How to cite this paper
Li, Y., Gao, K., Meng, L., Jing, X & Zhang, B. (2023). Heuristics and metaheuristics to minimize makespan for flowshop with peak power consumption constraints.International Journal of Industrial Engineering Computations , 14(2), 221-238.
Refrences
Chou, Y., Yang, J. & Wu, C. (2020). An energy-aware scheduling algorithm under maximum power consumption constraints. Journal of Manufacturing Systems, 57,182-197.
Çolak, M. & Keskin, G.A. (2022). An extensive and systematic literature review for hybrid flowshop scheduling problems. International Journal of Industrial Engineering Computations, 13(2),185-222.
Cui, W. & Lu, B. (2020). A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint. Sustainability, 12(10),4110.
Zhao, F., Ma, R., & Wang, L. (2021). A self-learning discrete jaya algorithm for multiobjective energy-efficient distributed no-idle flow-shop scheduling problem in heterogeneous factory system. IEEE Transactions on Cybernetics, 52(12), 12675-12686.
Fang, K., Uhan, N., Zhao, F. & Sutherland, J.W. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems, 30(4),234-240.
Fang, K., Uhan, N.A., Zhao, F. & Sutherland, J.W. (2013). Flow shop scheduling with peak power consumption constraints. Annals of Operations Research, 206(1),115-145.
Fernandez-Viagas, V., Sanchez-Mediano, L., Angulo-Cortes, A., Gomez-Medina, D. & Molina-Pariente, J.M. (2022). The Permutation Flow Shop Scheduling Problem with Human Resources: MILP Models, Decoding Procedures, NEH-Based Heuristics, and an Iterated Greedy Algorithm. Mathematics, 10(19),3446.
González-Neira, E., Montoya-Torres, J., & Barrera, D. (2017). Flow-shop scheduling problem under uncertainties: Review and trends. International Journal of Industrial Engineering Computations, 8(4), 399-426.
Li, M., & Wang, G. G. (2022). A review of green shop scheduling problem. Information Sciences, 589, 478-496.
Li, Y., Pan, Q., Gao, K., Tasgetiren, M.F., Zhang, B. & Li, J. (2021). A green scheduling algorithm for the distributed flowshop problem. Applied Soft Computing, 109(9),107526.
Li, Y., Pan, Q., Ruiz, R. & Sang, H. (2022). A referenced iterated greedy algorithm for the distributed assembly mixed no-idle permutation flowshop scheduling problem with the total tardiness criterion. Knowledge-Based Systems, 239(3),108036.
Luo, H., Du, B., Huang, G.Q., Chen, H. & Li, X. (2013). Hybrid flow shop scheduling considering machine electricity consumption cost. International Journal of Production Economics, 146(2),423-439.
Meng, L., Gao, K., Ren, Y., Zhang, B., Sang, H. & Chaoyong, Z. (2022). Novel MILP and CP models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times. Swarm and Evolutionary Computation, 71, 101058.
Mishra, A., & Shrivastava, D. (2020). A discrete Jaya algorithm for permutation flow-shop scheduling problem. International Journal of Industrial Engineering Computations, 11(3), 415-428.
Qin, S., Pi, D., & Shao, Z. (2022). AILS: A budget-constrained adaptive iterated local search for workflow scheduling in cloud environment. Expert Systems with Applications, 198, 116824.
Ramezanian, R., Vali-Siar, M.M. & Jalalian, M. (2019). Green permutation flowshop scheduling problem with sequence-dependent setup times: a case study. International Journal of Production Research, 57(10),3311-3333.
Renna, P. & Materi, S. (2021). A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems. Applied Sciences, 11(16),7366.
Ribas, I. & Companys, R. (2021). A computational evaluation of constructive heuristics for the parallel blocking flow shop problem with sequence-dependent setup times. International Journal of Industrial Engineering Computations, 12(3),321-328.
Tao, X., Pan, Q. & Gao, L. (2022). An efficient self-adaptive artificial bee colony algorithm for the distributed resource-constrained hybrid flowshop problem. Computers & Industrial Engineering, 169,108200.
Wang, J. & Wang, L. (2019). Decoding methods for the flow shop scheduling with peak power consumption constraints. International Journal of Production Research, 57(10),3200-3218.
Wang, J. & Wang, L. (2020). A Knowledge-Based Cooperative Algorithm for Energy-Efficient Scheduling of Distributed Flow-Shop. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(5),1-15.
Yu, Y., Zhang, F., Yang, G., Wang, Y., Huang, J. & Han, Y. (2022). A discrete artificial bee colony method based on variable neighborhood structures for the distributed permutation flowshop problem with sequence-dependent setup times. Swarm and Evolutionary Computation, 75,101179.
