Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 5, September-October 2024
Page(s) 4181 - 4195
DOI https://doi.org/10.1051/ro/2024159
Published online 14 October 2024
  • A.H. Alhilali and A. Montazerolghaem, Artificial intelligence based load balancing in SDN: a comprehensive survey. Internet Things 22 (2023) 100814. [CrossRef] [Google Scholar]
  • F.L. Chen, Y.C. Chen and J.Y. Kuo, Applying moving back-propagation neural network and Moving fuzzy-neuron network to predict the requirement of critical spare parts. Expert Syst. Appl. Int. J. 37 (2010) 6695–6704. [CrossRef] [MathSciNet] [Google Scholar]
  • J.D. Croston, Forecasting and stock control for intermittent demands. Oper. Res. Q. (1970–1977) 23 (1972) 289–303. [CrossRef] [Google Scholar]
  • A. Goli, Integration of blockchain-enabled closed-loop supply chain and robust product portfolio design. Comput. Ind. Eng. 179 (2023) 109211. [CrossRef] [Google Scholar]
  • A. Goli and E.B. Tirkolaee, Designing a portfolio-based closed-loop supply chain network for dairy products with a financial approach: accelerated benders decomposition algorithm. Comput. Oper. Res. 155 (2023) 106244. [CrossRef] [Google Scholar]
  • A. Goli, A. Ala and M. Hajiaghaei-Keshteli, Efficient multi-objective meta-heuristic algorithms for energy-aware non-permutation flow-shop scheduling problem. Expert Syst. Appl. 213 (2023) 119077. [CrossRef] [Google Scholar]
  • A. Goli, A. Ala and S. Mirjalili, A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty. Ann. Oper. Res. 328 (2023) 493–530. [CrossRef] [MathSciNet] [Google Scholar]
  • E. Gonzalez-Romera, M.A. Jaramillo-Moran and D. Carmona-Fernandez, Forecasting of the electric energy demand trend and monthly fluctuation with neural networks. Comput. Ind. Eng. 52 (2007) 336–343. [CrossRef] [Google Scholar]
  • F. Guo, C.Y. Liu and L.I. Wei-Ling, Research on spares consumption quota prediction based on exponential smoothing method. Comput. Modern. 1 (2012) 163–165. [Google Scholar]
  • F. Guo, J. Diao, Q. Zhao, D. Wang and Q. Sun, A double-level combination approach for demand forecasting of repairable airplane spare parts based on turnover data. Comput. Ind. Eng. 110 (2017) 92–108. [CrossRef] [Google Scholar]
  • N. Kourentzes, Intermittent demand forecasts with neural networks. Int. J. Prod. Econ. 143 (2013) 198–206. [CrossRef] [Google Scholar]
  • Y. Li, K. Wu and J. Liu, Self-paced ARIMA for robust time series prediction. Knowl. Based Syst. 269 (2023) 110489. [CrossRef] [Google Scholar]
  • A. Montazerolghaem, M. Khosravi, F. Rezaee and M.R. Khayyambashi, An optimal workflow scheduling method in cloud-fog computing using three-objective Harris-Hawks algorithm, in 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE (2022) 300–306. [Google Scholar]
  • A. Nazim and A. Afthanorhan, A comparison between single exponential smoothing (SES), double exponential smoothing (DES), holt’s (brown) and adaptive response rate exponential smoothing (ARRES) techniques in fore-casting Malaysia population. Glob. J. Math. Anal. 2 (2014) 276–280. [CrossRef] [Google Scholar]
  • F. Petropoulos and N. Kourentzes, Forecasts combinations for intermittent demand. J. Oper. Res. Soc. 66 (2015) 914–924. [CrossRef] [Google Scholar]
  • D.E. Rumelhart, G.E. Hinton and R.J. Williams, Learning representations by back propagating errors. Nature 323 (1986) 533–536. [CrossRef] [Google Scholar]
  • S. Sareminia, A support vector based hybrid forecasting model for chaotic time series: spare part consumption prediction. Neural Process. Lett. 55 (2023) 2825–2841. [CrossRef] [Google Scholar]
  • A.A. Syntetos and F. Jeb, On the bias of intermittent demand estimates. Int. J. Prod. Econ. 71 (2001) 457–466. [CrossRef] [Google Scholar]
  • H.P. Thadakamaila, U.N. Raghavan, S. Kumara and R. Albert, Survivability of multiagent-based supply networks: a topological perspective. IEEE Intell. Syst. 19 (2005) 24–31. [Google Scholar]
  • Y. Wang and Q. Shi, Improved dynamic PSO-based algorithm for critical spare parts supply optimization under (T, S) inventory policy. IEEE Access 7 (2019) 153694–153709. [CrossRef] [Google Scholar]
  • Y. Yang, Y. Chen, Y. Wang, C. Li and L. Li, Modelling a combined method based on ANFIS and neural network improved by DE algorithm: a case study for short-term electricity demand forecasting. Appl. Soft Comput. 49 (2016) 663–675. [Google Scholar]
  • K. Zhao, A. Kumar, T.P. Harrison and J. Yen, Analyzing the resilience of complex supply network topologies against random and targeted disruptions. IEEE Syst. J. 5 (2011) 28–39. [CrossRef] [Google Scholar]
  • Q. Zhou, B. Xiong, B. Li, J. Huang and S. Lu, Analysing the resilience of military supply network and simulation against disruptions. Int. J. Eng. Syst. Model. Simul. 8 (2016) 195–204. [Google Scholar]

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