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All issues Volume 58 / No 1 (January-February 2024) RAIRO-Oper. Res., 58 1 (2024) 303-326 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 1, January-February 2024
Page(s) 303 - 326
DOI https://doi.org/10.1051/ro/2023110
Published online 08 February 2024
  • A. Alptekinoğlu, A. Banerjee, A. Paul and N. Jain, Inventory pooling to deliver differentiated service. Manuf. Serv. Oper. Manag. 15 (2013) 33–44. [CrossRef] [Google Scholar]
  • R. Batarfi, M.Y. Jaber and C.H. Glock, Pricing and inventory decisions in a dual-channel supply chain with learning and forgetting. Comput. Ind. Eng. 136 (2019) 397–420. [CrossRef] [Google Scholar]
  • R. Behnamfar, S.M. Sajadi and M. Tootoonchy, Developing environmental hedging point policy with variable demand: A machine learning approach. Int. J. Prod. Econ. 254 (2022) 108640. [CrossRef] [Google Scholar]
  • H. Birkel and J.M. Müller, Potentials of industry 4.0 for supply chain management within the triple bottom line of sustainability-A systematic literature review. J. Clean. Prod. 289 (2021) 125612. [CrossRef] [Google Scholar]
  • A.G. De Kok, Hierarchical production planning for consumer goods. Eur. J. Oper. Res. 45 (1990) 55–69. [CrossRef] [Google Scholar]
  • N.K. Dev, R. Shankar and S. Swami, Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system. Int. J. Prod. Econ. 223 (2020) 107519. [CrossRef] [Google Scholar]
  • D. Fan, Q. Xu, T. Fan and F. Cheng, Inventory optimization model considering consumer shift and inventory transshipment in dual-channel supply chains. RAIRO: OR 53 (2019) 59–79. [CrossRef] [EDP Sciences] [Google Scholar]
  • G. Gao-feng and S. Hua-qian, Modeling and effectiveness analysis on business process collaboration of humanitarian relief supply chain based on stochastic Petri net. J. Saf. Sci. Technol. 9 (2017) 58–65. [Google Scholar]
  • C.A. Garcia, A. Ibeas, J. Herrera and R. Vilanova, Inventory control for the supply chain: An adaptive control approach based on the identification of the lead-time. Omega 40 (2012) 314–327. [CrossRef] [Google Scholar]
  • N.T.M. Hanh, J.M. Chen and N. Van Hop, Pricing strategy and order quantity allocation with price-sensitive demand in three-echelon supply chain. Expert Syst. Appl. 206 (2022) 117879. [Google Scholar]
  • B. Huang and S.M. Iravani, Optimal production and rationing decisions in supply chains with information sharing. Oper. Res. Lett. 35 (2007) 669–676. [CrossRef] [MathSciNet] [Google Scholar]
  • A.M. Jalal, E.A. Toso, C.P. Tautenhain and M.C. Nascimento, An integrated location-transportation problem under value-added tax issues in pharmaceutical distribution planning. Expert Syst. Appl. 206 (2022) 117780. [CrossRef] [Google Scholar]
  • A. Kanda and S.G. Deshmukh, Supply chain coordination: perspectives, empirical studies and research directions. Int. J. Prod. Econ. 115 (2008) 316–335. [CrossRef] [Google Scholar]
  • M. Karimi, H. Khademi-Zare, Y. Zare-Mehrjerdi and M.B. Fakhrzad, Optimizing service level, price, and inventory decisions for a supply chain with retailers’ competition and cooperation under VMI strategy. RAIRO: OR 56 (2022) 1051–1078. [CrossRef] [EDP Sciences] [Google Scholar]
  • N. Khilwani, M.K. Tiwari and I. Sabuncuoglu, Hybrid Petri-nets for modelling and performance evaluation of supply chains. Int. J. Prod. Res. 49 (2011) 4627–4656. [CrossRef] [Google Scholar]
