Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 6, November-December 2024
Page(s) 4741 - 4768
DOI https://doi.org/10.1051/ro/2024141
Published online 05 November 2024
  • A. Ali and M. Haseeb, Radio frequency identification (RFID) technology as a strategic tool towards higher performance of supply chain operations in textile and apparel industry of Malaysia. Uncert. Supply Chain Manag. 7 (2019) 215–226. [CrossRef] [Google Scholar]
  • S.M.H. Bamakan, P. Malekinejad, M. Ziaeian and A. Motavali, Bullwhip effect reduction map for COVID-19 vaccine supply chain. Sustain. Oper. Comput. 2 (2021) 139–148. [CrossRef] [Google Scholar]
  • P. Biswas and B.R. Sarker, Operational planning of supply chains in a production and distribution center with just-in-time delivery. J. Ind. Eng. Manag. (JIEM) 13 (2020) 332–351. [Google Scholar]
  • M. de Arquer, B. Ponte and R. Pino, Examining the balance between efficiency and resilience in closed-loop supply chains. Cent. Eur. J. Oper. Res. 30 (2022) 1307–1336. [CrossRef] [PubMed] [Google Scholar]
  • F. Delfani, H. Samanipour, H. Beiki, A.V. Yumashev and E.M. Akhmetshin, A robust fuzzy optimisation for a multi-objective pharmaceutical supply chain network design problem considering reliability and delivery time. Int. J. Syst. Sci. Oper. Logist. 9 (2022) 155–179. [Google Scholar]
  • A. Delgoshaei and C. Gomes, A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Appl. Soft Comput. 49 (2016) 27–55. [CrossRef] [Google Scholar]
  • A. Delgoshaei, M. Farhadi, S.H. Esmaeili, A. Delgoshaei and A. Mirzazadeh, A new method for distributing and transporting of fashion goods in a closed-loop supply chain in the presence of market uncertainty. Ind. Eng. Manag. Syst. 18 (2019) 825–844. [Google Scholar]
  • A. Delgoshaei, A.K. Aram, S. Ehsani, A. Rezanoori, S.E. Hanjani, G.H. Pakdel and F. Shirmohamdi, A supervised method for scheduling multi-objective job shop systems in the presence of market uncertainties. RAIRO:RO 55 (2021) S1165–S1193. [CrossRef] [EDP Sciences] [Google Scholar]
  • T. Ezaki, N. Imura and K. Nishinari, Model retraining and information sharing in a supply chain with long-term fluctuating demands. Sci. Rep. 11 (2021) 20277. [CrossRef] [Google Scholar]
  • S. Faramarzi-Oghani, P. Dolati Neghabadi, E.-G. Talbi and R. Tavakkoli-Moghaddam, Meta-heuristics for sustainable supply chain management: a review. Int. J. Prod. Res. 61 (2022) 1–31. [Google Scholar]
  • K. Govindan, H. Mina, A. Esmaeili and S.M. Gholami-Zanjani, An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. J. Clean. Prod. 242 (2020) 118317. [CrossRef] [Google Scholar]
  • J. Hemant, R. Rajesh and Y. Daultani, Causal modelling of the enablers of CPFR for building resilience in manufacturing supply chains. RAIRO:RO 56 (2022) 2139–2158. [CrossRef] [EDP Sciences] [Google Scholar]
  • J.M. Hernández and C. Pedroza-Gutiérrez, Estimating the influence of the network topology on the agility of food supply chains. Plos One 14 (2019) e0218958. [CrossRef] [PubMed] [Google Scholar]
  • D.S. Hochbaum, Complexity and algorithms for nonlinear optimization problems. Ann. Oper. Res. 153 (2007) 257–296. [CrossRef] [MathSciNet] [Google Scholar]
  • Y.-C. Hsieh, Y.-C. Lee and P.-S. You, Solving nonlinear constrained optimization problems: an immune evolutionary based two-phase approach. Appl. Math. Modell. 39 (2015) 5759–5768. [CrossRef] [Google Scholar]
