Free Access
RAIRO-Oper. Res.
Volume 55, Number 2, March-April 2021
Page(s) 921 - 945
Published online 07 May 2021
  • R. Aboolian, O. Berman and D. Krass, Competitive facility location and design problem. Eur. J. Oper. Res. 182 (2007) 40–62. [Google Scholar]
  • V. Acha, A. Davies, M. Hobday and A. Salter, Exploring the capital goods economy: complex product systems in the UK. Ind. Corporate Change 13 (2004) 505–529. [Google Scholar]
  • E.J. Anderson and Y. Bao, Price competition with integrated and decentralized supply chains. Eur. J. Oper. Res. 200 (2010) 227–234. [Google Scholar]
  • A. Atasu, V.D.R. Guide Jr and L.N. Van Wassenhove, So what if remanufacturing cannibalizes my new product sales? California Manage. Rev. 52 (2010) 56–76. [Google Scholar]
  • R.D. Banker, Estimating most productive scale size using data envelopment analysis. Eur. J. Oper. Res. 17 (1984) 35–44. [CrossRef] [Google Scholar]
  • M. Bashiri, H. Badri and J. Talebi, A new approach to tactical and strategic planning in production-distribution networks. Appl. Math. Model. 36 1703–1717. [Google Scholar]
  • O. Berman and D. Krass, Flow intercepting spatial interaction model: a new approach to optimal location of competitive facilities. Location Sci. 6 (1998) 41–65. [Google Scholar]
  • T. Boyaci and G. Gallego, Supply chain coordination in a market with customer service competition. Prod. Oper. Manage. 13 (2004) 3–22. [Google Scholar]
  • P. Chanintrakul, A.E. Coronado Mondragon, C. Lalwani and C.Y. Wong, Reverse logistics network design: a state-of-the-art literature review. Int. J. Bus. Perform. Supply Chain Model. 1 (2009) 61–81. [Google Scholar]
  • A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1978) 429–444. [Google Scholar]
  • J.-M. Chen and C.-I. Chang, The co-opetitive strategy of a closed-loop supply chain with remanufacturing. Transp. Res. Part E: Logistics Transp. Rev. 48 (2012) 387–400. [Google Scholar]
  • T.-M. Choi, Y. Li and L. Xu, Channel leadership, performance and coordination in closed loop supply chains. Int. J. Prod. Econ. 146 (2013) 371–380. [Google Scholar]
  • S.K. Das, S.K. Roy and G.W. Weber, Application of type-2 fuzzy logic to a multi-objective green solid transportation-location problem with dwell time under carbon tax, cap and offset policy: fuzzy vs. Non-fuzzy techniques. IEEE Trans. Fuzzy Syst. 28 (2020) 2711–2725. [Google Scholar]
  • A. Davies and T. Brady, Policies for a complex product system. Futures 30 (1998) 293–304. [Google Scholar]
  • A. Davies and M. Hobday, The Business of Projects: Managing Innovation in Complex Products and Systems. Cambridge University Press (2005). [CrossRef] [Google Scholar]
  • P. De Giovanni and G. Zaccour, A two-period game of a closed-loop supply chain. Eur. J. Oper. Res. 232 (2014) 22–40. [Google Scholar]
  • O. Dedehayir, T. Nokelainen and S.J. Mäkinen, Disruptive innovations in complex product systems industries: a case study. J. Eng. Technol. Manage. 33 (2014) 174–192. [Google Scholar]
  • R. Dekker, M. Fleischmann, K. Inderfurth, L.N. van Wassenhove, Reverse Logistics: Quantitative Models for Closed-Loop Supply Chains. Springer Science & Business Media (2013). [Google Scholar]
  • B. Du and S. Guo, Production planning conflict resolution of complex product system in group manufacturing: a novel hybrid approach using ant colony optimization and Shapley value. Comput. Ind. Eng. 94 (2016) 158–169. [Google Scholar]
  • B. Du, S. Guo, X. Huang, Y. Li and J. Guo, A Pareto supplier selection algorithm for minimum the life cycle cost of complex product system. Expert Syst. App. 42 (2015) 4253–4264. [Google Scholar]
  • D. Dubois and H. Prade, Possibility theory. In: Computational Complexity. Springer, New York (2012) 2240–2252. [Google Scholar]
  • R.Z. Farahani, S. Rezapour, T. Drezner and S. Fallah, Competitive supply chain network design: an overview of classifications, models, solution techniques and applications. Omega 45 (2014) 92–118. [Google Scholar]
  • M.J. Farrell, The measurement of productive efficiency. J. R. Stat. Soc. Ser. A (General) 120 (1957) 253–281. [Google Scholar]
