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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
Baumol, W. J., & Wolfe, P. (1958). A warehouse-location problem. Operations Research, 6(2), 252–263.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
Gunasekaran, A., Lai, K., & Cheng, T. C. E. (2008). Responsive supply chain: A competitive strategy in a networked economy. Omega, 36(4), 549–564.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
Holland, J. (1975). Adaptation in artificial and natural systems. Ann Arbor: The University of Michigan Press.
Google Scholar
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.
Article
Google Scholar
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
Nahmias, S., & Olsen, T. L. (2015). Production and operations analysis. Long Grove: Waveland Press.
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
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.
Article
Google Scholar
Zhao, N., & Lian, Z. (2011). A queueing-inventory system with two classes of customers. International Journal of Production Economics, 129(1), 225–231.
Article
Google Scholar