances 45 CHAPTER 6: CONCLUSIONS & RECOMMENDATIONS 49 6.1. Conclusions 49 6.2. Research Contributions 49 6.3. Future Research Directions 50 REFERENCES 51 APPENDIX 56

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