RAIRO-Oper. Res.
Volume 56, Number 5, September-October 2022
Recent developments of operations research and data sciences
Page(s) 3581 - 3609
Published online 19 October 2022
  • M. Abdel-Basset, R. Mohamed, K. Sallam and M. Elhoseny, A novel decision-making model for sustainable supply chain finance under uncertainty environment. J. Cleaner Prod. 269 (2020) 122324. [CrossRef] [Google Scholar]
  • O. Abdolazimi and A. Abraham, Meta-heuristic Based Multi Objective Supply Chain Model for the Oil Industry in Conditions of Uncertainty. In: International Conference on Innovations in Bio-inspired Computing and Applications. Springer, Cham (2020, December) 141–153. [Google Scholar]
  • O. Abdolazimi, M.S. Esfandarani and D. Shishebori, Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods. Neural Comput. App. 33 (2020) 6641–6656. [Google Scholar]
  • O. Abdolazimi, M.S. Esfandarani, M. Salehi and D. Shishebori, Robust design of a multi-objective closed-loop supply chain by integrating on-time delivery, cost, and environmental aspects, case study of a Tire Factory. J. Cleaner Prod. 264 (2020) 121566. [CrossRef] [Google Scholar]
  • O. Abdolazimi, M.S. Esfandarani and A. Abraham, Design of a closed supply chain under uncertainty with regards to social and environmental impacts. In: International Conference on Soft Computing and Pattern Recognition. Springer, Cham (2020, December) 476–488. [Google Scholar]
  • O. Abdolazimi, M. Salehi Esfandarani, M. Salehi and D. Shishebori, A comparison of solution methods for the multi-objective closed loop supply chains. Adv. Ind. Eng. 54 (2020) 75–98. [Google Scholar]
  • O. Abdolazimi, F. Bahrami, D. Shishebori and M.A. Ardakani, A multi-objective closed-loop supply chain network design problem under parameter uncertainty: comparison of exact methods. Environ. Dev. Sustainability (2021) 1–35. DOI: 10.1007/s10668-021-01883-2. [Google Scholar]
  • O. Abdolazimi, M.S. Esfandarani, M. Salehi, D. Shishebori and M. Shakhsi-Niaei, Development of sustainable and resilient healthcare and non-cold pharmaceutical distribution supply chain for COVID-19 pandemic: a case study. Int. J. Logistics Manage. (2021) DOI: 10.1108/IJLM-04-2021-0232. [Google Scholar]
  • O. Abdolazimi, D. Shishebori, F. Goodarzian, P. Ghasemi and A. Appolloni, Designing a new mathematical model based on ABC analysis for inventory control problem: a real case study. RAIRO: Oper. Res. 55 (2021) 2309–2335. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • A. Abraham, H. Guo and H. Liu, Swarm intelligence: foundations, perspectives and applications. In: Swarm Intelligent Systems. Springer, Berlin, Heidelberg (2006) 3–25. [CrossRef] [Google Scholar]
  • D. Ambrosino and M.G. Scutella, Distribution network design: new problems and related models. Eur. J. Oper. Res. 165 (2005) 610–624. [CrossRef] [Google Scholar]
  • M.S.J. Ameli, N. Azad and A. Rastpour, Designing a supply chain network model with uncertain demands and lead times. J. Uncertain Syst. 3 (2009) 123–130. [Google Scholar]
  • A. Amiri, Designing a distribution network in a supply chain system: formulation and efficient solution procedure. Eur. J. Oper. Res. 171 (2006) 567–576. [CrossRef] [Google Scholar]
  • B. Aouni, C. Colapinto and D. La Torre, Financial portfolio management through the goal programming model: current state-of-the-art. Eur. J. Oper. Res. 234 (2014) 536–545. [CrossRef] [Google Scholar]
  • C. Araz, P.M. Ozfirat and I. Ozkarahan, An integrated multicriteria decision-making methodology for outsourcing management. Comput. Oper. Res. 34 (2007) 3738–3756. [Google Scholar]
  • M. Azadi, M. Jafarian, R.F. Saen and S.M. Mirhedayatian, A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context. Comput. Oper. Res. 54 (2015) 274–285. [CrossRef] [MathSciNet] [Google Scholar]
  • M. Bashiri and M. Sherafati, A three echelons supply chain network design in a fuzzy environment considering inequality constraints. In: International Constraints in Industrial Engineering and Engineering Management (IEEM), Hong Kong (2012) 10–13. [Google Scholar]