  • Y. Huang, B. Zheng and Z. Wang, The value of information sharing in a dual-channel closed-loop supply chain. RAIRO:RO 55 (2021) 2001–2022. [CrossRef] [EDP Sciences] [Google Scholar]
  • T.A. Jessin, A. Rajeev and R. Rajesh, Supplier selection framework to evade pseudo-resilience and to achieve sustainability in supply chains. Int. J. Emerg. Mark. 18 (2023) 1425–1452. [CrossRef] [Google Scholar]
  • P.T. Lautala, M.R. Hilliard, E. Webb, I. Busch, J. Richard Hess, M.S. Roni and A. Valente, Opportunities and challenges in the design and analysis of biomass supply chains. Environ. Manag. 56 (2015) 1397–1415. [CrossRef] [PubMed] [Google Scholar]
  • F. Li, Optimization design of short life cycle product logistics supply chain scheme based on support vector machine. Comput. Intell. Neurosci. 2022 (2022) 1–13. [CrossRef] [Google Scholar]
  • Y. Li, F. Chu, C. Feng, C. Chu and M. Zhou, Integrated production inventory routing planning for intelligent food logistics systems. IEEE Trans. Intell. Transp. Syst. 20 (2018) 867–878. [Google Scholar]
  • W.-J. Lin, Z.-B. Jiang, R. Liu and L. Wang, The bullwhip effect in hybrid supply chain. Int. J. Prod. Res. 52 (2014) 2062–2084. [CrossRef] [Google Scholar]
  • M.F.B. Mad Ali, M.K.A.B.M. Ariffin, F.B. Mustapha and E.E.B. Supeni, An unsupervised machine learning-based framework for transferring local factories into supply chain networks. Mathematics 9 (2021) 3114. [CrossRef] [Google Scholar]
  • A.I. Malik and B. Sarkar, Optimizing a multi-product continuous-review inventory model with uncertain demand, quality improvement, setup cost reduction, and variation control in lead time. Ieee Access 6 (2018) 36176–36187. [CrossRef] [Google Scholar]
  • N.M. Modak and P. Kelle, Managing a dual-channel supply chain under price and delivery-time dependent stochastic demand. Eur. J. Oper. Res. 272 (2019) 147–161. [Google Scholar]
  • J.G. Nahr, H. Nozari and M.E. Sadeghi, Green supply chain based on artificial intelligence of things (AIoT). Int. J. Innov. Manag. Econ. Soc. Sci. 1 (2021) 56–63. [Google Scholar]
  • C. Paciarotti and F. Torregiani, The logistics of the short food supply chain: a literature review. Sustain. Prod. Cons. 26 (2020) 428–442. [Google Scholar]
  • A. Pal and K. Kant, IoT-based sensing and communications infrastructure for the fresh food supply chain. Computer 51 (2018) 76–80. [CrossRef] [Google Scholar]
  • E. Rafati, The bullwhip effect in supply chains: review of recent development. J. Future Sustain. 2 (2022) 81–84. [CrossRef] [Google Scholar]
  • L. Rajabion, M. Khorraminia, A. Andjomshoaa, M. Ghafouri-Azar and H. Molavi, A new model for assessing the impact of the urban intelligent transportation system, farmers’ knowledge and business processes on the success of green supply chain management system for urban distribution of agricultural products. J. Retail. Consum. Serv. 50 (2019) 154–162. [CrossRef] [Google Scholar]
  • S.H. Razavi Hajiagha, M. Daneshvar and J. Antucheviciene, A hybrid fuzzy-stochastic multi-criteria ABC inventory classification using possibilistic chance-constrained programming. Soft Comput. 25 (2021) 1065–1083. [CrossRef] [PubMed] [Google Scholar]
  • M. Rizou, I.M. Galanakis, T.M. Aldawoud and C.M. Galanakis, Safety of foods, food supply chain and environment within the COVID-19 pandemic. Trends Food Sci. Technol. 102 (2020) 293–299. [CrossRef] [Google Scholar]
