Alimardani, M., Jolai, F., & 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, 9(1), 22.
Amiri-Aref, M., Klibi, W., & Babai, M. Z. (2018). The multi-sourcing location inventory problem with stochastic demand. European Journal of Operational Research, 266(1), 72–87.
Arabzad, S. M., Ghorbani, M., & Tavakkoli-Moghaddam, R. (2015). An evolutionary algorithm for a new multi-objective location-inventory model in a distribution network with transportation modes and third-party logistics providers. International Journal of Production Research, 53(4), 1038–1050.
Araya-Sassi, C., Paredes-Belmar, G., & Gutiérrez-Jarpa, G. (2020). Multi-commodity inventory-location problem with two different review inventory control policies and modular stochastic capacity constraints. Computers & Industrial Engineering, 143, 106410.
Asadi-Gangraj, E., & Nayeri, S. (2018). A hybrid approach based on LP metric method and genetic algorithm for the vehicle-routing problem with time windows, driver-specific times, and vehicles-specific capacities. International Journal of Operations Research and Information Systems (IJORIS), 9(4), 51–67.
Asl-Najafi, J., Zahiri, B., Bozorgi-Amiri, A., & Taheri-Moghaddam, A. (2015). A dynamic closed-loop location-inventory problem under disruption risk. Computers & Industrial Engineering, 90, 414–428.
Baek, J. W., Bae, Y. H., Lee, H. W., & Ahn, S. (2018). Continuous-type (s, Q)-inventory model with an attached M/M/1 queue and lost sales. Performance Evaluation, 125, 68–79.
Baek, J. W., & Moon, S. K. (2016). A production–inventory system with a Markovian service queue and lost sales. Journal of the Korean Statistical Society, 45(1), 14–24.
Bairamzadeh, S., Saidi-Mehrabad, M., & Pishvaee, M. S. (2018). Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach. Renewable Energy, 116, 500–517.
Baumol, W. J., & Wolfe, P. (1958). A warehouse-location problem. Operations Research, 6(2), 252–263.
Candas, M. F., & Kutanoglu, E. (2020). Integrated location and inventory planningin service parts logistics with customer-based service levels. European Journal of Operational Research, 285(1), 279–295. https://doi.org/10.1016/j.ejor.2020.01.058.
Cardoso, S. R., Barbosa-Póvoa, A. P., Relvas, S., & Novais, A. Q. (2015). Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty. Omega, 56, 53–73.
Dai, Z., Aqlan, F., Zheng, X., & Gao, K. (2018). A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Computers & Industrial Engineering, 119, 338–352.
Dehghani, E., Pishvaee, M. S., & Jabalameli, M. S. (2018). A hybrid Markov process-mathematical programming approach for joint location-inventory problem under supply disruptions. RAIRO-Operations Research, 52(4–5), 1147–1173.
Diabat, A., Battaïa, O., & Nazzal, D. (2015). An improved Lagrangian relaxation-based heuristic for a joint location-inventory problem. Computers & Operations Research, 61, 170–178.
Diabat, A., Dehghani, E., & Jabbarzadeh, A. (2017). Incorporating location and inventory decisions into a supply chain design problem with uncertain demands and lead times. Journal of Manufacturing Systems, 43, 139–149.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science, 1995. MHS’95 (pp. 39–43). IEEE.
Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation Research Part E: Logistics and Transportation Review, 101, 176–200.
Fazli-Khalaf, M., Mirzazadeh, A., & Pishvaee, M. S. (2017). A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network. Human and Ecological Risk Assessment: An International Journal, 23(8), 2119–2149.
Gholizadeh, H., Tajdin, A., & Javadian, N. (2020). A closed-loop supply chain robust optimization for disposable appliances. Neural Computing and Applications, 32(8), 3967–3985. https://doi.org/10.1007/s00521-018-3847-9.
Ghorbani, A., & Jokar, M. R. A. (2016). A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem. Computers & Industrial Engineering, 101, 116–127.
Gong, W., Li, D., Liu, X., Yue, J., & Fu, Z. (2007). Improved two-grade delayed particle swarm optimisation (TGDPSO) for inventory facility location for perishable food distribution centres in Beijing. New Zealand Journal of Agricultural Research, 50(5), 771–779.
