Open Access
| Issue |
RAIRO-Oper. Res.
Volume 60, Number 4, July-August 2026
|
|
|---|---|---|
| Page(s) | 2227 - 2252 | |
| DOI | https://doi.org/10.1051/ro/2026066 | |
| Published online | 28 July 2026 | |
- B. Alhijawi and A. Awajan, Genetic algorithms: theory, genetic operators, solutions, and applications. Evol. Intell. 17 (2024) 1245–1256. [Google Scholar]
- E. Badakhshan, N. Mustafee and R. Bahadori, Application of simulation and machine learning in supply chain management: a synthesis of the literature using the SIM-ML literature classification framework. Comput. Ind. Eng. 198 (2024) 110649. [Google Scholar]
- M. Beck, K. Pöppel, M. Spanring, A. Auer, O. Prudnikova, M. Kopp, G. Klambauer, J. Brandstetter and S. Hochreiter, XLSTM: extended long short-term memory. Preprint arXiv:2405.04517 (2024). [Google Scholar]
- Y. Belkourchia, L. Azrar and E.-S.M. Zeriab, A hybrid optimization algorithm for solving constrained engineering design problems, in 2019 5th International Conference on Optimization and Applications (ICOA) (2019) 1–7. [Google Scholar]
- A. Bell, M. Fairbrother and K. Jones, Fixed and random effects models: making an informed choice. Qual. Quant. 53 (2019) 1051–1074. [Google Scholar]
- S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press (2004). [Google Scholar]
- J. Braun and M. Griebel, On a constructive proof of Kolmogorov's superposition theorem. Constr. Approx. 30 (2009) 653–675. [Google Scholar]
- H.W. Chen and A. Lim, A network price elasticity of demand model with product substitution. J. Revenue Pricing Manag. 22 (2023) 235–247. [Google Scholar]
- H.Y.S. Chien, J.S. Turek, N. Beckage, V.A. Vo, C. J. Honey and T.L. Willke, Slower is better: revisiting the forgetting mechanism in LSTM for slower information decay. Preprint arXiv:2105.05944 (2021). [Google Scholar]
- G. Ellison and S.F. Ellison, Search, obfuscation, and price elasticities on the internet. Econometrica 77 (2009) 427–452. [Google Scholar]
- Z. Fu, X. Yang, Y. Ma, Y. Sun and T. Wang, Integrating explainable AI and causal inference to unveil regional air quality drivers in China. J. Environ. Manag. 390 (2025) 126270. [Google Scholar]
- A.G. Gad, Particle swarm optimization algorithm and its applications: a systematic review. Arch. Comput. Methods Eng. 29 (2022) 2531–2561. [Google Scholar]
- R. Genet and H. Inzirillo, TKAN: temporal Kolmogorov–Arnold networks. Preprint arXiv:2405.07344 (2024). [Google Scholar]
- B. Gülmez, Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Syst. Appl. 227 (2023) 120346. [Google Scholar]
- Z. Gong, Optimization of cross-border e-commerce (CBEC) supply chain management based on fuzzy logic and auction theory. Sci. Rep. 14 (2024) 14088. [Google Scholar]
- O. Hashemi-Amiri, F. Ghorbani and R. Ji, Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: a distributionally robust approach. Comput. Ind. Eng. 175 (2023) 108845. [Google Scholar]
- S. Hochreiter and J. Schmidhuber, Long short-term memory. Neural Comput. 9 (1997) 1735–1780. [CrossRef] [PubMed] [Google Scholar]
- E. Hofmann and E. Rutschmann, Big data analytics and demand forecasting in supply chains: a conceptual analysis. Int. J. Logist. Manag. 29 (2018) 739–766. [Google Scholar]
- V. Ismailov, On the representation by linear superpositions. J. Approx. Theory 151 (2008) 113–125. [Google Scholar]
- W.A. Kamakura, B.-D. Kim and J. Lee, Modeling preference and structural heterogeneity in consumer choice. Mark. Sci. 15 (1996) 152–172. [Google Scholar]
- J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of ICNN'95 – International Conference on Neural Networks, Perth, WA, Australia (1995) 1942–1948. [Google Scholar]
- Y. Kong, Z. Wang, Y. Nie, T. Zhou, S. Zohren, Y. Liang, P. Sun and Q. Wen, Unlocking the power of LSTM for long term time series forecasting. Proc. AAAI Conf. Artif. Intell. 39 (2025) 11968–11976. [Google Scholar]
