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) S1369 - S1394
DOI https://doi.org/10.1051/ro/2020041
Published online 02 March 2021
  • S. Aghamohammadi-Bosjin, M. Rabbani and R. Tavakkoli-Moghaddam, Agile two-stage lot-sizing and scheduling problem with reliability, customer satisfaction and behaviour under uncertainty: a hybrid metaheuristic algorithm. Eng. Optim. 52 (2020) 1–21. [Google Scholar]
  • R. Babazadeh, J. Razmi and R. Ghodsi, Supply chain network design problem for a new market opportunity in an agile manufacturing system. J. Ind. Eng. Int. 8 (2012) 19–27. [Google Scholar]
  • B. Beemsterboer, M. Land and R. Teunter, Hybrid MTO-MTS production planning: an explorative study. Eur. J. Oper. Res. 248 (2016) 453–461. [Google Scholar]
  • B. Beemsterboer, M. Land and R. Teunter, Flexible lot sizing in hybrid make-to-order/make-to-stock production planning. Eur. J. Oper. Res. 260 (2017) 1014–1023. [Google Scholar]
  • A. Ben-Tal, L. El-Ghaoui and A. Nemirovski, Robust Optimization. Princeton University Press, Princeton, NJ (2009). [Google Scholar]
  • X. Brusset, Does supply chain visibility enhance agility? Int. J. Prod. Econ. 171 (2016) 46–59. [Google Scholar]
  • H. Carvalho, S. Azevedo and V. Cruz-Machado, Agile and resilient approaches to supply chain management: influence on performance and competitiveness. Logist. Res. 4 (2012) 49–62. [Google Scholar]
  • F. Chan and A. Kumar, Performance optimization of a leagility inspired supply chain model: a CFGTSA algorithm based approach. Int. J. Prod. Res. 47 (2009) 777–799. [Google Scholar]
  • M. Ding, W. Ross and V. Rao, Price as an indicator of quality: implications for utility and demand functions. J. Retail. 86 (2010) 69–84. [Google Scholar]
  • M. El Mokadem, The classification of supplier selection criteria with respect to lean or agile manufacturing strategies. J. Manuf. Technol. Manage. 28 (2017) 232–249. [Google Scholar]
  • A. Faiza, Significance of lean, agile and leagile decoupling point in supply chain management. J. Econ. Behav. Stud. 3 (2011) 287–295. [Google Scholar]
  • B. Giri, C. Mondal and T. Maiti, Optimal product quality and pricing strategy for a two-period closed-loop supply chain with retailer variable markup. RAIRO: OR 53 (2019) 609–626. [Google Scholar]
  • R. Gössinger and S. Kalkowski, Robust order promising with anticipated customer response. Int. J. Prod. Econ. 170 (2015) 529–542. [Google Scholar]
  • A. Hasani, S. 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]
  • A. Haq and V. Boddu, Analysis of enablers for the implementation of leagile supply chain management using an integrated fuzzy QFD approach. J. Intell. Manuf. 28 (2017) 1–12. [Google Scholar]
  • S. Hum, M. Parlar and Y. Zhou, Measurement and optimization of responsiveness in supply chain networks with queueing structures. Eur. J. Oper. Res. 264 (2018) 106–118. [Google Scholar]
  • M. Inuiguchi and J. Rami, 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. [Google Scholar]
  • A. Jakubovskis, Flexible production resources and capacity utilization rates: a robust optimization perspective. Int. J. Prod. Econ. 189 (2017) 77–85. [Google Scholar]
  • M. Khan, M. Hussain and L. Cárdenas-Barrón, Learning and screening errors in an EPQ inventory model for supply chains with stochastic lead time demands. Int. J. Prod. Res. 55 (2017) 4816–4832. [Google Scholar]
  • H. Li and K. Womer, Optimizing the supply chain configuration for make-to-order manufacturing. Eur. J. Oper. Res. 221 (2012) 118–128. [Google Scholar]
