Open Access
RAIRO-Oper. Res.
Volume 56, Number 4, July-August 2022
Page(s) 2721 - 2749
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). [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]

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.