Alimardani, M., Jolai, F., and Rafiei, H. (2013). Bi-product inventory planning in a three-echelon supply chain with backordering, Poisson demand, and limited warehouse space. Journal of Industrial Engineering International, Vol. 9 (22), pp. 9-22.
Amiri A. (2006). Designing a distribution network in a supply chain system: Formulation and efficient solution procedure.European Journal of Operational Research,Vol.171, pp. 567-576.
Azaron, A., Brown, K.N., Tarim, S.A., and Modarres, M. (2008). A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, Vol. 116, pp.129-138.
Bandyopadhyay, S., and Bhattacharya, R. (2014). Solving a tri-objective supply chain problem withmodified NSGA-II algorithm. Journal of Manufacturing Systems, Vol. 33, pp. 41-50.
Belgin, O., Karaoglan, I., and Altiparmak, F. (2018) Two-echelon vehicle routing problem with simultaneous pickup and delivery: Mathematical model and heuristic approach. Computers & Industrial Engineering, Vol.115, pp.1-16
Bidhandi, H.M., and Yusuff R.M. (2011). Integrated supply chain planning under uncertainty using an improved stochastic approach. Applied Mathematical Modeling, Vol. 35, pp. 2618-2630.
Cardona-Valdés, Y., Alvarez, A., and Ozdemir, D. (2011). A bi-objective supply Alvarez chain design problem with uncertainty. Transportation Research, Vol.19, pp. 821-832.
Cerny V. (1985). A thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, Vol.45, pp. 41-51.
Chandra, C, and Kumar, S. (2001). Enterprise architectural framework for supply chain integration. Industrial Management and Data Systems Vol.101, pp. 290-303.
Chen, C.L., and Lee, W.C. (2004). Multi-objective optimization of multi-echelon supply chainnetworks with uncertain product demands and prices. Computers and Chemical Engineering ,Vol.28, pp.1131-1144.
Chen, X., Wan, W., and Xu, X. (1998). Modeling rolling batch planning as vehicle routing problem with time windows. Computers and Operations Research, Vol. 25, pp. 1127-1136.
Costa, A., Celano, G., Fichera, S., and Trovato, E. (2010). A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms. Computers and Industrial Engineering, Vol.59, pp. 986-999.
Dai, Z., Aqlan, F., and Gao, K. (2017) Optimizing multi-echelon inventory with three types of demand in supply chain Transportation Research Part E. Logistics and Transportation Review, Vol.107, pp. 141-177.
El-Sayed, M., Afia, N., and El-Kharbotly, A. (2010). A stochastic model for forward–revers logistics network design under risk. Computers and Industrial Engineering, Vol.58, pp. 423-431.
Gebennini, E., Gamberini, R., and Manzini, R. (2009). An integrated production–distribution model for the dynamic location and allocation problem with safety stock optimization. International Journal of Production Economics ,Vol. 122, pp. 286-304.
Gen, M., and Cheng, R. (2000). Genetic algorithms and engineering optimization. New York: John Wiley and Sons.
Gen, M. (1997). Genetic algorithm and engineering design. New York: John Wiley & Sons
Georgiadis, M.C., Tsiakis, P., Longinidis, P., and Sofioglou, M.K. (2011). Optimal design of supply chain networks under uncertain transient demand variations. Omega,Vol. 39, pp. 254-272.
Ghasemy Yaghin, R. (2018). Integrated multi-site aggregate production-pricing planning in a two-echelon supply chain with multiple demand classes Applied Mathematical Modelling,Vol.53, pp.276-295.
Goldberg, D. (1989). Genetic algorithms in search, Optimization, and machine learning. MA, USA: Addison-Wesley, Reading.
Habibi-Kouchaksaraei, M., Paydar, M.M., and Asadi-Gangraj, E. (2018) Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Applied Mathematical Modelling ,Vol. 55, pp. 583-599.
Holland, J.H. (1975). Adaption in natural and artificial systems. Ann Arbor, Michigan: University of Michigan Press.
Hwang, C.L., and Yoon, K.L.(1981). Multiple attribute decision making: Methods and applications. Springer-Verlag, New York.
Jamshidi, R., Fatemi-Ghomi S.M.T., and Karimi E. (2012). Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method. Scientia Iranica,Vol. 19, pp.1876-1886.
