Multi-objective flexible job-shop scheduling in hospital using discrete particle swarm optimization algorithm with adaptive inertia weight (DPSO-AIW)
A multi-objective Flexible Job-shop Scheduling technique for hospitals is proposed using DPSO-AIW i.e. discrete particle swarm optimization with adaptive inertia weight method. The approach encodes the layer of the chromosomes using an operation sequence (OS) and machine assignment (MA) whi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2024-01-01
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| Series: | Journal of Project Management |
| Online Access: | http://www.growingscience.com/jpm/Vol9/jpm_2024_21.pdf |
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| Summary: | A multi-objective Flexible Job-shop Scheduling technique for hospitals is proposed using DPSO-AIW i.e. discrete particle swarm optimization with adaptive inertia weight method. The approach encodes the layer of the chromosomes using an operation sequence (OS) and machine assignment (MA) which is a two-layer coding structure. Global selection based on the operation (GSO) of MA and random selection of OS are coupled in the initial population. Rapid non-dominated sorting yields fronts of non-domination, which are necessary for getting the Pareto optimum solution. The diversity of the population is increased during the evolution process by adaptive adjustment of the variation of the weight of inertia, expressed by ω. Then, the Pareto optimal solution found during the process is kept in the Pareto optimal solution set (POS). The discrete particle swarm optimization algorithm is utilized to solve the values of the next generation chromosomes in the discrete domain directly. Lastly, comparisons with certain current techniques and numerical simulation based on two sets of international standard examples are performed, which are already established. The findings from the comparison show that the suggested DPSO-AIW is practical, effective, and more feasible for solving the problem related to the Multi-objective Flexible Job-shop Scheduling Problem. |
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| ISSN: | 2371-8366 2371-8374 |