EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 56 / No 1 (January-February 2022) RAIRO-Oper. Res., 56 1 (2022) 431-444 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 56, Number 1, January-February 2022
Page(s) 431 - 444
DOI https://doi.org/10.1051/ro/2022002
Published online 14 February 2022
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [Google Scholar]
  • N. Azad and H. Davoudpour, Designing a stochastic distribution network model under risk. Int. J. Adv. Manuf. Technol. 64 (2013) 23–40. [CrossRef] [Google Scholar]
  • 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. [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [Google Scholar]
  • 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. [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [Google Scholar]
  • M. Ziari and M.S. Sajadieh, A behavior-based pricing model in retail systems considering vertical and horizontal competition. Comput. Ind. Eng. 152 (2021). [Google Scholar]
  • 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. [CrossRef] [MathSciNet] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.