Volume 3 Issue 4 pp. 681-694 Summer, 2012


Bi-objective supply chain problem using MOPSO and NSGA-II


 Hassan Javanshir Sadoullah Ebrahimnejad and Samaneh Nouri


The increase competition and decline economy has increased the relevant importance of having reliable supply chain. The primary objective of many supply chain problems is to reduce the cost of services and, at the same time, to increase the quality of services. In this paper, we present a multi-level supply chain network by considering multi products, single resource and deterministic cost and demand. The proposed model of this paper is formulated as a mixed integer programming and we present two metaheuristics namely MOPSO and NSGA-II to solve the resulted problems. The performance of the proposed models of this paper has been examined using some randomly generated numbers and the results are discussed. The preliminary results indicate that while MOPSO is able to generate more Pareto solutions in relatively less amount of time, NSGA-II is capable of providing better quality results.


DOI: 10.5267/j.ijiec.2012.02.003

Keywords: Supply Chain, Bi-objective optimization, MOPSO, NSGA-II, Supply chain network design responsiveness

References

References 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., Álvarez, 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., Espuña, 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-López, 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., & Önüt, 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.