Open Access
Issue |
RAIRO-Oper. Res.
Volume 56, Number 4, July-August 2022
|
|
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Page(s) | 2721 - 2749 | |
DOI | https://doi.org/10.1051/ro/2022094 | |
Published online | 18 August 2022 |
- I. Agrebi, M. Jemmali, H. Alquhayz and T. Ladhari, Metaheuristic algorithms for the two-machine flowshop scheduling problem with release dates and blocking constraint. J. Ch. Inst. Eng. 44 (2021) 573–582. [CrossRef] [Google Scholar]
- F. al Fayez, L.K.B. Melhim and M. Jemmali, Heuristics to optimize the reading of railway sensors data. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE (2019) 1676–1681. [Google Scholar]
- M. Alharbi and M. Jemmali, Algorithms for investment project distribution on regions. Comput. Intell. Neurosci. 2020 (2020). DOI: 10.1155/2020/3607547. [CrossRef] [Google Scholar]
- M. Alkhelaiwi, W. Boulila, J. Ahmad, A. Koubaa and M. Driss, An efficient approach based on privacy-preserving deep learning for satellite image classification. Remote Sens. 13 (2021) 2221. [CrossRef] [Google Scholar]
- H. Alquhayz and M. Jemmali, Max–min processors scheduling. Inf. Technol. Control 50 (2021) 5–12. [CrossRef] [Google Scholar]
- A. Altay and M. Baykal-Gürsoy, Imperfect rail-track inspection scheduling with zero-inflated miss rates. Transp. Res. Part C Emerg. Technol. 138 (2022) 103608. [CrossRef] [Google Scholar]
- H. Amdouni, M. Jemmali, M. Mrad and T. Ladhari, An exact algorithm minimizing the makespan for the twomachine flowshop scheduling under release dates and blocking constraints. Int. J. Ind. Eng. 28 (2021) 631–643. [Google Scholar]
- A. Bakhtiary, J.A. Zakeri and S. Mohammadzadeh, An opportunistic preventive maintenance policy for tamping scheduling of railway tracks. Int. J. Rail Transp. 9 (2021) 1–22. [CrossRef] [Google Scholar]
- W. Boulila, A top-down approach for semantic segmentation of big remote sensing images. Earth Sci. Inf. 12 (2019) 295–306. [CrossRef] [Google Scholar]
- W. Boulila, M. Sellami, M. Driss, M. Al-Sarem, M. Safaei and F.A. Ghaleb, Rs-dcnn: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification. Comput. Electron. Agri. 182 (2021) 106014. [CrossRef] [Google Scholar]
- R.L. Burdett and E. Kozan, Performance profiling for predictive train schedules. J. Rail Transp. Planning Manage. 4 (2014) 98–114. [CrossRef] [Google Scholar]
- C. Dao, R. Basten and A. Hartmann, Maintenance scheduling for railway tracks under limited possession time. J. Transp. Eng. Part A Syst. 144 (2018) 04018039. [CrossRef] [Google Scholar]
- M. Dell’Amico, M. Iori, S. Martello and M. Monaci, Heuristic and exact algorithms for the identical parallel machine scheduling problem. INFORMS J. Comput. 20 (2008) 333–344. [CrossRef] [MathSciNet] [Google Scholar]
- H. Dong, H. Zhu, Y. Li, Y. Lv, S. Gao, Q. Zhang and B. Ning, Parallel intelligent systems for integrated high-speed railway operation control and dynamic scheduling. IEEE Trans. Cybern. 48 (2018) 3381–3389. [CrossRef] [PubMed] [Google Scholar]
- F. Donzella, M. del Cacho Estil-les, C. Bersani, R. Sacile and L. Zero, Train scheduling and rescheduling model based oncustomer satisfaction. Application to genoa railway network. In: 2018 13th Annual Conference on System of Systems Engineering (SoSE). IEEE (2018) 593–600. [Google Scholar]
- J. Garca, P. Moraga, M. Valenzuela, B. Crawford, R. Soto, H. Pinto, A. Peña, F. Altimiras and G. Astorga, A db-scan binarization algorithm applied to matrix covering problems. Comput. Intell. Neurosci. 2019 (2019). DOI: 10.1155/2019/3238574. [Google Scholar]
- H. Ghandorh, W. Boulila, S. Masood, A. Koubaa, F. Ahmed and J. Ahmad, Semantic segmentation and edge detection – approach to road detection in very high resolution satellite images. Remote Sens. 14 (2022) 613. [CrossRef] [Google Scholar]
- M. Haouari and M. Jemmali, Tight bounds for the identical parallel machine-scheduling problem: Part II. Int. Trans. Oper. Res. 15 (2008) 19–34. [Google Scholar]
- M. Haouari, A. Gharbi and M. Jemmali, Tight bounds for the identical parallel machine scheduling problem. Int. Trans. Oper. Res. 13 (2006) 529–548. [Google Scholar]
- M. Jemmali, An optimal solution for the budgets assignment problem. RAIRO: Oper. Res. 55 (2021) 873–897. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
- M. Jemmali, Projects distribution algorithms for regional development. ADCAIJ 10 (2021). http://hdl.handle.net/10366/147245. [Google Scholar]
- M. Jemmali, Intelligent algorithms and complex system for a smart parking for vaccine delivery center of covid-19. Complex Intell. Syst. 8 (2022) 597–609. [CrossRef] [PubMed] [Google Scholar]