Zhang, B., Pan, Q., Meng, L., Lu, C., Mou, J. & Li, J. (2022). An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots. Knowledge-Based Systems, 238,107819.
Çolak, M. & Keskin, G.A. (2022). An extensive and systematic literature review for hybrid flowshop scheduling problems. International Journal of Industrial Engineering Computations, 13(2),185-222.
Cui, W. & Lu, B. (2020). A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint. Sustainability, 12(10),4110.
Zhao, F., Ma, R., & Wang, L. (2021). A self-learning discrete jaya algorithm for multiobjective energy-efficient distributed no-idle flow-shop scheduling problem in heterogeneous factory system. IEEE Transactions on Cybernetics, 52(12), 12675-12686.
Fang, K., Uhan, N., Zhao, F. & Sutherland, J.W. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems, 30(4),234-240.
Fang, K., Uhan, N.A., Zhao, F. & Sutherland, J.W. (2013). Flow shop scheduling with peak power consumption constraints. Annals of Operations Research, 206(1),115-145.
Fernandez-Viagas, V., Sanchez-Mediano, L., Angulo-Cortes, A., Gomez-Medina, D. & Molina-Pariente, J.M. (2022). The Permutation Flow Shop Scheduling Problem with Human Resources: MILP Models, Decoding Procedures, NEH-Based Heuristics, and an Iterated Greedy Algorithm. Mathematics, 10(19),3446.
González-Neira, E., Montoya-Torres, J., & Barrera, D. (2017). Flow-shop scheduling problem under uncertainties: Review and trends. International Journal of Industrial Engineering Computations, 8(4), 399-426.
Li, M., & Wang, G. G. (2022). A review of green shop scheduling problem. Information Sciences, 589, 478-496.
Li, Y., Pan, Q., Gao, K., Tasgetiren, M.F., Zhang, B. & Li, J. (2021). A green scheduling algorithm for the distributed flowshop problem. Applied Soft Computing, 109(9),107526.
Li, Y., Pan, Q., Ruiz, R. & Sang, H. (2022). A referenced iterated greedy algorithm for the distributed assembly mixed no-idle permutation flowshop scheduling problem with the total tardiness criterion. Knowledge-Based Systems, 239(3),108036.
Luo, H., Du, B., Huang, G.Q., Chen, H. & Li, X. (2013). Hybrid flow shop scheduling considering machine electricity consumption cost. International Journal of Production Economics, 146(2),423-439.
Meng, L., Gao, K., Ren, Y., Zhang, B., Sang, H. & Chaoyong, Z. (2022). Novel MILP and CP models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times. Swarm and Evolutionary Computation, 71, 101058.
Mishra, A., & Shrivastava, D. (2020). A discrete Jaya algorithm for permutation flow-shop scheduling problem. International Journal of Industrial Engineering Computations, 11(3), 415-428.
Qin, S., Pi, D., & Shao, Z. (2022). AILS: A budget-constrained adaptive iterated local search for workflow scheduling in cloud environment. Expert Systems with Applications, 198, 116824.
Ramezanian, R., Vali-Siar, M.M. & Jalalian, M. (2019). Green permutation flowshop scheduling problem with sequence-dependent setup times: a case study. International Journal of Production Research, 57(10),3311-3333.
Renna, P. & Materi, S. (2021). A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems. Applied Sciences, 11(16),7366.
Ribas, I. & Companys, R. (2021). A computational evaluation of constructive heuristics for the parallel blocking flow shop problem with sequence-dependent setup times. International Journal of Industrial Engineering Computations, 12(3),321-328.
Tao, X., Pan, Q. & Gao, L. (2022). An efficient self-adaptive artificial bee colony algorithm for the distributed resource-constrained hybrid flowshop problem. Computers & Industrial Engineering, 169,108200.
Wang, J. & Wang, L. (2019). Decoding methods for the flow shop scheduling with peak power consumption constraints. International Journal of Production Research, 57(10),3200-3218.
Wang, J. & Wang, L. (2020). A Knowledge-Based Cooperative Algorithm for Energy-Efficient Scheduling of Distributed Flow-Shop. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(5),1-15.
Yu, Y., Zhang, F., Yang, G., Wang, Y., Huang, J. & Han, Y. (2022). A discrete artificial bee colony method based on variable neighborhood structures for the distributed permutation flowshop problem with sequence-dependent setup times. Swarm and Evolutionary Computation, 75,101179.
Zhang, B., Pan, Q., Meng, L., Lu, C., Mou, J. & Li, J. (2022). An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots. Knowledge-Based Systems, 238,107819.