Cost-based hybrid flow shop scheduling with uniform machine optimization using an improved tiki-taka algorithm
Cost is the foremost factor in decision-making for profit-driven organizations. However, hybrid flow shop scheduling (HFSS) research rarely prioritizes cost as its optimization objective. Existing studies primarily focus on electricity costs linked to machine utilization. This paper introduces a com...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Taylor and Francis Ltd.
2024
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/41081/1/2023%20Cost-based%20HFSS%20using%20Improved%20TTA.pdf http://umpir.ump.edu.my/id/eprint/41081/7/Cost-based%20hybrid%20flow%20shop%20scheduling%20with%20uniform%20machine%20optimization.pdf http://umpir.ump.edu.my/id/eprint/41081/ https://doi.org/10.1080/21681015.2023.2276108 https://doi.org/10.1080/21681015.2023.2276108 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Cost is the foremost factor in decision-making for profit-driven organizations. However, hybrid flow shop scheduling (HFSS) research rarely prioritizes cost as its optimization objective. Existing studies primarily focus on electricity costs linked to machine utilization. This paper introduces a comprehensive cost-based HFSS model, encompassing electricity, labor, maintenance, and penalty costs. Next, the Tiki-Taka Algorithm (TTA) is improved by increasing the exploration capability to optimize the problem. The cost-based HFSS model and TTA algorithm have been tested using benchmark and case study problems. The results indicated that the TTA consistently outperforms other algorithms. It delivers the best mean fitness and better solution distribution. In industrial contexts, the TTA able to reduces costs by 2.8% to 12.0% compared to other approaches. This holistic cost-based HFSS model empowers production planners to make more informed decisions. Furthermore, the improved TTA shows promise for broader applicability in various combinatorial optimization domains. |
|---|