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All issues Volume 56 / No 6 (November-December 2022) RAIRO-Oper. Res., 56 6 (2022) 3991-4022 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 56, Number 6, November-December 2022
Page(s) 3991 - 4022
DOI https://doi.org/10.1051/ro/2022164
Published online 25 November 2022
  • G. Ahmadi, S.A. Torabi and R. Tavakkoli-Moghaddam, A bi-objective location-inventory model with capacitated transportation and lateral transshipments. Int. J. Prod. Res. 54 (2016) 2035–2056. [CrossRef] [Google Scholar]
  • M. Akbarpour, S.A. Torabi and A. Ghavamifar, Designing an integrated pharmaceutical relief chain network under demand uncertainty. Transp. Res. Part E Logist. Transp. Rev. 136 (2020) 101867. [CrossRef] [Google Scholar]
  • I. Ali, S. Gupta and A. Ahmed, Multi-objective linear fractional inventory problem under intuitionistic fuzzy environment. Int. J. Syst. Assur. Eng. Manag. 10 (2019) 173–189. [CrossRef] [Google Scholar]
  • E.M. Alvarez, M.C. Van der Heiden, I.M.H. Vliegen and W.H.M. Zijm, Service differentiation through selective lateral transshipments. Eur. J. Oper. Res. 237 (2014) 824–835. [CrossRef] [Google Scholar]
  • S.H. Amin and F. Baki, A facility location model for global closed-loop supply chain network design. App. Math. Model. 41 (2017) 316–330. [CrossRef] [Google Scholar]
  • A. Arasteh, Supply chain management under uncertainty with the combination of fuzzy multi-objective planning and real options approaches. Soft Comput. 24 (2021) 5177–5198. [Google Scholar]
  • M.G. Avci and H. Selim, A multi-objective simulation-based optimization approach for inventory replenishment problem with premium freights in convergent supply chains. Omega 80 (2018) 153–165. [CrossRef] [Google Scholar]
  • S. Bandyopadhyay and R. Bhattacharya, Solving a tri-objective supply chain problem with modified NSGA-II algorithm. J. Manuf. Syst. 33 (2014) 41–50. [CrossRef] [Google Scholar]
  • M. Bashiri, H. Badri and J. Talebi, A new approach to tactical and strategic planning in production–distribution networks. App. Math. Model. 36 (2012) 1703–1717. [CrossRef] [Google Scholar]
  • A.K. Bera, D.K. Jana, D. Banerjee and T. Nandy, A two-phase multi-criteria fuzzy group decision making approach for supplier evaluation and order allocation considering multi-objective, multi-product and multi-period. Ann. Data Sci. 8 (2020) 577–601. [Google Scholar]
  • B. Bilgen, Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem. Expert Syst. App. 37 (2010) 4488–4495. [CrossRef] [Google Scholar]
  • C. Bilir, S.O. Ekici and F. Ulengin, An integrated multi-objective supply chain network and competitive facility location model, Comput. Ind. Eng. 108 (2017) 136–148. [CrossRef] [Google Scholar]
  • H.L. Chan, X. Wei, S. Guo and W.H. Leung, Corporate social responsibility (CSR) in fashion supply chains: a multi-methodological study. Transp. Res. Part E Logist. Transp. Rev. 142 (2020) 102063. [CrossRef] [Google Scholar]
  • V. Charles, S. Gupta and I. Ali, A fuzzy goal programming approach for solving multi-objective supply chain network problems with pareto-distributed random variables. Int. J. Uncertainty Fuzziness Knowlege Based Syst. 27 (2019) 559–593. [CrossRef] [Google Scholar]
  • S.B. Choi, B.K. Dey, S.J. Kim and B. Sarkar, Intelligent servicing strategy for an online-to-offline (O2O) supply chain under demand variability and controllable lead time. RAIRO: Oper. Res. 56 (2022) 1623–1653. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • F. Delfani, H. Samanipour, H. Beiki, A.V. Yumashev and E.M. Akhmetshin, A robust fuzzy optimisation for a multi-objective pharmaceutical supply chain network design problem considering reliability and delivery time. Int. J. Syst. Sci. Oper. Logist. 9 (2020) 155–179. [Google Scholar]
  • M. Daz-Madroñero, D. Peidro and J. Mula, A fuzzy optimization approach for procurement transport operational planning in an automobile supply chain. App. Math. Model. 38 (2014) 5705–5725. [CrossRef] [Google Scholar]
  • H. Ensafian and S. Yaghoubi, Robust optimization model for integrated procurement, production and distribution in platelet supply chain. Transp. Res. Part E Logist. Transp. Rev. 103 (2017) 32–55. [CrossRef] [Google Scholar]
