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学者姓名：李德彪
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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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In textile production, fabric dyeing is the most time-consuming and energy-intensive process, particularly with batch processing machines (BPMs), which affects both production efficiency and sustainable manufacturing. Failed fabric redyeing further complicates scheduling by causing due date misses. This study proposes a green parallel BPMs scheduling problem (GPBSP) with redyeing operations, due windows, and sequence-dependent setup times. A novel hybrid algorithm called teaching-learning-based optimisation with ensemble estimation of distribution algorithm (TLBO-eEDA) is introduced to simultaneously minimise the makespan, total energy consumption, and total weighted earliness and tardiness. TLBO-eEDA integrates the teacher-learner mechanism of TLBO with the global statistical learning capability of EDA, dynamically balancing exploration and exploitation. It employs a hybrid initialisation strategy that combines heuristic rule combinations with random methods to generate a high-quality population. A hierarchical team structure comprising of teachers, an elite class, and a regular class, is adopted to enhance search efficiency through dynamic interactions and bidirectional communication. To prevent premature convergence, a diversity reinforcement operator replaces stagnant solutions using the ensemble probability model of EDA, while a local search refines solutions via critical operations and machines. Extensive experiments with industrial and synthetic datasets are conducted, and computational results confirm its superiority in solving the considered problem.
Keyword ：
due windows due windows estimation of distribution algorithm estimation of distribution algorithm Fabric dyeing process Fabric dyeing process green scheduling problem green scheduling problem redyeing operations redyeing operations teaching-learning-based optimisation teaching-learning-based optimisation
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| GB/T 7714 | Wang, Jing , Lian, Jingsheng , Lei, Deming et al. Sustainable multi-objective scheduling in fabric dyeing: a hybrid TLBO-eEDA approach with redyeing operations and due windows [J]. | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH , 2025 . |
| MLA | Wang, Jing et al. "Sustainable multi-objective scheduling in fabric dyeing: a hybrid TLBO-eEDA approach with redyeing operations and due windows" . | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2025) . |
| APA | Wang, Jing , Lian, Jingsheng , Lei, Deming , Li, Debiao , Cheng, Lixin , Tang, Hongtao et al. Sustainable multi-objective scheduling in fabric dyeing: a hybrid TLBO-eEDA approach with redyeing operations and due windows . | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH , 2025 . |
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Machine scheduling serves as a vital function for industrial and service operations, and uncertainties always pose a significant challenge in real-world scheduling practices. In this paper, we propose to solve the stochastic machine scheduling problems with uncertain processing times by an improved prescriptive tree-based (IPTB) model. Our approach includes a novel way of combining historical processing time data with current scheduling constraints to strengthen the quality of historical decisions. We apply these improved historical decisions and incorporate an improved model for calculating the optimisation loss and accelerate the training of our IPTB model. Our trained model can directly prescribe downstream scheduling solutions with high robustness in the face of uncertainties. We evaluate the proposed IPTB method on a stochastic parallel machine scheduling problem originating from printed circuit board assembly lines. Through a series of comparative experiments, our findings demonstrate the IPTB method’s superior accuracy and robustness, highlighting its resilience in noisy data environments. Additionally, we interpret the model through feature importance analysis and examine the model’s behaviours under noisy conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Adversarial machine learning Adversarial machine learning Scheduling algorithms Scheduling algorithms Stochastic models Stochastic models Stochastic systems Stochastic systems
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| GB/T 7714 | Chen, Siping , Li, Debiao , Noman, Nasimul et al. An Improved Prescriptive Tree-Based Model for Stochastic Parallel Machine Scheduling [C] . 2025 : 354-365 . |
| MLA | Chen, Siping et al. "An Improved Prescriptive Tree-Based Model for Stochastic Parallel Machine Scheduling" . (2025) : 354-365 . |
