在二十一世紀，企業不再是單打獨鬥，而是強調企業與企業之間協同合作的關係，隨著企業間的合作越來越普遍，供應鏈管理這門科學也越來越受到重視。長久以來，供應鏈管理也一直是一個很熱門的議題，一條好的供應鏈，可以幫助企業減少成本與浪費，更甚者可以縮短貨運、配送的時間。而要組成一條好的供應鏈有許多因素：採購、遞貨、轉運與配送，都是重要的課題，唯有協調好每一部分作出系統最佳化，整體成本才可以達到理想的數字。
　　而從兩千年以來，許多科學家與企業家開始研究「供應鏈管理」這個熱門的議題。要找出一條好的、節省成本的供應鏈，從運輸路網、供應商選擇到設廠位置，無一不是關鍵，然而，最重要的是決定適當的配貨量與配貨方式，這是影響成本最關鍵的因素。回顧過往的文獻，大多在求解供應鏈路網的成本最佳化問題時，往往忽略的現實面的考量。不是忽略了運輸途中的遞貨損失，就是將路網過度簡化而使路網無法貼近於現實生活，因此，本研究在考慮衰退效應的情況下，以三層式的供應鏈路網結構來作最佳配貨的研究。目標是使整條供應鏈路網的總成本達到最低。
　　由於本研究的數學模式為一NP-hard問題，需要一個高效率的演算法來尋求最佳解，本研究遂使用一個新的、人工智慧的啟發式演算法，簡化群體演算法，並搭配延伸區域的搜尋策略，來作供應鏈路網的成本的最佳化。再將演算結果與過去文獻的基因演算法與粒子群演算法作比較，結果顯示本研究所提出的簡化群體演算法與基因演算法及粒子群演算法的績效具顯著差異。在複雜的供應鏈路網的運輸與配送問題，藉由本研究所提出的簡化群體演算法，管理者便能在其供應鏈路網上做出合適的決策及安排，企業的營運成本得以下降，利潤也能獲得提升。

In the 21th century, the importance of the integration among enterprises has been raised. To reduce the cost incurred in supply chain is a big issue in the supply chain management. To compose an efficient supply chain, there are several elements: procurement, transport, delivery, and distribution.
Since 2000, many researchers and enterprisers have spent a lot of time in developing an efficient supply chain, which including transportation route, supplier-choosing, and location-determining. Most importantly, the distribution way and amount should be properly determined. However, as the plants and suppliers become more miscellaneous and the network scale becomes larger, the supply chain network (SCN) problem becomes more complicated.
To make the supply chain network problem more close to reality, the paper considers the deteriorate effect. The products might lose when delivered due to deterioration, lost, theft or other factors. Hence for the sake of safety, the amount delivered tends to be different between upstream suppliers and the terminate retailers.
This problem is considered to be an NP-hard problem, which needs an effective algorithm to solve. Currently, artificial intelligence algorithm is one of the main techniques to solve NP-hard problem. This paper presents a novel artificial intelligence algorithm named SSO to solve deteriorated SCN problem. An extend local search (ELS) strategy is embedded. SSO-ELS provides a good solution with less computational effort in ideal time. With a good solution provided by SSO-ELS, enterprisers can arrange its supply chain network system more efficiently.

Table of Contents

Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Research Framework 3
Chapter 2 Literature Review 4
2.1 Supply Chain Network (SCN) 4
2.2 Deteriorate-effect Network (SCN) 5
2.3 Genetic Algorithm (GA) 5
2.4 Particle Swarm Optimization (PSO) 7
2.5 Simplified Swarm Optimization (SSO) 8
2.6 Summary and Conclusion 9
Chapter 3 Model Formulation 11
3.1 Problem Statement 11
3.2 The Mathematical Model 12
3.3 Summary and Conclusion 14
Chapter 4 Proposed SSO-ELS Algorithm 15
4.1 Solution Representation 15
4.2 Fitness function 15
4.3 Penalty Cost 16
4.4 SSO Algorithm Procedures 16
4.5 Extend Local Search Procedures 19
4.6 Parameters Tuning 20
Chapter 5 Example and Computational Result 23
5.1 An Illustrative Example 23
5.2 Computational Result 25
Chapter 6 Conclusions and Future Research 28
6.1 Conclusions 28
6.2 Future Research 28
REFERENCES 30

