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

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.

Reçu le :
Accepté le :
Première publication :
Publié le :
DOI : 10.1051/ro/2022002
Keywords: Pricing, network design, closed-loop supply chain, disruption
@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] E. Ahmadzadeh and B. Vahdani, 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] S. H. Amin and G. Zhang, 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] M.-B. Aryanezhad, S. G. Jalali and A. Jabbarzadeh, An integrated supply chain design model with random disruptions consideration. Afr. J. Bus. Manage. 4 (2010) 2393.

[4] N. Azad and H. Davoudpour, Designing a stochastic distribution network model under risk. Int. J. Adv. Manuf. Technol. 64 (2013) 23–40.

[5] N. Azad, G. K. Saharidis, H. Davoudpour, H. Malekly and S. A. Yektamaram, 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] E. Bazan, M. Y. Jaber and S. Zanoni, 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] E. Dehghani, M. S. Jabalameli, A. Jabbarzadeh and M. S. Pishvaee, Resilient solar photovoltaic supply chain network design under business-as-usual and hazard uncertainties. Comput. Chem. Eng. 111 (2018) 288–310.

[8] N. Demirel, E. Özceylan, T. Paksoy and H. Gökçen, 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] M. Esmaeilikia, B. Fahimnia, J. Sarkis, K. Govindan, A. Kumar and J. Mo, A tactical supply chain planning model with multiple flexibility options: an empirical evaluation. Ann. Oper. Res. 2 (2014) 429–454. | MR

[10] M. Farrokh, A. Azar, G. Jandaghi and E. Ahmadi, 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] M. Fleischmann, P. Beullens, J. M. Bloemhof-Ruwaard and L. N. Wassenhove, The impact of product recovery on logistics network design Prod. Oper. Manage. 10 (2001) 156–173.

[12] M. Ghomi-Avili, S. G. J. Naeini, R. Tavakkoli-Moghaddam, A. Jabbarzadeh, 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] A. Hasani and A. Khosrojerdi, 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] S. Hatefi and F. Jolai, Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions. Appl. Math. Modell. 38 (2014) 2630–2647. | MR

[15] S. Hatefi, F. Jolai, S. Torabi and R. Tavakkoli-Moghaddam, 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] A. Jabbarzadeh, M. Haughton and A. Khosrojerdi, Closed-loop supply chain network design under disruption risks: a robust approach with real world application. Comput. Ind. Eng. 116 (2018) 178–191.

[17] A. Jabbarzadeh, M. Haughton and F. Pourmehdi, A robust optimization model for efficient and green supply chain planning with postponement strategy. Int. J. Prod. Econ. 214 (2019) 266–283.

[18] E. Keyvanshokooh, M. Fattahi, S. Seyed-Hosseini and R. Tavakkoli-Moghaddam, A dynamic pricing approach for returned products in integrated forward/reverse logistics network design. Appl. Math. Modell. 37 (2013) 10182–10202. | MR

[19] E. Keyvanshokooh, S. M. Ryan and E. Kabir, 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] M. Lalmazloumian, K. Y. Wong, K. Govindan and D. Kannan, A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Ann. OR 240 (2016) 435–470. | MR

[21] Y.-K. Lin, C.-T. Yeh and C.-F. Huang, Reliability evaluation of a stochastic-flow distribution network with delivery spoilage. Comput. Ind. Eng. 66 (2013) 352–359.

[22] O. Listeş and R. Dekker, A stochastic approach to a case study for product recovery network design. Eur. J. Oper. Res. 160 (2005) 268–287. | Zbl

[23] Z. Lu and N. Bostel, 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] E. Özceylan, T. Paksoy and T. Bektaş, 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] M. Pishvaee and S. Torabi, A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets Syst. 161 (2010) 2668–2683. | MR | Zbl

[26] M. S. Pishvaee, R. Z. Farahani and W. Dullaert, A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Comput. Oper. Res. 37 (2010) 1100–1112. | Zbl

[27] 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. | MR | Zbl

[28] L. Qi, Z.-J. M. Shen and L. V. Snyder, The effect of supply disruptions on supply chain design decisions. Transp. Sci. 44 (2010) 274–289.

[29] Q. Qiang, K. Ke, T. Anderson and J. Dong, The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega 41 (2013) 186–194.

[30] M. Ramezani, M. Bashiri and R. Tavakkoli-Moghaddam, A robust design for a closed-loop supply chain network under an uncertain environment. Int. J. Adv. Manuf. Technol. 66 (2013) 825–843.

[31] M. Ramezani, A. M. Kimiagari, B. Karimi and T. H. Hejazi, Closed-loop supply chain network design under a fuzzy environment. Knowl.-Based Syst. 59 (2014) 108–120.

[32] S. Rezapour, R. Z. Farahani, B. Fahimnia, K. Govindan and Y. Mansouri, Competitive closed-loop supply chain network design with price-dependent demands. J. Cleaner Prod. 93 (2015) 251–272.

[33] M. I. G. Salema, A. P. Barbosa-Povoa and A. Q. Novais, 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] N. Tahirov, P. Hasanov and M. Y. Jaber, Optimization of closed-loop supply chain of multi-items with returned subassemblies. Int. J. Prod. Econ. 174 (2016) 1–10.

[35] B. Vahdani and M. Mohammadi, 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] B. Vahdani, R. Tavakkoli-Moghaddam, M. Modarres and A. Baboli 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] C. J. Vidal and M. Goetschalckx, A global supply chain model with transfer pricing and transportation cost allocation. Eur. J. Oper. Res. 129 (2001) 134–158. | Zbl

[38] E. Yadegari, H. Najmi, M. Ghomi-Avili and M. Zandieh, A flexible integrated forward/reverse logistics model with random path-based memetic algorithm. Iran. J. Manage. Stud. 8 (2015) 287.

[39] M. Ziari and M. S. Sajadieh, A behavior-based pricing model in retail systems considering vertical and horizontal competition. Comput. Ind. Eng. 152 (2021).

[40] S. Zokaee, A. Jabbarzadeh, B. Fahimnia and S. J. Sadjadi, Robust supply chain network design: an optimization model with real world application. Ann. Oper. Res. 257 (2017) 15–44. | MR

Cité par Sources :