A Self-Adaptive Multiobjective Differential Evolution Algorithm for the Unrelated Parallel Batch Processing Machine Scheduling Problem

Abstract

In this paper, the unrelated parallel batch processing machine (UPBPM) scheduling problem is addressed to minimize the total energy consumption (TEC) and makespan. Firstly, a mixed-integer line programming model (MILP) of the UPBPM scheduling problem is presented. Secondly, a self-adaptive multiobjective differential evolution (AMODE) algorithm is put forward. Since the parameter value can affect the performance of the algorithm greatly, an adaptive parameter control method is proposed according to the convergence index of the individual and the evolution degree of the population to improve the exploitation and exploration ability of the algorithm. Meanwhile, an adaptive mutation strategy is proposed to improve the algorithm’s convergence and the solutions’ diversity. Finally, to verify the effectiveness of the algorithm, comparative experiments are carried out on 20 instances with 5 different scales. Numerical comparisons indicate that the proposed method can achieve high comprehensive performance.

Scheduling parallel batch processing machines: A case study in the semiconductor industry

“…Ozturk (2020) presented a decomposition method that uses column generation to schedule parallel batches of jobs on identical parallel machines with different job release dates, processing times, and sizes while machines have limited capacity. In Song's (2022) paper, the unrelated parallel BPM scheduling problem is addressed to minimize the total energy consumption and makespan. A self-adaptive multi-objective differential evolution algorithm was put forward.…”

A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

“…Kong et al [19] applied a shuffled frog-leaping algorithm with variable neighborhood search for the problem with nonlinear processing time. Song et al [20] developed a self-adaptive multi-objective differential evolution algorithm to minimize total energy consumption and makespan. Zhang et al [21] formulated a mixed-integer programming (MIP) model and presented an improved particle swarm optimization algorithm for the problem with production and delivery in cloud manufacturing.…”