Johansson, L., and Olsson, F. (2018) Age-based inventory control in a multi-echelon system with emergency replenishments. European Journal of Operational Research ,Vol.265, pp.951-961
Kayvanfar, V., Moattar Husseini, S.M., Sajadieh, M.S., and Karimi, B. (2018). A multi-echelon multi-product stochastic model to supply chain of small-and-medium enterprises in industrial clusters. Computers&IndustrialEngineering,Vol.115, pp.69-79.
Kirkpatrick, S., GelattJr, C.D., and Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science ,Vol. 220, pp. 671-680.
Liu, T., Luo, Z., Qin, H., and Lim, A. (2018) A branch-and-cut algorithm for the two-echelon capacitated vehicle routing problem with grouping constraints. European Journal of Operational Research ,Vol. 266, pp.487-497.
Maghsoudlou, H., Kahag, M.R., Niaki, S.T.A., and Pourvaziri, H. (2016) Bi-objective optimization of a three-echelon multi-server supply-chain problem in congested systems: Modeling and solution Computers & Industrial Engineering ,Vol. 99, pp.41-62
Mele, F.D., Guill´en, G., Espuna, A., and Puigjaner, L. (2007). An agent-based approach for supply chain retrofitting under uncertainty.Computers and Chemical Engineering,Vol. 31, pp. 722-735.
Michalewicz, Z. (1996). Genetic algorithms + data structures = evolution programs (3rd Ed.). Berlin, Germany: Springer.
Miranbeigi, M., Moshiri, B., Rahimi-Kian, A., and Razmi, J. (2015). Demand satisfaction in supply chain management system using a full online optimal control method. The International Journal of Advanced Manufacturing Technology,Vol. 77, pp.1401-1417.
Mirzapour, Al-e-hashem, Malekly, H., Aryanezhad, M.B.(2010).A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics ,Vol. 134, pp. 28-42.
Modak, N.M., Panda, S., and Sana, S.S. (2016). Three-echelon supply chain coordination considering duopolistic retailers with perfect quality products. International Journal of Production Economics ,Vol. 182, pp. 564-578.
Mohammadi, A., Abbasi, A., Alimohammadlou, M., Eghtesadifard, M., and Khalifeh, M. (2017). Optimal design of a multi-echelon supply chain in a system thinking framework: An integrated financial-operational approach. Computers & Industrial Engineering,Vol. 114, pp. 297-315.
Murthy, D.N.P., Solem, B.O., and Roren, T. (2004). Product warranty logistics: Issues and challenges. European Journal of Operational Research ,Vol. 156, pp. 110-126.
Olivares-Benitez, E., González-Velarde, J.L., and Ríos-Mercado, R.Z. (2012). A supply chain design problem with facility location and bi-objective transportation choices. Sociedad de Estadística e Investigación Operativa ,Vol. 20, pp. 729-753.
Owen, S.H., and Daskin, M.S. (1998). Strategic facility location: A review. European Journal of Operational Research ,Vol. 111, pp. 423-47.
Park Y. A. (2001). hybrid genetic algorithm for the vehicle scheduling problem with due times and time deadlines. International Journal of Production Economics, Vol. 73, pp.175-188.
Panda, S., Modak, N.M., and Cárdenas-Barrón, L.E. (2017). Coordination and benefit sharing in a three-echelon distribution channel with deteriorating product. Computers & Industrial Engineering ,Vol. 113, pp. 630-645.
Pasandideh, S.H.R., Niaki
, S.T.A., and Aryan Yeganeh
, J. A. (2010). Parameter-tuned genetic algorithm for multi-product economic production quantity model with space constraint, discrete delivery orders and shortages. Advances in Engineering Software,
Vol. 41, pp. 306-314.
Pasandideh, S.H.R., and Niaki, S.T.A. (2008). A genetic algorithm approach to optimize a multi-products EPQ model with discrete delivery orders andconstrained space.Applied Mathematics and Computation,Vol. 195, pp. 506-514.
Pishvaee, M.S., Razmi, J., and Torabi, S.A. (2014). An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain. Transportation Research Part E: Logistics and Transportation Review, ,Vol. 67, pp.14-38.
