Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 6, November-December 2025
Page(s) 3945 - 3967
DOI https://doi.org/10.1051/ro/2024230
Published online 07 January 2026
  • A.A. Javid and N. Azad, Incorporating location, routing and inventory decisions in supply chain network design. Transp. Res. Part E Logistics Transp. Rev. 46 (2010) 582–597. [Google Scholar]
  • J.-H. Lee, I.-K. Moon and J.-H. Park, Multi-level supply chain network design with routing. Int. J. Prod. Res. 48 (2010) 3957–3976. [Google Scholar]
  • V. Schmid, K.F. Doerner and G. Laporte, Rich routing problems arising in supply chain management. Eur. J. Oper. Res. 224 (2013) 435–448. [Google Scholar]
  • M. Awad, M. Ndiaye and A. Osman, Vehicle routing in cold food supply chain logistics: a literature review. Int. J. Logistics Manage. 32 (2021) 592–617. [Google Scholar]
  • M. Musavi and A. Bozorgi-Amiri, A multi-objective sustainable hub location-scheduling problem for perishable food supply chain. Comput. Ind. Eng. 113 (2017) 766–778. [Google Scholar]
  • J.X. Cao, Z. Zhang and Y. Zhou, A location-routing problem for biomass supply chains. Comput. Ind. Eng. 152 (2021) 107017. [Google Scholar]
  • M. Tavana, H. Tohidi, M. Alimohammadi and R. Lesansalmasi, A location-inventory-routing model for green supply chains with low-carbon emissions under uncertainty. Environ. Sci. Pollut. Res. 28 (2021) 50636–50648. [Google Scholar]
  • K. Govindan, A. Jafarian, R. Khodaverdi and K. Devika, Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food. Int. J. Prod. Econ. 152 (2014) 9–28. [Google Scholar]
  • G. Iassinovskaia, S. Limbourg and F. Riane, The inventory-routing problem of returnable transport items with time windows and simultaneous pickup and delivery in closed-loop supply chains. Int. J. Prod. Econ. 183 (2017) 570–582. [Google Scholar]
  • M. Yavari, H. Enjavi and M. Geraeli, Demand management to cope with routes disruptions in location-inventory-routing problem for perishable products. Res. Transp. Bus. Manage. 37 (2020) 100552. [Google Scholar]
  • M.M. Nasiri, H. Mousavi and S. Nosrati-Abarghooee, A green location-inventory-routing optimization model with simultaneous pickup and delivery under disruption risks. Decis. Anal. J. 6 (2023) 100161. [Google Scholar]
  • F. Rayat, M. Musavi and A. Bozorgi-Amiri, Bi-objective reliable location-inventory-routing problem with partial backordering under disruption risks: a modified AMOSA approach. Appl. Soft Comput. 59 (2017) 622–643. [Google Scholar]
  • J.M. Mulvey, R.J. Vanderbei and S.A. Zenios, Robust optimization of large-scale systems. Oper. Res. 43 (1995) 264–281. [Google Scholar]
  • A. Ben-Tal, L. El Ghaoui and A. Nemirovski, Robust Optimization. Vol. 28. Princeton University Press (2009). [Google Scholar]
  • D. Bertsimas and M. Sim, The price of robustness. Oper. Res. 52 (2004) 35–53. [Google Scholar]
  • M.S. Pishvaee, M. Rabbani and S.A. Torabi, A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl. Math. Modell. 35 (2011) 637–649. [Google Scholar]
  • A. Rahbari, M.M. Nasiri, F. Werner, M. Musavi and F. Jolai, The vehicle routing and scheduling problem with cross-docking for perishable products under uncertainty: two robust bi-objective models. Appl. Math. Modell. 70 (2019) 605–625. [Google Scholar]
  • A. Ala, V. Simic, N. Bacanin and E.B. Tirkolaee, Blood supply chain network design with lateral freight: a robust possibilistic optimization model. Eng. App. Artif. Intell. 133 (2024) 108053. [Google Scholar]
  • A. Goli, A. Ala and S. Mirjalili, A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty. Ann. Oper. Res. 328 (2023) 493–530. [CrossRef] [MathSciNet] [Google Scholar]
  • F. Habibzadeh Boukani, B. Farhang Moghaddam and M.S. Pishvaee, Robust optimization approach to capacitated single and multiple allocation hub location problems. Comput. Appl. Math. 35 (2016) 45–60. [Google Scholar]
  • M. Varas, S. Maturana, R. Pascual, I. Vargas and J. Vera, Scheduling production for a sawmill: a robust optimization approach. Int. J. Prod. Econ. 150 (2014) 37–51. [Google Scholar]
  • R. Lotfi, Z. Sheikhi, M. Amra, M. AliBakhshi and G.-W. Weber, Robust optimization of risk-aware, resilient and sustainable closed-loop supply chain network design with Lagrange relaxation and fix-and-optimize. Int. J. Logistics Res. App. 27 (2024) 705–745. [Google Scholar]