  • B.K. Rubel, Increasing the efficiency and effectiveness of inventory management by optimizing supply chain through enterprise resource planning technology. Efflatounia Multidiscip. J. 5 (2021) 1739–1756. [Google Scholar]
  • M. Sarkar, B.K. Dey, B. Ganguly, N. Saxena, D. Yadav and B. Sarkar, The impact of information sharing and bullwhip effects on improving consumer services in dual-channel retailing. J. Retail. Consum. Serv. 73 (2023) 103307. [CrossRef] [Google Scholar]
  • H.K. Sarmah and B.B. Hazarika, Importance of the size of sample and its determination in the context of data related to the schools of greater Guwahati. Bull. Gauhati Univ. Math. Assoc. 12 (2012) 55–76. [Google Scholar]
  • O. Schiffmann, B. Hicks, A. Nassehi, J. Gopsill and M. Valero, A cost–benefit analysis simulation for the digitalisation of cold supply chains. Sensors 23 (2023) 4147. [CrossRef] [PubMed] [Google Scholar]
  • J. Shi, G. Zhang and J. Sha, Optimal production planning for a multi-product closed loop system with uncertain demand and return. Comput. Oper. Res. 38 (2011) 641–650. [Google Scholar]
  • P. Tavakkol, B. Nahavandi and M. Homayounfar, Analyzing the drivers of bullwhip effect in pharmaceutical industry’s supply chain. J. Syst. Manag. 9 (2023) 97–117. [Google Scholar]
  • O. Theophilus, M.A. Dulebenets, J. Pasha, Y.-Y. Lau, A.M. Fathollahi-Fard and A. Mazaheri, Truck scheduling optimization at a cold-chain cross-docking terminal with product perishability considerations. Comput. Ind. Eng. 156 (2021) 107240. [CrossRef] [Google Scholar]
  • J. Um and N. Han, Understanding the relationships between global supply chain risk and supply chain resilience: the role of mitigating strategies. Supply Chain Manag. Int. J. 26 (2021) 240–255. [CrossRef] [Google Scholar]
  • S. Validi, A. Bhattacharya and P. Byrne, Integrated low-carbon distribution system for the demand side of a product distribution supply chain: a DoE-guided MOPSO optimiser-based solution approach. Int. J. Prod. Res. 52 (2014) 3074–3096. [CrossRef] [Google Scholar]
  • M. Wang and F. Jie, Managing supply chain uncertainty and risk in the pharmaceutical industry. Health Serv. Manag. Res. 33 (2020) 156–164. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • S.-P. Wang, W. Lee and C.-Y. Chang, Modeling the consignment inventory for a deteriorating item while the buyer has warehouse capacity constraint. Int. J. Prod. Econ. 138 (2012) 284–292. [CrossRef] [Google Scholar]
  • Y. Xu and J. Szmerekovsky, A multi-product multi-period stochastic model for a blood supply chain considering blood substitution and demand uncertainty. Health Care Manag. Sci. 25 (2022) 441–459. [CrossRef] [PubMed] [Google Scholar]
  • X. Xu, H.-S. Kim, S.-S. You and S.-D. Lee, Active management strategy for supply chain system using nonlinear control synthesis. Int. J. Dyn. Control 10 (2022) 1981–1995. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • W. Yu, Robust competitive facility location model with uncertain demand types. Plos One 17 (2022) e0273123. [CrossRef] [PubMed] [Google Scholar]
  • F. Zhao, Y. Hong, D. Yu, Y. Yang and Q. Zhang, A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems. Int. J. Comput. Integr. Manuf. 23 (2010) 20–39. [CrossRef] [Google Scholar]
  • Y. Zhou, H. Li, S. Hu and X. Yu, Two-stage supply chain inventory management based on system dynamics model for reducing bullwhip effect of sulfur product. Ann. Oper. Res. 337 (2022) 1–19. [Google Scholar]

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