臺灣博碩士論文加值系統

隨著全球產業競爭加劇，如何快速地反應顧客的實際需求與有效地降低成本為管理階層所重視之焦點。在傳統的存貨管理之中，上、下游廠商各自追求本身成本的最小化而並非整體供應鏈之利益最佳化，進而無法達成上述之目標。因此，本研究提出一運送前置時間不確定及品質不可靠環境下之多階整理即時化存貨模型，在供應鏈中各階層成員可以交換相關資訊的條件下同時進行生產、採購和運送等決策，以達成整體供應鏈總成本最小化之目標。
求解運送前置時間不確定及品質不可靠環境下之多階整理即時化存貨模型等同於求解一個非線性混整數(Mixed integer)問題，此類NP-hard問題通常無法以傳統方法解決。啟發式演算法近年來常被用於複雜度高且難以解決的NP-hard問題，而粒子群最佳化(Particle Swarm Optimization; PSO)即是一種具有高求解效率與品質的啟發式方法。
本研究透過三種不同的PSO權重遞減策略針對序列式多階存貨模型進行求解並分析不同參數下的結果，再與最佳化軟體LINGO比較。經研究結果顯示，在適當的參數設定下PSO能相當有迅速且有效地解決多階整合即時化存貨模型。

How to respond the real require of customer and reduce the total cost effectively are the major focuses in this highly competitive global supply chain environment. In traditional inventory management system, the vendor and the buyer find their own optimal economic-lot-size respectively but not result in an optimal policy for whole supply chain. In addition, uncertain delivery lead time and the quality unreliability are two common factors that could make a great influence the optima inventory policy. Therefore, a serial multi-echelon integrated JIT inventory model with uncertain delivery lead time and quality unreliability (SMEIJI model) was proposed.
Particle Swarm Optimization (PSO) is a burgeoning heuristic approach that has proven to have good performance and quality in solving NP-hard problems. In this research, PSO is applied to search the optimal solution of SMEIJI problem. Experiment results show that PSO is rapid and efficient in solving the multi-echelon inventory problem under the proper parameter setting.

摘 要 i
ABSTRACT ii
誌 謝 iii
CONTENT iv
LIST OF TABLES vi
LIST OF FIGURES vii
Chapter 1 INTRODUCTION 1
1.1. Research Background and Motivation 1
1.2 The Objectives 3
1.3 Problem Description 3
1.4 Research Process and Architecture 4
Chapter 2 LITERATURE REVIEW 6
2.1 Serial Multi-echelon Integrated JIT Inventory Model 6
2.1.1 Multi-echelon Inventory 7
2.1.2 Concept of Just-in-time manufacturing and purchasing 9
2.1.3 Uncertain Delivery Lead Time 11
2.1.4 Quality Unreliability 12
2.2 Particle Swarm Optimization 12
2.2.1 The Concept of Particle Swarm Optimization 12
2.2.2 The Development of Particle Swarm Optimization 15
2.2.3 The Parameters of Particle Swarm Optimization 19
Chapter 3 METHODOLOGY 21
3.1 Serial Multi-echelon Integrated JIT Inventory Model 23
3.1.1 Notation and assumptions 23
3.1.2 Model Formulation 26
3.2 The proposed Particle Swarm Optimization 40
3.2.1 Generating good initial solution 41
3.2.2 The PSO algorithm 43
3.2.3 Measure index 44
Chapter 4 EXPERIMENT RESULTS AND ANALYSIS 46
4.1 Experiment of Different Kinds of Decreasing Weight PSO 46
4.2 Experiment of Particle Number 49
4.3 Experiment of Proportion of Good Initial Solution 51
4.4 Sensitive Analysis of Defective Rate 53
Chapter 5 CONCLUSION 56
5.1 Contributions 56
5.2 Future Research Directions 57
REFERENCE 58

