Closed-loop supply chains have attracted more attention by researchers and practitioners due to strong government regulations, environmental issues, social responsibilities and natural resource constraints over past few years. This paper presents a mixed-integer linear programming model to design a closed-loop supply chain network and optimizing pricing policies under random disruption. Reusing the returned products is applied as a resilience strategy to cope with the waste of energy and improving supply efficiency. Moreover, it is necessary to find the optimal prices for both final and returned products. Therefore, the model is formulated based on demand function and it maximizes total supply chain’s profit. Finally, its application is explored through using the real data of an industrial company in glass industry.
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DOI : 10.1051/ro/2022002
@article{RO_2022__56_1_431_0,
author = {Ziari, Matineh and Sajadieh, Mohsen Sheikh},
title = {A joint pricing and network design model for a closed-loop supply chain under disruption (glass industry)},
journal = {RAIRO. Operations Research},
pages = {431--444},
year = {2022},
publisher = {EDP-Sciences},
volume = {56},
number = {1},
doi = {10.1051/ro/2022002},
mrnumber = {4379610},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2022002/}
}
TY - JOUR AU - Ziari, Matineh AU - Sajadieh, Mohsen Sheikh TI - A joint pricing and network design model for a closed-loop supply chain under disruption (glass industry) JO - RAIRO. Operations Research PY - 2022 SP - 431 EP - 444 VL - 56 IS - 1 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2022002/ DO - 10.1051/ro/2022002 LA - en ID - RO_2022__56_1_431_0 ER -
%0 Journal Article %A Ziari, Matineh %A Sajadieh, Mohsen Sheikh %T A joint pricing and network design model for a closed-loop supply chain under disruption (glass industry) %J RAIRO. Operations Research %D 2022 %P 431-444 %V 56 %N 1 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2022002/ %R 10.1051/ro/2022002 %G en %F RO_2022__56_1_431_0
Ziari, Matineh; Sajadieh, Mohsen Sheikh. A joint pricing and network design model for a closed-loop supply chain under disruption (glass industry). RAIRO. Operations Research, Tome 56 (2022) no. 1, pp. 431-444. doi: 10.1051/ro/2022002
[1] and , A location-inventory-pricing model in a closed loop supply chain network with correlated demands and shortages under a periodic review system. Comput. Chem. Eng. 101 (2017) 148–166.
[2] and , A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Appl. Math. Modell. 37 (2013) 4165–4176. | MR
[3] , and , An integrated supply chain design model with random disruptions consideration. Afr. J. Bus. Manage. 4 (2010) 2393.
[4] and , Designing a stochastic distribution network model under risk. Int. J. Adv. Manuf. Technol. 64 (2013) 23–40.
[5] , , , and , Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Ann. Oper. Res. 210 (2013) 125–163. | MR | Zbl
[6] , and , A review of mathematical inventory models for reverse logistics and the future of its modeling: an environmental perspective. Appl. Math. Modell. 40 (2016) 4151–4178. | MR
[7] , , and , Resilient solar photovoltaic supply chain network design under business-as-usual and hazard uncertainties. Comput. Chem. Eng. 111 (2018) 288–310.
[8] , , and , A genetic algorithm approach for optimising a closed-loop supply chain network with crisp and fuzzy objectives. Int. J. Prod. Res. 52 (2014) 3637–3664.
[9] , , , , and , A tactical supply chain planning model with multiple flexibility options: an empirical evaluation. Ann. Oper. Res. 2 (2014) 429–454. | MR
[10] , , and , A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy Sets Syst. 341 (2018) 69–91. | MR
[11] , , and , The impact of product recovery on logistics network design Prod. Oper. Manage. 10 (2001) 156–173.
[12] , , , , A fuzzy pricing model for a green competitive closed-loop sup.ply chain network design in the presence of disruptions, J. Cleaner Prod. 188 (2018) 425–442.
[13] and , Robust global supply chain network design under disruption and uncertainty considering resilience strategies: a parallel memetic algorithm for a real-life case study. Transp. Res. Part E: Logistics Transp. Rev. 87 (2016) 20–52.
[14] and , Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions. Appl. Math. Modell. 38 (2014) 2630–2647. | MR
[15] , , and , A credibility-constrained programming for reliable forward–reverse logistics network design under uncertainty and facility disruptions. Int. J. Comput. Integr. Manuf. 28 (2015) 664–678.
[16] , and , Closed-loop supply chain network design under disruption risks: a robust approach with real world application. Comput. Ind. Eng. 116 (2018) 178–191.
[17] , and , A robust optimization model for efficient and green supply chain planning with postponement strategy. Int. J. Prod. Econ. 214 (2019) 266–283.
[18] , , and , A dynamic pricing approach for returned products in integrated forward/reverse logistics network design. Appl. Math. Modell. 37 (2013) 10182–10202. | MR
[19] , and , Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition. Eur. J. Oper. Res. 249 (2016) 76–92. | MR
[20] , , and , A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Ann. OR 240 (2016) 435–470. | MR
[21] , and , Reliability evaluation of a stochastic-flow distribution network with delivery spoilage. Comput. Ind. Eng. 66 (2013) 352–359.
[22] and , A stochastic approach to a case study for product recovery network design. Eur. J. Oper. Res. 160 (2005) 268–287. | Zbl
[23] and , A facility location model for logistics systems including reverse flows: the case of remanufacturing activities. Comput. Oper. Res. 34 (2007) 299–323. | Zbl | MR
[24] , and , Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing. Transp. Res. Part E: Logistics Transp. Rev. 61 (2014) 142–164.
[25] and , A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets Syst. 161 (2010) 2668–2683. | MR | Zbl
[26] , and , A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Comput. Oper. Res. 37 (2010) 1100–1112. | Zbl
[27] , and , A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl. Math. Modell. 35 (2011) 637–649. | MR | Zbl
[28] , and , The effect of supply disruptions on supply chain design decisions. Transp. Sci. 44 (2010) 274–289.
[29] , , and , The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega 41 (2013) 186–194.
[30] , and , A robust design for a closed-loop supply chain network under an uncertain environment. Int. J. Adv. Manuf. Technol. 66 (2013) 825–843.
[31] , , and , Closed-loop supply chain network design under a fuzzy environment. Knowl.-Based Syst. 59 (2014) 108–120.
[32] , , , and , Competitive closed-loop supply chain network design with price-dependent demands. J. Cleaner Prod. 93 (2015) 251–272.
[33] , and , An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. Eur. J. Oper. Res. 179 (2007) 1063–1077. | Zbl
[34] , and , Optimization of closed-loop supply chain of multi-items with returned subassemblies. Int. J. Prod. Econ. 174 (2016) 1–10.
[35] and , A bi-objective interval-stochastic robust optimization model for designing closed loop supply chain network with multi-priority queuing system. Int. J. Prod. Econ. 170 (2015) 67–87.
[36] , , and Reliable design of a forward/reverse logistics network under uncertainty: a robust-M/M/C queuing model. Transp. Res. Part E: Logistics Transp. Rev. 48 (2012) 1152–1168.
[37] and , A global supply chain model with transfer pricing and transportation cost allocation. Eur. J. Oper. Res. 129 (2001) 134–158. | Zbl
[38] , , and , A flexible integrated forward/reverse logistics model with random path-based memetic algorithm. Iran. J. Manage. Stud. 8 (2015) 287.
[39] and , A behavior-based pricing model in retail systems considering vertical and horizontal competition. Comput. Ind. Eng. 152 (2021).
[40] , , and , Robust supply chain network design: an optimization model with real world application. Ann. Oper. Res. 257 (2017) 15–44. | MR
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