Multi-objective Evolutionary Algorithms for Optimal Scheduling
- Y. Wang
- Wednesday 19 January 2022
2311 GJ Leiden
- Prof. T.H.W. Bäck
Multi-objective optimization is an effective technique for finding optimal solutions that balance conflicting objectives. The research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Scheduling problems appear in various real-world application fields such as manufacturing, transportation, logistics, education and healthcare where multiple conflicting scheduling objectives should be simultaneously considered.
Several novel algorithms are proposed: the diversity indicator-based multi-objective evolutionary algorithm (DI-MOEA) can achieve a uniformly distributed solution set; the preference-based MOEA can obtain preferred solutions in an automatically generated preference region; the edge-rotated cone can improve the performance of MOEAs for optimization with a high number of objectives; and dynamic MOEA takes the stability as an extra objective.
Besides the classical flexible job shop scheduling, the thesis proposes solutions for the novel problem domain of vehicle fleet maintenance scheduling optimization (VFMSO). The problem originated from the CIMPLO (Cross-Industry Predictive Maintenance Optimization Platform) project and the project partners Honda and KLM. The VFMSO problem is to determine the maintenance schedule for the vehicle fleet, meaning to find the best maintenance order, location and time for each component in the vehicle fleet based on the predicted remaining useful lifetimes of components and conditions of available workshops. The maintenance schedule is optimized to bring business advantages to industries, i.e., to reduce maintenance time, increase safety and save repair expenses. After formulating the problem as a scalable benchmark in an industrially relevant setting, the proposed algorithms have been successfully used to solve VFMSO problem instances.
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