A two-stage assembly flow-shop scheduling problem with bi-level products structure and machines’ availability constraints

ABSTRACT

This paper incorporates the preventive maintenance activities into the two-stage assembly flow-shop scheduling problem, which has m machines at the production stage and an assembly machine at the second one. In this paper, three maintenance policies are employed based on the maximization of machine’s reliability and availability. This problem is NP-hard. So, two hybrid optimization methods, namely variable neighborhood search together with simulated annealing algorithm, and variable neighborhood search along with tabu search algorithm, are used to find the proper job sequencing. Statistical methods are conducted based on the hypothetical testing and analysis of variance to determine the better solution method and sensitivity analysis. Also, the performances of the presented algorithms on the problems of different sizes are analyzed. Results have shown that variable neighborhood search with simulated annealing outperforms another algorithm in the quality of solutions and computational time.

KEYWORDS:

• Scheduling
• maintenance
• variable neighborhood search
• simulated annealing

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mohammad Ali Nikouei

Mohammad Ali Nikouei is a PhD candidate of Industrial Management in Allameh Tabataba’i University, Tehran, Iran. He received his MSc and BSc degrees in industrial engineering. His research interests are scheduling, multi-criteria decision-making (MCDM), data envelopment analysis (DEA), design of experiments (DOE), fuzzy MCDM, and simulation.

Mostafa Zandieh

Mostafa Zandieh is Professor of Industrial Engineering at Shahid Beheshti University. He is also in the editorial board of more journals. He has published over 300 research papers. His research interests include scheduling and planning, flexible and assembly flow shop problems, meta-heuristic, and heuristics algorithms.

Maghsoud Amiri

Maghsoud Amiri is a Professor with the Department of Industrial Management of Allameh Tabataba’i University, Tehran, Iran. He received PhD degree in industrial engineering from Sharif University of Technology, Tehran, Iran. He has published many papers in leading international journals. His research interests include multi-criteria decision-making (MCDM), data envelopment analysis (DEA), design of experiments (DOE), response surface methodology (RSM), fuzzy MCDM, inventory control, supply chain management, simulation and reliability engineering.