運用遺傳演算法求解在資訊分享投資考量下的多買方供應鏈協同合作模式

Using Genetic Algorithm Solving the Supply Chain Collaboration Model in Considering Information Sharing

沈育樹(Yu-Shu Shen) ； 楊菁倩(Ching-Chien Yang) ； 鍾峻任(Chun-Jen Chung) ； 林淑玲(Shu-Lin Lin)

《修平學報》 26期 (2013/03) Pp. 11-28

摘要

本研究延伸單一買方的生產存貨模式為多買方存貨模式，此亦稱為多買方供應鏈協同合作模式，在考慮資訊分享投資的情況下，尋求最佳的補貨策略。決策變數包含投資資訊分享成本、遞送頻率和遞送批量。遺傳演算法（Genetic Algorithm, GA）是本研究採用的求解工具，首先針對GA中的參數進行分析，確定演算法參數中的群體數目，並且確定交配率和突變率。接者，我們利用選定的GA參數對模式求解，並提出各項模式參數之敏感度分析，以了解模式參數值變動對總成本之影響。結果對於總成本降低改善而言，首須針對供應商的購買成本、間接成本和物料處理成本的變動成本上做改善，總成本方能大幅下降。

關鍵字

供應鏈協同合作模式 ； 生產存貨模式 ； 資訊分享 ； 遺傳演算法

並列摘要

This paper extends the single-buyer production inventory model to a multi-buyer inventory model, which is also known as the supply chain collaboration model, considers information sharing investment, finds the best replenishment policy. The decision variables include the cost of investment information sharing, delivery frequency and delivery quantity. The Genetic Algorithm (Genetic Algorithm, GA) is used for solving tools to solve the model solution. First, this paper finds the GA parameters and GA population size which were analyzed in the algorithm parameters, and determines the crossover rate and mutation rate. Then, we use the GA parameters selected for solving the model, and show the sensitivity analysis of the model parameters in order to understand the impact of changes in model parameter values to the total cost. The results in terms of total cost reduction is to improve the supplier's purchase costs, indirect costs, and material handling costs such that the total cost can be reduced significantly.

並列關鍵字

Supply chain collaboration ； Production inventory model ； Information sharing ； Genetic algorithm

參考文獻

Agrell, P., Lindroth, R. and Norrman, A. (2004) Risk, information and incentives in telecom supply chains. International Journal of Production Economics, 90(1), 1–16.

Ahmad, S., Schroeder, R. G.. and Sinha, K. K. (2003) The Role of Infrastructure Practices in the Effectiveness of JIT Practices: Implications for Plant Competitiveness. Journal of Engineering and Technology Management, 20, 161-191.

Altiparmak, F., Gen, M., Lin, L. and Paksoy, T. (2006) A Genetic Algorithm Approach for Multi-objective Optimization of Supply Chain Networks. Computers and Industrial Engineering, 51, 197-216.

Ben-Daya, M. and Hariga, M. (2000) Economic Lot Scheduling Problem with Imperfect Production Processes. Journal of the Operational Research Society, 51, 875-881.

Chopra, S. and Meindl, P. (2007) Supply Chain Management: Strategy, Planning and Operation, 3rd edition, Pearson.