1. Thomas, D.J. and P.M. Griffin, Coordinated supply chain management. European Journal of Operational Research, 1996. 94(1): p. 1-15.
2. Mentzer, J.T., et al., Defining supply chain management. Journal of Business logistics, 2001. 22(2): p. 1-25.
3. Hicks, D.A. A four step methodology for using simulation and optimization technologies in strategic supply chain planning. in Simulation Conference Proceedings, 1999 Winter. 1999. IEEE.
4. Lesnaia, E., I. Vasilescu, and S.C. Graves, The complexity of safety stock placement in general-network supply chains. 2005.
5. Woeginger, G.J., Exact algorithms for NP-hard problems: A survey, in Combinatorial Optimization—Eureka, You Shrink! 2003, Springer. p. 185-207.
6. Ayala-Ramirez, V., et al., Soft Computing Applications in Robotic Vision Systems. na.
7. Haupt, R.L., Phase-only adaptive nulling with a genetic algorithm. Antennas and Propagation, IEEE Transactions on, 1997. 45(6): p. 1009-1015.
8. Kadadevaramath, R.S., et al., Application of particle swarm intelligence algorithms in supply chain network architecture optimization. Expert Systems with Applications, 2012. 39(11): p. 10160-10176.
9. Liang, J.J., et al., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. Evolutionary Computation, IEEE Transactions on, 2006. 10(3): p. 281-295.
10. Bae, C., et al., A new simplified swarm optimization (SSO) using exchange local search scheme. International Journal of Innovative Computing, Information and Control, 2012. 8(6): p. 4391-4406.
11. Kennedy, J. and R. Eberhart, Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, 1995: p. 1942-1948.
12. Kennedy, J. and R.C. Eberhart. A discrete binary version of the particle swarm algorithm. in Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on. 1997.
13. Yuhui, S. and R.C. Eberhart. Empirical study of particle swarm optimization. in Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on. 1999.
14. Blumenfeld, D.E., et al., Reducing logistics costs at General Motors. Interfaces, 1987. 17(1): p. 26-47.
15. Arntzen, B.C., et al., Global supply chain management at Digital Equipment Corporation. Interfaces, 1995. 25(1): p. 69-93.
16. Lazzarini, S.G., F.R. Chaddad, and M.L. Cook, Integrating supply chain and network analyses: the study of netchains. Journal on chain and network science, 2001. 1(1): p. 7-22.
17. Nagurney, A., J. Dong, and D. Zhang, A supply chain network equilibrium model. Transportation Research Part E: Logistics and Transportation Review, 2002. 38(5): p. 281-303.
18. Talluri, S. and R. Baker, A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operational Research, 2002. 141(3): p. 544-558.
19. Jawahar, N. and A.N. Balaji, A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge. European Journal of Operational Research, 2009. 194(2): p. 496-537.
20. Yan, H., Z. Yu, and T. Edwin Cheng, A strategic model for supply chain design with logical constraints: formulation and solution. Computers & Operations Research, 2003. 30(14): p. 2135-2155.
21. Yeh, W.-C., Evaluating the reliability of a novel deterioration-effect multi-state flow network. Information Sciences, 2013. 243: p. 75-85.
22. Tajima, M., Strategic value of RFID in supply chain management. Journal of purchasing and supply management, 2007. 13(4): p. 261-273.
23. Kärkkäinen, M., Increasing efficiency in the supply chain for short shelf life goods using RFID tagging. International Journal of Retail & Distribution Management, 2003. 31(10): p. 529-536.
24. Holland, J.H., Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 1973. 2(2): p. 88-105.
25. Booker, L.B., D.E. Goldberg, and J.H. Holland, Classifier systems and genetic algorithms. Artificial Intelligence, 1989. 40(1–3): p. 235-282.
26. Leardi, R., Genetic algorithms in chemometrics and chemistry: a review. Journal of chemometrics, 2001. 15(7): p. 559-569.
27. Simpson, A.R., G.C. Dandy, and L.J. Murphy, Genetic algorithms compared to other techniques for pipe optimization. Journal of Water Resources Planning and Management, 1994. 120(4): p. 423-443.
28. Antony Arokia Durai Raj, K. and C. Rajendran, A genetic algorithm for solving the fixed-charge transportation model: Two-stage problem. Computers & Operations Research, 2012. 39(9): p. 2016-2032.
29. Xie, F. and R. Jia, Nonlinear fixed charge transportation problem by minimum cost flow-based genetic algorithm. Computers & Industrial Engineering, 2012. 63(4): p. 763-778.
30. Kennedy, J., Particle swarm optimization, in Encyclopedia of Machine Learning. 2010, Springer. p. 760-766.
31. Kennedy, J. and R.C. Eberhart. A discrete binary version of the particle swarm algorithm. in Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on. 1997. IEEE.
32. Yeh, W.-C., Study on quickest path networks with dependent components and apply to RAP. Aug. 1, 2008–Jul. 31, 2011: Taiwan, National Tsinghua University.
33. Yeh, W.-C., A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems. Expert Systems with Applications, 2009. 36(5): p. 9192-9200.
34. Yeh, W.-C., W.-W. Chang, and Y.Y. Chung, A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method. Expert Systems with Applications, 2009. 36(4): p. 8204-8211.
35. Yeh, W.-C., Orthogonal simplified swarm optimization for the series–parallel redundancy allocation problem with a mix of components. Knowledge-Based Systems, 2014. 64: p. 1-12.
36. Yeh, W.-C., Simplified swarm optimization in disassembly sequencing problems with learning effects. Computers & Operations Research, 2012. 39(9): p. 2168-2177.
37. Yeh, W.-C., Optimization of the Disassembly Sequencing Problem on the Basis of Self-Adaptive Simplified Swarm Optimization. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 2012. 42(1): p. 250-261.