A Branch-and-Bound Enhanced Cooperative Evolutionary Algorithm for the Hybrid Seru System Scheduling Considering Worker Heterogeneity

Abstract

The hybrid seru manufacturing mode widely exists in many real-world production enterprises, where workers are usually partially cross-trained due to high-training costs and employee turnover. However, the hybrid seru system scheduling problem considering worker heterogeneity (HSSWH) has rarely been studied in academia. To fill the gap, this article introduces a branch-and-bound enhanced cooperative evolutionary algorithm (BBCEA) to solve the HSSWH. Three core search components and an evaluation component are proposed in BBCEA, which are crafted to be problem-specific. In the exploration search component, a probability model sampling method and crossover collaborate to generate offspring with high quality and diversity. In the exploitation search component, five knowledge-based operators collaborate with a knowledge-guided operator selection strategy, which is designed by fully utilizing the problem properties and feedback information. In the exact search component, a branch-and-bound method is designed to solve the bottom layer subproblem precisely, which can greatly improve the effectiveness of the algorithm. In the evaluation component, a look-up table method is proposed to reduce computation effort by avoiding duplicate calculations. Numerical experimental results validate the superiority of the BBCEA in addressing the HSSWH, which can obtain the best solution on 95% of the instances compared with the state-of-the-art algorithms.