  • M. Fattahi, K. Govindan and E. Keyvanshokooh, A multi-stage stochastic program for supply chain network redesign problem with price-dependent uncertain demands. Comput. Operat. Res. 100 (2018) 314–332. [CrossRef] [Google Scholar]
  • A. Garai and B. Sarkar, Economically independent reverse logistics of customer-centric closed-loop supply chain for herbal medicines and biofuel. J. Clean. Prod. 334 (2022) 129977. [CrossRef] [Google Scholar]
  • B. Gaudenzi and M. Christopher, Achieving supply chain “Leagility” through a project management orientation. Int. J. Logist. Res. App. 19 (2016) 3–18. [CrossRef] [Google Scholar]
  • A.L. Guiffrida and M.Y. Jaber, Managerial and economic impacts of reducing delivery variance in the supply chain. App. Math. Model. 32 (2008) 2149–2161. [CrossRef] [Google Scholar]
  • S. Gupta, I. Ali and A. Ahmed, Efficient fuzzy goal programming model for multi-objective production distribution problem. Int. J. App. Comput. Math. 4 (2018) 76. [CrossRef] [Google Scholar]
  • S. Gupta, H. Garg and S. Chaudhary, Parameter estimation and optimization of multi-objective capacitated stochastic transportation problem for gamma distribution. Complex Intell. Syst. 6 (2020) 651–667. [CrossRef] [Google Scholar]
  • S. Gupta, S. Chaudhary, P. Chatterjee and M. Yazdani, An efficient stochastic programming approach for solving integrated multi-objective transportation and inventory management problem using goodness of fit. Kybernetes 51 (2021) 768–803. [Google Scholar]
  • D. Han, Y. Yang, D. Wang, T.C.E. Cheng and Y. Yin, Integrated production, inventory, and outbound distribution operations with fixed departure times in a three-stage supply chain. Transp. Res. Part E Logist. Transp. Rev. 125 (2019) 334–347. [CrossRef] [Google Scholar]
  • M. Kadziński, T. Tervonen, M.K. Tomczyk and R. Dekker, Evaluation of multi-objective optimization approaches for solving green supply chain design problems. Omega 68 (2017) 168–184. [CrossRef] [Google Scholar]
  • A.S.H. Kugele, W. Ahmed and B. Sarkar, Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system. RAIRO: OR 56 (2022) 1013–1029. [CrossRef] [EDP Sciences] [Google Scholar]
  • S. Kumar, M. Sigroha, K. Kumar and B. Sarkar, Manufacturing/remanufacturing based supply chain management under advertisements and carbon emission process. RAIRO: OR 56 (2022) 831–851. [CrossRef] [EDP Sciences] [Google Scholar]
  • T.F. Liang, Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain. Comput. Ind. Eng. 55 (2008) 676–694. [CrossRef] [Google Scholar]
  • S. Liu, L.G. Papageorgiou, Multi-objective optimization of production, distribution and capacity planning of global supply chains in the process industry. Omega 41 (2013) 369–382. [CrossRef] [Google Scholar]
  • A.S. Mahapatra, M.S. Mahapatra, B. Sarkar and S.K. Majumder, Benefit of preservation technology with promotion and time-dependent deterioration under fuzzy learning. Expert. Syst. App. 201 (2022) 117169. [CrossRef] [Google Scholar]
  • M. Mahmoodi, A new multi-objective model of agile supply chain network design considering transportation limits. Prod. Manuf. Res. 7 (2019) 1–22. [Google Scholar]
  • J.T. Margolis, K.M. Sullivan, S.J. Mason and M. Magagnotti, A multi-objective optimization model for designing resilient supply chain networks. Int. J. Prod. Econ. 204 (2018) 174–185. [Google Scholar]
  • S. Min, Z.G. Zacharia and C.D. Smith, Defining supply chain management: in the past, present, and future. J. Bus. Logist. 40 (2019) 44–55. [CrossRef] [MathSciNet] [Google Scholar]
  • A.M. Mohammed and S.O. Duffuaa, A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks. Expert. Syst. App. 140 (2020) 112808. [CrossRef] [Google Scholar]
  • I. Moon, W.Y. Yun and B. Sarkar, Effects of variable setup cost, reliability, and production costs under controlled carbon emissions in a reliable production system. Eur. J. Ind. Eng. 16 (2022) 371–397. [CrossRef] [Google Scholar]
  • K.P. Nurjanni, M.S. Carvalho and L. Costa, Green supply chain design: a mathematical modeling approach based on a multi-objective optimization model. Int. J. Prod. Econ. 183 (2017) 421–432. [Google Scholar]
  • D. Peidro, J. Mula, M.M.E. Alemany and F.C. Lario, Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. Int. J. Prod. Res. 50 (2012) 3011–3020. [CrossRef] [Google Scholar]