[1]A. Allahverdi and F. S. Al-Anzi, “A PSO and Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application,” Computers and Operations Research, Vol. 33, No. 4, 2006, pp. 1056-1080.
[2]A. Stacey, M. Jancic and I. Grundy, “Particle Swarm Optimization with Mutation,” Evolutionary Computation, Vol. 2, 2003, pp. 1425- 1430.
[3]A. Banerjee and S. L. Kim, “An integrated JIT inventory model,” International Journal of Operations & Production Management, Vol. 15, lss. 9, 1995, pp 237-244.
[4]C. B. Lee, “Multi-Echelon Inventory Optimization,” Evant White Paper Series, 2003.
[5]C. K. Huang, “An Optimal Policy for a Single-vendor Single-buyer Integrated Production-inventory Problem with Process Unreliability Consideration,” International Journal of Produc tion Economics, Vol. 91, 2004, pp. 91-98.
[6]D. Ha and S. L. Kim, “Implementation of JIT purchasing: an integrated approach,” Production Planning & Control, Vol. 8, no. 2, 1997, pp. 152-157.
[7]E. L. Porteus, “Optimal Lot Sizing, Process Quality Improvement and Setup Cost Reduction,” Operations Research, Vol. 34, No. 1, 1986, pp. 137-144.
[8]F. Chen, “Optimal Policies for Multi-Echelon Inventory Problems with Batch Ordering,” Operations Research, Vol. 48, No. 3, 2000, pp. 376-389.
[9]G. A. Graman, D. F. Rogers, “Delivery Delay Variation in Multi-echelon Inventory Model,” Journal of the Operational Research Society, Vol. 48, No. 10, 1997, pp. 1029-1036.
[10]G. C. Mahata, A Goswami, and D. K. Gupta, “A Joint Economic-Lot-Size Model for Purchaser and Vender in Fuzzy Sense,” An International Journal Computers & Mathematics with Application, 50, No. 10-12, 2005, pp. 1767-1790.
[11]G. C. Zheng, “Integrating Particle Swarm Optimization and Simulated Annealing for Flexible Job Shop Scheduling Problem,” Master paper, National Taipei University of Technology, Taipei, 2006.
[12]H. N. Chiu, “A Cost Saving Technique for Solving Capacitated Multi-Stage Lot-Sizing Problems,” Computers & Industrial Engineering, Vol. 24, No. 3, 1993, pp. 367-377.
[13]H. N. Chiu and H. L. Huang, “A Multi-Echelon Integrated JIT Inventory Model Using the Time Buffer and Emergency Borrowing Policies to deal with Random Delivery Lead Times,” International Journal of Production Research, Vol. 41, No. 13, 2003, pp. 2911-2931.
[14]H. L. Lee, and M. J. Rosenblatt, “Simultaneous Determination of Production Cycles and Inspection Schedules in a Production System,” Management Science, Vol. 33, 1987, pp. 1125-1137.
[15]P. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, Vol.4, 1995, pp. 1942-1948.
[16]J. C. H. Pan and J. S. Yang, “A Study of an Integrated Inventory with Controllable Lead Time,” International Journal of Production Research, Vol. 40, No. 5, 2002, pp. 1263-1273.
[17]J. M. Betts and R. B. Johnson, “Just-in-time Component Replenishment Decisions for Assemble-to-order Manufacturing under Capital Constraint and Stochastic Demand,” International Journal of Production Economics, Vol. 95, 2005, pp. 51-70.
[18]K. Moinazdeh, “A Multi-Echelon Inventory System with Information Exchange,” Management Science, Vol. 48, No. 3, 2002, pp. 414-426.
[19]L. Y. Ouyang, “The Single-Vendor Single-Buyer Integrated Inventory Problem with Quality Improvement and lead Time Reduction – Minimax Distribution-free Approach,” Asia-Pacific Journal of Operational Research, Vol. 23, No. 3, 2006, pp. 407-424.
[20]M. Clerc, “The Swarm and the Queen: Towards a Deterministic and Adaptive particle Swarm Optimization,” Evolutionary Computation, Vol. 3, 1999, pp. 1951-1957.
[21] P. Alfredsson and J. Verrijdt, “Model Emergency Supply Flexibility in a Two-Echelon Inventory System,” Management Science, Vol. 45, No. 10, 1999, pp. 1416-1431.
[22]R. Eberhart and J. Kennedy, “A New Optimizer Using Particle Swarm Theory,” Sixth International Symposium on Micro Machine and human Science, Nagoya, Japan, 1995, pp. 39-43.
[23]R. Eberhart and Y. Shi, “Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization,” Proceedings of IEEE Congress on Evolutionary Computation, Piscataway, NJ, IEEE Service Center, 2000, pp.84-88
[24]R. Eberhart and Y. Shi, “Tracking and Optimizing Dynamic Systems with Particle Swarm,” Evolutionary Computation, Vol. 1, 2001, pp. 94-100.
[25]S. K. Goyal, “An Integrated Inventory Model for a Single-supplier Single-customer Problem,” International Journal of Production Research, Vol. 15, 1977, pp. 107-111.
[26]S. K. Goyal, “A Joint Economic Lot Size Model for Purchaser and Vendor: a comment,” Decision Sciences, Vol. 19, 1988, pp. 236-241
[27]S. K. Goyal and L. E. Cardenas-Barron, “Economic Production Quantity Model for Items with Imperfect Quality – A Practical Approach,” International Journal of Production Economics, Vol. 77, 2002, pp. 85-87.
[28]W. Lee, “A Joint Economic Lot Size Model for Raw material Ordering, Manufacturing Setup, and Finished Goods Delivering,” The International Journal of Management Science, Vol. 33, 2003, pp. 163-174.
[29]X. H. Shi, X. L. Xing, Q. X. Wang, L. H. Zhang, X. W. Yang, C. G.. Zhou and Y. C. Liang, “A Discrete PSO Method for Generalized TSP Problem,” Proceedings of the Third International Conference, Shanghai, 2004, pp. 26-29.
[30]Y. Seo, “Controlling general multi-echelon distribution supply chains with improved reorder decision policy utilizing real-time shared stock information,” Computer & Industry Engineering, Vol. 51, Iss. 2, 2006, pp. 229-246.
[31]Y. Shi and R. Eberhart, “A Modified Particle Swarm Optimizer,” IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, 1998, pp. 4-9.
[32]Y. Shi and R. Eberhart, “Parameter Selection in Particle Swarm Optimization,” Evolutionary Programming, 1998, pp. 591-600.
[33]Y. Y. Chiu, “Modification of Particle Swarm Optimizer for the Global Optimization of Multi-modal Function,” Master paper, Yuan Ze University, Jung-Li, 2004.