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All issues Volume 56 / No 4 (July-August 2022) RAIRO-Oper. Res., 56 4 (2022) 2115-2137 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 56, Number 4, July-August 2022
Page(s) 2115 - 2137
DOI https://doi.org/10.1051/ro/2022072
Published online 11 July 2022
  • R. Abyazi-Sani and R. Ghanbari, An efficient tabu search for solving the uncapacitated single allocation hub location problem. Comput. Ind. Eng. 93 (2016) 99–109. [CrossRef] [Google Scholar]
  • A. Anderluh, P.C. Nolz, V.C. Hemmelmayr and T.G. Crainic, Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and “grey zone” customers arising in urban logistics. Eur. J. Oper. Res. 289 (2021) 940–958. [CrossRef] [Google Scholar]
  • M.S. Atabaki, A.A. Khamseh and M. Mohammadi, A priority-based firefly algorithm for network design of a closed-loop supply chain with price-sensitive demand. Comput. Ind. Eng. 135 (2019) 814–837. [CrossRef] [Google Scholar]
  • C. Claycomb, K. Iyer and R. Germain, Predicting the level of B2B e-commerce in industrial organizations. Ind. Mark. Manag. 34 (2005) 221–234. [CrossRef] [Google Scholar]
  • J.F. Cordeau, M. Gendreau and G. Laporte, A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks 30 (1997) 105–119. [CrossRef] [Google Scholar]
  • T.L. Dam, K. Li and P. Fournier-Viger, Chemical reaction optimization with unified tabu search for the vehicle routing problem. Soft Comput. 21 (2017) 6421–6433. [CrossRef] [Google Scholar]
  • A. Dwivedi, A. Jha, D. Prajapati, N. Sreenu and S. Pratap, Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem. Mod. Supply Chain Res. Appl. (2020). DOI: 10.1108/MSCRA-04-2020-0007. [Google Scholar]
  • S. Elhedhli and R. Merrick, Green supply chain network design to reduce carbon emissions. Transp. Res. Part D Transp. Environ. 17 (2012) 370–379. [CrossRef] [Google Scholar]
  • B. Fahimnia, J. Sarkis, F. Dehghanian, N. Banihashemi and S. Rahman, The impact of carbon pricing on a closed-loop supply chain: an Australian case study. J. Clean. Prod. 59 (2013) 210–225. [CrossRef] [Google Scholar]
  • J. Hong, A. Diabat, V.V. Panicker and S. Rajagopalan, A two-stage supply chain problem with fixed costs: an ant colony optimization approach. Int. J. Prod. Econ. 204 (2018) 214–226. [CrossRef] [Google Scholar]
  • A.Y.S. Lam and V.O.K. Li, Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans. Evol. Comput. 14 (2009) 381–399. [Google Scholar]
  • A.Y.S. Lam, V.O.K. Li and J.J.Q. Yu, Real-coded chemical reaction optimization. IEEE Trans. Evol. Comput. 16 (2012) 339–353. [CrossRef] [Google Scholar]
  • K.H. Leung, C.K.M. Lee and K.L. Choy, An integrated online pick-to-sort order batching approach for managing frequent arrivals of B2B e-commerce orders under both fixed and variable time-window batching. Adv. Eng. Inf. 45 (2020) 101125. [CrossRef] [Google Scholar]
  • J. Li and Q. Pan, Chemical-reaction optimization for solving fuzzy job-shop scheduling problem with flexible maintenance activities. Int. J. Prod. Econ. 145 (2013) 4–17. [CrossRef] [Google Scholar]
  • W.C. Ling, A.B. Verasingham, V. Andiappan, Y.K. Wan, I.M.L. Chew and D.K.S. Ng, An integrated mathematical optimisation approach to synthesise and analyse a bioelectricity supply chain network. Energy 178 (2019) 554–571. [CrossRef] [Google Scholar]
  • I. Mallidis, R. Dekker and D. Vlachos, The impact of greening on supply chain design and cost: a case for a developing region. J. Transp. Geogr. 22 (2012) 118–128. [CrossRef] [Google Scholar]
  • D.G. Mogale, S.K. Kumar, F.P.G. Marquez and M.K. Tiwari, Bulk wheat transportation and storage problem of public distribution system. Comput. Ind. Eng. 104 (2017) 80–97. [CrossRef] [Google Scholar]
  • F. Mohebalizadehgashti, H. Zolfagharinia and S.H. Amin, Designing a green meat supply chain network: a multi-objective approach. Int. J. Prod. Econ. 219 (2020) 312–327. [CrossRef] [Google Scholar]
  • R. Ngambusabongsopa, Z. Li and E. Eldesouky, A hybrid mutation chemical reaction optimization algorithm for global numerical optimization. Math. Probl. Eng. 2015 (2015). DOI: 10.1155/2015/375902. [CrossRef] [Google Scholar]
  • T.T. Nguyen, Z. Li, S. Zhang and T.K. Truong, A hybrid algorithm based on particle swarm and chemical reaction optimization. Expert Syst. Appl. 41 (2014) 2134–2143. [CrossRef] [Google Scholar]
  • F.R. Nilsson, A complexity perspective on logistics management: rethinking assumptions for the sustainability era. Int. J. Logist. Manag. 30 (2019) 681–698. [CrossRef] [Google Scholar]
  • J. Noh and J.S. Kim, Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand. J. Clean. Prod. 208 (2019) 1421–1435. [CrossRef] [Google Scholar]
  • I. Nouira, R. Hammami, Y. Frein and C. Temponi, Design of forward supply chains: impact of a carbon emissions-sensitive demand. Int. J. Prod. Econ. 173 (2016) 80–98. [CrossRef] [Google Scholar]