| APA | Chen, Siping , Li, Debiao , Noman, Nasimul , Harrison, Kyle , Chiong, Raymond . An Improved Prescriptive Tree-Based Model for Stochastic Parallel Machine Scheduling . (2025) : 354-365 . |
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In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased tardiness. To tackle this multi-constrained problem, a stochastic integer programming model is formulated to minimize total estimated tardiness. A novel symmetry-driven two-population collaborative differential evolution (TCDE) algorithm is then proposed. It features two symmetrically complementary subpopulations that achieve a balance between global exploration and local exploitation. One subpopulation employs chaotic parameter adaptation through a logistic map for symmetrically enhanced exploration, while the other adjusts parameters based on population diversity and convergence speed to facilitate symmetry-aware exploitation. Moreover, it also incorporates a symmetrical collaborative mechanism that includes the periodic migration of top individuals between subpopulations, along with elite-set guidance, to enhance both population diversity and convergence efficiency. Extensive computational experiments were conducted on 21 small-scale (optimally validated via CVX) and 15 large-scale synthetic datasets, as well as 21 small-scale (similarly validated) and 20 large-scale industrial datasets. These experiments demonstrate that TCDE significantly outperforms state-of-the-art comparative methods. Ablation studies also further verify the critical role of its symmetry-based components, with computational results confirming its superiority in solving the considered problem.
Keyword ：
collaborative mechanism collaborative mechanism lace dyeing lace dyeing parallel machine scheduling parallel machine scheduling probabilistic re-dyeing operations probabilistic re-dyeing operations symmetry-driven differential evolution symmetry-driven differential evolution
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| GB/T 7714 | Wang, Jing , Lian, Jingsheng , Deng, Youpeng et al. Symmetry-Driven Two-Population Collaborative Differential Evolution for Parallel Machine Scheduling in Lace Dyeing with Probabilistic Re-Dyeing Operations [J]. | SYMMETRY-BASEL , 2025 , 17 (8) . |
| MLA | Wang, Jing et al. "Symmetry-Driven Two-Population Collaborative Differential Evolution for Parallel Machine Scheduling in Lace Dyeing with Probabilistic Re-Dyeing Operations" . | SYMMETRY-BASEL 17 . 8 (2025) . |
| APA | Wang, Jing , Lian, Jingsheng , Deng, Youpeng , Pan, Lang , Xue, Huan , Chen, Yanming et al. Symmetry-Driven Two-Population Collaborative Differential Evolution for Parallel Machine Scheduling in Lace Dyeing with Probabilistic Re-Dyeing Operations . | SYMMETRY-BASEL , 2025 , 17 (8) . |
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In recent years, the urgent need to mitigate stormwater runoff and address urban waterlogging has garnered significant attention. Low Impact Development (LID) has emerged as a promising strategy for managing urban runoff sustainably. However, the vast array of potential LID layout combinations presents challenges in quantifying their effectiveness and often results in high construction costs. To address these issues, this study proposes a simulation-optimization framework that integrates the Storm Water Management Model (SWMM) with advanced optimization techniques to minimize both runoff volume and costs. The framework incorporates random variations in rainfall intensity within the basin, ensuring robustness under diverse climatic conditions. By leveraging a multi-objective scatter search algorithm, this research optimizes LID layouts to achieve effective stormwater management. The algorithm is further enhanced by two local search techniques-namely, the 'cost-benefit' local search and path-relinking local search-which significantly improve computational efficiency. Comparative analysis reveals that the proposed algorithm outperforms the widely used NSGA-II (Non-dominated Sorting Genetic Algorithm II), reducing computation time by an average of 8.89%, 16.98%, 1.72%, 3.85%, and 1.23% across various scenarios. The results demonstrate the method's effectiveness in achieving optimal LID configurations under variable rainfall intensities, highlighting its practical applicability for urban flood management. This research contributes to advancing urban sponge city initiatives by providing a scalable, efficient, and scientifically grounded solution for sustainable urban water management. The proposed framework is expected to support decision-makers in designing cost-effective and resilient stormwater management systems, paving the way for more sustainable urban development.