- C. Lea, R. Vidal, A. Reiter and G.D. Hager, Temporal convolutional networks: a unified approach to action segmentation, in European Conference on Computer Vision (ECCV), Vol. 9915 (2016) 47–54. [Google Scholar]
- Y. Li, Z. Song, L. Xia, J. Sun and Z. Wang, Application of the adaptive sparrow search algorithm in medical supply engineering. Sci. Rep. 15 (2025) 35775. [Google Scholar]
- Z. Li and Z. Yang, ESG rating disagreement and corporate total factor productivity: inference and prediction. Finance Res. Lett. 78 (2025) 107127. [Google Scholar]
- J. Lu, X. Zheng, E. Nervino, Y. Li, Z. Xu and Y. Xu, Retail store location screening: a machine learning-based approach. J. Retail. Consum. Serv. 77 (2024) 103620. [Google Scholar]
- A.S. Mahapatra, S. Sengupta, A. Dasgupta, B. Sarkar and R.T. Goswami, What is the impact of demand patterns on integrated online-offline and buy-online-pickup in-store (BOPS) retail in a smart supply chain management? J. Retail. Consum. Serv. 82 (2025) 104093. [Google Scholar]
- C. Mangla, M. Ahmad and M. Uddin, Optimization of complex nonlinear systems using genetic algorithm. Int. J. Inf. Technol. 13 (2021) 1913–1925. [Google Scholar]
- M.K. Mantrala, P.B. Seetharaman, R. Kaul, S. Gopalakrishna and A. Stam, Optimal pricing strategies for an automotive aftermarket retailer. J. Mark. Res. 43 (2006) 588–604. [Google Scholar]
- S. Mehroliya, S. Tomar, A. Arya and B. Singh, A novel hybrid GA-PSO algorithm-based optimization of transmission and expansion planning. SN Comput. Sci. 4 (2023) 690. [Google Scholar]
- F. Milisav, V. Bazinet, R.F. Betzel and B. Misic, A simulated annealing algorithm for randomizing weighted networks. Nat. Comput. Sci. 5 (2025) 48–64. [Google Scholar]
- D. Mircetic, B. Rostami-Tabar, S. Nikolicic and M. Maslaric, Forecasting hierarchical time series in supply chains: an empirical investigation. Int. J. Prod. Res. 60 (2021) 2514–2533. [Google Scholar]
- N. Niroumandrad, N. Lahrichi and A. Lodi, Learning tabu search algorithms: a scheduling application. Comput. Oper. Res. 170 (2024) 106751. [Google Scholar]
- J.J. Rushchitsky, On the constants of the nonlinear Murnaghan's hyperelastic material model. Int. Appl. Mech. 52 (2016) 508–519. [Google Scholar]
- R.S. Tavares, T.C. Martins and M.S.G. Tsuzuki, Simulated annealing with adaptive neighborhood: a case study in off-line robot path planning. Expert Syst. Appl. 38 (2011) 2951–2965. [Google Scholar]
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser and I. Polosukhin, Attention is all you need, in Advances in Neural Information Processing Systems (NeurIPS), Vol. 30 (2017) 5998–6008. [Google Scholar]
- R.G. Walters and W. Bommer, Measuring the impact of product and promotion-related factors on product category price elasticities. J. Bus. Res. 36 (1996) 203–216. [Google Scholar]
- A. Wan, Q. Chang, K. AL-Bukhaiti and J. He, Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism. Energy 282 (2023) 128274. [Google Scholar]
- L. Yunzhu, Improvement of simulated annealing algorithm and its application in water environment engineering, in Frontier Computing, FC 2022, edited by J.C. Hung, N.Y. Yen, and J.W. Chang, Vol. 1031 of Lecture Notes in Electrical Engineering. Springer, Singapore (2023) 1717–1729. [Google Scholar]
- O. Zabraoui, Y. Hmamou, A. Chafi and S. Kammouri Alami, A comparative study of multi-algorithm optimization for inventory analytics in supply chains. Supply Chain Anal. 12 (2025) 100154. [Google Scholar]
- J. Zhang, H. Jiang, X. Li, L. Zhang and Z. Li, A CNN-transformer hybrid approach for decoding visual neural activities evoked by natural images into texts. NeuroImage 247 (2022) 118377. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.