Two-objective optimization of preventive maintenance orders scheduling as a multi-skilled resource-constrained flow shop problem

Fekri, M.; Heydari, M.; Mazdeh, M.

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Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print) Quarterly Publication Volume 12 Issue 1 pp. 41-54 , 2023

Two-objective optimization of preventive maintenance orders scheduling as a multi-skilled resource-constrained flow shop problem Pages 41-54 Download PDF

Authors: Masoud Fekri, Mehdi Heydari, Mohammad Mahdavi Mazdeh

DOI: 10.5267/j.dsl.2022.10.007

Keywords: Flow shop Scheduling, Maintenance Scheduling, Genetic Algorithm, Multi-Skilled Resource-Constrained Scheduling

Abstract: In this article, the application of the Multi-Skilled Resource-Constrained Flow Shop Scheduling Problem (MSRC-FSSP) in preventive maintenance as a case study has been investigated. In other words, to complete each maintenance order at each stage, in addition to the machine, a set of required human resources with different skills must be available. According to human resources skills, each of them can perform at least one order or at most N orders, and each maintenance order must be done by a set of human resources with different skills. To carry out a maintenance order, different human resources must be in communication and cooperation so that a preventive maintenance order can be completed. In this article, these resources are considered as technical supervisors, repairmen and maintenance managers who complete all maintenance orders in a flow shop environment as a job. For this problem, a new Mixed Integer Linear Programming (MILP) model has been formulated with the two-objective functions, minimizing total orders completion time and the human resources idle time. To solve the model on a small scale, CPLEX is used, and to solve it on a large scale, due to the fact that this problem is NP-Hard, a meta-heuristic algorithm named Genetic Algorithm (GA) is presented. Finally, the computational results have been done to validate the model, along with the analysis of the human resources idle time.

How to cite this paper

 Fekri, M., Heydari, M & Mazdeh, M. (2023). Two-objective optimization of preventive maintenance orders scheduling as a multi-skilled resource-constrained flow shop problem.Decision Science Letters , 12(1), 41-54.

Refrences

Awad, M., & Ertem, M. (2017). Stochastic scheduling of workforce-constrained preventive maintenance activities in petroleum plants. In 2017 Annual Reliability and Maintainability Symposium (RAMS) (pp.1-5). IEEE.
Azadeh, A., Fekri, M., Asadzadeh, S. M., Barazandeh, B., & Barrios, B. (2016). A unique mathematical model for maintenance strategies to improve energy flows of the electrical power sector. Energy Exploration & Exploitation, 34(1), 19-41.
Blazewicz, J., Ecker, K. H., Pesch, E., Schmidt, G., Sterna, M., & Weglarz, J. (2019). Handbook on Scheduling: From Theory to Practice. Springer.
Cheng, T. E., Lin, B. M., & Huang, H. L. (2012). Resource-constrained flow shop scheduling with separate resource recycling operations. Computers & Operations Research, 39(6), 1206-1212.
Coit, D. W., & Zio, E. (2019). The evolution of system reliability optimization. Reliability Engineering & System Safety, 192, 106259.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., Stein, C. (2009). Introduction to algorithms. MIT press.
Costa, A., Fernandez-Viagas, V., & Framinan, J. M. (2020). Solving the hybrid flow shop scheduling problem with limited human resource constraint. Computers & Industrial Engineering, 146, 106545.
De Jonge, B., & Scarf, P. A. (2020). A review on maintenance optimization. European journal of operational research, 285(3), 805-824.
Golab, A., Gooya, E., Falou, A., & Cabon, M. (2022). A multilayer feed-forward neural network (MLFNN) for the resource-constrained project scheduling problem (RCPSP). Decision Science Letters, 11(4), 407-418.
Ertem, M., As' ad, R., Awad, M., & Al-Bar, A. (2022). Workers-constrained shutdown maintenance scheduling with skills flexibility: Models and solution algorithms. Computers & Industrial Engineering, 172, 108575.
Habibi, F., Barzinpour, F., & Sadjadi, S. (2018). Resource-constrained project scheduling problem: review of past and recent developments. Journal of project management, 3(2), 55-88.
Hans, E. W., Herroelen, W., Leus, R., & Wullink, G. (2007). A hierarchical approach to multi-project planning under uncertainty. Omega, 35, 563–577. doi:10.1016/j.omega.2005.10.004.
Kazemipoor, H., Tavakkoli-Moghaddam, R., & Shahnazari-Shahrezaei, P. (2002). Solving a mixed-integer linear programming model for a multi-skilled project scheduling problem by simulated annealing. Management Science Letters, 2(2), 681-688.
Keizer, M. C. O., Flapper, S. D. P., & Teunter, R. H. (2017). Condition-based maintenance policies for systems with multiple dependent components: A review. European Journal of Operational Research, 261(2), 405-420.
Mild, P., & Salo, A. (2009). Combining a multi attribute value function with an optimization model: An application to dynamic resource allocation for infrastructure maintenance. Decision Analysis, 6(3), 139-152.
Nguyen, K. A., Do, P., & Grall, A. (2017). Joint predictive maintenance and inventory strategy for multi-component systems using Birnbaum’s structural importance. Reliability Engineering & System Safety, 168, 249-261.
Snauwaert, J., & Vanhoucke, M. (2022). A classification and new benchmark instances for the multi-skilled resource-constrained project scheduling problem. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2022.05.049
Tao, S., & Dong, Z. S. (2017). Scheduling resource-constrained project problem with alternative activity chains. Computers & Industrial Engineering, 114, 970 288–296. doi:10.1016/j.cie.2017.10.027.
Urbani, M., Brunelli, M., & Punkka, A. (2022). An approach for bi-objective maintenance scheduling on a networked system with limited resources. European Journal of Operational Research, 305(1), 101-113.
Witteman, M., Deng, Q., & Santos, B. F. (2021). A bin packing approach to solve the aircraft maintenance task allocation problem. European Journal of Operational Research, 294(1), 365-376.
Yunusoglu, P., & Topaloglu Yildiz, S. (2022). Constraint programming approach for multi-resource-constrained unrelated parallel machine scheduling problem with sequence-dependent setup times. International Journal of Production Research, 60(7), 2212-2229.
Zio, E. (2009). Reliability engineering: Old problems and new challenges. Reliability engineering & system safety, 94(2), 125-141.