The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation
Source abstract
Abstract
Production and maintenance tasks apply for access to the same resources. Maintenance-related machine downtime reduces productivity, but the costs incurred due to unplanned machine failures often outweigh the costs associated with predictive maintenance. Costs incurred due to unplanned machine failure include corrective maintenance, reworks, delays in deliveries, breaks in the work of employees and machines. Therefore, scheduling of production and maintenance tasks should be considered jointly. The problem of generating a predictive schedule with given constrains is considered. The objective of the paper is to develop a scheduling method that reflects the operation of the production system and nature of disturbances. The original value of the paper is the development of the method of a basic schedule generation with the application of the Ant Colony Optimisation. A predictive schedule is built by planning the technical inspection of the machine at time of the predicted failure-free time. The numerical simulations are performed for job/flow shop systems.
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Research context
OpenAlex topics: Scheduling and Optimization Algorithms · Assembly Line Balancing Optimization · Advanced Manufacturing and Logistics Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Engineering Processes Automation and Integrated Manufacturing Systems, Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A str., 44-100 Gliwice, Poland
Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wrocław, Poland
References (16)
Show source-provided references
1. On the robustness of joint production and maintenance scheduling in presence of uncertainties10.1007/s10845-017-1303-9 ↗
2. A dynamic predictive maintenance model considering spare parts inventory based on hidden semi-Markov model10.1177/0954406212469773 ↗
3. Predictable scheduling of a job subject to breakdowns10.1109/70.678447 ↗
4. A proactive approach to solve integrated production scheduling and maintenance planning problem in flow shops10.1016/j.cie.2017.11.020 ↗
5. Rescheduling of identical parallel machines under machine eligibility constraints10.1016/S0377-2217(02)00499-X ↗
6. Optimal Generator Maintenance Scheduling Using a Hybrid Metaheuristic Approach10.1142/S146902681550011X ↗
7. Integrated production scheduling and maintenance policy for robustness in a single machine10.1016/j.cor.2014.02.006 ↗
8. Boryczka, U. (2006). Anthill Optimization Algorithms, Wydawnictwo Uniwersytetu Śląskiego. (In Polish).
9. Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns10.1080/00207543.2010.495088 ↗
10. Benbouzid-Sitayeb, F., Ammi, I., Varnier, C., and Zerhouni, N. (2008, January 7–11). Applying Ant Colony Optimization for the Joint Production and Preventive Maintenance Scheduling Problem in the Flowshop Sequencing Problem. Proceedings of the 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, Syria.10.1109/ICTTA.2008.4530343 ↗
11. Fei, Y., and Ma, H. (2018, January 26–28). Multi-objective joint optimization of batch-discrete hybrid flow shop scheduling integrated with machine maintenance. Proceedings of the 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), Singapore.10.1109/IEA.2018.8387105 ↗
12. Integrating production, inventory and maintenance planning for a parallel system with dependent components10.1016/j.ress.2012.02.001 ↗
13. Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling10.1016/j.cor.2009.11.017 ↗
14. The influence of algorithms for basic-schedule generation on the performance of predictive and reactive schedules
15. A Hybrid-Multi Objective Immune Algorithm for predictive and reactive scheduling10.1007/s10951-016-0494-9 ↗
16. Searching for a Method of Basic Schedules Generation which influences over the Performance of Predictive and Reactive Schedules10.1007/978-3-319-46592-0_20 ↗