Integrated supply chain includes different components of order, production and distribution and it plays an important role on reducing the cost of manufacturing system. In this paper, an integrated supply chain in a form of multi-objective decision-making problem is presented. The proposed model of this paper considers different parameters with uncertainty using trapezoid numbers. We first implement a ranking method to covert the fuzzy model into a crisp one and using multi-objective particle swarm optimization, we solve the resulted model. The results are compared with the performance of NSGA-II for some randomly generated problems and the preliminary results indicate that the proposed model of the paper performs better than the alternative method.

Pourrousta, A., dehbari, S., Tavakkoli-Moghaddam, R & amalnik, M. (2012). A multi-objective particle swarm optimization for production-distribution planning in supply chain network.Management Science Letters -, 2(2), 603-614-.

Abido, M.A. (2007). Two-level of nondominated solutions approach to multiobjective particle swarm optimization. In: Proceedings of the 2007 genetic and evolutionary computation conference, GECCO’ 2007, 7–11 July 2007, London, UK, 726–733.

Aliev, R.A., Fazlollahi, B., Guirimov, B.G., & Aliev, R.R.(2007). Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management. Information Sciences, 177, 4241-4255.

Bilgen, B. (2010). Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem. Expert system with applications, 37, 4488-4495.

Chen, S.P., & Chang, P.C.(2006). A mathematical programming approach to supply chain models with fuzzy parameters. Engineering Optimization, 38, 647-669.

Coello Coello, C.A., & Lechuga, M.S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation, 2, 1051–1056.

Davis, T. (1993). Effective supply chain management. Sloan Management Review, 34, 35-46.

Dehbari, S., Pourrousta, A., Ebrahim Neghad, S., Tavakkoli-Moghaddam, R., & Javanshir, H.(2012). A new supply chain management method with one-way time window: A hybrid PSO-SA approach. International Journal of Industrial Engineering Computations,3(2), 241-252.

Dubois, D., Fargier, H., & Fortemps, P. (2003). Fuzzy scheduling: modelling flexible constraints vs. coping with incomplete knowledge. European Journal of Operational Research, 147, 231-252.

Jayaraman, V., & Ross, A. (2003). A simulated annealing methodology to distribution network design and management. European Journal of Operational Research, 144, 629-645.

Kennedy, J., & Eberhart, R.C. (1995) Particle swarm optimization. In:Proceedings of the 1995 IEEE International Conference on Neural Networks 4, 1942–1948.

Liang, T.F. (2008). Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain. Computers & Industrial Engineering, 55(3), 676-694.

Liang, T.F.(2008). Integrating production-transportation planning decision with fuzzy multiple goals in supply chains. International Journal of Production Research, 46, 1477-1494.

Liang, T.F., Cheng, & H.W. (2009). Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains. Expert Systems with Applications, 36, 3367-3377.

Moore, J., & Chapman, R. (1999). Application of particle swarm to multi objective optimization, Department of Computer Science and Software Engineering, Auburn University.

McDonald, C.M., & Karimi, I.A.(1997). Planning and scheduling of parallel semi-continuous processes. Industrial & Engineering Chemical Research, 36, 2691-2700.

Mula, J., Peidro, D., & Poler, R. (2010). The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. International Journal of Production Economics, 128, 136-143.

Peidro, D., Mula, J., Poler, R., & Verdegay, J.L. (2009). Fuzzy optimization for supply chain planning under supply, demand, and process uncertainties. Fuzzy Sets and Systems, 160, 2640-2657.

Peidro, D., Mula, J., Jimenez, M., & Botela, M.D.M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205, 65-80.

Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2000). Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies. McGraw-Hill, New York.

Sabri, E.H., & Beamon, B.N. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28, 581-598.

Syarif, N., Yun, Y., & Gen, M. (2002). Study on multi-stage logistic chain network: a spanning tree based genetic algorithm approach. Computers & Industrial Engineering, 43, 299-314.

Torabi, S.A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159, 193-214.

Yao, J.S., & Wu, K.(2000). Ranking of fuzzy numbers based on decomposition principle and signed distanced. Fuzzy Sets & Systems, 116, 275-288.

Zhou, G., Min, H., & Gen, M. (2002). The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach. Computers & Industrial Engineering, 43, 251-261.