How to cite this paper
Javanshir, H., Ebrahimnejad, S & Nouri, S. (2012). Bi-objective supply chain problem using MOPSO and NSGA-II.International Journal of Industrial Engineering Computations , 3(4), 681-694.
Refrences
Altiparmak, F., Gen, M., Lin, L., & Karaoglan, I. (2009). A steady-state genetic algorithm for multi-product supply chain network design. Computers & Industrial Engineering, 56(2), 521-537.
Altiparmak, F., Gen, M., Lin, L., & Paksoy, T. (2006). A genetic algorithm approach for multi–objective optimization of supply chain networks. Computers & Industrial Engineering. 51, 196–215.
Beamon, B. M. (1998). Supply chain design and analysis: Models and Methods International Journal of Production Economics. 55, 281-294.
Bottani, E., & Rizzi, A. (2006). Strategic management of logistics service: A fuzzy QFD approach. International Journal of Production Economics. 103, 585-599.
Cardona-Valdés, Y., ?lvarez, A., & Ozdemir, D. (2010). A bi–objective supply chain design problem with uncertainty. Transportation Research Part C, 19, 821-823.
Chan, F. T. S., & Kumar, N. (2009). Effective allocation of customers to distribution centres: A multiple ant colony optimization approach. Robotics and Computer–Integrated Manufacturing. 25, 1–12.
Chan, F. T. S., Chung, S. H., & Wadhwa, S. (2005). A hybrid genetic algorithm for production and distribution.Omega. 33, 345-355.
Chen, Y.M., & Wang, W.S. (2010). A particle swarm approach to solve environmental/economic dispatch problem. International Journal of Industrial Engineering Computations, 1(1), 157–172.
CoelloCoello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handliny multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation. 8, 256-279.
CoelloCoello, C. A.Aamont, G.B., & Van Veldhuizen, D.A. (2007).Evolutionary Algorithms for Solving Multi-Objective Problems.SpringerScience+Business Media.ISBN 978-0-387-33254-3.
Das, S., Abraham, A., & Konar, A. (2008). Particle swarm optimization and differential evolution Algorithms. Technical Analysis, Applications and Hybridization Perspectives. Studies in Computational Intelligence, 116, 1-38.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 6, 182-197.
Du, F., & Evans, G. W. (2008). A bi-objective reverse logistics network analysis for post-sale service. Computers & Operations Research, 35, 2617-2634.
Erol, I., Ferrell Jr, W. G. (2004). A methodology to support decision making across the supply chain of an industrial distributor. International Journal of Production Economics. 89, 119-129.
Gen, M., & Syarif, A. (2005). Hybrid genetic algorithm for multi–time period production/distribution planning. Computers & Industrial Engineering. 48, 799–809.
Guillén, G., Mele, F.D., Bagajewicz, M.J., Espu?a, A., & Puigjaner, L. (2005). Multiobjective supply chain design under uncertainty. Chemical Engineering Science. 60, 1535–1553.
Guliashki, V., Toshev, H., & Korsemov, C. (2009). Survey of Evolutionary Algorithms Used in Multiobjective Optimization. Institute of Information Technologies,1113 Sofia.
Lee, Y. H., & Kim, S. H. (2002). Production–distribution planning in supply chain considering capacity constraints. Computer & Industrial Engineering. 43, 169–190.
Papageorgiou, L. G. (2009). Supply chain optimisation for the process industries: Advances and opportunities. Computers and Chemical Engineering, 33, 1931-1938.
Perea-L?pez, E., Ydstie, B.E., & Grossmann, I.E. (2003). A model predictive control strategy for supply chain optimization. Computers and Chemical Engineering, 27, 1201–1218.
Pishvaee, M. S., Zanjirani Farahani, R., & Dullaert, W. (2010). A memetic algorithm for bi–objective integrated forward/reverse logistics network design. Computers & Operations Research, 37, 1100–1112.
Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization: An overview. Swarm Intelligence. 1, 33-57.
Reyes-Sierra, M., & CoelloCoello, C. A. (2006). Muti-objective particle swarm optimizers: A survey of the state-of-the-Art. International Journal of Computational Intelligence Research. 2, 287-308.
Sabri, E. H., & Beamon, B. M. (2000). A multi–objective approach to simultaneous strategic and operational planning in supply chain design. Omega. 28, 581–598.
Sedighizadeh, D., & Masehian, E. (2009). Particle swarm optimization methods, taxonomy and applications. International Journal of Computer Theory and Engineering, 1, 1793-8201.
Sharaf, A. M., & El.Gammal, A. A. (2009). A multi objective multi-stage particle swarm optimization MOPSO search scheme for power quality and loss reduction on radial distribution system. International Conference on Renewable Energies and Power Quality (ICREPQ’09).
Taghavi-fard, M.T., Javanshir, H., Roueintan, M.A., & Soleimany, E. (2011). Multi-objective group scheduling with learning effect in the cellular manufacturing system. International Journal of Industrial Engineering Computations, 2, 617-630.
Tuzkaya, U.R., & ?nüt, S. (2009). A holonic approach based integration methodology for transportation and warehousing functions of the supply network. Computers & Industrial Engineering, 56, 708–723.
Voudouris, V.T. & Consulting, A. (1996). Mathematical programming techniques to debottleneck the supply chain of fine chemicals. Computers & Chemical Engineering, 20S, S1269-S1274.
Xu, J., Liu, Q., & Wang, R. (2008). A class of multi–objective supply chain network optimal model under random fuzzy environment and its application to the industry of Chinese Liquor. Information Sciences. 178, 2022–2043.
You, F., & Grossmann, E. (2008). Design of responsive supply chain under demand uncertainty. Computers & Chemical Engineering. 32, 3090-3111.