Guo, H., Zhang, Y., Zhang, C., Liu, Y., & Zhou, Y. (2018). Location-inventory decisions for closed-loop supply chain management in the presence of the secondary market. Annals of Operations Research, 291(1–2), 1–26. https://doi.org/10.1007/s10479-018-3039-0.
Gunasekaran, A., Lai, K., & Cheng, T. C. E. (2008). Responsive supply chain: A competitive strategy in a networked economy. Omega, 36(4), 549–564.
Hanukov, G., Avinadav, T., Chernonog, T., Spiegel, U., & Yechiali, U. (2017). A queueing system with decomposed service and inventoried preliminary services. Applied Mathematical Modelling, 47, 276–293.
Hiassat, A., Diabat, A., & Rahwan, I. (2017). A genetic algorithm approach for location-inventory-routing problem with perishable products. Journal of Manufacturing Systems, 42, 93–103.
Holland, J. (1975). Adaptation in artificial and natural systems. Ann Arbor: The University of Michigan Press.
Javid, A. A., & Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review, 46(5), 582–597.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.
Liao, S.-H., Hsieh, C.-L., & Lin, Y.-S. (2011). A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory systems. Annals of Operations Research, 186(1), 213–229.
Liu, B., Chen, H., Li, Y., & Liu, X. (2015). A pseudo-parallel genetic algorithm integrating simulated annealing for stochastic location-inventory-routing problem with consideration of returns in e-commerce. Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2015/586581.
Liu, Y., Dai, J., Zhao, S., Zhang, J., Shang, W., Li, T., et al. (2020a). Optimization of five-parameter BRDF model based on Hybrid GA–PSO algorithm. Optik, 219, 164978.
Liu, Y., Dehghani, E., Jabalameli, M. S., Diabat, A., & Lu, C.-C. (2020b). A coordinated location-inventory problem with supply disruptions: A two-phase queuing theory–optimization model approach. Computers & Industrial Engineering, 142, 106326.
Mir, M. S. S., & Rezaeian, J. (2016). A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines. Applied Soft Computing, 41, 488–504.
Mondal, P., Neogy, S. K., Gupta, A., & Ghorui, D. (2020). A policy improvement algorithm for solving a mixture class of perfect information and AR-AT semi-markov games. International Game Theory Review (IGTR), 22(02), 1–19.
Mondal, P., Neogy, S. K., Sinha, S., & Ghorui, D. (2017). Completely mixed strategies for two structured classes of semi-markov games, principal pivot transform and its generalizations. Applied Mathematics & Optimization, 76(3), 593–619.
Mondal, P., Sinha, S., Neogy, S. K., & Das, A. K. (2013). Ordered field property in subclasses of finite discounted AR-AT semi-markov games. In Game theory and management. Collected abstracts of papers presented on the seventh international conference game theory and management/editors Leon A. Petrosyan and Nikolay A. Zenkevich.–SPb.: Graduate School of Management SPbU, 2013.–274 p. The collectio (Vol. 26, p. 164).
Mondal, P., Sinha, S., Neogy, S. K., & Das, A. K. (2016). On discounted AR–AT semi-Markov games and its complementarity formulations. International Journal of Game Theory, 45(3), 567–583.
Mousavi, S. M., Bahreininejad, A., Musa, S. N., & Yusof, F. (2017). A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. Journal of Intelligent Manufacturing, 28(1), 191–206.
Naderi, B., Ghomi, S. M. T. F., Aminnayeri, M., & Zandieh, M. (2011). Scheduling open shops with parallel machines to minimize total completion time. Journal of Computational and Applied Mathematics, 235(5), 1275–1287.
Nahmias, S., & Olsen, T. L. (2015). Production and operations analysis. Long Grove: Waveland Press.
Nayeri, S., Asadi-Gangraj, E., & Emami, S. (2019). Metaheuristic algorithms to allocate and schedule of the rescue units in the natural disaster with fatigue effect. Neural Computing and Applications, 31(11), 7517–7537. https://doi.org/10.1007/s00521-018-3599-6.