- M. Jemmali and A. Alourani, Mathematical model bounds for maximizing the minimum completion time problem. J. Appl. Math. Comput. Mech. 20 (2021) 43–50. [CrossRef] [MathSciNet] [Google Scholar]
- M. Jemmali and H. Alquhayz, Equity data distribution algorithms on identical routers. In: International Conference on Innovative Computing and Communications. Springer (2020) 297–305. [CrossRef] [Google Scholar]
- M. Jemmali, L.K.B. Melhim and M. Alharbi, Randomized-variants lower bounds for gas turbines aircraft engines. In: World Congress on Global Optimization. Springer (2019) 949–956. [Google Scholar]
- M. Jemmali, L.K.B. Melhim, S.O.B. Alharbi and A.S. Bajahzar, Lower bounds for gas turbines aircraft engines. Commun. Math. App. 10 (2019) 637–642. [Google Scholar]
- M. Jemmali, M.M. Otoom and F. al Fayez, Max–min probabilistic algorithms for parallel machines. In: Proceedings of the 2020 International Conference on Industrial Engineering and Industrial Management. ACM (2020) 19–24. [CrossRef] [Google Scholar]
- M. Jemmali, L. Hidri and A. Alourani, Two-stage hybrid flowshop scheduling problem with independent setup times. Int. J. Simul. Model. (IJSIMM) 21 (2022) 5–16. [CrossRef] [Google Scholar]
- M. Jemmali, L.K.B. Melhim, M.T. Alharbi, A. Bajahzar and M.N. Omri, Smart-parking management algorithms in smart city. Sci. Rep. 12 (2022) 1–15. [CrossRef] [Google Scholar]
- B. Johannes, Scheduling parallel jobs to minimize the makespan. J. Scheduling 9 (2006) 433–452. [CrossRef] [MathSciNet] [Google Scholar]
- S. Jütte, D. Müller and U.W. Thonemann, Optimizing railway crew schedules with fairness preferences. J. Scheduling 20 (2017) 43–55. [CrossRef] [MathSciNet] [Google Scholar]
- D. Kovenkin and V. Podverbnyy, Issues of planning work on the current maintenance of the railway track. Transp. Res. Proc. 61 (2022) 636–640. [Google Scholar]
- D. Laha and J.N. Gupta, An improved cuckoo search algorithm for scheduling jobs on identical parallel machines. Comput. Ind. Eng. 126 (2018) 348–360. [CrossRef] [Google Scholar]
- J. Lan, Y. Jiang, G. Fan, D. Yu and Q. Zhang, Real-time automatic obstacle detection method for traffic surveillance in urban traffic. J. Signal Process. Syst. 82 (2016) 357–371. [CrossRef] [Google Scholar]
- S. Li and Y. Zhang, On-line scheduling on parallel machines to minimize the makespan. J. Syst. Sci. Complexity 29 (2016) 472–477. [CrossRef] [MathSciNet] [Google Scholar]
- L.K.B. Melhim, M. Jemmali and M. Alharbi, Intelligent real-time intervention system applied in smart city. In: 2018 21st Saudi Computer Society National Computer Conference (NCC). IEEE (2018) 1–5. [Google Scholar]
- H. Mezni, M. Driss, W. Boulila, S.B. Atitallah, M. Sellami and N. Alharbi, Smartwater: a service-oriented and sensor cloud-based framework for smart monitoring of water environments. Remote Sens. 14 (2022) 922. [CrossRef] [Google Scholar]
- M. Movaghar and S. Mohammadzadeh, Bayesian monte carlo approach for developing stochastic railway track degradation model using expert-based priors. Struct. Infrastruct. Eng. 18 (2022) 145–166. [CrossRef] [Google Scholar]
- D. Pisinger, Dynamic programming on the word ram. Algorithmica 35 (2003) 128–145. [Google Scholar]
- J. Pradeep, M. Harikrishnan and K. Vijayakumar, Automatic railway detection and tracking inspecting system. Springer (2022) 309–318. [Google Scholar]
- Z. Qi, Y. Tian and Y. Shi, Efficient railway tracks detection and turnouts recognition method using hog features. Neural Comput. App. 23 (2013) 245–254. [CrossRef] [Google Scholar]
- B. Roy and A.K. Sen, Meta-heuristic techniques to solve resource-constrained project scheduling problem. In: International Conference on Innovative Computing and Communications. Springer (2019) 93–99. [CrossRef] [Google Scholar]
- M. Sedghi, O. Kauppila, B. Bergquist, E. Vanhatalo and M. Kulahci, A taxonomy of railway track maintenance planning and scheduling: a review and research trends. Reliab. Eng. Syst. Saf. 215 (2021) 107827. [CrossRef] [Google Scholar]
- L.P. Veelenturf, D. Potthoff, D. Huisman and L.G. Kroon, Railway crew rescheduling with retiming. Transp. Res. Part C Emerg. Technol. 20 (2012) 95–110. [CrossRef] [Google Scholar]
- X. Xu, K. Li, L. Yang and Z. Gao, An efficient train scheduling algorithm on a single-track railway system. J. Scheduling 22 (2019) 85–105. [CrossRef] [MathSciNet] [Google Scholar]
- C. Zhang, Y. Gao, L. Yang, U. Kumar and Z. Gao, Integrated optimization of train scheduling and maintenance planning on high-speed railway corridors. Omega 87 (2019) 86–104. [CrossRef] [Google Scholar]
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