  • E.H. Sabri and B.M. Beamon, A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega 28 (2000) 581–598. [CrossRef] [Google Scholar]
  • J. Sadeghi, S.M. Mousavi, S.T.A. Niaki and S. Sadeghi, Optimizing a bi-objective inventory model of a three-echelon supply chain using a tuned hybrid bat algorithm. Transp. Res. Part E Logist. Transp. Rev. 70 (2014) 274–292. [CrossRef] [Google Scholar]
  • A.N. Sadigh, H. Fallah and N. Nahavandi, A multi-objective supply chain model integrated with location of distribution centers and supplier selection decisions. Int. J. Adv. Manuf. Tech. 69 (2013) 225–235. [CrossRef] [Google Scholar]
  • B. Sarkar and S. Bhuniya, A sustainable flexible manufacturing–remanufacturing model with improved service and green investment under variable demand. Expert. Syst. App. 202 (2022) 117154. [CrossRef] [Google Scholar]
  • A. Sarkar, R. Guchhait and B. Sarkar, Application of the artificial neural network with multithreading within an inventory model under uncertainty and inflation. Int. J. Fuzzy Syst. 24 (2022) 2318–2332. [CrossRef] [Google Scholar]
  • B. Sarkar, J. Joo, Y. Kim, H. Park and M. Sarkar, Controlling defective items in a complex multi-phase manufacturing system. RAIRO: OR 56 (2022) 871–889. [CrossRef] [EDP Sciences] [Google Scholar]
  • B. Sarkar, M. Ullah and M. Sarkar, Environmental and economic sustainability through innovative green products by remanufacturing. J. Clean. Prod. 332 (2022) 129813. [CrossRef] [Google Scholar]
  • K. Sarrafha, S.H.A. Rahmati, S.T.A. Niaki and A. Zaretalab, A bi-objective integrated procurement, production, and distribution problem of a multi-echelon supply chain network design: a new tuned MOEA. Comput. Oper. Res. 54 (2015) 35–51. [Google Scholar]
  • A. Seidscher and S. Minner, A Semi-Markov decision problem for proactive and reactive transshipments between multiple warehouses. Eur. J. Oper. Res. 230 (2013) 42–52. [CrossRef] [Google Scholar]
  • S.K. Singh and M. Goh, Multi-objective mixed integer programming and an application in a pharmaceutical supply chain. Int. J. Prod. Res. 57 (2019) 1214–1237. [Google Scholar]
  • F.J. Tapia-Ubeda, P.A. Miranda, I. Roda, M. Macchi and O. Durán, Modelling and solving spare parts supply chain network design problems. Int. J. Prod. Res. 58 (2020) 5299–5319. [CrossRef] [Google Scholar]
  • S.A. Torabi, E. Hassini and M. Jeihoonian, Fulfillment source allocation, inventory transshipment, and customer order transfer in e-tailing. Transp. Res. Part E Logist. Transp. Rev. 79 (2015) 128–144. [CrossRef] [Google Scholar]
  • S.C. Tsai and S.T. Chen, A simulation-based multi-objective optimization framework: a case study on inventory management. Omega 70 (2017) 148–159. [CrossRef] [Google Scholar]
  • U.R. Tuzkaya and S. Önüt, A holonic approach based integration methodology for transportation and warehousing functions of the supply network. Comput. Indust. Eng. 56 (2009) 708–723. [CrossRef] [Google Scholar]
  • S. Validi, A. Bhattacharya and P.J. Byrne, A case analysis of a sustainable food supply chain distribution system – A multi-objective approach. Int. J. Prod. Econ. 152 (2014) 71–87. [CrossRef] [Google Scholar]
  • K.J. Wang, B. Makond and S.Y. Liu, Location and allocation decisions in a two-echelon supply chain with stochastic demand – A genetic-algorithm based solution. Expert. Syst. App. 8 (2011) 6125–6131. [CrossRef] [Google Scholar]
  • J. Xu, Q. Liu and R. Wang, A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor. Inf. Sci. 178 (2008) 2022–2043. [CrossRef] [Google Scholar]
  • D. Yadav, R. Singh, A. Kumar and B. Sarkar, Reduction of pollution through sustainable and flexible production by controlling by-products. J. Environ. Inform. 40 (2022) 106–124. [Google Scholar]
  • S. Zandkarimkhani, H. Mina, M. Biuki and K. Govindan, A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design. Ann. Oper. Res. 295 (2020) 425–452. [CrossRef] [MathSciNet] [Google Scholar]
  • S. Zhang, C.K.M. Lee, K. Wu and K.L. Choy, Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels. Expert. Syst. App. 65 (2016) 87–99. [CrossRef] [Google Scholar]

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