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.
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