Optimization of hybrid flow shop scheduling in a machine shop: Achieving energy efficiency and minimizing machine idleness with multi-objective Tiki Taka optimization
Hybrid Flow Shop Scheduling (HFS) has garnered significant interest in terms of problem formulation and solution approaches. This work introduces an optimization approach for a case study on a hybrid flow shop scheduling problem. The objective is to minimize the makespan, energy consumption, and idl...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Faculty Mechanical Engineering, UMP
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46511/1/Optimization%20of%20hybrid%20flow%20shop%20scheduling%20in%20a%20machine%20shop.pdf https://doi.org/10.15282/jmes.19.3.2025.5.0843 https://umpir.ump.edu.my/id/eprint/46511/ |
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| Summary: | Hybrid Flow Shop Scheduling (HFS) has garnered significant interest in terms of problem formulation and solution approaches. This work introduces an optimization approach for a case study on a hybrid flow shop scheduling problem. The objective is to minimize the makespan, energy consumption, and idle machines in manufacturing shop. The HFS consists of many concurrent production lines, each containing several machines, operating in one or more stages. A case study was conducted using fourteen jobs across three stages, which utilized lathe, milling, and deburring machines. The EE-HFS was optimized using Multi-Objective Tiki Taka Optimization (MOTTA). The study considered machine idle time as a key factor influencing energy efficiency, incorporating it into the scheduling evaluation. The optimization result was compared to established algorithms, such as the Non-dominated Sorting Genetic Algorithm-II, the Multi Objectives Evolutionary Algorithm Based on Decomposition, the Multi Objectives Particle Swarm Optimization, and the recent algorithm Multi Objectives Grey Wolf Optimizer. The metrics used for comparison include Error Ratio (ER), Pareto Percentage (%), Spacing, Maximum Spread, computational speed, Hyper Volume, Inverted Generational Distance (IGD), and Generational Distance (GD). The results indicate that MOTTA exhibits superior performance with 78.42% best overall and 100% better in the convergence and domination of the case study solution (ER, ND, GD, and IGD). Overall, the findings have important implications for Hybrid flow shop scheduling in terms of the energy utilization model, reducing idle machine time, and the promising potential of MOTTA for application in other combinatorial scheduling challenges. This case study provides substantial advantages to the organization by effectively decreasing its daily energy consumption, equipment usage, and enhancing resource management. |
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