  • A. Ben-Tal and A. Nemirovski, Robust solutions of linear programming problems contaminated with uncertain data. Math. Prog. 88 (2000) 411–424. [CrossRef] [Google Scholar]
  • M. Beranek and U. Buscher, Optimal price and quality decisions of a supply chain game considering imperfect quality items and market segmentation. Appl. Math. Modell. 91 (2020) 1227–1244. [Google Scholar]
  • D. Bertsimas and V. Goyal, On the power of robust solutions in two-stage stochastic and adaptive optimization problems. Math. Oper. Res. 35 (2010) 284–305. [CrossRef] [MathSciNet] [Google Scholar]
  • R. Bhattacharya and A. Kaur, Allocation of external returns of different quality grades to multiple stages of a closed loop supply chain. J. Manuf. Syst. 37 (2015) 692–702. [CrossRef] [Google Scholar]
  • A. Charnes, W.W. Cooper and R.O. Ferguson, Optimal estimation of executive compensation by linear programming. Manage. Sci. 1 (1955) 138–151. [Google Scholar]
  • C.L. Chen, T.W. Yuan and W.C. Lee, Multi-criteria fuzzy optimization for locating warehouses and distribution centers in a supply chain network. J. Ch. Inst. Chem. Eng. 38 (2007) 393–407. [CrossRef] [Google Scholar]
  • A. Cheraghalipour and M. Hajiaghaei-Keshteli, Tree growth algorithm (TGA): an effective metaheuristic algorithm inspired by trees behavior. In: 13th International Conference on Industrial Engineering. Vol. 13. Scientific Information Databases Babolsar (2017). [Google Scholar]
  • A. Cheraghalipour, M. Hajiaghaei-Keshteli and M.M. Paydar, Tree Growth Algorithm (TGA): a novel approach for solving optimization problems. Eng. App. Artif. Intell. 72 (2018) 393–414. [CrossRef] [Google Scholar]
  • G. Cornuejols, Designing a closed-looped network in a supply chain system. Eur. J. Oper. Res. (2007) 667–676. [Google Scholar]
  • Z. Dai, K. Gao and B.C. Giri, A hybrid heuristic algorithm for cyclic inventory-routing problem with perishable products in VMI supply chain. Exp. Syst. App. 153 (2020) 113322. [CrossRef] [Google Scholar]
  • M.S. Daskin, Network and Discrete Location: Models, Algorithms, and Applications. John Wiley & Sons (2011). [Google Scholar]
  • K. Devika, A. Jafarian and V. Nourbakhsh, Designing a sustainable closed-loop supply chain network based on triple bottom line approach: a comparison of metaheuristics hybridization techniques. Eur. J. Oper. Res. 235 (2014) 594–615. [CrossRef] [MathSciNet] [Google Scholar]
  • Z. Drezner and H.W. Hamacher, editors Facility Location: Applications and Theory. Springer Science & Business Media (2001). [Google Scholar]
  • Z. Drezner and G.O. Wesolowsky, Network design: selection and design of links and facility location. Transp. Res. Part A Policy Pract. 37 (2003) 241–256. [CrossRef] [Google Scholar]
  • D.E. D’Souza and F.P. Williams, Toward a taxonomy of manufacturing flexibility dimensions. J. Oper. Manage. 18 (2000) 577–593. [CrossRef] [Google Scholar]
  • J.S. Dyer, Remarks on the analytic hierarchy process. Manage. Sci. 36 (1990) 249–258. [CrossRef] [Google Scholar]
  • EIA (U.S. Department of Energy, Energy Information Administration), Commercial Buildings Energy Consumption Survey. Washington, DC (2003). [Google Scholar]
  • A.M.F. Fard and M. Hajaghaei-Keshteli, A tri-level location-allocation model for forward/reverse supply chain. Appl. Soft Comput. 62 (2018) 328–346. [CrossRef] [Google Scholar]
  • A.M. Fathollahi-Fard, M. Hajiaghaei-Keshteli and R. Tavakkoli-Moghaddam, Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft Comput. 24 (2020) 14637–14665. [CrossRef] [Google Scholar]
  • K. Govindan, H. Soleimani and D. Kannan, Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. Eur. J. Oper. Res. 240 (2015) 603–626. [Google Scholar]
  • S. Gunpinar and G. Centeno, An integer programming approach to the bloodmobile routing problem. Transp. Res. part E: Logistics Transp. Rev. 86 (2016) 94–115. [CrossRef] [Google Scholar]
