Presenting a model for scheduling parallel machines considering divisible tasks, and preparation time by reduction of energy cost | ||
| Journal of Engineering Management and Soft Computing | ||
| مقاله 5، دوره 11، شماره 2 - شماره پیاپی 21، اسفند 2025، صفحه 86-106 اصل مقاله (1.07 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2025.12352.1256 | ||
| نویسندگان | ||
| Samira Sameye1؛ Javad Rezaeian* 2؛ Mohammad Reza Lotfi3 | ||
| 1PhD Student, Department of Industrial Management, Firouzkooh Branch, Islamic Azad University, Firouzkooh, Iran | ||
| 2Associate Professor, Department of Industrial Engineering, Mazandaran University of Science and Technology , Babol, Iran | ||
| 3Assistant Professor, Department of Industrial Engineering, Firouzkooh Branch, Islamic Azad University, Firouzkooh, Iran | ||
| چکیده | ||
| Scheduling parallel machines is an important problem in manufacturing systems, one of the important goals of which is to minimize the completion time, but the goals in this problem are not limited to scheduling and can include costs, including energy costs. In this research, a model for scheduling parallel machines by considering divisible jobs, sequence-dependent startup, and setup time to minimize the completion time and energy costs is presented. First, a two-objective model was designed and then solved with two algorithms, NSGAII and MOGWO. The results showed that the MOGWO algorithm had better performance than the NSGAII algorithm and achieved better results both in terms of objective function values and multi-objective criteria. Setup time can influence on make span to more than 75 percent. But NSGAII algorithm gives better results and make span can be improved based on this algorithm. | ||
| کلیدواژهها | ||
| Scheduling problem؛ parallel machines؛ energy cost؛ metaheuristic algorithm | ||
| مراجع | ||
|
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 Cataldo, A., Perizzato, A., & Scattolini, R. (2015). Production scheduling of parallel machines with model predictive control. Control Engineering Practice, 42, 28-40. https://doi.org/10.1016/j.conengprac.2015.05.007 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 Zhang, Z. (2024). Multi-objective optimization method for building energy-efficient design based on multi-agent-assisted NSGA-II. Energy Informatics, 7(1), 1-17. https://doi.org/10.1186/s42162-024-00394-4 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 | ||
|
آمار تعداد مشاهده مقاله: 200 تعداد دریافت فایل اصل مقاله: 72 |
||