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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Multi-Objective Flexible Job-Shop Scheduling with Limited Resource Constraints in Hospitals Using Hybrid Discrete Firefly Algorithm
https://orcid.org/http://orcid.org/0009-0006-7754-2045
This paper focuses on solving the multi-objective flexible job shop problem with resource-limited constraints in hospitals where scheduling plays a vital role. A hybrid discrete firefly algorithm (HDFA) was utilized here to tackle this complex problem, focusing on three main objectives: minimizing the overall time that a particular job takes to be complete (makespan), minimizing the number of times that a specific machine is loaded with work and improving the overall utilization of the total machines available. In the proposed HDFA method, a discrete firefly approach is coupled with a local search (LS) to develop an efficient search heuristic. Our empirical study on benchmark instances confirms that HDFA can provide better solutions in the makespan and workload than other algorithms, such as PSO + TS, AL + CGA, and ESM. For example, in the 4 × 5 problem size, it obtained a makespan of 11; in the 10 × 10 and 15 × 10 problem sizes, the makespan was 15 and 12, respectively. Furthermore, regarding the working load on the essential machines, HDFA presented optimized results of 63 and 77 in different cases. The total workload across all machines was also improved, proving that the practical formulation of HDFA was very effective. This research implies that the algorithm is a powerful tool for improving operations and resource performance, hence, patients’ health. The results provide new knowledge about automated scheduling techniques for dynamic healthcare systems and can inform future research on optimization methods in operational settings.
Multi-Objective Flexible Job-Shop Scheduling with Limited Resource Constraints in Hospitals Using Hybrid Discrete Firefly Algorithm
https://orcid.org/http://orcid.org/0009-0006-7754-2045
This paper focuses on solving the multi-objective flexible job shop problem with resource-limited constraints in hospitals where scheduling plays a vital role. A hybrid discrete firefly algorithm (HDFA) was utilized here to tackle this complex problem, focusing on three main objectives: minimizing the overall time that a particular job takes to be complete (makespan), minimizing the number of times that a specific machine is loaded with work and improving the overall utilization of the total machines available. In the proposed HDFA method, a discrete firefly approach is coupled with a local search (LS) to develop an efficient search heuristic. Our empirical study on benchmark instances confirms that HDFA can provide better solutions in the makespan and workload than other algorithms, such as PSO + TS, AL + CGA, and ESM. For example, in the 4 × 5 problem size, it obtained a makespan of 11; in the 10 × 10 and 15 × 10 problem sizes, the makespan was 15 and 12, respectively. Furthermore, regarding the working load on the essential machines, HDFA presented optimized results of 63 and 77 in different cases. The total workload across all machines was also improved, proving that the practical formulation of HDFA was very effective. This research implies that the algorithm is a powerful tool for improving operations and resource performance, hence, patients’ health. The results provide new knowledge about automated scheduling techniques for dynamic healthcare systems and can inform future research on optimization methods in operational settings.
Multi-Objective Flexible Job-Shop Scheduling with Limited Resource Constraints in Hospitals Using Hybrid Discrete Firefly Algorithm
https://orcid.org/http://orcid.org/0009-0006-7754-2045
This paper focuses on solving the multi-objective flexible job shop problem with resource-limited constraints in hospitals where scheduling plays a vital role. A hybrid discrete firefly algorithm (HDFA) was utilized here to tackle this complex problem, focusing on three main objectives: minimizing the overall time that a particular job takes to be complete (makespan), minimizing the number of times that a specific machine is loaded with work and improving the overall utilization of the total machines available. In the proposed HDFA method, a discrete firefly approach is coupled with a local search (LS) to develop an efficient search heuristic. Our empirical study on benchmark instances confirms that HDFA can provide better solutions in the makespan and workload than other algorithms, such as PSO + TS, AL + CGA, and ESM. For example, in the 4 × 5 problem size, it obtained a makespan of 11; in the 10 × 10 and 15 × 10 problem sizes, the makespan was 15 and 12, respectively. Furthermore, regarding the working load on the essential machines, HDFA presented optimized results of 63 and 77 in different cases. The total workload across all machines was also improved, proving that the practical formulation of HDFA was very effective. This research implies that the algorithm is a powerful tool for improving operations and resource performance, hence, patients’ health. The results provide new knowledge about automated scheduling techniques for dynamic healthcare systems and can inform future research on optimization methods in operational settings.
Multi-Objective Flexible Job-Shop Scheduling with Limited Resource Constraints in Hospitals Using Hybrid Discrete Firefly Algorithm
J. Inst. Eng. India Ser. C
Lingkon, Md. Limonur Rahman (Autor:in) / Dash, Adri (Autor:in)
Journal of The Institution of Engineers (India): Series C ; 106 ; 403-423
01.02.2025
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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