Journal of Industrial Engineering and Management Studies

A flow shop scheduling model considering no-wait constraints between sequential tasks

Document Type : Original Article

Authors

Fatemeh Zahmatkesh 1
Maryam Daneshmand-Mehr 1
Milad Abolghasemian 2

1 Department of Industrial Engineering, La. C., Islamic Azad University, Lahijan, Iran.

2 Department of Industrial Engineering, Ayandegan University, Tonekabon, Iran

10.22116/jiems.2026.555292.1620
Abstract
Flow shop scheduling has traditionally focused on minimizing completion time; however, growing energy costs and sustainability concerns necessitate the simultaneous consideration of energy consumption as an optimization objective. In addition, in many real-world manufacturing systems, such as chemical, food, and continuous production industries, jobs must be processed without waiting between successive operations, a constraint that significantly increases problem complexity. Despite its practical relevance, the combined consideration of no-wait flow shop scheduling and energy-aware optimization has received limited attention in the literature. This study develops a bi-objective no-wait flow shop scheduling model that simultaneously minimizes makespan and total energy consumption, accounting for both active and idle energy usage of machines. A mathematical formulation is proposed, and due to the NP-hard nature of the problem, the NSGA II metaheuristic algorithm is employed to obtain high-quality Pareto-optimal solutions. Model validity is verified using small-scale instances solved optimally, followed by large-scale computational experiments. Furthermore, a comprehensive parametric sensitivity analysis is conducted to examine the influence of key factors, including processing time, number of tasks, and machine energy consumption in active and idle states, on both objectives. The results reveal that idle energy consumption has a disproportionately high impact on total energy usage and completion time, underscoring its critical role in sustainable scheduling decisions. The findings provide both theoretical insights and practical guidelines for designing energy-efficient schedules in no-wait production environments.

Keywords

Flow shop scheduling
Mathematical modeling
Energy consumption
NSGA II
Alfaro-Fernández, P., Ruiz, R., Pagnozzi, F., & Stützle, T. (2020). Automatic algorithm design for hybrid flowshop scheduling problems. European Journal of Operational Research, 282(3), 835-845.
Ayough, A., adeghi Nouri, F., Khorshidvand, B., Farhadi, F. (2025). Modeling workers rotation in divisional seru production systems. Computers & Industrial Engineering, 2025, 111141.
Ayough, A., Khorshidvand, B. (2019). Designing a manufacturing cell system by assigning workforce. Journal of Industrial Engineering and Management, 12(1), 13-26.
Fernandez-Viagas, V. (2022). A speed-up procedure for the hybrid flow shop scheduling problem. Expert Systems with Applications, 187, 115903.
Fernandez-Viagas, V., Talens, C., & Framinan, J. M. (2022). Assembly flowshop scheduling problem: Speed-up procedure and computational evaluation. European Journal of Operational Research, 299(3), 869-882.
Han, X., Han, Y., Zhang, B., Qin, H., Li, J., Liu, Y., & Gong, D. (2022). An effective iterative greedy algorithm for distributed blocking flowshop scheduling problem with balanced energy costs criterion. Applied Soft Computing, 129, 109502.
Karimi-Mamaghan, M., Mohammadi, M., Pasdeloup, B., & Meyer, P. (2023). Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 304(3), 1296-1330.
Khatami, M., Salehipour, A., & Cheng, T. C. E. (2023). Flow-shop scheduling with exact delays to minimize makespan. Computers & Industrial Engineering, 183, 109456.
Khurshid, B., Maqsood, S., Khurshid, Y., Naeem, K., & Khalid, Q. S. (2024). A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems. Scientific Reports, 14(1), 2376.
Neufeld, J. S., Schulz, S., & Buscher, U. (2023). A systematic review of multi-objective hybrid flow shop scheduling. European Journal of Operational Research, 309(1), 1-23.
Perez-Gonzalez, P., & Framinan, J. M. (2024). A review and classification on distributed permutation flowshop scheduling problems. European Journal of Operational Research, 312(1), 1-21.
Tasgetiren, M., Kizilay, D., & Kandiller, L. (2024). Solving blocking flowshop scheduling problem with makespan criterion using q-learning-based iterated greedy algorithms. Journal of Project Management (Canada), 9(2).
Wang, Y., Wang, Y., Han, Y., Li, J., Gao, K., & Nojima, Y. (2023). Intelligent optimization under multiple factories: Hybrid flow shop scheduling problem with blocking constraints using an advanced iterated greedy algorithm. Complex System Modeling and Simulation, 3(4), 282-306.
Wang, Z. Y., Pan, Q. K., Gao, L., & Wang, Y. L. (2022). An effective two-stage iterated greedy algorithm to minimize total tardiness for the distributed flowshop group scheduling problem. Swarm and Evolutionary Computation, 74, 101143.
Zhang, W., Hao, J., & Liu, F. (2024). Effective social spider optimization algorithms for distributed assembly permutation flowshop scheduling problem in automobile manufacturing supply chain. Scientific Reports, 14(1), 6370.
Journal of Industrial Engineering and Management Studies
Volume 13, Issue 1
July 2026
Pages 86-96

Home

Guide for Authors

Publication Ethics

Submit Manuscript

Reviewers

Contact Us