Prakash, A., Chan, F.T.S, Liao, H., and Deshmukh, S.G. (2012). Network optimization in supply chain: AKBGA approach. Decision Support Systems,Vol. 52, pp. 528-538.
Rodriguez, M.A., Vecchietti, A.R., Harjunkoski, L., and Grossmann, L.E. (2014). Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part I:MINLP and MILP models. Computers & Chemical Engineering,Vol. 62, pp.194-210.
Ross, A., Khajehnezhad, M., Otieno, W., and Aydas, O. (2017). Integrated location-inventory modelling under forward and reverse product flows in the used merchandise retail sector: A multi-echelon formulation. European Journal of Operational Research, Vol.259, pp.664-676
Ruiz-Femenia, R., Guillén-Gosálbez, G., Jiménez, L., and Caballero, J.A. (2013). Multi-objective optimization of environmentally conscious chemical supply chains under demand uncertainty. Chemical Engineering Science,Vol. 96, pp. 1-11.
Schüt, P.Z., Tomasgard, A., and Ahmed, S. (2009). Supply chain design under uncertainty using sample average approximation and dual decomposition. European Journal of Operational Research ,Vol. 199, pp.409-419.
Shen Z.(2007). Integrated supply chain design models: A survey and future research directions. Journal of Industrial and Management Optimization,Vol. 3, pp. 1-27.
Simchi-Levi, D., Kaminsky, P., and Simchi-Levi, E. (2000).Designing and managing the supply chain. New York: Irwin McGraw-Hill.
Snyder, L.V. (2006). Facility location under uncertainty: A review. IIE Transactions,Vol. 38, pp. 537-554.
Song, D.P., Dong, J.X, and Xu, J. (2014). Integrated inventory management and supplier base reduction in a supply chain with multiple uncertainties. European Journal of Operational Research,Vol. 232, pp. 522-536.
Stenius, O., Marklund, J., and Axsäter, S. (2017). Sustainable Multi-echelon Inventory Control with Shipment Consolidation and Volume Dependent Freight Costs. European Journal of Operational Research, in press.
Topan, E., Bayındır, Z.P., and Tan, T. (2017) Heuristics for multi-item two-echelon spare parts inventory control subject to aggregate and individual service measures. European Journal of Operational Research ,Vol. 256, pp.126-138.
Tsai C.F., and Chao K.M. (2009). Chromosome refinement for optimizing multiple supplychains. Information Sciences ,Vol. 179, pp. 2403-2415.
Van Landeghem, H., and Vanmaele, H. (2002).Robust planning: A new paradigm for demand chain planning. Journal of Operation Management ,Vol. 20, pp. 769-783.
Wan,g H.F., and Hsu, H.W.(2010). A closed-loop logistic model with a spanning-tree based genetic algorithm. Computers & Operations Research ,Vol. 37, pp. 376-389.
Wang, K.J., Makond, B., and 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,Vol. 38, pp. 6125-6131.
Weber, C.A., Current, J., and Desai. A.(2000). An optimization approach to determining the number of vendors to employ. Supply Chain Management,Vol. 5, pp. 90-98.
Wu, D., Wu, D.D., Zhang, Y., and Olson, D.L. (2013). Supply chain outsourcing risk using anintegrated stochastic-fuzzy optimization approach. Information Sciences ,Vol. 235, pp. 242-258.
You, F., Grossmann, and I.E. (2008). Design of responsive supply chains under demand uncertainty.Computers and Chemical Engineering,Vol. 32, pp. 3090-3111.
Zegordi, S.H., Abadi, L.N.K., and Beheshtinia, M.A. (2010). A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain. Computers and Industrial Engineering ,Vol. 58, pp. 373-281.
Zhou, L., Baldacci, R., Vigo, D.,and Wang, X. (2018) A Multi-Depot Two-Echelon Vehicle Routing Problem with Delivery Options Arising in the Last Mile Distribution. European Journal of Operational Research ,Vol. 265, pp.765-778.
Zhou, W.Q., Chen, L., and Ge, H.M. (2013). A multi-product multi-echelon inventory control model with joint replenishment strategy. Applied Mathematical Modelling,Vol.37, pp. 2039-2050.