2024年6月21日，Elsevier 旗下top期刊《Swarm and Evolutionary Computation》在线发表了云南师范大学数学学院李俊青教授团队的最新研究成果《A double-Q network collaborative multi-objective optimization algorithm for precast scheduling with curing constraints》。云南师范大学数学学院为第一单位，通讯作者为云南师范大学数学学院李俊青教授。合作单位为澳门科技大学（Macau University of Science and Technology）、恒星学院（HengXing University）
Abstract
Precast scheduling is a special case of optimization problem, in which the curing stage requires parallel processing of a batch of jobs. In this study, distributed flexible job shop scheduling with crane transportation, fuzzy processing, and curing constraints is modeled for precast scheduling. Three objectives are considered simultaneously, namely, fuzzy makespan, energy consumption, and economic cost. To solve this complex problem, a bi-level collaborative multi-objective optimization evolution algorithm (BCMOEA) is developed. First, a double-Q network is designed considering different groups of features to generate valuable solutions for the second-level components. Then, a dynamic-adjusted reference point set is embedded to divide the current population into three sub-populations. Furthermore, a collaboration mechanism is developed to learn different knowledge from these sub-populations to balance the convergence and diversity abilities. Moreover, a knowledge driven curing batching heuristic is designed to improve the fuzzy makespan. Finally, a set of instances generated based on the realistic precast process is tested, and detailed comparisons with the state-of-the-art algorithms show the competitive performances of the proposed algorithm.
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