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Application of Simplified Swarm Optimization Algorithm in Deteriorated Supply Chain Network Problem
Thesis

Application of Simplified Swarm Optimization Algorithm in Deteriorated Supply Chain Network Problem

Lin. Wei Ting
Masters, 國立清華大學, 工業工程與工程管理學系
2014

Abstract

最佳化 簡化群體演算法 供應鏈管理 三層供應鏈路網 衰退型路網 Optimization Simplified Swarm Optimization (SSO) Supply chain management Three-stage supply chain network Deteriorated network
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.

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