  • A.G. Lagodimos, Multi-echelon service models for inventory systems under different rationing policies. Int. J. Prod. Econ. 30 (1992) 939–956. [CrossRef] [Google Scholar]
  • A.G. Lagodimos and S. Koukoumialos, Service performance of two-echelon supply chains under linear rationing. Int. J. Prod. Econ. 112 (2008) 869–884. [CrossRef] [Google Scholar]
  • R. Liu and L. Zhao, Evolutionary game study on information nodes setting in supply chain tracing based on compensation mechanism. RAIRO: OR 56 (2022) 3405–3428. [CrossRef] [EDP Sciences] [Google Scholar]
  • J. Liu, X. Liu, F. Wang, X. Ma and X. Yue, Equilibrium decisions and coordination of a logistics service supply chain with bilateral corporate social responsibility. RAIRO: OR 56 (2022) 2595–2612. [CrossRef] [EDP Sciences] [Google Scholar]
  • S.V.S. Padiyar, V. Vandana, N. Bhagat, S.R. Singh and B. Sarkar, Joint replenishment strategy for deteriorating multi-item through multi-echelon supply chain model with imperfect production under imprecise and inflationary environment. RAIRO: OR 56 (2022) 3071–3096. [CrossRef] [EDP Sciences] [Google Scholar]
  • B. Paul and C. Rajendran, Optimal and heuristic base-stock levels and rationing policy for a divergent supply chain. Int. J. Ind. Syst. Eng. 5 (2010) 460–484. [Google Scholar]
  • S. Perotti, R.F.B. Santacruz, P. Bremer and J.E. Beer, Logistics 4.0 in warehousing: a conceptual framework of influencing factors, benefits and barriers. Int. J. Logist. Manag. 33 (2022) 193–220. [CrossRef] [Google Scholar]
  • T.F. Rodrigues, M.M. Dantas and C.A. Cavalcante, A dynamic inventory rationing policy for business-to-consumer e-tail stores in a supply disruption context. Comput. Ind. Eng. 142 (2020) 106379. [CrossRef] [Google Scholar]
  • A.B. Samii, Impact of nested inventory allocation policies in a newsvendor setting. Int. J. Prod. Econ. 181 (2016) 247–256. [CrossRef] [Google Scholar]
  • T. Sawik, On the fair optimization of cost and customer service level in a supply chain under disruption risks. Omega 53 (2015) 58–66. [CrossRef] [Google Scholar]
  • A.J. Schmitt, S.A. Sun, L.V. Snyder and Z.J.M. Shen, Centralization versus decentralization: Risk pooling, risk diversification, and supply chain disruptions. Omega 52 (2015) 201–212. [CrossRef] [Google Scholar]
  • N. Tahirov and C.H. Glock, Manufacturer encroachment and channel conflicts: A systematic review of the literature. Eur. J. Oper. Res. 302 (2022) 403–426. [CrossRef] [Google Scholar]
  • M. Taiping, Z. Peisi and D. Renhang, Modeling and performance analysis of manufacturing execution system basedon Petrinet. Comput. Integr. Manuf. Syst. 2l (2015) 2064–2071. [Google Scholar]
  • J.P. van der Gaast and F. Weidinger, A deep learning approach for the selection of an order picking system. Eur. J. Oper. Res. 302 (2022) 530–543. [CrossRef] [Google Scholar]
  • M.C. Van der Heijden, E.B. Diks and A.G. De Kok, Stock allocation in general multi-echelon distribution systems with (R, S) order-up-to-policies. Int. J. Prod. Econ. 49 (1997) 157–174. [CrossRef] [Google Scholar]
  • P. Wanke, H. Alvarenga, H. Correa, A. Hadi-Vencheh and M.A.K. Azad, Fuzzy inference systems and inventory allocation decisions: Exploring the impact of priority rules on total costs and service levels. Expert Syst. Appl. 85 (2017) 182–193. [CrossRef] [Google Scholar]
  • S.H. Zegordi and H. Davarzani, Developing a supply chain disruption analysis model: Application of colored Petri-nets. Expert Syst. Appl. 39 (2012) 2102–2111. [CrossRef] [Google Scholar]

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