Abikarram, J. B., McConky, K., & Proano, R. (2019). Energy cost minimization for unrelated parallel machine scheduling under real time and demand charge pricing.
Journal of cleaner production,
208, 232-242.
https://doi.org/10.1016/j.jclepro.2018.10.048
Al-Harkan, I. M., & Qamhan, A. A. (2019). Optimize unrelated parallel machines scheduling problems with multiple limited additional resources, sequence-dependent setup times and release date constraints.
IEEE Access, 7, 171533-171547.
https://doi.org/10.1109/ACCESS.2019.2955975
Al-Harkan, I. M., Qamhan, A. A., Badwelan, A., Alsamhan, A., & Hidri, L. (2021). Modified Harmony Search Algorithm for Resource-Constrained Parallel Machine Scheduling Problem with Release Dates and Sequence-Dependent Setup Times.
Processes, 9(4), 654.
https://doi.org/10.3390/pr9040654
Bernal, C. P., Salido, M. A., & Moya, C. M. (2025). Optimizing energy efficiency in unrelated parallel machine scheduling problem through reinforcement learning.
Information Sciences,
693, 121674.
https://doi.org/10.1016/j.ins.2024.121674
Catanzaro, D., Pesenti, R., & Ronco, R. (2023). Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey.
European Journal of Operational Research,
308(3), 1091-1109.
https://doi.org/10.1016/j.ejor.2023.01.029
Ding, J. Y., Song, S., Zhang, R., Chiong, R., & Wu, C. (2015). Parallel machine scheduling under time-of-use electricity prices: New models and optimization approaches.
IEEE Transactions on Automation Science and Engineering, 13(2), 1138-1154.
https://doi.org/10.56578/jimd030303
Gao, J., Sun, G. X., & Qian, T. H. (2024). Optimization of production scheduling through a multi-objective constrained greedy model.
J. Intell Manag. Decis,
3(3), 159-174.
https://doi.org/10.1109/TASE.2015.2495328
Javadi, M., Fatemi, S., Azizi, A., & Najafi, E. (2024). Designing an intelligent dynamic model of preventive maintenance and repairs in the textile and clothing industry in interaction with production using fuzzy-artificial neural network.
Engineering Management and Soft Computing, 9(2), 63-90.
https://doi.org/10.22091/jemsc.2024.9078.1169
Kianpour, P., Gupta, D., Krishnan, K., & Gopalakrishnan, B. (2021). Optimising unrelated parallel machine scheduling in job shops with maximum allowable tardiness limit.
International Journal of Industrial and Systems Engineering, 37(3), 359-381.
https://doi.org/10.1504/IJISE.2021.113443
Lai, X., Zhang, K., Li, Z., Mao, N., Chen, Q., & Zhang, S. (2023). Scheduling air conditioner testing tasks under time-of-use electricity tariff: A predict in and for optimization approach.
Computers & Industrial Engineering,
175, 108850.
https://doi.org/10.1016/j.cie.2022.108850
Li, X., & Liu, C. (2024). Energy-efficient scheduling in an identical parallel machine environment with peak power consumption and deadline constraints. Computers & Operations Research, 170, 106777.
Meng, L., Zhang, C., Shao, X., Ren, Y., & Ren, C. (2019). Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines.
International Journal of Production Research, 57(4), 1119-1145.
https://doi.org/10.1080/00207543.2018.1501166
Mohamadifar, L., Moosavirad, S. H., & Mirhosseini, M. (2024). Application of linear programming model for optimizing the components of combined photovoltaic and battery system. Engineering Management and Soft Computing, 10(1), 143-154.
https://doi.org/10.22091/jemsc.2024.11166.1194
Moon, J. Y., Shin, K., & Park, J. (2013). Optimization of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency.
The International Journal of Advanced Manufacturing Technology, 68(1), 523-535.
https://doi.org/10.1007/s00170-013-4749-8
Nagari, T. B., Suletra, I. W., & Hisjam, M. (2024, November). Multiple objective traveling salesman problem using NSGA-II: A case study of ice tube distribution. In
AIP Conference Proceedings (Vol. 3215, No. 1). AIP Publishing.
https://doi.org/10.1063/5.0235626
Pan, Z., Lei, D., & Zhang, Q. (2018). A new imperialist competitive algorithm for multiobjective low carbon parallel machines scheduling.
Mathematical problems in engineering,
2018(1), 5914360.
https://doi.org/10.1155/2018/5914360
Rasti Barzoki, M., & Raeisi, S. (2022). Comparison of the Effect of Various Types of Genetic Algorithm Operators on the Total Amount of Tardiness in Flow Shop Problem.
Engineering Management and Soft Computing, 7(2), 49-65.
https://doi.org/10.22091/jemsc.2017.877
Rubaiee, S., & Yildirim, M. B. (2019). An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling.
Computers & Industrial Engineering,
127, 240-252.
https://doi.org/10.1016/j.cie.2018.12.020
Shao, W., Shao, Z., & Pi, D. (2020). Modeling and multi-neighborhood iterated greedy algorithm for distributed hybrid flow shop scheduling problem.
Knowledge-Based Systems, 194, 105527.
https://doi.org/10.1016/j.knosys.2020.105527
Zhang, Y., Cheng, Z., Zhang, N., & Chiong, R. (2025). A weighted distribution-free model for parallel machine scheduling with uncertain job processing times.
European Journal of Operational Research,
324(3), 814-824.
https://doi.org/10.1016/j.ejor.2024.12.027
Zhou, S., Li, X., Du, N., Pang, Y., & Chen, H. (2018). A multi-objective differential evolution algorithm for parallel batch processing machine scheduling considering electricity consumption cost.
Computers & Operations Research, 96, 55-68.
https://doi.org/10.1016/j.cor.2018.04.009
Tanhaeean, M., Tavakkoli-Moghaddam, R., & Akbari, A. H. (2022). Boxing match algorithm: A new meta-heuristic algorithm. Soft Computing, 26(24), 13277-13299. https://doi.org/10.1007/s00500-022-07518-6
Akbari, A. H., Jafari, M., & Akhavan, P. (2025). Deep Reinforcement Learning Algorithm: Dynamic Job Shop Scheduling Problem with Order Rejection and Inventory. Journal of Advanced Manufacturing Systems. https://doi.org/10.1142/S0219686727500156
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