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学者姓名：朱光宇
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中国的制造业正经历着数字化和绿色低碳转型.为实现节能减排,提高设备利用率,针对考虑可再生能源的混合流水车间,建立机器与自动导引车(AGV)联合利用再生能源的混合流水车间调度问题(HFSP-MA-RE)数学模型.为求解该模型,提出基于提前调度的机器与AGV联合调度策略、能源分配策略,在考虑AGV路径优化和充电约束的情况下,实现对最大完工时间、碳排放量、总能耗和AGV利用率4个目标的优化.采用基于正向灰靶模型的多目标最佳觅食算法(PPGT OFA)求解该问题.通过24个测试实例及1个工程应用,将所提算法与5个多目标优化算法进行实验,验证了 HFSP-MA-RE模型及PPGT OFA算法解决多目标优化问题的有效性.
Keyword ：
A*算法 A*算法 可再生能源 可再生能源 多目标优化 多目标优化 混合流水车间调度 混合流水车间调度 联合调度 联合调度
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| GB/T 7714 | 朱光宇 , 贾唯鸿 , 李德彪 . 机器与AGV联合利用再生能源的混合流水车间调度问题 [J]. | 北京航空航天大学学报 , 2025 , 51 (2) : 368-379 . |
| MLA | 朱光宇 等. "机器与AGV联合利用再生能源的混合流水车间调度问题" . | 北京航空航天大学学报 51 . 2 (2025) : 368-379 . |
| APA | 朱光宇 , 贾唯鸿 , 李德彪 . 机器与AGV联合利用再生能源的混合流水车间调度问题 . | 北京航空航天大学学报 , 2025 , 51 (2) , 368-379 . |
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不同于快速成型(RP)制造最优方案选择常采用单一质量属性或者成本属性评价方法,提出考虑距离-形状因素的属性评价方法,基于新方法结合RP制造过程资源环境属性实现快速成型制造的最优方案选择.首先,建立适合于RP制造过程的资源环境属性评价体系,提出可量化指标.然后,建立考虑距离-形状因素的属性评价方法计算模型,利用综合熵权法避免指标权重或主观或仅客观的倾向,TOPSIS法和灰色关联分析法结合避免传统单一方法偏重序列距离分析或者偏重序列形状分析的缺点.最后,以SLA快速成型制造过程为例,以该计算模型对法兰零件在相同工况下不同摆放角度的5个制造方案进行综合评价.实验结果表明:该计算模型具有可行性和实用性,可用于RP制造过程的资源环境属性综合评价和决策.
Keyword ：
SLA快速成型 SLA快速成型 多属性决策 多属性决策 综合评价 综合评价
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| GB/T 7714 | 杨志锋 , 朱光宇 , 王海燕 et al. 考虑资源环境属性评价的快速成型制造方案优化研究 [J]. | 制造业自动化 , 2025 , 47 (1) : 169-176 . |
| MLA | 杨志锋 et al. "考虑资源环境属性评价的快速成型制造方案优化研究" . | 制造业自动化 47 . 1 (2025) : 169-176 . |
| APA | 杨志锋 , 朱光宇 , 王海燕 , 吴春泽 . 考虑资源环境属性评价的快速成型制造方案优化研究 . | 制造业自动化 , 2025 , 47 (1) , 169-176 . |
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The job-shop scheduling problem (JSP) is one of the most famous production scheduling problems, and it is an NP-hard problem. Reinforcement learning (RL), a machine learning method capable of feedback-based learning, holds great potential for solving shop scheduling problems. In this paper, the literature on applying RL to solve JSPs is taken as the review object and analyzed in terms of RL methods, the number of agents, and the agent upgrade strategy. We discuss three major issues faced by RL methods for solving JSPs: the curse of dimensionality, the generalizability and the training time. The interconnectedness of the three main issues is revealed and the main factors affecting them are identified. By discussing the current solutions to the above issues as well as other challenges that exist, suggestions for solving these problems are given, and future research trends are proposed.
