An enhanced two phase estimation of distribution algorithm for solving scheduling problem

作者：Xinchang Hao, Jing Tian, Hui Ding, Keheng Zhao, Mitsuo Gen · 发表于：International Journal of Management Science and Engineering Management · 年份：2022 · DOI：10.1080/17509653.2022.2085205 · 被引用次数：4 · 研究领域：Scheduling and Optimization Algorithms、Metaheuristic Optimization Algorithms Research、Advanced Manufacturing and Logistics Optimization

Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method of enhanced hybrid Estimation of Distribution Algorithm (EDA) with Teaching and Learning-Based Optimization Algorithm (TLBO). First, in order to concentrate their respective advantages, two algorithms of EDA and TLBO are integrated to enhance the capability of both global and local search. Second, scenario-based simulation is adopted to deal with uncertainty, and an adaptive sampling strategy is involved to dynamically adjust the number of scenarios during the evolving process. Third, a problem-specific local search is designed to further improve the optimality of candidate solutions. By comparing with existing algorithms on the benchmark problems of flexible job shop scheduling problem (FJSP), it is to demonstrate that our proposal can obtain better solutions in the aspects of optimality and computational efficiency.