2023年10月6日和13日，云师大李俊青教授课题组在Elsevier 旗下国际著名期刊《Swarm and Evolutionary Computation》（影响因子10.267）杂志发表2篇最新成果研究《A Collaboration-based Multi-objective Algorithm for Distributed Hybrid Flowshop Scheduling with Resource Constraints》《Q-learning based multi-objective immune algorithm for fuzzy flexible job shop scheduling problem considering dynamic disruptions》。第一作者单位分贝为聊城大学和山东师范大学，云南师范大学数学学院李俊青教授为通讯作者。

Abstract

With the development of the realistic manufacturing process, the distributed scheduling, machine velocity, and resource constraints have attracted much attention. This paper addresses the distributed hybrid flowshop scheduling problem (DHFSP) with machine velocity and resource constraints to minimize the makespan and total energy consumption simultaneously. A mathematical model of the problem is formulated. To solve the proposed problem, a collaboration-based multi-objective algorithm (CBMA) is developed. First, a machine velocity adjustment rule considering resource constraints is proposed by analyzing the characteristics of the problem. In the proposed algorithm, each solution is represented by a well-designed three-dimensional vector. Then, an objective-balanced machine selection strategy is employed to balance the quality and diversity of the initial population. Next, a Pareto knowledge-based collaborative search mechanism enhances the global search ability in each iteration. To improve the convergence of the algorithm, a distributed machine velocity adjustment rule is embedded into the local search. Finally, a set of instances based on realistic industrial processes are tested. The effective performance of the proposed algorithm is verified through computational comparisons.

Abstract

Confronted with complex industrial environments, dynamic disruptions like new job arrival and machine breakdown bring significant challenges to the robustness and stability of the manufacturing process, making the static production depart from the original scheduling scheme. To address this problem, a flexible job shop scheduling problem with fuzzy processing time, dynamic disruptions, and variable processing speeds is considered simultaneously. As well as three objectives of maximum completion time, total energy consumption, and average agreement index are demonstrated in this study. Then, a predictive-reactive dynamic/static rescheduling model is developed, where the off-line based mixed integer linear programming model and the on-line based rescheduling heuristics are proposed. Next, a multi-objective immune algorithm combined with a Q-learning algorithm (Q-MOIA) is developed. In the proposed algorithm, an active decoding heuristic based on the interval insertion mechanism is used to optimize the initial solutions. After that, the clonal selection-based immune algorithm and the Q-learning algorithm are adopted to improve the exploration and exploitation capabilities, respectively, where four objective-driven neighborhood structures are designed. Eventually, extensive computational experiments were conducted on 27 instances under static and dynamic scenarios to demonstrate the superiority and stability of the proposed predictive-reactive dynamic/static rescheduling model and the Q-MOIA. Comparative analysis with four state-of-the-art approaches revealed that proposed Q-MOIA outperformed in approximately 51.9, 66.7, and 83.3 % of the instances for the three multi-objective metrics.

《Swarm and Evolutionary Computation》是人工智能与智能计算领域重要学术期刊。该期刊主要报道自然启发式智能计算、跨学科领域的最新研究和发展成果，长期位于中国科学院计算机科学和工程技术等学科一区，也是该领域的Top期刊。李俊青，汉族，1976年8月出生，山东冠县人，教授、博士生导师。入选全球顶尖前10万科学家，全球前2%顶尖科学家终身成就榜。担任SCI一区期刊《Expert Systems With Applications》副主编，H-因子41。现为中国仿真学会智能仿真优化与调度专委会常务委员，中国运筹学会排序分委会理事、中国建筑学会绿色建筑委员会委员、中国人工智能学会自然计算及数字智能城市专委会委员，山东省自动化学会理事、山东计算机学会理事，中国计算机学会会员，IEEE会员、ACM会员、中国自动化学会会员、中国运筹学会会员。担任NSFC项目的通讯评议专家、省科技进步奖评审专家、教育部学位中心论文评审专家、中国博士后科学基金评审专家。

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