  • M. Ferguson and B. Toktay, The effect of external competition on recovery strategies. Georgia Institute of Technology College of Business Working Paper (2004). [Google Scholar]
  • S. Green, Principles of Biopsychology Lawrence Erlbaum Associates Ltd. Hove, England (1994). [Google Scholar]
  • T.G. Gutowski, S. Sahni, A. Boustani and S.C. Graves, Reply to Comment on “Remanufacturing and Energy Savings’’. Environ. Sci. Technol. 45 (2011) 7604–7604. [Google Scholar]
  • D. Hammond and P. Beullens, Closed-loop supply chain network equilibrium under legislation. Eur. J. Oper. Res. 183 (2007) 895–908. [CrossRef] [Google Scholar]
  • K.L. Hansen and H. Rush, Hotspots in complex product systems: emerging issues in innovation management. Technovation 18 (1998) 555–590. [CrossRef] [Google Scholar]
  • S. Heilpern, The expected value of a fuzzy number. Fuzzy Sets Syst. 47 (1992) 81–86. [CrossRef] [Google Scholar]
  • C.-J. Ho, Evaluating the impact of operating environments on MRP system nervousness. Int. J. Prod. Res. 27 (1989) 1115–1135. [Google Scholar]
  • M. Hobday, Editor’s Introduction: The Scope of Martin Bell’s Contribution. (2007). [Google Scholar]
  • M. Hobday, The project-based organisation: an ideal form for managing complex products and systems? Res. Policy 29 (2000) 871–893. [Google Scholar]
  • I.-H. Hong and J.-S. Yeh, Modeling closed-loop supply chains in the electronics industry: a retailer collection application. Transp. Res. Part E: Logistics Transp. Rev. 48 (2012) 817–829. [Google Scholar]
  • C. Hongzhuan, F. Zhigeng, L. Sifeng and M. Shuai, The optimal cost-sharing incentive model of main manufacturer-suppliers for complex equipment under grey information. Paper presented at the Proceedings of 2013 IEEE International Conference on Grey systems and Intelligent Services (GSIS) (2013). [Google Scholar]
  • M. Inuiguchi and J. Ramk, Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets Syst. 111 (2000) 3–28. [CrossRef] [Google Scholar]
  • I. Karakayali, H. Emir-Farinas and E. Akcali, An analysis of decentralized collection and processing of end-of-life products. J. Oper. Manage. 25 (2007) 1161–1183. [Google Scholar]
  • S. Khalilpourazari, A. Mirzazadeh, G.-W. Weber and S.H.R. Pasandideh, A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process. Optimization 69 (2019) 63–90. [Google Scholar]
  • W. Klibi, A. Martel and A. Guitouni, The design of robust value-creating supply chain networks: a critical review. Eur. J. Oper. Res. 203 (2010) 283–293. [Google Scholar]
  • H. Kurata, D.-Q. Yao and J.J. Liu, Pricing policies under direct vs. indirect channel competition and national vs. store brand competition. Eur. J. Oper. Res. 180 (2007) 262–281. [Google Scholar]
  • B. Liu and K. Iwamura, A note on chance constrained programming with fuzzy coefficients. Fuzzy sets and Syst. 100 (1998) 229–233. [Google Scholar]
  • S.C.H. Leung, S.O.S. Tsang, W.-L. Ng and Y. Wu, A robust optimization model for multi-site production planning problem in an uncertain environment. Eur. J. Oper. Res. 181 (2007) 224–238. [CrossRef] [Google Scholar]
  • O. Listes, A generic stochastic model for supply-and-return network design. Comput. Oper. Res. 34 (2007) 417–442. [CrossRef] [Google Scholar]
  • Y. Liu, S. Fang, Z. Fang and K.W. Hipel, Petri net model for supply-chain quality conflict resolution of a complex product. Kybernetes 41 (2012) 920–928. [Google Scholar]
  • R. Lotfi, Y.Z. Mehrjerdi, M.S. Pishvaee, A. Sadeghieh and G.-W. Weber, A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk. Numer. Algebra Control Optim. 11 (2021) 221–253. [Google Scholar]
  • T.W. McGuire and R. Staelin, An industry equilibrium analysis of downstream vertical integration. Marketing Sci. 2 (1983) 161–191. [CrossRef] [Google Scholar]
  • E. Özceylan, T. Paksoy and T. Bektas, 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. [Google Scholar]
  • E. Ozceylan, B. Ozkan, M. Kabak and M. Dagdeviren, A survey on spherical fuzzy sets and clustering the literature. Paper presented at the International Conference on Intelligent and Fuzzy Systems (2020). [Google Scholar]
  • E.D. Özdemir, M. Härdtlein, T. Jenssen, D. Zech and L. Eltrop, A confusion of tongues or the art of aggregating indicators – Reflections on four projective methodologies on sustainability measurement. Renew. Sustainable Energy Rev. 15 (2011) 2385–2396. [Google Scholar]
