EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 55 (2021) Supplement RAIRO-Oper. Res., 55 (2021) S2859-S2877 References
Free Access
Issue
RAIRO-Oper. Res.
Volume 55, 2021
Regular articles published in advance of the transition of the journal to Subscribe to Open (S2O). Free supplement sponsored by the Fonds National pour la Science Ouverte
Page(s) S2859 - S2877
DOI https://doi.org/10.1051/ro/2020131
Published online 02 March 2021
  • 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. Model. 37 (2013) 4165–4176. [Google Scholar]
  • J. Ashayeri, N. Ma and R. Sotirov, The redesign of a warranty distribution network with recovery processes. Transp. Res. Part E: Logistics Transp. Rev. 77 (2015) 184–197. [Google Scholar]
  • J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, in Advanced Applications in Pattern Recognition. Springer, New York, NY (1981). [Google Scholar]
  • E. Dehghani, M.S. Pishvaee and M.S. Jabalameli, A hybrid Markov process-mathematical programming approach for joint location-inventory problem under supply disruptions. RAIRO:OR 52 (2018) 1147–1173. [Google Scholar]
  • F. Du and G.W. Evans, A bi-objective reverse logistics network analysis for post-sale service. Comput. Oper. Res. 35 (2008) 2617–2634. [Google Scholar]
  • J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3 (1973) 32–57. [Google Scholar]
  • M.B. Fakhrzad and F. Goodarzian, A fuzzy multi-objective programming approach to develop a green closed-loop supply chain network design problem under uncertainty: modifications of imperialist competitive algorithm. RAIRO:OR 53 (2019) 963–990. [Google Scholar]
  • M. Fazli-Khalaf and A. Hamidieh, A robust reliable forward-reverse supply chain network design model under parameter and disruption uncertainties. Int. J. Eng.-Trans. B: App. 30 (2017) 1160–1169. [Google Scholar]
  • M. Fazli-Khalaf and N.G. Nemati, A socially responsible supplier selection model under uncertainty: case study of pharmaceutical department of an Iranian hospital. Int. J. Logistics Syst. Manage. 32 (2019) 69–90. [Google Scholar]
  • M. Fazli-Khalaf, B. Naderi and M. Mohammadi, Design of a reliable supply chain network with responsiveness considerations under uncertainty: case study of an Iranian tire manufacturer. Special issue: 14th International Industrial Engineering Conference. J. Ind. Syst. Eng. 11 (2018) 120–131. [Google Scholar]
  • M. Fazli-Khalaf, K. Fathollahzadeh, A. Mollaei, B. Naderi and M. Mohammadi, A robust possibilistic programming model for water allocation problem. RAIRO:OR 53 (2019) 323–338. [Google Scholar]
  • M. Fazli-Khalaf, S.K. Chaharsooghi and M.S. Pishvaee, A new robust possibilistic programming model for reliable supply chain network design: a case study of lead-acid battery supply chain. RAIRO-OR 53 (2019) 1489–1512. [Google Scholar]
  • J. Ghahremani-Nahr, R. Kian and E. Sabet, A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert Syst. App. 116 (2019) 454–471. [Google Scholar]
  • A. Hamidieh, A. Arshadikhamseh and M. Fazli-Khalaf, A robust reliable closed loop supply chain network design under uncertainty: a case study in equipment training centers. Int. J. Eng. Trans. A Basics 31 (2017) 648–658. [Google Scholar]
  • A. Hasani, S.H. Zegordi and E. Nikbakhsh, Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. Int. J. Prod. Res. 50 (2012) 4649–4669. [Google Scholar]
  • S.T. John, R. Sridharan and P.R. Kumar, Reverse logistics network design: a case of mobile phones and digital cameras. Int. J. Adv. Manuf. Technol. 94 (2018) 615–631. [Google Scholar]
  • T.-Y. Liao, Reverse logistics network design for product recovery and remanufacturing. Appl. Math. Model. 60 (2018) 145–163. [Google Scholar]