Nayeri, S., Paydar, M. M., Asadi-Gangraj, E., & Emami, S. (2020). Multi-objective fuzzy robust optimization approach to sustainable closed-loop supply chain network design. Computers & Industrial Engineering, 148, 106716.
Nekooghadirli, N., Tavakkoli-Moghaddam, R., Ghezavati, V. R., & Javanmard, S. (2014). Solving a new bi-objective location-routing-inventory problem in a distribution network by meta-heuristics. Computers & Industrial Engineering, 76, 204–221.
Puga, M. S., & Tancrez, J.-S. (2017). A heuristic algorithm for solving large location–inventory problems with demand uncertainty. European Journal of Operational Research, 259(2), 413–423.
Rahimi, M., & Fazlollahtabar, H. (2018). Optimization of a closed loop green supply chain using particle Swarm and genetic algorithms. Jordan Journal of Mechanical & Industrial Engineering, 12, 2.
Rahimikelarijani, B., Fazlollahtabar, H., & Nayeri, S. (2020). Multi-objective multi-load tandem autonomous guided vehicle for robust workload balance and material handling optimization. SN Applied Sciences, 2(7), 1–11.
Ramezankhani, M. J., Torabi, S. A., & Vahidi, F. (2018). Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach. Computers & Industrial Engineering, 126, 531–548.
Rayat, F., Musavi, M., & Bozorgi-Amiri, A. (2017). Bi-objective reliable location-inventory-routing problem with partial backordering under disruption risks: A modified AMOSA approach. Applied Soft Computing, 59, 622–643.
Razavi, N., Gholizadeh, H., Nayeria, S., & Ashrafi, T. A. (2020). A robust optimization model of the field hospitals in the sustainable blood supply chain in crisis logistics. Journal of the Operational Research Society, 2020, 1–26.
Rezapour, S., Farahani, R. Z., & Pourakbar, M. (2017). Resilient supply chain network design under competition: A case study. European Journal of Operational Research, 259(3), 1017–1035.
Roh, J., Hong, P., & Min, H. (2014). Implementation of a responsive supply chain strategy in global complexity: The case of manufacturing firms. International Journal of Production Economics, 147, 198–210.
Sadjadi, S. J., Makui, A., Dehghani, E., & Pourmohammad, M. (2016). Applying queuing approach for a stochastic location-inventory problem with two different mean inventory considerations. Applied Mathematical Modelling, 40(1), 578–596.
Saffari, M., Asmussen, S., & Haji, R. (2013). The M/M/1 queue with inventory, lost sale, and general lead times. Queueing Systems, 75(1), 65–77.
Saha, A. K., Paul, A., Azeem, A., & Paul, S. K. (2020). Mitigating partial-disruption risk: A joint facility location and inventory model considering customers’ preferences and the role of substitute products and backorder offers. Computers & Operations Research, 117, 104884.
Savasaneril, S., & Sayin, E. (2017). Dynamic lead time quotation under responsive inventory and multiple customer classes. OR Spectrum, 39(1), 95–135. https://doi.org/10.1007/s00291-016-0445-z.
Seyedhosseini, S. M., Bozorgi-Amiri, A., & Daraei, S. (2014). An integrated location-Routing-Inventory problem by considering supply disruption. iBusiness, 2014(2), 29–37. https://doi.org/10.4236/ib.2014.62004.
Vahdani, B., Soltani, M., Yazdani, M., & Mousavi, S. M. (2017). A three level joint location-inventory problem with correlated demand, shortages and periodic review system: Robust meta-heuristics. Computers & Industrial Engineering, 109, 113–129.
Wright, M. H. (1996). Direct search methods: Once scorned, now respectable. Pitman Research Notes in Mathematics Series, 191–208.
Zhang, D., Yang, S., Li, S., Fan, J., & Ji, B. (2020). Integrated optimization of the location-inventory problem of maintenance component distribution for high-speed railway operations. Sustainability, 12(13), 5447.
Zhao, N., & Lian, Z. (2011). A queueing-inventory system with two classes of customers. International Journal of Production Economics, 129(1), 225–231.