Keyword ：
computational efficiency simulation optimization computational efficiency simulation optimization cost-benefit analysis cost-benefit analysis low impact development low impact development multi-objective scatter search algorithm multi-objective scatter search algorithm path-relinking path-relinking
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| GB/T 7714 | Huang, Yuzhou , Li, Debiao , Li, Qiusha et al. Optimization of Low Impact Development Layouts for Urban Stormwater Management: A Simulation-Based Approach Using Multi-Objective Scatter Search Algorithm [J]. | WATER , 2025 , 17 (6) . |
| MLA | Huang, Yuzhou et al. "Optimization of Low Impact Development Layouts for Urban Stormwater Management: A Simulation-Based Approach Using Multi-Objective Scatter Search Algorithm" . | WATER 17 . 6 (2025) . |
| APA | Huang, Yuzhou , Li, Debiao , Li, Qiusha , Xu, Kai-Qin , Xie, Jiankun , Qiang, Wei et al. Optimization of Low Impact Development Layouts for Urban Stormwater Management: A Simulation-Based Approach Using Multi-Objective Scatter Search Algorithm . | WATER , 2025 , 17 (6) . |
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This paper proposes a Wasserstein distributionally robust learning (WDRL) model to predict the production cycle time of highly mixed printed circuit board (PCB) orders on multiple production lines. The PCB production cycle time is essential for optimizing production plans. However, the design of the PCB, production line configuration, order combinations, and personnel factors make the prediction of the PCB production cycle time difficult. In addition, practical production situations with significant disturbances in feature data make traditional prediction models inaccurate, especially when there is new data. Therefore, we establishe a WDRL model, derive a tight upper bound for the expected loss function, and reformulate a tractable equivalent model based on the bound. To demonstrate the effectiveness of this method, we collected data related to surface mounted technology (SMT) production lines from a leading global display manufacturer for our computational experiments. In addition, we also designed experiments with perturbations in the training and testing datasets to verify the WDRL model's ability to handle perturbations. This proposed method has also been compared with other machine learning methods, such as the support vector regression combined with symbiotic organism search, decision tree, and kernel extreme learning machine, among others. Experimental results indicate that the WDRL model can make robust predictions of PCB cycle time, which helps to effectively plan production capacity in uncertain situations and avoid overproduction or underproduction. Finally, we implement the WDRL model for the actual SMT production to predict the production cycle time and set it as the target for production. We observed a 98-103 % achievement rate in the last 20 months since the implementation in September 2022.
Keyword ：
Distributionally robust learning Distributionally robust learning PCB production cycle time prediction PCB production cycle time prediction SMT production SMT production Wasserstein distance Wasserstein distance
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| GB/T 7714 | Liu, Feng , Lu, Yingjie , Li, Debiao et al. Wasserstein distributionally robust learning for predicting the cycle time of printed circuit board production [J]. | COMPUTERS IN INDUSTRY , 2025 , 164 . |
| MLA | Liu, Feng et al. "Wasserstein distributionally robust learning for predicting the cycle time of printed circuit board production" . | COMPUTERS IN INDUSTRY 164 (2025) . |
| APA | Liu, Feng , Lu, Yingjie , Li, Debiao , Chiong, Raymond . Wasserstein distributionally robust learning for predicting the cycle time of printed circuit board production . | COMPUTERS IN INDUSTRY , 2025 , 164 . |
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Distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with batch processing machines (BPMs) is rarely considered, let alone DEHFSP with BPMs and uncertainty. In this study, a fuzzy DEHFSP with BPMs at a middle stage and no precedence between some stages is presented, and a dynamic artificial bee colony (DABC) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and fuzzy total energy consumption. To produce high quality solutions, Metropolis criterion is used, dynamic employed bee phase based on neighborhood structure dynamic selection is implemented, and group-based onlooker bee phase with bidirectional communication is given. Migration operator is also adopted to replace scout bee phase. Extensive experiments are conducted, and the optimal combination of key parameters for DABC is decided by the Taguchi method. Comparative results and statistical analysis show that new strategies of DABC are effective, and DABC is highly competitive in solving the considered fuzzy DEHFSP.