Keyword ：
Job-shop scheduling problem Job-shop scheduling problem Machine learning Machine learning Reinforcement learning (RL) Reinforcement learning (RL) Shop scheduling Shop scheduling
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| GB/T 7714 | Zhang, Xiehui , Zhu, Guang-Yu . A literature review of reinforcement learning methods applied to job-shop scheduling problems☆ [J]. | COMPUTERS & OPERATIONS RESEARCH , 2025 , 175 . |
| MLA | Zhang, Xiehui et al. "A literature review of reinforcement learning methods applied to job-shop scheduling problems☆" . | COMPUTERS & OPERATIONS RESEARCH 175 (2025) . |
| APA | Zhang, Xiehui , Zhu, Guang-Yu . A literature review of reinforcement learning methods applied to job-shop scheduling problems☆ . | COMPUTERS & OPERATIONS RESEARCH , 2025 , 175 . |
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In this article, we propose sampling-focused marching tree (SMT) to guarantee optimal solutions in complex environments efficiently. By synergistically integrating heuristic path planning, homotopy space computation, and adaptive sampling exploration, SMT swiftly identifies homotopic solutions to the optimal solution and focuses sampling efforts in their vicinity, continually refining the solution to efficiently attain the global optimum. In the heuristic path planning phase, the generalized Voronoi graph feature nodes are extracted by a filter to facilitate subsequent computations. Next, the feature visibility graph is constructed based on the feature nodes to plan a heuristic path. In the homotopy space computation phase, feature cell decomposition is executed using the feature nodes as well to refine the obstacle-free space. Then, the homotopy space is computed by examining the topological connections between cells and the heuristic path to narrow down the sampling space. In the adaptive sampling exploration phase, the sampling factor is adjusted based on the area of the sampling space to enhance the quality of samples. After adaptive sampling based on the factor, the fast marching tree is leveraged to rapidly explore the samples and find the optimal solution. A thorough analysis of SMT is provided, including completeness, asymptotic optimality, and computational complexity. Comprehensive simulation comparisons with current-leading planning approaches in complex scenarios, along with a series of convincing real-world studies have been conducted to provide evidence for verifying optimality and high-efficiency computation of the proposed SMT.
Keyword ：
Convergence Convergence Costs Costs Feature extraction Feature extraction Homotopy-heuristic exploration Homotopy-heuristic exploration Measurement Measurement Mechanical engineering Mechanical engineering Mechatronics Mechatronics mobile robot mobile robot Mobile robots Mobile robots optimality optimality Path planning Path planning Planning Planning Research and development Research and development sampling-based planning sampling-based planning topological refinement topological refinement
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| GB/T 7714 | Chen, Yanjie , Jiang, Wensheng , Zhang, Zhixing et al. Sampling-Focused Marching Tree: Optimal Planning Based on Minimized Topological Refinement and Homotopy-Heuristic Exploration [J]. | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2024 . |
| MLA | Chen, Yanjie et al. "Sampling-Focused Marching Tree: Optimal Planning Based on Minimized Topological Refinement and Homotopy-Heuristic Exploration" . | IEEE-ASME TRANSACTIONS ON MECHATRONICS (2024) . |
| APA | Chen, Yanjie , Jiang, Wensheng , Zhang, Zhixing , Zhang, Liping , Zhu, Guangyu , Zhang, Hui et al. Sampling-Focused Marching Tree: Optimal Planning Based on Minimized Topological Refinement and Homotopy-Heuristic Exploration . | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2024 . |
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As one of the most fundamental operations in mechanical production, hole-making plays a crucial role. However, existing hole-making sequence optimization models are not suitable for workshops with variable production parameters. To address this issue, a new model, named multi-objective multi-tool hole-making sequence optimization with precedence constraints (MO-MTpcHSO), is proposed in this paper. The model has two objectives: spindle travel distance and tool switching time. To solve MO-MTpcHSO, a customized Q-learning based genetic algorithm (QLGA) is proposed. The adaptive encoding method allows chromosomes to express feasible solutions, the population is considered as the agent, and the states are intervals of the diversity coefficient. Different insertion methods in the crossover operator are set as actions, and the reward is related to the diversity and values of objective functions of the population. The effectiveness of QLGA is validated by comparing it with other algorithms in practical workpieces. Moreover, the reasonability of actions and the necessity of the Q-learning framework in QLGA are validated.
Keyword ：
genetic algorithm genetic algorithm hole-making sequence optimization hole-making sequence optimization multi-objective problem multi-objective problem Q-learning Q-learning
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| GB/T 7714 | Zhang, Desong , Chen, Yanjie , Zhu, Guangyu . Multi-Objective Hole-Making Sequence Optimization by Genetic Algorithm Based on Q-Learning [J]. | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2024 , 8 (6) : 3793-3806 . |
| MLA | Zhang, Desong et al. "Multi-Objective Hole-Making Sequence Optimization by Genetic Algorithm Based on Q-Learning" . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 8 . 6 (2024) : 3793-3806 . |
| APA | Zhang, Desong , Chen, Yanjie , Zhu, Guangyu . Multi-Objective Hole-Making Sequence Optimization by Genetic Algorithm Based on Q-Learning . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2024 , 8 (6) , 3793-3806 . |
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孔加工是机械制造的基本工序之一.针对数控机床的刀具路径优化问题,提出一种新颖的孔加工刀具路径优化模型—-带可决策孔的孔加工多刀具路径优化问题(MTdDPO).在该模型中,工件上的孔分为两类:固定孔和可决策孔.MTdDPO的目标是通过判断可决策孔的路径归属和路径内各孔的加工顺序来实现加工路径长度的最小化.为实现MTdDPO的优化,提出基于强化学习的分段遗传算法(RLSGA).在RLSGA中,种群被视为智能体,智能体的状态是种群的多样性系数,3种不同的分段交叉算子是智能体的动作,智能体的奖励与种群的适应度值和多样性系数的变化有关.针对MTdDPO,新建5个基准测试问题,并在测试问题上将RLSGA与其他4个算法进行对比.结果表明,RLSGA的表现明显优于其他算法,RLSGA能够有效地解决MTdDPO问题.