  • T. Paksoy, A. Çalik, A. Kumpf and G.W. Weber, A new model for lean and green closed-loop supply chain optimization. In: Lean and Green Supply Chain Management. Springer (2019) 39–73. [Google Scholar]
  • P. Peykani and J. Gheidar-Kheljani, Performance appraisal of research and development projects value-chain for complex products and systems: the fuzzy three-stage DEA approach. J. New Res. Math. 6 (2020) 41–58. [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. [CrossRef] [Google Scholar]
  • M.S. Pishvaee, J. Razmi and S.A. Torabi, An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: a case study of medical needle and syringe supply chain. Transp. Res. Part E: Logistics Transp. Rev. 67 (2014) 14–38. [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. [CrossRef] [Google Scholar]
  • C. ReVelle, A.T. Murray and D. Serra, Location models for ceding market share and shrinking services. Omega 35 (2007) 533–540. [Google Scholar]
  • S. Rezapour and R.Z. Farahani, Strategic design of competing centralized supply chain networks for markets with deterministic demands. Adv. Eng. Softw. 41 (2010) 810–822. [Google Scholar]
  • P.J. Rushton, M.T. Bokowiec, S. Han, H. Zhang, J.F. Brannock, X. Chen, T.W. Laudeman and M.P. Timko, Tobacco transcription factors: novel insights into transcriptional regulation in the Solanaceae. Plant Physiol. 147 (2008) 280–295. [PubMed] [Google Scholar]
  • M. Safdari Ranjbar, T.-Y. Park and M. Kiamehr, What happened to complex product systems literature over the last two decades: progresses so far and path ahead. Technol. Anal. Strategic Manage. 30 (2018) 948–966. [Google Scholar]
  • W.-F. Shen, D.-Q. Zhang, W.-B. Liu and G.-L. Yang, Increasing discrimination of DEA evaluation by utilizing distances to anti-efficient frontiers. Comput. Oper. Res. 75 (2016) 163–173. [Google Scholar]
  • O. Solgi, J. Gheidar-Kheljani, E. Dehghani and A. Taromi, Resilient supplier selection in complex product and its subsystems’ supply chain under uncertainty and risk disruption: a case study for satellite components. Sci. Iran. DOI: 10.24200/sci.2019.52556.2773 (2019). [Google Scholar]
  • O. Solgi, J. Gheidar-Kheljani, M. Saidi-Mehrabad and E. Dehghani, Implementing an efficient data envelopment analysis method for assessing suppliers of complex product systems. J. Ind. Syst. Eng. 12 (2019) 113–137. [Google Scholar]
  • T. Sueyoshi and M. Goto, Measurement of Returns to Scale and Damages to Scale for DEA-based operational and environmental assessment: How to manage desirable (good) and undesirable (bad) outputs? Eur. J. Oper. Res. 211 (2011) 76–89. [Google Scholar]
  • E.B. Tirkolaee, A. Goli and G.W. Weber, Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option. IEEE Trans. Fuzzy Syst. 28 (2020) 2772–2783. [Google Scholar]
  • A.A. Tsay and N. Agrawal, Channel dynamics under price and service competition. Manuf. Serv. Oper. Manage. 2 (2000) 372–391. [CrossRef] [Google Scholar]
  • H. Üster, G. Easwaran, E. Akçali and S. Çetinkaya, Benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain network design model. Nav. Res. Logistics (NRL) 54 (2007) 890–907. [Google Scholar]
  • J. Wei, K. Govindan, Y. Li and J. Zhao, Pricing and collecting decisions in a closed-loop supply chain with symmetric and asymmetric information. Comput. Oper. Res. 54 (2015) 257–265. [Google Scholar]
  • O. Wu and H. Chen, Chain-to-Chain Competition Under Demand Uncertainty. The University of British Columbia, Vancouver (2003) 1–10. [Google Scholar]
  • T. Xiao and D. Yang, Price and service competition of supply chains with risk-averse retailers under demand uncertainty. Int. J. Prod. Econ. 114 (2008) 187–200. [Google Scholar]
  • D. Zhang, A network economic model for supply chain versus supply chain competition. Omega 34 (2006) 283–295. [Google Scholar]
  • J. Zhao, W. Tang and J. Wei, Pricing decision for substitutable products with retail competition in a fuzzy environment. Int. J. Prod. Econ. 135 (2012) 144–153. [Google Scholar]

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