Keyword ：
Artificial bee colony Artificial bee colony Batch processing machines Batch processing machines Distributed energy-efficient hybrid flow shop Distributed energy-efficient hybrid flow shop Fuzzy scheduling Fuzzy scheduling Precedence Precedence
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| GB/T 7714 | Wang, Jing , Lei, Deming , Li, Debiao et al. A dynamic artificial bee colony for fuzzy distributed energy-efficient hybrid flow shop scheduling with batch processing machines [J]. | JOURNAL OF MANUFACTURING SYSTEMS , 2025 , 78 : 94-108 . |
| MLA | Wang, Jing et al. "A dynamic artificial bee colony for fuzzy distributed energy-efficient hybrid flow shop scheduling with batch processing machines" . | JOURNAL OF MANUFACTURING SYSTEMS 78 (2025) : 94-108 . |
| APA | Wang, Jing , Lei, Deming , Li, Debiao , Li, Xixing , Tang, Hongtao . A dynamic artificial bee colony for fuzzy distributed energy-efficient hybrid flow shop scheduling with batch processing machines . | JOURNAL OF MANUFACTURING SYSTEMS , 2025 , 78 , 94-108 . |
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This paper investigates the energy-efficient room scheduling (ERS) problem by considering uncertainties in energy consumption and renewable energy generation in buildings. Rather than the conventional 'predict, then optimise' approach, we propose an improved prescriptive tree-based (IPTB) model that directly 'prescribes' scheduling solutions. Our model utilises contextual information on energy consumption (e.g., temperature and humidity) and renewable energies (e.g., wind speeds and sunlight) to generate direct ERS solutions. It is trained using a novel optimisation loss function that aligns historical ERS solutions with current conditions, ensuring robustness and tractability by exploiting problem-specific properties. To evaluate the proposed model's performance, experiments on randomly generated ERS instances demonstrate that the IPTB model is trained efficiently across various problem sizes and consistently outperforms advanced data-driven optimisation methods in prescriptive accuracy. Moreover, the IPTB model achieves more balanced energy consumption, particularly under practical scenarios emphasising on energy demand charges. A case study using real-world datasets from six buildings at Monash University, Australia, validates the model's effectiveness in addressing complex practical constraints inherent in ERS problems.
Keyword ：
Data-driven decision-making Data-driven decision-making Energy-efficient room scheduling Energy-efficient room scheduling Prescriptive analytics Prescriptive analytics Tree-based machine learning models Tree-based machine learning models Uncertainty Uncertainty
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| GB/T 7714 | Chen, Siping , Chiong, Raymond , Li, Debiao . Innovative Applications of OR A prescriptive tree-based model for energy-efficient room scheduling: Considering uncertainty in energy generation and consumption [J]. | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH , 2025 , 326 (2) . |
| MLA | Chen, Siping et al. "Innovative Applications of OR A prescriptive tree-based model for energy-efficient room scheduling: Considering uncertainty in energy generation and consumption" . | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 326 . 2 (2025) . |
| APA | Chen, Siping , Chiong, Raymond , Li, Debiao . Innovative Applications of OR A prescriptive tree-based model for energy-efficient room scheduling: Considering uncertainty in energy generation and consumption . | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH , 2025 , 326 (2) . |
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Batch processing machines, which operate multiple jobs at a time, are commonly used in energy-intensive industries. A significant amount of energy can be saved in such industries using production scheduling as an approach to enhance efficiency. This study deals with an energy-aware scheduling problem for parallel batch processing machines with incompatible families and job release times. In such an environment, a machine may need to wait until all the jobs in the next batch become ready. During waiting time, a machine can be switched off or kept on standby for more energy-efficient scheduling. We first present a mixed-integer linear programming (MILP) model to solve the problem. However, the presented MILP model can only solve small problem instances. We therefore propose an energy-efficient tabu search (ETS) algorithm for solving larger problem instances. The proposed solution framework incorporates multiple neighborhood methods for efficient exploration of the search space. An energy-related heuristic is also integrated into the ETS for minimizing energy consumption during the waiting time. The performance of our proposed ETS algorithm is validated by comparing it with CPLEX for small problem instances and with two other heuristic algorithms for larger problem instances. The contribution of different components in ETS is also established in our experimental studies. The proposed solution framework is expected to bring many benefits in energy-intensive industries both economically and environmentally.
