Dynamic Maintenance Scheduling with Fuzzy Data via Biogeography-based Optimization Algorithm and its Hybridizations
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Abstract
A multi-objective maintenance problem of a plaza building is presented using a dynamic fuzzy maintenance scheduling model (DFMS). There are multiple component machines and jobs with different fuzzy processing time. Generally, it aims to simultaneously minimize total labor cost on regular, overtime and subcontract including equipment cost and minimize the makespan of all jobs, teams and consecutive time periods under fuzzy natures. Nature-inspired intelligence algorithms have become increasingly popular to implement complex problems. Some features of biogeography-based optimization algorithm (BBO) are unique among biology-based methods. This study applied the BBO and its hybridizations based on the variable neighborhood search (BBOVNS) and particle swarm optimization (BBOPSO) mechanisms to the DFMS. Analytical findings indicated that the proposed BBOPVNS is powerful in terms of dispersion effects. The proposed DFMS demonstrates an efficient compromise method and the overall levels of decision making satisfaction with the multi-objective problem.
Keywords: dynamic maintenance; fuzzy data; metaheuristic; biogeography-based optimization; variable neighborhood search; particle swarm optimization
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