  • R. Lotfi, F. Shoushtari, S.S. Ali, S.M.R. Davoodi, M. Afshar and M.M. Sharifi Nevisi, A viable and bi-level supply chain network design by applying risk, robustness and considering environmental requirements. Cent. Eur. J. Oper. Res. (2024) 1–29. [Google Scholar]
  • D. Bertsimas, V. Gupta and N. Kallus, Data-driven robust optimization. Math. Program. 167 (2018) 235–292. [CrossRef] [MathSciNet] [Google Scholar]
  • C. Shang, X. Huang and F. You, Data-driven robust optimization based on kernel learning. Comput. Chem. Eng. 106 (2017) 464–479. [Google Scholar]
  • M. Musavi and A. Bozorgi-Amiri, Data-driven robust optimization for hub location-routing problem under uncertain environment. J. Ind. Syst. Eng. 15 (2024) 109–129. [Google Scholar]
  • S. Mohseni, M.S. Pishvaee and R. Dashti, Privacy-preserving energy trading management in networked microgrids via data-driven robust optimization assisted by machine learning. Sustain. Energy Grids Networks 34 (2023) 101011. [Google Scholar]
  • Y. Li, Y. Sun, J. Liu, C. Liu and F. Zhang, A data driven robust optimization model for scheduling near-zero carbon emission power plant considering the wind power output uncertainties and electricity-carbon market. Energy 279 (2023) 128053. [Google Scholar]
  • R. Lotfi, R. Hazrati, S. Aghakhani, M. Afshar, M. Amra and S.S. Ali, A data-driven robust optimization in viable supply chain network design by considering Open Innovation and Blockchain Technology. J. Clean. Prod. 436 (2024) 140369. [Google Scholar]
  • M. Karimi-Mamaghan, M. Mohammadi, A. Pirayesh, A.M. Karimi-Mamaghan and H. Irani, Hub-and-spoke network design under congestion: a learning based metaheuristic. Transp. Res. Part E: Logistics Transp. Rev. 142 (2020) 102069. [Google Scholar]
  • C.-Y. Cheng, P. Pourhejazy, K.-C. Ying, S.-F. Li and C.-W. Chang, Learning-based metaheuristic for scheduling unrelated parallel machines with uncertain setup times. IEEE Access 8 (2020) 74065–74082. [Google Scholar]
  • A. Seyyedabbasi, R. Aliyev, F. Kiani, M.U. Gulle, H. Basyildiz and M.A. Shah, Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems. Knowl.-Based Syst. 223 (2021) 107044. [Google Scholar]
  • W. Qin, Z. Zhuang, Z. Huang and H. Huang, A novel reinforcement learning-based hyper-heuristic for heterogeneous vehicle routing problem. Comput. Ind. Eng. 156 (2021) 107252. [Google Scholar]
  • V.A. de Santiago Jr., E. Özcan and V.R. de Carvalho, Hyper-heuristics based on reinforcement learning, balanced heuristic selection and group decision acceptance. Appl. Soft Comput. 97 (2020) 106760. [Google Scholar]
  • İ. Gölcük and F.B. Ozsoydan, Q-learning and hyper-heuristic based algorithm recommendation for changing environments. Eng. App. Artif. Intell. 102 (2021) 104284. [Google Scholar]
  • B. Xi and D. Lei, Q-learning-based teaching-learning optimization for distributed two-stage hybrid flow shop scheduling with fuzzy processing time. Complex Syst. Model. Simul. 2 (2022) 113–129. [Google Scholar]
  • X. Ni, W. Hu, Q. Fan, Y. Cui and C. Qi, A Q-learning based multi-strategy integrated artificial bee colony algorithm with application in unmanned vehicle path planning. Expert Syst. App. 236 (2024) 121303. [Google Scholar]
  • Z. Zhang, Z. Wu, H. Zhang and J. Wang, Meta-learning-based deep reinforcement learning for multiobjective optimization problems. IEEE Trans. Neural Netw. Learn. Syst. 34 (2022) 7978–7991. [Google Scholar]
  • J. Kallestad, R. Hasibi, A. Hemmati and K. Sörensen, A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems. Eur. J. Oper. Res. 309 (2023) 446–468. [Google Scholar]
  • Z. Zhang, Z. Shao, W. Shao, J. Chen and D. Pi, MRLM: a meta-reinforcement learning-based metaheuristic for hybrid flow-shop scheduling problem with learning and forgetting effects. Swarm Evol. Comput. 85 (2024) 101479. [Google Scholar]
  • S. Kirkpatrick, C.D. Gelatt Jr. and M.P. Vecchi, Optimization by simulated annealing. Science 220 (1983) 671–680. [CrossRef] [MathSciNet] [Google Scholar]
  • J.F. Cordeau, M. Gendreau and G. Laporte, A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks: An Int. J. 30 (1997) 105–119. [Google Scholar]

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