Zanjirani Farahani, R., & Elahipanah, M. (2008). A genetic algorithm to optimize the total cost and service level for just–in–time distribution in a supply chain. International Journal of production Economics. 111, 229–243.
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.
Altiparmak, F., Gen, M., Lin, L., & Paksoy, T. (2006). A genetic algorithm approach for multi–objective optimization of supply chain networks. Computers & Industrial Engineering. 51, 196–215.
Beamon, B. M. (1998). Supply chain design and analysis: Models and Methods International Journal of Production Economics. 55, 281-294.
Bottani, E., & Rizzi, A. (2006). Strategic management of logistics service: A fuzzy QFD approach. International Journal of Production Economics. 103, 585-599.
Cardona-Valdés, Y., ?lvarez, A., & Ozdemir, D. (2010). A bi–objective supply chain design problem with uncertainty. Transportation Research Part C, 19, 821-823.
Chan, F. T. S., & Kumar, N. (2009). Effective allocation of customers to distribution centres: A multiple ant colony optimization approach. Robotics and Computer–Integrated Manufacturing. 25, 1–12.
Chan, F. T. S., Chung, S. H., & Wadhwa, S. (2005). A hybrid genetic algorithm for production and distribution.Omega. 33, 345-355.
Chen, Y.M., & Wang, W.S. (2010). A particle swarm approach to solve environmental/economic dispatch problem. International Journal of Industrial Engineering Computations, 1(1), 157–172.
CoelloCoello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handliny multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation. 8, 256-279.
CoelloCoello, C. A.Aamont, G.B., & Van Veldhuizen, D.A. (2007).Evolutionary Algorithms for Solving Multi-Objective Problems.SpringerScience+Business Media.ISBN 978-0-387-33254-3.
Das, S., Abraham, A., & Konar, A. (2008). Particle swarm optimization and differential evolution Algorithms. Technical Analysis, Applications and Hybridization Perspectives. Studies in Computational Intelligence, 116, 1-38.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 6, 182-197.
Du, F., & Evans, G. W. (2008). A bi-objective reverse logistics network analysis for post-sale service. Computers & Operations Research, 35, 2617-2634.
Erol, I., Ferrell Jr, W. G. (2004). A methodology to support decision making across the supply chain of an industrial distributor. International Journal of Production Economics. 89, 119-129.
Gen, M., & Syarif, A. (2005). Hybrid genetic algorithm for multi–time period production/distribution planning. Computers & Industrial Engineering. 48, 799–809.
Guillén, G., Mele, F.D., Bagajewicz, M.J., Espu?a, A., & Puigjaner, L. (2005). Multiobjective supply chain design under uncertainty. Chemical Engineering Science. 60, 1535–1553.
Guliashki, V., Toshev, H., & Korsemov, C. (2009). Survey of Evolutionary Algorithms Used in Multiobjective Optimization. Institute of Information Technologies,1113 Sofia.
Lee, Y. H., & Kim, S. H. (2002). Production–distribution planning in supply chain considering capacity constraints. Computer & Industrial Engineering. 43, 169–190.
Papageorgiou, L. G. (2009). Supply chain optimisation for the process industries: Advances and opportunities. Computers and Chemical Engineering, 33, 1931-1938.
Perea-L?pez, E., Ydstie, B.E., & Grossmann, I.E. (2003). A model predictive control strategy for supply chain optimization. Computers and Chemical Engineering, 27, 1201–1218.
Pishvaee, M. S., Zanjirani Farahani, R., & Dullaert, W. (2010). A memetic algorithm for bi–objective integrated forward/reverse logistics network design. Computers & Operations Research, 37, 1100–1112.
Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization: An overview. Swarm Intelligence. 1, 33-57.
Reyes-Sierra, M., & CoelloCoello, C. A. (2006). Muti-objective particle swarm optimizers: A survey of the state-of-the-Art. International Journal of Computational Intelligence Research. 2, 287-308.
Sabri, E. H., & Beamon, B. M. (2000). A multi–objective approach to simultaneous strategic and operational planning in supply chain design. Omega. 28, 581–598.
Sedighizadeh, D., & Masehian, E. (2009). Particle swarm optimization methods, taxonomy and applications. International Journal of Computer Theory and Engineering, 1, 1793-8201.
Sharaf, A. M., & El.Gammal, A. A. (2009). A multi objective multi-stage particle swarm optimization MOPSO search scheme for power quality and loss reduction on radial distribution system. International Conference on Renewable Energies and Power Quality (ICREPQ’09).
Taghavi-fard, M.T., Javanshir, H., Roueintan, M.A., & Soleimany, E. (2011). Multi-objective group scheduling with learning effect in the cellular manufacturing system. International Journal of Industrial Engineering Computations, 2, 617-630.
Tuzkaya, U.R., & ?nüt, S. (2009). A holonic approach based integration methodology for transportation and warehousing functions of the supply network. Computers & Industrial Engineering, 56, 708–723.
Voudouris, V.T. & Consulting, A. (1996). Mathematical programming techniques to debottleneck the supply chain of fine chemicals. Computers & Chemical Engineering, 20S, S1269-S1274.
Xu, J., Liu, Q., & Wang, R. (2008). A class of multi–objective supply chain network optimal model under random fuzzy environment and its application to the industry of Chinese Liquor. Information Sciences. 178, 2022–2043.
You, F., & Grossmann, E. (2008). Design of responsive supply chain under demand uncertainty. Computers & Chemical Engineering. 32, 3090-3111.
Zanjirani Farahani, R., & Elahipanah, M. (2008). A genetic algorithm to optimize the total cost and service level for just–in–time distribution in a supply chain. International Journal of production Economics. 111, 229–243.
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.