Energy-efficient flexible job-shop scheduling with transportation times and selectable speeds
Khaled Abdelaziz Mili; Majdi Argoubi
Abstract
Keywords
References
Abreu, A., & Fuchigami, H. Y. (2026). Optimising manufacturing schedules with tailored metrics to reduce unproductive times.
Ahmadipour, M., Abadi, M. S. S., & Ahmadi, M. (2024). MG-Jaya: an advanced multi-objective Jaya algorithm for optimal microgrid energy management.
Berterottière, L., Dauzère-Pérès, S., & Yugma, C. (2024). Flexible job-shop scheduling with transportation resources.
Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search.
Brucker, P., & Schlie, R. (1990). Job-shop scheduling with multi-purpose machines.
Caldeira, R. H., & Gnanavelbabu, A. (2021). A Pareto based discrete Jaya algorithm for multi-objective flexible job shop scheduling problem.
Camargo, F. G., Rossomando, F. G., Gandolfo, D. C., Sarroca, E. A., Faure, O. R., & Sosa, G. (2025). A novel hybrid methodology for multi-objective optimisation of dual-axis solar tracking systems with artificial intelligence.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II.
He, P., Jiang, X., Wang, Q., & Zhang, B. (2025). Multi-objective human-robot collaborative batch scheduling in distributed hybrid flowshop via automatic design of local search-reconstruction-feedback algorithm.
Li, Z., & Chen, Y. (2023). Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects.
Li, S., Han, K., Pei, Z., Yi, W., & Lv, R. (2026). Green flexible job shop integrated scheduling optimization for machines and AGVs based on the INSGA-II algorithm.
Luo, S., Zhang, L., & Fan, Y. (2019). Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization.
Nabati, E. G., Alvela Nieto, M. T., Bode, D., Schindler, T. F., Decker, A., & Thoben, K.-D. (2022). Challenges of manufacturing for energy efficiency: towards a systematic approach through applications of machine learning.
Rao, R. V. (2016). Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems.
Rao, R. V. (2019).
Rao, R. V., & Rai, D. P. (2017). Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm.
Pal, M., Mittal, M. L., Soni, G., Chouhan, S. S., & Kumar, M. (2023). A multi-agent system for FJSP with setup and transportation times.
Shen, L., Dauzère-Pérès, S., & Maecker, S. (2023). Energy cost efficient scheduling in flexible job-shop manufacturing systems.
Vishnu, M., & Kumar, S. (2022). Hybrid Firefly–JAYA algorithm for complex engineering design optimization problems.
Wei, Z., Liao, W., & Zhang, L. (2022). Hybrid energy-efficient scheduling measures for flexible job-shop problem with variable machining speeds.
Xu, G. J., Bao, Q., & Zhang, H. L. (2023). Multi-objective green scheduling of integrated flexible job shop and automated guided vehicles.
Yuan, Y., Xu, H., & Yang, J. (2013). A hybrid harmony search algorithm for the flexible job shop scheduling problem.
Zitzler, E., Laumanns, M., & Thiele, L. (2001).
Zhang, G., Gao, L., & Shi, Y. (2011). An effective genetic algorithm for the flexible job-shop scheduling problem.
Zhang, F., Li, R., & Gong, W. (2024). Deep reinforcement learning-based memetic algorithm for energy-aware flexible job shop scheduling with multi-AGV.
Zhang, B., Wang, Z.-X., Meng, L.-L., Sang, H.-Y., & Jiang, X.-C. (2025). Multi-objective scheduling for surface mount technology workshop: automatic design of two-layer decomposition-based approach.
Submitted date:
04/10/2026
Accepted date:
08/17/2026