Keyword ：
孔加工路径优化 孔加工路径优化 强化学习 强化学习 组合优化 组合优化 遗传算法 遗传算法
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| GB/T 7714 | 朱光宇 , 张德颂 . 基于强化学习的遗传算法求解一种新的钻削路径优化问题 [J]. | 控制与决策 , 2024 , 39 (2) : 697-704 . |
| MLA | 朱光宇 et al. "基于强化学习的遗传算法求解一种新的钻削路径优化问题" . | 控制与决策 39 . 2 (2024) : 697-704 . |
| APA | 朱光宇 , 张德颂 . 基于强化学习的遗传算法求解一种新的钻削路径优化问题 . | 控制与决策 , 2024 , 39 (2) , 697-704 . |
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The batch layout of various types of parts in additive manufacturing has problems such as high printing time cost and low utilization of workbench space, and the impact of the height of the parts needs to be considered. The batch layout problem includes two sub-problems, the part placement strategy on the workbench and the allocation of molding batches. The placement strategies involve the forming direction selection, the part collision detection, and the positioning strategies. Based on the established forming direction criterion, the polygons were used to represent the projected contour of parts. Based on no-fit polygon, an improved orbital sliding approach was proposed to determine the collision-free emission range of polygonal parts with three categories. A new bottom-left positioning strategy to place parts was proposed, which integrated an improved constructive approach and fit degree. A strategy of the used maximum area in a worktable was proposed to achieve the part molding batch allocation. On the basis of above research, a batch layout algorithm based on the optimal foraging algorithm was proposed. The algorithm used the double coding to express the part placement order and the rotation angle, and realized batch layout optimization by taking minimum completion time as the objective. The proposed algorithm was compared with three comparison algorithms based on some library parts, and the results showed that the proposed method could effectively improve the space utilization of additive manufacturing and shorten the completion time. © 2024 CIMS. All rights reserved.
Keyword ：
additive manufacturing additive manufacturing batch layout batch layout optimal foraging algorithm optimal foraging algorithm three-dimensional nesting three-dimensional nesting
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| GB/T 7714 | Zhu, G. , Jiang, Q. , Lin, Q. . Batch layout of various parts in additive manufacturing based on optimal foraging algorithm; [基于最优觅食算法的增材制造中多种类零件分批排样研究] [J]. | Computer Integrated Manufacturing Systems, CIMS , 2024 , 30 (7) : 2340-2349 . |
| MLA | Zhu, G. et al. "Batch layout of various parts in additive manufacturing based on optimal foraging algorithm; [基于最优觅食算法的增材制造中多种类零件分批排样研究]" . | Computer Integrated Manufacturing Systems, CIMS 30 . 7 (2024) : 2340-2349 . |
| APA | Zhu, G. , Jiang, Q. , Lin, Q. . Batch layout of various parts in additive manufacturing based on optimal foraging algorithm; [基于最优觅食算法的增材制造中多种类零件分批排样研究] . | Computer Integrated Manufacturing Systems, CIMS , 2024 , 30 (7) , 2340-2349 . |
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The optimal foraging algorithm (OFA) is a swarm-based algorithm motivated by animal behavioral ecology theory. When solving complex optimization problems characterized by multiple peaks, OFA is easy to get trapped in local minima and encounters slow convergence. Therefore, this paper presents an improved optimal foraging algorithm with social behavior based on quasi-opposition (QOS-OFA) to address these problems. First, quasi-opposition-based learning (QOBL) is introduced to improve the overall quality of the population in the initialization phase. Second, an efficient cosine-based scale factor is designed to accelerate the exploration of the search space. Third, a new search strategy with social behavior is designed to enhance local exploitation. The cosine-based scale factor is used as a regulator to achieve a balance between global exploration and local exploitation. The proposed QOS-OFA is compared with seven meta-heuristic algorithms on a CEC benchmark test suite and three real-world optimization problems. The experimental results show that QOS-OFA is better than other competitors on most of the test problems.