  • N. Haghjoo, R. Tavakkoli-Moghaddam, H. Shahmoradi-Moghadam and Y. Rahimi, Reliable blood supply chain network design with facility disruption: a real-world application. Eng. App. Artif. Intell. 90 (2020) 103493. [CrossRef] [Google Scholar]
  • H. Hishamuddin, R. Sarker and D. Essam, A simulation model of a three echelon supply chain system with multiple suppliers subject to supply and transportation disruptions. IFAC-PapersOnLine 48 (2015) 2036–2040. [CrossRef] [Google Scholar]
  • S.M. Hosseini-Motlagh, M.R.G. Samani and V. Shahbazbegian, Innovative strategy to design a mixed resilient-sustainable electricity supply chain network under uncertainty. Appl. Energy 280 (2020) 115921. [CrossRef] [Google Scholar]
  • Y. Ji, X. Jin, Z. Xu and S. Qu, A mixed 0–1 programming approach for multiple attribute strategic weight manipulation based on uncertainty theory. J. Intell. Fuzzy Syst. 41 (2021) 6739–6754. [CrossRef] [Google Scholar]
  • Y. Ji, H. Li and H. Zhang, Risk-averse two-stage stochastic minimum cost consensus models with asymmetric adjustment cost. Group Decis. Negotiation 31 (2022) 261–291. [Google Scholar]
  • Q.I.N. Jin, S.H.I. Feng, L.X. Miao and G.J. Tan, Optimal model and algorithm for multi-commodity logistics network design considering stochastic demand and inventory control. Syst. Eng. Theory Prac. 29 (2009) 176–183. [CrossRef] [Google Scholar]
  • J. Kennedy and R. Eberhart, Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks. Vol. 4. IEEE (1995, November) 1942–1948. [Google Scholar]
  • C.H. Kuei and C.N. Madu, Identifying critical success factors for supply chain quality management (SCQM). Asia Pac. Manage. Rev. 6 (2001) 409–423. [Google Scholar]
  • S.H. Lashine, M. Fattouh and A. Issa, Location/allocation and routing decisions in supply chain network design. J. Modell. Manage. 1 (2006) 173–183. [CrossRef] [Google Scholar]
  • S. Li, B. Ragu-Nathan, T.S. Ragu-Nathan and S.S. Rao, The impact of supply chain management practices on competitive advantage and organizational performance. Omega 34 (2006) 107–124. [CrossRef] [Google Scholar]
  • Y.Z. Mehrjerdi and M. Shafiee, A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies. J. Cleaner Prod. 289 (2021) 125141. [CrossRef] [Google Scholar]
  • M.T. Melo, S. Nickel and F. Saldanha-Da-Gama, Facility location and supply chain management: a review. Eur. J. Oper. Res. 196 (2009) 401–412. [CrossRef] [Google Scholar]
  • Q. Meng, Y. Huang and R.L. Cheu, Competitive facility location on decentralized supply chains. Eur. J. Oper. Res. 196 (2009) 487–499. [CrossRef] [Google Scholar]
  • M.K. Mohammed, U. Umer and A. Al-Ahmari, Optimization of laser micro milling of alumina ceramic using radial basis functions and MOGA-II. Int. J. Adv. Manuf. Technol. 91 (2017) 2017–2029. [CrossRef] [Google Scholar]
  • A. Nagurney, S. Saberi, S. Shukla and J. Floden, Supply chain network competition in price and quality with multiple manufacturers and freight service providers. Transp. Res. Part E Logistics Transp. Rev. 77 (2015) 248–267. [CrossRef] [Google Scholar]
  • C. Obreque, M. Donoso, G. Gutiérrez and V. Marianov, A branch and cut algorithm for the hierarchical network design problem. Eur. J. Oper. Res. 200 (2010) 28–35. [CrossRef] [Google Scholar]
  • M.S. Pishvaee, S.A. Torabi and J. Razmi, Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Comput. Ind. Eng. 62 (2012) 624–632. [Google Scholar]
  • L. Qi and Z.J.M. Shen, A supply chain design model with unreliable supply. Nav. Res. Logistics (NRL) 54 (2007) 829–844. [CrossRef] [Google Scholar]
  • R. Rajesh and V. Ravi, Supplier selection in resilient supply chains: a grey relational analysis approach. J. Cleaner Prod. 86 (2015) 343–359. [CrossRef] [Google Scholar]
  • C.J. Robinson and M.K. Malhotra, Defining the concept of supply chain quality management and its relevance to academic and industrial practice. Int. J. Prod. Econ. 96 (2005) 315–337. [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]
  • N.I. Saragih, N. Bahagia and I. Syabri, A heuristic method for location-inventory-routing problem in a three-echelon supply chain system. Comput. Ind. Eng. 127 (2019) 875–886. [CrossRef] [Google Scholar]
