Document Type : Research Paper


Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University


In nowadays market, the increased level of competitiveness and uneven fall of the product/service demands are pushing enterprises to make key efforts for optimization of their process management. It involves collaboration in multiple dimensions including information sharing, capacity planning, and reliability among players. One of the most important dimensions of the supply chain network is to determine its optimal operating conditions incurring minimum total costs. However, this is even a tough job due to the complexities inherit the dynamic interaction among multiple facilities and locations. In order to resolve these complexities and to identify the optimal operating conditions, we have proposed a hybrid approach via integrating the simulation technique, Taguchi method, robust multiple non-linear regression analysis and the Harmony Search algorithm, which is the main contribution of the research. In the first experiment, design concepts are used to define a number of scenarios for the supply chain. Then each of these scenarios is implemented in a simulated environment. The results of the simulation used to estimate the relationship between the chain and chain cost factors. This relationship can be used to optimize the supply chain which minimizes the system costs. This research provides a framework to understand the intricacies of the dynamics and interdependency among the various factors involved in the supply chain. It provides guidelines to the manufacturers for the selection of appropriate plant capacity and proposes a justified strategy for delayed differentiation.


chain management: Strategy, planning and operation. Englewood Cliffs: Prentice-Hall.
 [2] Sahin, F., & Robinson, E. (2002). Flow coordination and information sharing in supply chains: Review, implications and directions for future research. Decision Sciences, 33, 505–536.
[3] Gavirneni, S., Kapuscinski, R., & Tayur, S. (1999). Value of information in capacitated supply chains. Management Science, 45(1).
[4] Strader, T. J., Lin, F. R., & Shaw, M. (1999). The impact of information sharing on order fulfillment in divergent differentiation supply chains. Journal of Global Information Management, Harrisburg, 7(1).
[5] Hewitt, F. (1999). Information technology mediated business process management– Lessons from the supply chain. International Journal of Technology Management, Geneva, 17(1–2).
[6] Shunk, D. L., Kim, J. I., & Nam, H. Y. (2003). The application of an integrated enterprise modeling methodology—FIDO—to supply chain integration modeling. Computers and Industrial Engineering, 45(1), 167–193.
[7] Spekman, R. E. (1988). Strategic supplier selection: Understanding long-term buyer relation-ships. Business Horizons (July–August), 80–81.
[8] Tompkins, J. A. (1998). Time to rise above supply chain management. Transportation and Distribution, 39(10).
[9] Kwak, T. C., Kim, J. S., & Chiung, M. (2006). Supplier–buyer models for the bargaining process over a long-term replenishment contract. Computers and Industrial Engineering, 51(2), 219–228.
[10] Zhang, D. Z., Anosike, A. I., Ming, K. L., & Akanle, O. M. (2006). An agent-based approach for remanufacturing and supply chain integration. Computers and Industrial Engineering, 51(2), 343–360.
[11] Altiparmak, F., Gen, M., Lin, L., & Paksoy, T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers and Industrial Engineering, 51(1), 196–215.
[12] Cohen, M. A., & Lee, H. L. (1988). Strategic analysis of integrated production distribution system: Models and methods. Operations Research, 36(2), 216–228.
[13] Arntzen, B. C., Brown, G. G., Harrison, T. P., & Trafton, L. L. (1995). Global supply chain management at digital equipment corporation. Interface, 25(1), 69–93.
[14] Hariharan, R., & Zipkin, P. (1995). Customer-order information, lead times, and inventories. Management Science, 41(1), 1599–1607.
[15] Zheng, Y., & Zipkin, P. (1990). A queuing model to analyze the value of centralized inventory information. Operations Research, 38(2), 296–300.
[16] Vanhoutum, G., Inderfurth, K., & Zijm, W. (1996). Material coordination in stochastic multi-echelon systems. European Journal of Operational Research, 95, 1–23.
 [17] Montgomery, D. C. (2001).Design and analysis of experiments. New York: Wiley.
[18] Sanjay Kumar Shukla, M. K. Tiwari, Hung-Da Wan, Ravi Shankar: (2010), Optimization of the supply chain network: Simulation, Taguchi, and Psychoclonal algorithm embedded approach. Computers & Industrial Engineering 58(1): 29-39.
[19] Z.W. Geem, J-H. Kim, G.V. Loganathan, A new heuristic optimization algorithm: harmony search, Simulation 76 (2001) 60–68.
[20] Bhaskaran, S. (1998). Simulation analysis of manufacturing supply chain. Decision Sciences, 29(3), 633–657.
[21] Beamon, B. M., & Chen, V. C. P. (2001). Performance analysis of conjoined supply chains. International Journal of Production Research, 39(14), 3195–3218.
[22] Alderson, W. (1950). Marketing efficiency and the principal of postponement cost.Profit outlook, September 1950.
[23] Swaminathan, J. M., & Tayue, S. R. (1998). Managing broader product line through delayed differentiation using vanilla boxes. Management Science (December), 161-172.
[24] Gaonkar, R., & Viswanadham, N. (2001). Collaboration and information sharing in global contract manufacturing networks. IEEE/ASME on Mechatronics, 6, 366-367.
[25] Gavirneni, S. (1997). Inventories in supply chains under cooperation. Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA, September.
[26] Towill, D. R., Naim, M. M., & Wikner, J. (1992). Industrial dynamics simulation models in the design of supply chains. International Journal of Distribution and Logistics Management, 22, 3–13.
[27] Lee, H., Padmanabhan, V., & Whang, S. (1994). Information distortion in a supply chain: The bullwhip effect. Management Sciences, 43(4), 546–558.
[28] Zhao, X., & Xie, J. (2002). Forecasting errors and value of information sharing in a supply chain. International Journal of Production Research, 40(2), 311-335.
[29] Chung, C. S., & Lin, C. H. M. (1988). An O(T2) algorithm for the NI/G/NI/ND capacitated lot size problem. Management Science, 34, 420–426.
[30] Dengiz, B., & Akbay, K. S. (2000). Computer simulation of a PCB production line: Metamodelling approach. International Journal of Production Economics, 63, 195-205.