Browsing by Author "Wang, Rui"

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemOpen Access
    A multi-action deep reinforcement learning based on BiLSTM for flexible job shop scheduling problem with tight time
    (ACM, 2024-12) Wang, Rui; Liu, Chang; Wang, Xinzhuo; Yang, Shengxiang; Hou, Yaqi
    The Flexible Job Shop Scheduling Problem (FJSP) with tight time is a significant challenge in both academic and industrial fields of production scheduling. This paper addresses the FJSP with tight time using a Multi-action Deep Reinforcement Learning (MDRL) method. First, a multi-action Markov Decision Process (MDP) is formulated, integrating operation and machine sets into a unified multi-action space. Then, a scheduling policy is developed using a Bi-Directional Long Short-Term Memory Network (BiLSTM) to extract intrinsic scheduling information. Finally, Proximal Policy Optimization (PPO) enhanced with reward shaping is employed to train the model, enabling intelligent decision-making in action selections. Extensive experiments are conducted on four problem instances of varying scales. Comparisons among 20 priority dispatch rules and two closely rated DRL methods demonstrate the superior performance of the proposed MDRL approach.
  • Thumbnail Image
    ItemOpen Access
    An adaptive localized decision variable analysis approach to large scale multi-objective and many-objective optimization
    (IEEE, 2021-01) Ma, Lianbo; Huang, Min; Yang, Shengxiang; Wang, Rui; Wang, Xingwei
    This paper proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large scale multi-objective and many objective optimization problems. Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large scale multiobjective and many-objective optimization problems.
  • Thumbnail Image
    ItemOpen Access
    A Mahalanobis distance-based approach for dynamic multi-objective optimization with stochastic changes
    (IEEE, 2023-03-08) Hu, Yaru; Zheng, Jinhua; Jiang, Shouyong; Yang, Shengxiang; Zou, Juan; Wang, Rui
    In recent years, researchers have made significant progress in handling dynamic multi-objective optimization problems (DMOPs), particularly for environmental changes with predictable characteristics. However, little attention has been paid to DMOPs with stochastic changes. It may be difficult for existing dynamic multi-objective evolutionary algorithms (DMOEAs) to effectively handle this kind of DMOPs because most DMOEAs assume that environmental changes follow regular patterns and consecutive environments are similar. This paper presents a Mahalanobis Distance-based approach (MDA) to deal with DMOPs with stochastic changes. Specifically, we make an all-sided assessment of search environments via Mahalanobis distance on saved information to learn the relationship between the new environment and historical ones. Afterward, a change response strategy applies the learning to the new environment to accelerate the convergence and maintain the diversity of the population. Besides, the change degree is considered for all decision variables to alleviate the impact of stochastic changes on the evolving population. MDA has been tested on stochastic DMOPs with 2 to 4 objectives. The results show that MDA performs significantly better than the other latest algorithms in this paper, suggesting that MDA is effective for DMOPs with stochastic changes.
  • Thumbnail Image
    ItemOpen Access
    Meta-heuristics in microgrid management: A survey
    (Elsevier, 2023-02-07) Zheng, Zedong; Yang, Shengxiang; Guo, Yinan; Jin, Xiaolong; Wang, Rui
    As a small energy system, microgrid plays an important role in utilizing distributed energy resources, improving traditional energy networks, and building intelligent integrated energy systems. However, microgrid management is always a challenging optimization problem due to different factors. Meta-heuristics have been widely used to solve complex optimization problems in many fields, including energy systems. However, there is a lack of a systematic summary of the application of meta-heuristics in microgrid management. This paper aims to review the application of meta-heuristics in microgrid management, summarize the contributions and influences of different methods, and provide further insights and suggestions for future research.