Unrelated Parallel Batch Machine Scheduling Using a Modified ABC Algorithm

Abstract

This paper introduces a multi-objective discrete artificial bee colony (MODABC) algorithm, which aims to simultaneously minimize the makespan, total earliness and tardiness (ET), and total energy consumption (TEC) by efficiently scheduling a variety of jobs on unrelated parallel batch machines. Machines have different capacities and consume varying amounts of processing energy, whereas the jobs differ in sizes, release times, and due dates. In the employed bee and follower bee phase, three neighborhood structures are employed to generate feasible solutions, improving the population’s quality. In the scout bee phase, three multi-objective local search strategies are used to fully search the solution space. The experimental results show that the MODABC algorithm is superior to the NSGA-III algorithm in terms of convergence and diversity.

Keywords

• artificial bee colony algorithm
• multi-objective optimization
• unrelated parallel batch machine scheduling