Document Type : Research Paper
Author
Abstract
Today, intense competition in global markets has forced companies to design and manage of supply chains in a better way. Since the role of three factors: location, routing and inventory is important to continue the life of a supply chain so, integration of these three elements will create an efficient and effective supply chain. In this study, we investigate the problem of supply chain network design that including routing and inventory problem consist of flow allocation, vehicle routing between facilities, locating distribution centers and also consider the maximum coverage for respond to customer demand. Proposed mathematical model is a nonlinear mixed integer programming model for location-routing-inventory problem in the four-echelon supply chain by considering the multiple conflicting goals of total cost, travel time and maximum coverage. In order to solve the proposed model, three meta-heuristic algorithms (MOPSO, MSGA_II and NRGA) has been used. The accuracy of mathematical model and proposed algorithms are evaluated through numerical examples
Keywords
Almeder, C., Preusser, M., &Hartl, R. F. (2009). Simulation and optimization of supply chains: alternative or complementary approaches?. OR spectrum, 31(1), 95-119.
Asl-Najafi, J., Zahiri, B., Bozorgi-Amiri, A., Taheri-Moghaddam, A., )2015(. A dynamic closed-loop location-inventory problem under disruption risk. Comput.Indust. Eng. 90, 414–428.
Blanquero, R., Carrizosa, E. and Boglárka, G., )2015(. Maximal covering location problems on networks with regional demand. Omega.
Coello, C. C., & Lechuga, M. S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization. In Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on (Vol. 2, pp. 1051-1056). IEEE.
Davari, S., Zarandi, M.H.F. and Turksen, I.B., )2013(. A greedy variable neighborhood search heuristic for the maximal covering location problem with fuzzy coverage radii. Knowledge-Based Systems, 41, pp.68-76.
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000, September). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In International Conference on Parallel Problem Solving From Nature (pp. 849-858). Springer Berlin Heidelberg.
Gaur, S., & Ravindran, A. R. (2006). A bi-criteria model for the inventory aggregation problem under risk pooling. Computers & industrial engineering, 51(3), 482-501.
Ghaffari-Nasab N., Ahari S. G., Ghazanfari M., (2013), A hybrid simulated annealing based heuristic for solving the location-routing problem with fuzzy demands. Scientia Iranica20(3), 919-930.
Guerrero, W.J., Prodhon, C., Velasco, N. and Amaya, C.A., )2013(. Hybrid heuristic for the inventory location-routing problem with deterministic demand. International Journal of Production Economics, 146(1), pp.359-370.
Huang S. H., (2015), Solving the multi-compartment capacitated location routing problem with pickup–delivery routes and stochastic demands. Computers & Industrial Engineering 87, 104-113.
Karaoglan I., Altiparmak F., Kara I., Dengiz, B., (2011), A branch and cut algorithm for the location-routing problem with simultaneous pickup and delivery. European Journal of Operational Research211(2), 318-332.
Karasakal, O., &Karasakal, E. K. (2004). A maximal covering location model in the presence of partial coverage. Computers & Operations Research, 31(9), 1515-1526.
Kaya, O., Urek, B., )2016(. A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loopsupply chain. Comput. Oper. Res. 65, 93–103.
Kumar, R.S., Kondapaneni, K., Dixit, V., Goswami, A., Thakur, L., Tiwari, M., )2015(. Multi-objective modeling of production and pollution routing problem withtime window: a self-learning particle swarm optimization approach. Comput. Indust. Eng.
Lee, B. K., Kang, K. H., & Lee, Y. H. (2008). Decomposition heuristic to minimize total cost in a multi-level supply chain network. Computers & Industrial Engineering, 54(4), 945-959.
Liao, S. H., Hsieh, C. L., & Lai, P. J. (2011). An evolutionary approach for multi-objective optimization of the integrated location–inventory distribution network problem in vendor-managed inventory. Expert Systems with Applications, 38(6), 6768-6776.
Liu S. C., Lee S. B., (2003), A two-phase heuristic method for the multi-depot location routing problem taking inventory control decisions into consideration. The International Journal of Advanced Manufacturing Technology22(11-12), 941-950.
Ma Z., Dai Y.,(2010), Stochastic dynamic location-routing-inventory problem in two-echelon multi-product distribution systems. Logistics for Sustained Economic Development, pp. 2562-2568.
Rajagopalan H. K., Saydam C., Xiao J., (2008), A multiperiod set covering location model for dynamic redeployment of ambulances. Computers & Operations Research35(3), 814-826.
Sarkar, B., & Majumder, A. (2013). A study on three different dimensional facility location problems. Economic Modelling, 30, 879-887.
Simchi-Levi, D., Kaminsky, P., Simchi-Levi E. )2000(. Designing and Managing the Supply Chain Concepts, Strategies and Case Studies. McGraw-Hill, Boston.
Syarif, A., Yun, Y., & Gen, M. (2002). Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach. Computers & Industrial Engineering, 43(1), 299-314.
Tancrez, J. S., Lange, J. C., &Semal, P. (2012). A location-inventory model for large three-level supply chains. Transportation Research Part E: Logistics and Transportation Review, 48(2), 485-502.
Tavakkoli-Moghaddam R., Makui A., Mazloomi Z., (2010), A new integrated mathematical model for a bi-objective multi-depot location-routing problem solved by a multi-objective scatter search algorithm. Journal of Manufacturing Systems29(2), 111-119..
Teo CP., Shu J., (2004), Warehouse-retailer network design problems. Operations Research 52, 396–408.
Vidović, M., Ratković, B., Bjelić, N., &Popović, D. (2016). A two-echelon location-routing model for designing recycling logistics networks with profit: MILP and heuristic approach. Expert Systems with Applications, 51, 34-48.
Wang, K. J., Makond, B., & Liu, S. Y. (2011). Location and allocation decisions in a two-echelon supply chain with stochastic demand–A genetic-algorithm based solution. Expert Systems with Applications, 38(5), 6125-6131.
Zachariadis, E. E., Tarantilis, C. D., &Kiranoudis, C. T. (2009). An integrated local search method for inventory and routing decisions. Expert Systems with Applications, 36(7), 10239-10248.
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., &Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182-214.
Zhao J., Verter V., (2014), A bi-objective model for the used oil location-routing problem. Computers & Operations Research62, 157-168.
)