Affinity Propagation Hierarchical Memetic Algorithm for Multimodal Multiobjective Flexible Job Shop Scheduling With Variable Speed

Abstract

The flexible job shop scheduling, as the most typical production mode in industrial manufacturing, aims to improve production efficiency. However, the proposal of energy-saving and emission-reduction policy implies that it is impossible to increase the processing speed to improve productivity, and energy consumption is also becoming another important optimization objective. For the multiobjective flexible job shop scheduling problem, the optimization process tends to converge faster in some regions. This is because different scheduling sequences obtain the same objective values, i.e., there is a multimodal characteristic, which is still hardly investigated. Therefore, optimizing the decision space and the objective space simultaneously has become an urgent challenge that needs to be solved. To overcome the above challenges, we model the multimodal multiobjective flexible job shop scheduling problem with variable speed (MMFJSP-S) and propose an affinity propagation hierarchical memetic algorithm (APHMA) to minimize makespan and total energy consumption. First, four problem-specific neighborhood structures are employed to enhance the convergence. Then, an affinity propagation clustering combined with the random forests strategy is proposed to classify the global and local Pareto sets. Finally, a hierarchical environmental selection strategy is designed to ensure the convergence and diversity in the decision and objective spaces. Evaluations against seven advanced algorithms on MK and DP benchmarks demonstrate the competitive performance of APHMA in solving MMFJSP-S.