Keyword ：
Global exploration Global exploration Local exploitation Local exploitation Optimal foraging algorithm Optimal foraging algorithm Quasi-opposition-based learning Quasi-opposition-based learning Social behavior Social behavior
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| GB/T 7714 | Ding, Chen , Zhu, Guangyu . Improved optimal foraging algorithm for global optimization [J]. | COMPUTING , 2024 , 106 (7) : 2293-2319 . |
| MLA | Ding, Chen et al. "Improved optimal foraging algorithm for global optimization" . | COMPUTING 106 . 7 (2024) : 2293-2319 . |
| APA | Ding, Chen , Zhu, Guangyu . Improved optimal foraging algorithm for global optimization . | COMPUTING , 2024 , 106 (7) , 2293-2319 . |
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Hole-making is one of the basic processes of mechanical production. For the problem of toolpath optimization of computer numerical control (CNC) machine tools, a novel toolpath model for hole-making called as multi-tool drilling path optimization problems with decidable holes (MTdDPO) is proposed. In the MTdDPO, holes on workpieces are divided into two categories: fixed holes and decidable holes. The goal of the MTdDPO is to minimize the length of the machining path by judging the path ownership of decidable holes and the machining sequence of all holes in each path. To realize the optimization of the MTdDPO, a segmented genetic algorithm based on reinforcement learning (RLSGA) is proposed. The population of the RLSGA is regarded as the agent, the states of the agent are the intervals of the diversity coefficient of the population, three different segmental crossover operators are the actions of the agent, and the reward of the agent is related to the changes in fitness value and diversity coefficients of the population. Based on the MTdDPO, 5 benchmark test problems are designed, and the RLSGA is compared with other 4 algorithms on these test problems. Results show that the performance of the RLSGA is significantly better than other algorithms, which means the RLSGA can effectively solve the MTdDPO problems. © 2024 Northeast University. All rights reserved.
Keyword ：
Benchmarking Benchmarking Combinatorial optimization Combinatorial optimization Computer control systems Computer control systems Genetic algorithms Genetic algorithms Infill drilling Infill drilling Learning algorithms Learning algorithms Machine tools Machine tools Reinforcement learning Reinforcement learning
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| GB/T 7714 | Zhu, Guang-Yu , Zhang, De-Song . Genetic algorithm based on reinforcement learning for a novel drilling path optimization problem [J]. | Control and Decision , 2024 , 39 (2) : 697-704 . |
| MLA | Zhu, Guang-Yu et al. "Genetic algorithm based on reinforcement learning for a novel drilling path optimization problem" . | Control and Decision 39 . 2 (2024) : 697-704 . |
| APA | Zhu, Guang-Yu , Zhang, De-Song . Genetic algorithm based on reinforcement learning for a novel drilling path optimization problem . | Control and Decision , 2024 , 39 (2) , 697-704 . |
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增材制造中多种类零件分批排样存在打印时间成本高和工作台空间利用率低等问题,且需考虑零件高度的影响.分批排样问题包括零件在工作台上放置策略和成型批次分配两个子问题.放置策略涉及成型方向选择、零件碰撞检测和定位策略.基于建立的成型方向准则,利用多边形表示零件投影轮廓,提出基于临界多边形的改进移动碰撞法以确定3种不同形态多边形零件的免碰撞排放范围,提出新的左下定位策略放置零件,新定位策略融合了改进的建设性方法和契合度;提出单机台面积占用最大化策略实现零件成型批次分配.基于上述研究,提出基于最优觅食算法的分批排样算法,算法采用双重编码表达零件放置顺序和旋转角度,以最小化完工时间为目标实现优化分批排样.以案例库零件为对象,与3种对比算法比较,表明所提方法的方案能有效提高增材制造的空间利用率和缩短完工时间.
Keyword ：
三维排样 三维排样 分批排样 分批排样 增材制造 增材制造 最优觅食算法 最优觅食算法
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| GB/T 7714 | 朱光宇 , 蒋起爽 , 林晓斌 . 基于最优觅食算法的增材制造中多种类零件分批排样研究 [J]. | 计算机集成制造系统 , 2024 , 30 (7) : 2340-2349 . |
| MLA | 朱光宇 et al. "基于最优觅食算法的增材制造中多种类零件分批排样研究" . | 计算机集成制造系统 30 . 7 (2024) : 2340-2349 . |
| APA | 朱光宇 , 蒋起爽 , 林晓斌 . 基于最优觅食算法的增材制造中多种类零件分批排样研究 . | 计算机集成制造系统 , 2024 , 30 (7) , 2340-2349 . |
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