Solving Makespan and Energy Utilization in Hybrid Flow Shop Scheduling Problem Using Artificial Bee Colony (ABC)

Hybrid Flow shop Scheduling (HFS) problem is one the most sought after researched work either in dealing with modelling of the schedule or finding optimum ways to solve the problem. However, there are still gaps in the literature where the study on multi-objective HFS with energy utilization (EE) remains unsolved. The proposed study presents a model to solve scheduling in HFS and several optimization approaches to solve EE-HFS problem. The aim of this work is to present the best approach to minimize both energy utilization and completion time in HFS. The work will consider unrelated machine capabilities that are independent of one machine to another. The optimization of EE-HFS was performed utilizing the Artificial Bee Colony Optimization (ABC) across 12 benchmark HFS problems. Based on the optimization results, it was observed that the ABC algorithm exhibited superior performance compared to 8 other algorithms in most of the problem scenarios. The ABC algorithm performed better than 46% of the optimization objectives from other algorithms and demonstrated the most stable convergence when compared to other algorithms dependent on iterations under consideration.