  • T. Paksoy, T. Bektaş and E. Özceylan, Operational and environmental performance measures in a multi-product closed-loop supply chain. Transp. Res. Part E Logist. Transp. Rev. 47 (2011) 532–546. [CrossRef] [Google Scholar]
  • D. Prajapati, F. Zhou, N. Cheikhrouhou and S. Pratap, Minimizes the time window for delivery of orders in B2B e-commerce. In: Proceedings of the 5th International Conference on Industrial Engineering (ICIE). Sochi, Russia (2019) 18–19. [Google Scholar]
  • D. Prajapati, A.R. Harish, Y. Daultani, H. Singhand S. Pratap, A clustering based routing heuristic for last-mile logistics in fresh food e-Commerce. Glob. Bus. Rev. (2020). DOI: 10.1177/0972150919889797. [Google Scholar]
  • D. Prajapati, F. Zhou, M. Zhang, H. Chelladurai and S. Pratap, Sustainable logistics network design for multi-products delivery operations in B2B e-commerce platform. Sādhanā 46 (2021) 1–13. [Google Scholar]
  • A. Ramudhin, A. Chaabane, M. Kharoune and M. Paquet, Carbon market sensitive green supply chain network design. 2008 IEEE Int. Conf. Ind. Eng. Eng. Manag. 5 (2008) 1093–1097. [CrossRef] [Google Scholar]
  • H.S. Rao, Reducing carbon emissions from transport projects. Eval. Knowl. Br. 16 (2010) 1–96. [Google Scholar]
  • Scenario Building Team, Long term mitigation scenarios: strategic options for South Africa. Long Term Mitig. Scenar. Scenar. Doc. Deaprtment Environ. Tour. (2007) 34. http://hdl.handle.net/11427/16804. [Google Scholar]
  • A. Shamsuzzoha, E. Ndzibah and K. Kettunen, Data-driven sustainable supply chain through centralized logistics network: case study in a Finnish pharmaceutical distributor company. Curr. Res. Environ. Sustain. 2 (2020) 100013. [CrossRef] [Google Scholar]
  • K. Shaw, M. Irfan, R. Shankar and S.S. Yadav, Low carbon chance constrained supply chain network design problem: a benders decomposition based approach. Comput. Ind. Eng. 98 (2016) 483–497. [CrossRef] [Google Scholar]
  • M. Sreenivas and T. Srinivas,, The Role of Transportation in Logistics Chain. Alluri Institute of Management Science, Warangal. AP India (1814) 1–10. [Google Scholar]
  • S.K. Srivastava, Green supply-chain management: a state-of-the-art literature review. Int. J. Manag. Rev. 9 (2007) 53–80. [CrossRef] [Google Scholar]
  • A. Stefaniec, K. Hosseini, J. Xie and Y. Li, Sustainability assessment of inland transportation in China: a triple bottom line-based network DEA approach. Transp. Res. Part D Transp. Environ. 80 (2020) 102258. [CrossRef] [Google Scholar]
  • Y. Tan, Y. Shi, F. Buarque, A. Gelbukh, S. Das and A. Engelbrecht, Advances in swarm and computational intelligence. In: 6th International Conference, ICSI 2015 held in conjunction with the Second BRICS Congress, CCI 2015 Beijing, China, June 25–28, 2015 Proceedings, Part II. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinf.). Vol. 9141 (2015) 229–237. [Google Scholar]
  • J. Tang, H. Heinimann, K. Han, H. Luo and B. Zhong, Evaluating resilience in urban transportation systems for sustainability: a systems-based Bayesian network model. Transp. Res. Part C Emerg. Technol. 121 (2020) 102840. [CrossRef] [Google Scholar]
  • P. Taylor, V.V. Panicker, R. Vanga and R. Sridharan, Ant colony optimisation algorithm for distribution-allocation problem in a two-stage supply chain with a fixed transportation charge. Int. J. Prod. Res. 51 (2013) 698–717. [CrossRef] [Google Scholar]
  • C.J. Vidal and M. Goetschalckx, Strategic production-distribution models: a critical review with emphasis on global supply chain models. Eur. J. Oper. Res. 98 (1997) 1–18. [CrossRef] [Google Scholar]
  • Y. Wang, Y. Sun, X. Guan and Y. Guo, Two-echelon location-routing problem with time windows and transportation resource sharing. J. Adv. Transp. 2021 (2021). DOI: 10.1155/2021/8280686 . [Google Scholar]
  • S.X. Xu, M. Cheng and G.Q. Huang, Efficient intermodal transportation auctions for B2B e-commerce logistics with transaction costs. Transp. Res. Part B 80 (2020) 322–337. [Google Scholar]
  • P. Yang and L. Zeng, Models and methods for two-echelon location routing problem with time constraints in city logistics. Math. Probl. Eng. 2018 (2018). DOI: 10.1155/2018/2549713 . [Google Scholar]
  • M. Zhang, S. Pratap, G.Q. Huang and Z. Zhao, Optimal collaborative transportation service trading in B2B e-commerce logistics. Int. J. Prod. Res. 7543 (2017) 1–17. [Google Scholar]
  • M. Zhang, L. Chen and X. Chen, An advanced chemical reaction optimization algorithm based on balanced local and global search. Math. Prob. Eng. 2018 (2018). DOI: 10.1155/2018/8042689. [Google Scholar]
  • M. Zhang, S. Pratap, Z. Zhao, D. Prajapati and G.Q. Huang, Forward and reverse logistics vehicle routing problems with time horizons in B2C e-commerce logistics. Int. J. Prod. Res. 59 (2021) 6291–6310. [CrossRef] [Google Scholar]

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