  • S. Liao, C. Hsieh and W. Ho, Multi-objective evolutionary approach for supply chain network design problem within online customer consideration. RAIRO: OR 51 (2017) 135–155. [Google Scholar]
  • M. Lim, H. Mak and Z. Shen, Agility and proximity considerations in supply chain design. Manage. Sci. 63 (2016) 1026–1041. [Google Scholar]
  • C. Lin and T.H. Wang, Build to order supply chain network design under supply and demand uncertainties. Trans. Res. B: Meth. 45 (2011) 1162–1176. [Google Scholar]
  • B. Liu and Y. Liu, Expected value of fuzzy variable and fuzzy expected value models. IEEE Trans. Fuzzy Syst. 10 (2002) 445–450. [Google Scholar]
  • Y. Liu, H. Dong, N. Lohse and S. Petrovic, A multi-objective genetic algorithm for nalyzingon of energy consumption and shop floor production performance. Int. J. Prod. Econ. 179 (2016) 259–272. [Google Scholar]
  • A. Lyons and A. Ma’aram, An examination of multi-tier supply chain strategy alignment in the food industry. Int. J. Prod. Res. 52 (2014) 1911–1925. [Google Scholar]
  • M. Naim and J. Gosling, On leanness, agility and leagile supply chains. Int. J. Prod. Econ. 131 (2011) 342–354. [Google Scholar]
  • P. Nieuwenhuis and E. Katsifou, More sustainable automotive production through understanding decoupling points in leagile manufacturing. J. Clean. Prod. 95 (2015) 232–241. [Google Scholar]
  • F. Pan and R. Nagi, Robust supply chain design under uncertain demand in agile manufacturing. Comput. Oper. Res. 37 (2010) 668–683. [Google Scholar]
  • D. Peidro, J. Mula, R. Poler and J.L. Verdegay, Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets Syst. 160 (2009) 2640–2657. [Google Scholar]
  • M. Pishvaee, M. Rabbani and S. Torabi, A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl. Math. Model. 35 (2011) 637–649. [Google Scholar]
  • M. Pishvaee, J. Razmi and S. Torabi, Robust possibilistic programming for socially responsible supply chain network design: a new approach. Fuzzy Sets Syst. 206 (2012) 1–20. [Google Scholar]
  • S. Raj, S. Sundeer, Vinodha and G. Anandc, A mathematical model to evaluate the role of agility enablers and criteria in a manufacturing environment. Int. J. Prod. Res. 51 (2013) 5971–5981. [Google Scholar]
  • U. Sağlam and A. Banerjee, Integrated multiproduct batch production and truck shipment scheduling under different shipping policies. Omega 74 (2018) 70–81. [Google Scholar]
  • M. Seliaman, M. Khan and L. Cárdenas-Barrón, Algebraic modelling of a two level supply chain with defective items. RAIRO: OR 52 (2018) 415–427. [Google Scholar]
  • A. Taleizadeh, S. Hadadpour, L. Cárdenas-Barrón, and A. Shaikh, Warranty and price optimization in a competitive duopoly supply chain with parallel importation. Int. J. Prod. Econ. 185 (2017) 76–88. [Google Scholar]
  • S. Torabi and E. Hassini, An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets Syst. 159 (2008) 193–214. [Google Scholar]
  • S. Vinodh and S. Aravindraj, Evaluation of leagility in supply chains using fuzzy logic approach. Int. J. Prod. Res. 51 (2013) 1186–1195. [Google Scholar]
  • B. Yan, J. Wu, L. Liu and Q. Chen, Inventory management models in cluster supply chains based on system dynamics. RAIRO: OR 51 (2017) 763–778. [Google Scholar]
  • A. Yimer and K. Demirli, A genetic approach to two-phase optimization of dynamic supply chain scheduling. Comput. Ind. Eng. 58 (2010) 411–422. [Google Scholar]
  • Y. Zhang, Y. Wang and L. Wu, Research on demand-driven leagile supply chain operation model: a simulation based on anylogic in system engineering. Syst. Eng. Proc. 3 (2012) 249–258. [Google Scholar]

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