  • S.-H. Liao, C.-L. Hsieh and W.-C. Ho, Multi-objective evolutionary approach for supply chain network design problem within online customer consideration. RAIRO:OR 51 (2017) 135–155. [Google Scholar]
  • A. Majumder, C.K. Jaggi and B. Sarkar, A multi-retailer supply chain model with backorder and variable production cost. RAIRO:OR 52 (2018) 943–954. [Google Scholar]
  • J.M. Mulvey, R.J. Vanderbei and S.A. Zenios, Robust optimization of large-scale systems. Oper. Res. 43 (1995) 264–281. [Google Scholar]
  • D.N.P. Murthy, O. Solem and T. Roren, Product warranty logistics: issues and challenges. Eur. J. Oper. Res. 156 (2004) 110–126. [Google Scholar]
  • A. Mutha and S. Pokharel, Strategic network design for reverse logistics and remanufacturing using new and old product modules. Comput. Ind. Eng. 56 (2009) 334–346. [Google Scholar]
  • T.G. Nguyen, J.-L. de Kok and M.J. Titus, A new approach to testing an integrated water systems model using qualitative scenarios. Environ. Model. Softw. 22 (2007) 1557–1571. [Google Scholar]
  • R. Piplani and A. Saraswat, Robust optimisation approach to the design of service networks for reverse logistics. Int. J. Prod. Res. 50 (2012) 1424–1437. [Google Scholar]
  • M.S. Pishvaee and S.A. Torabi, A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets Syst. 161 (2010) 2668–2683. [Google Scholar]
  • M.S. Pishvaee, M. Fathi and F. Jolai, A fuzzy clustering-based method for scenario analysis in strategic planning: the case of an Asian pharmaceutical company. S. Afr. J. Bus. Manage. 39 (2008) 21–31. [Google Scholar]
  • M.S. Pishvaee, J. Razmi and S.A. Torabi, Robust possibilistic programming for socially responsible supply chain network design: a new approach. Fuzzy Sets Syst. 206 (2012) 1–20. [Google Scholar]
  • M. Ramezani, M. Bashiri and R. Tavakkoli-Moghaddam, A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Appl. Math. Model. 37 (2013) 328–344. [Google Scholar]
  • E. Rezaei, M.M. Paydar and A.S. Safaei, Customer relationship management and new product development in designing a robust supply chain. RAIRO:OR 54 (2020) 369–391. [Google Scholar]
  • N. Saccani, P. Johansson and M. Perona, Configuring the after-sales service supply chain: a multiple case study. Int. J. Prod. Econ. 110 (2007) 52–69. [Google Scholar]
  • N. Sahebjamnia, A.M. Fathollahi-Fard and M. Hajiaghaei-Keshteli, Sustainable tire closed-loop supply chain network design: hybrid metaheuristic algorithms for large-scale networks. J. Cleaner Prod. 196 (2018) 273–296. [Google Scholar]
  • P. Schwab, F. Cerutti and U.H. von Reibnitz, Foresight-using scenarios to shape the future of agricultural research. Foresight 5 (2003) 55–61. [Google Scholar]
  • H. Soleimani, K. Govindan, H. Saghafi and H. Jafari, Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Comput. Ind. Eng. 109 (2017) 191–203. [Google Scholar]
  • B. Vahdani, J. Razmi and R. Tavakkoli-Moghaddam, Fuzzy possibilistic modeling for closed loop recycling collection networks. Environ. Model. Assess. 17 (2012) 623–637. [Google Scholar]
  • S. Verstrepen, D. Deschoolmeester and R.J. Van den Berg, Servitization in the automotive sector: creating value and competitive advantage through service after sales. In: Global Production Management. Vol. 24 of: IFIP The International Federation for Information Processing. Springer, Boston, MA (1999) 538–545. [Google Scholar]
  • C.-S. Yu and H.-L. Li, A robust optimization model for stochastic logistic problems. Int. J. Prod. Econ. 64 (2000) 385–397. [Google Scholar]
  • L.J. Zeballos, C.A. Méndez and A.P. Barbosa-Povoa, Integrating decisions of product and closed-loop supply chain design under uncertain return flows. Comput. Chem. Eng. 112 (2018) 211–238. [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.