Presenting a Bi-objective Integrated Model for Production-Distribution Problem in a Multi-level Supply Chain Network

Kazemi, Abolfazl; Khezrian, Vahid; Oroojeni Mohammad Javad, Mahsa; Alinezhad, Alireza

doi:10.22034/2014.4.07

Presenting a Bi-objective Integrated Model for Production-Distribution Problem in a Multi-level Supply Chain Network

Document Type : Research Paper

Authors

¹ Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

² Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA,

10.22034/2014.4.07

Abstract

In this study, a bi-objective model for integrated planning of production-distribution in a multi-level supply chain network with multiple product types and multi time periods is presented. The supply chain network including manufacturers, distribution centers, retailers and final customers is proposed. The proposed model minimizes the total supply chain costs and transforming time of products for customers in the chain. The proposed model is in the class of linear integer programming problems. The complexity of the problem is large and in the literatur, this problem has been shown to be NP-hard. Therefore, for solving this problem, two multi objective meta-heuristic approaches based on Pareto method including non-dominated Sorting Genetic Algorithm-II (NSGA-II) and non-dominated Ranking Genetic Algorithm (NRGA) have been suggested. Since the output of meta- heuristic algorithms are highly dependent on the input parameters of the algorithm, Taguchi method (Taguchi) is used to tune the parameters. Finally, in order to evaluate the performance of the proposed solution methods, different test problems with different dimensions have been produced and the performances of the proposed algorithms on the test problems have been analyzed.

Keywords

Main Subjects

optimization in supply chain management

References

Al Jadaan, O., Rajamani L. and Rao, C.R. (2008), Non-Dominated ranked genetic algorithm for solving Multi-Objective optimization problems: NRGA. Journal of Theoretical and Applied Information Technology, Vol. 2, pp. 60-67.

Bate, S.T. and Jones, B. (2008), A review of uniform crossover designs. Journal of Statistical Planning and Inference, Vol. 138(2), pp. 336-351.

Chan, F.T.S., Chung, S.H. and Wadhwa S. (2005), A hybrid genetic algorithm for production and distribution. Omega, Vol. 33(4), pp. 345-355.

Chandra, P. and Fisher, M.L. (1994), Coordination of production and distribution planning. European Journal of Operational Research, Vol. 72(3), pp. 503-517.

Chen, C. and Lee, W. (2004), Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers and Chemical Engineering, Vol. 28(6-7), pp. 1131-1144.

Chopra, S., and Meindle, P. (2004), Supply chain management- strategy. Planning and operation, 2nd Ed. Prentic Hall.

Cohen, M.A. and Lee, H.L. (1988), Strategic Analysis of Integrated Production-Distribution Systems: Models and Methods. Operations Research, Vol. 36(2), pp. 216-228.

Deb, K., Agrawal, S., Pratap, A. and Meyarivan, T. (2000), A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: proceedings of the parallel problem solving from nature VI (PPSN-VI), Conference, pp. 849-858.

Erenguc, S.S., Simpson, N.C. and Vakharia, A.J. (1999), Integrated production/distribution planning in supply chains: an invited review. European Journal of Operational Research, Vol. 115(2), pp. 219-236.

Fahimnia B., Zanjirani Frahani R., Marian R. and Luong, L. (2013), A review and critique on integrated production–distribution planning models and techniques. Journal of Manufacturing Systems, Vol. 32(1), pp. 1-19.

Fraley, S., Oom, M., Terrien, B., and Zalewski, J. (2006), Design of experiments via Taguchi methods: Orthogonal Arrays. The Michigan Chemical Process Dynamic and Controls Open Text Book, USA.

Jolai, F., Razmi, J. and Rostami, N.K.M. (2011), A fuzzy goal programming and meta heuristic algorithms for solving integrated production:distribution planning problem. Central European Journal of Operation Research, Vol. 19(4), pp. 547-569.

Kalaitzidou, M. A., Longinidis, P., Tsiakis, P., & Georgiadis, M. C. (2014), Optimal Design of Multiechelon Supply Chain Networks with Generalized Production and Warehousing Nodes. Industrial & Engineering Chemistry Research, Vol. 53, No. 33, PP. 13125-13138.

Kazemi, A., Fazel Zarandi, M. H. and Moattar Husseini S. M. (2009), A multi-agent system to solve the production–distribution planning problem for a supply chain: a genetic algorithm approach, The International Journal of Advanced Manufacturing Technology, Vol. 44, No. 1-2, PP. 180–193.

Keskin, B.B. and Uster, H. (2007), Meta-heuristic approaches with memory and evolution for a multi-product production/distribution system design problem, European Journal of Operational Research, Vol. 182(2), pp. 663-682.

Lee, Y.H. and Kim, S.H. (2002), Production-distribution planning in supply chain considering capacity constraints. Computers and Industrial Engineering, Vol. 43(1-2), pp. 169-190.

Liu, S. and Papageorgiou, L.G. (2013), Multi objective optimization of production, distribution and capacity planning of global supply chains in the process industry. Omega, Vol. 43(2), pp. 369-382.

Mohamed, Z. M. (1999), an integrated production-distribution model for a multi-national company operating under varying exchange rates. International Journal of Production Economics, Vol. 58(1), pp. 81-92.

Sabri, E.H. and Beamon, B.M. (2000), A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, Vol. 28(5), pp. 581-598.

Sarrafha, K., Rahmati, S.H.A., Akhavan Niaki, S.T., & Zaretalab, A. (2015), A bi-objective integrated procurement, production, and distribution problem of a multi-echelon supply chain network design: A new tuned MOEA. Computers & Operations Research, Vol. 54(1), pp. 35-51.

Schott, J.R., (1999), Fault tolerant design using single and multicriteria genetic algorithms optimization. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA.

Thomas, D.J. and Griffin, P.M. (1996), Coordinated supply chain management. European Journal of Operational Research, Vol. 94(1), pp. 1-15.

Yeniay, O. and Ankare, B. (2005), Penalty function methods for constrained optimization with genetic algorithms. Mathematical and Computational Application, Vol. 10(1), pp. 45-56.

Zitzler, E. and Thiele, L. (1998), Multiobjective optimization using evolutionary algorithms a comparative case study. Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), Berlin, Germany, pp. 292-301.