  • J. Sarkis, A strategic decision framework for green supply chain management. J. Cleaner Prod. 11 (2003) 397–409. [CrossRef] [Google Scholar]
  • J. Sarkis, Q. Zhu and K.H. Lai, An organizational theoretic review of green supply chain management literature. Int. J. Prod. Econ. 130 (2011) 1–15. [CrossRef] [Google Scholar]
  • M. Schenkel, H. Krikke, M.C. Caniëls and E. van der Laan, Creating integral value for stakeholders in closed loop supply chains. J. Purchasing Supply Manage. 21 (2015) 155–166. [CrossRef] [Google Scholar]
  • M. Seifbarghy, K. Nouhi and A. Mahmoudi, Contract design in a supply chain considering price and quality dependent demand with customer segmentation. Int. J. Prod. Econ. 167 (2015) 108–118. [CrossRef] [Google Scholar]
  • H. Selim, C. Araz and I. Ozkarahan, Collaborative production–distribution planning in supply chain: a fuzzy goal programming approach. Transp. Res. Part E Logistics Transp. Rev. 44 (2008) 396–419. [CrossRef] [Google Scholar]
  • M. Shafiee, Y. Zare Mehrjerdi and M. Keshavarz, Integrating lean, resilient, and sustainable practices in supply chain network: mathematical modelling and the AUGMECON2 approach. Int. J. Syst. Sci. Oper. Logistics (2021) 1–21. DOI: 10.1080/23302674.2021.1921878. [Google Scholar]
  • B. Shavazipour, J. Stray and T.J. Stewart, Sustainable planning in sugar-bioethanol supply chain under deep uncertainty: a case study of South African sugarcane industry. Comput. Chem. Eng. 143 (2020) 107091. [CrossRef] [Google Scholar]
  • D. Shishebori, A. Yousefi Babadi and Z. Noormohammadzadeh, A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem. Sci. Iran. 25 (2018) 1750–1767. [Google Scholar]
  • A. Shoja, S. Molla-Alizadeh-Zavardehi and S. Niroomand, Adaptive meta-heuristic algorithms for flexible supply chain network design problem with different delivery modes. Comput. Ind. Eng. 138 (2019) 106107. [CrossRef] [Google Scholar]
  • L.V. Snyder, M.S. Daskin and C.P. Teo, The stochastic location model with risk pooling. Eur. J. Oper. Res. 179 (2007) 1221–1238. [CrossRef] [Google Scholar]
  • S.K. Srivastava, Green supply-chain management: a state-of-the-art literature review. Int. J. Manage. Rev. 9 (2007) 53–80. [Google Scholar]
  • K. Subulan, A.S. Taşan and A. Baykasoğlu, A fuzzy goal programming model to strategic planning problem of a lead/acid battery closed-loop supply chain. J. Manuf. Syst. 37 (2015) 243–264. [CrossRef] [Google Scholar]
  • P.N. Thanh, N. Bostel and O. Péton, A dynamic model for facility location in the design of complex supply chains. Int. J. Prod. Econ. 113 (2008) 678–693. [CrossRef] [Google Scholar]
  • A. Verma and N. Singhal, A computing methodology for evaluating supply chain competitiveness. Mater. Today Proc. 5 (2018) 4183–4191. [CrossRef] [Google Scholar]
  • X. Zhao, P. Wang and R. Pal, The effects of agro-food supply chain integration on product quality and financial performance: Evidence from Chinese agro-food processing business. Int. J. Prod. Econ. 231 (2021) 107832. [CrossRef] [Google Scholar]
  • M. Zheng, W. Li, Y. Liu and X. Liu, A Lagrangian heuristic algorithm for sustainable supply chain network considering CO2 emission. J. Cleaner Prod. 270 (2020) 122409. [CrossRef] [Google Scholar]
  • H. Zhou and L. Li, The impact of supply chain practices and quality management on firm performance: evidence from China’s small and medium manufacturing enterprises. Int. J. Prod. Econ. 230 (2020) 107816. [CrossRef] [Google Scholar]
  • C. Zikopoulos and G. Tagaras, Reverse supply chains: effects of collection network and returns classification on profitability. Eur. J. Oper. Res. 246 (2015) 435–449. [CrossRef] [Google Scholar]
  • E. Zografidou, K. Petridis, N.E. Petridis and G. Arabatzis, A financial approach to renewable energy production in Greece using goal programming. Renew. Energy 108 (2017) 37–51. [CrossRef] [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.