Energy-aware scheduling optimization in hybrid flow shops using artificial bee colony algorithm

Hybrid flow shop scheduling (HFS) involves optimizing production processes, where different manufacturing stages have varying capacities, combining parallel machine and flow shop scheduling to improve efficiency and reduce production time. Incorporating energy considerations into HFS problems has emerged as a critical area of research, driven by the growing emphasis on environmental sustainability and cost-effectiveness in manufacturing operations. This study addresses the hybrid flow shop scheduling with energy consideration (HFSE) problem, aiming to simultaneously optimize makespan and total energy consumption, two conflicting objectives. An Artificial Bee Colony (ABC) algorithm is proposed as an effective solution methodology for tackling the HFSE problem. Through an extensive computational experiment involving a well-known benchmark suite, the ABC algorithm demonstrated remarkable performance, consistently outperforming several popular metaheuristic algorithms, including Genetic Algorithms, Particle Swarm Optimization, Memetic Algorithms, and Whale Optimization Algorithm in 75% of the problems. The proposed approach's ability to efficiently explore the search space and balance the trade-offs between makespan minimization and energy consumption reduction contributed to its superior results. The ABC algorithm reduces makespan and energy consumption by 2.95% and 3.43%, respectively. This finding suggests potential benefits for manufacturing operations, including decreased production time and lower operational costs.