Optimisation of energy and machine balance in the hybrid flowshop scheduling problem using differential evolution
DOI:
https://doi.org/10.15282/jmes.20.2.2026.3.0871Keywords:
Energy balanced, Hybrid Flowshop, Differential Evolution (DE), SchedulingAbstract
One of the most essential processes in manufacturing is continuous operations without any downtime. The Energy Balanced Hybrid Flow Shop Scheduling Problem (EMBHFSP) is an interesting study as it provides huge impact on machine effectiveness while balancing between minimizing completion time and energy consumption. Limited study has focused on hybrid flow shops (HFS) with a concentration on energy-machine balanced production. This paper aims to develop a computational model and evaluate the exploration effectiveness of Differential Evolution in optimizing the EMBHFSP. The most popular as well as latest optimization algorithms including Simulated Annealing, Grey Wolf Optimization, Henry Gas Solubility Optimization, Harmony Search, Imperialist Competitive Algorithm, Multi-verse Optimizer, and Thermal Exchange Optimization were evaluated against Differential Evolution utilizing the EMBHFSP model across 20 Optimization repetitions. The experimental results indicate that the Differential evolution algorithm surpassed others by 45% in mean fitness value and exhibited a 67.5% enhancement in standard deviation across all benchmark problems. In addition, a case study was performed in a manufacturing facility to validate the applicability of the model. Three different scheduling solutions, optimizing makespan, energy balance and machine utilization balance are generated using differential evolution. The results show the ability of the model to solve trade-offs between efficiency of production and sustainability in real industrial environments.
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