How to cite this paper
Pourrousta, A., dehbari, S., Tavakkoli-Moghaddam, R & amalnik, M. (2002). A multi-objective particle swarm optimization for production-distribution planning in supply chain network ,.Management Science Letters , 2(2), 603-614.
Refrences
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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.
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.