Keyword ：
Batch processing machine (BPM) scheduling Batch processing machine (BPM) scheduling energy-related heuristic (EH) energy-related heuristic (EH) incompatible families incompatible families neighborhood moves (NMs) neighborhood moves (NMs) release times release times tabu search (TS) tabu search (TS) total energy consumption (TEC) total energy consumption (TEC) total weighted tardiness (TWT) total weighted tardiness (TWT)
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| GB/T 7714 | Abedi, Mehdi , Chiong, Raymond , Noman, Nasimul et al. A Metaheuristic Framework for Energy-Intensive Industries With Batch Processing Machines [J]. | IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT , 2024 , 71 : 4502-4516 . |
| MLA | Abedi, Mehdi et al. "A Metaheuristic Framework for Energy-Intensive Industries With Batch Processing Machines" . | IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 71 (2024) : 4502-4516 . |
| APA | Abedi, Mehdi , Chiong, Raymond , Noman, Nasimul , Liao, Xiaoya , Li, Debiao . A Metaheuristic Framework for Energy-Intensive Industries With Batch Processing Machines . | IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT , 2024 , 71 , 4502-4516 . |
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Setup time is pivotal in printed circuit board (PCB) assembly line operations. However, PCB production encounters varying setup times due to multiple influencing factors. This paper addresses an uncertain setup time prediction problem in PCB assembly production lines. Unlike existing production time prediction models, our proposed approach integrates a comprehensive range of production features, not only with features related to PCBs but also production line operators, setup procedures and so on. To enhance model accuracy and mitigate overfitting, we implemented some data preprocessing phases and designed a random forest-integrated feature selection method. With the selected features, we used a light gradient boosting machine (LightGBM) as the predictive model and optimised its hyperparameters by a differential evolution (DE) algorithm. We validated our model's performance through extensive computational experiments based on real-world industrial data, focusing on feature selection efficiency and hyperparameter optimisation. The experimental results confirmed that our proposed DE-LightGBM can reduce redundant features and optimise the integral hyperparameters for model training. We also compared the DE-LightGBM model to some well-established machine learning approaches in different setup scenarios. The proposed DE-LightGBM outperformed other machine learning methods being compared, delivering accurate setup time predictions in both standard and complex scenarios. © 2024 Copyright held by the owner/author(s).
Keyword ：
Adaptive boosting Adaptive boosting Assembly machines Assembly machines Machine learning Machine learning Prediction models Prediction models Random forests Random forests
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| GB/T 7714 | Chen, Siping , Li, Debiao , Gan, Xiqin et al. An Optimised Light Gradient Boosting Machine Model for Setup Time Prediction in Electronic Production Lines [C] . 2024 : 647-650 . |
| MLA | Chen, Siping et al. "An Optimised Light Gradient Boosting Machine Model for Setup Time Prediction in Electronic Production Lines" . (2024) : 647-650 . |
| APA | Chen, Siping , Li, Debiao , Gan, Xiqin , Chiong, Raymond . An Optimised Light Gradient Boosting Machine Model for Setup Time Prediction in Electronic Production Lines . (2024) : 647-650 . |
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