An Optimal Configuration of Multiechelon Supply Chain with Multiple Sourcing Strategy

Abstract

Abstract: Based on a multiple sourcing strategy, a research is conducted on the multiechelon supply chain configuration (SCC) problem. Supply chain with uncertain number of multiple supplier options (such as more than two options) is studied. Sourcing proportion control factor (SPCF) is proposed as a multiple sourcing control mechanism. An approach based on ANP and TOPSIS is put forward to calculate the SPCF. Service time and actual sourcing proportion are adopted as two major decision variables to establish an SCC model for multiechelon supply chain. An improved Genetic Algorithm using mixed coding method of integer and real variable is proposed to solve the problem. Both the model and the algorithm are verified by demonstrative cases. At last, sensitivity analyses are conducted with a series of SPCF and several managerial insights drawn.

Key words: multiple sourcing, supply chain configuration, sourcing proportion control factor, improved genetic algorithm

Nie Du-xian1,2， Qu Ting1,3， Chen Xin1， Lei Tao1, Huang Guo-quan1,3. An Optimal Configuration of Multiechelon Supply Chain with Multiple Sourcing Strategy[J]. Industrial Engineering Journal , 2014, 17(4): 54-62.

References

［1］Graves S C, Willems S P. Optimizing the supply chain configuration for new products［J］. Management Science, 2005, 51(8):1165-1180. ［2］Truong T H, Azadivar F. Optimal design methodologies for configuration of supply chains［J］. International Journal of Production Research, 2005,43 (11), 2217-2236. ［3］Huang G Q, Zhang X Y, Liang L. Towards integrated optimal configuration of platform products, manufacturing processes, and supply chains［J］. Journal of Production Economics, 2005, 23(24):267-290. ［4］Amini M, Li H. Supply chain configuration for diffusion of new products: an integrated optimization approach［J］.Omega,2011,39(3):313-322. ［5］Qu T, Huang G Q, Cung VanDat, et al. Optimal configuration of assembly supply chains using analytical target cascading［J］. International Journal of Production Research,2010,48(23)：6883-6907. ［6］Dyer J H, Cho D S, Chu W. Strategic supplier segmentation: the next ‘best practice’ in supply chain management［J］.California Management Review,1998,40(2):57-77. ［7］Costantino N, Pellegrino R. Choosing between single and multiple sourcing based on supplier default risk: a real options approach［J］. Journal of Purchasing & Supply Management, 2010,16(1):27-40. ［8］Sheffi Y, Rice J B. A supply chain view of the resilient enterprise［J］.MIT Sloan Management Review,2005,47(1):41-48. ［9］Yu H, Zeng A Z, Zhao L. Single or dual sourcing: decisionmaking in the presence of supply chain disruption risks［J］. The International Journal of Management Science, 2009,37(4):788-800. ［10］Saaty R W．Descion making in complex environments: the analytic hierarchy process(ANP) for decision making and the analytic network process (ANP) for decision making with dependence and feedback［M］．Pittsburgh：RWS Publications，2003：73-138. ［11］Yoon K, Hwang C L. Manufacturing plant location analysis by multiple attribute decision making: part I—singleplant strategy［J］. International Journal of Production Research，1985,23（2）：345-359. ［12］Chopra S, Sodhi M S. Managing risk to avoid supplychain breakdown［J］.MIT Sloan Management Review,2004,46(1):5362. ［13］Vasilija S, Lidija P, Milan C, et al. Comparison between two target functions for optimization of single phase shadedpole motor using method of genetic algorithms［J］. Journal of Materials Processing Technology, 2005,161(2):89-95. ［14］Bradshaw J, Miles J C. Using standard fitnesses with genetic algorithms［J］. Advances in Engineering Software,1997,28(7):425-435. ［15］任庆生, 叶中行,曾进,等.对常用选择算子的分析［J］.上海交通大学学报,2000, 34(4):564-566. Ren Qing-sheng, Ye Zhong-xing, Zeng Jin, et al. Analysis of common select operator［J］. Journal of Shanghai Jiaotong University, 2000, 34(4):564-566. ［16］黄中瑞，牛朝阳，刘春生.基于改进遗传算法的圆阵列方向图联合优化［J］.计算机工程,2013,39(2): 182-186. Huang Zhongrui, Niu Zhaoyang, Liu Chunsheng. Joint optimization of circular array pattern based on modified genetic algorithm［J］. Computer Engineering, 2013,39(2):182-186. ［17］Chung H P, Woo L, Woo S H, et al. Improved genetic algorithm for multidisciplinary optimization of composite laminates［J］. Computers and Structures, 2008,86(20):1894-1903. ［18］李敬花,樊付见,余峰.求解船体分段堆场调度问题的改进遗传算法［J］. 计算机集成制造系统,2012,18(12):2674-2682. Li Jing-hua, Fan Fu-jian, Yu Feng. Improved generic algorithm for ship block yard scheduling problem［J］. Computer Integrated manufacturing systems,2012,18(12):2674-2682.

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