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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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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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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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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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Demand fluctuates in actual production. When manufacturers face demand under their maximum capacity, suspension shifts are crucial for cost reduction and on-time delivery. In this case, suspension shifts are needed to minimize idle time and prevent inventory buildup. Thus, it is essential to integrate suspension shifts with scheduling under an uncertain production environment. This paper addresses the two-stage hybrid flow shop scheduling problem (THFSP) with suspension shifts under uncertain processing times, aiming to minimize the weighted sum of earliness and tardiness. We develop a stochastic integer programming model and validate it using the Gurobi solver. Additionally, we propose a dual-space co-evolutionary biased random key genetic algorithm (DCE-BRKGA) with parallel evolution of solutions and scenarios. Considering decision-makers' risk preferences, we use both average and pessimistic criteria for fitness evaluation, generating two types of solutions and scenario populations. Testing with 28 datasets, we use the value of the stochastic solution (VSS) and the expected value of perfect information (EVPI) to quantify benefits. Compared to the average scenario, the VSS shows that the proposed algorithm achieves additional value gains of 0.9% to 69.9%. Furthermore, the EVPI indicates that after eliminating uncertainty, the algorithm yields potential improvements of 2.4% to 20.3%. These findings indicate that DCE-BRKGA effectively supports varying decision-making risk preferences, providing robust solutions even without known processing time distributions.
Keyword ：
biased random key genetic algorithm biased random key genetic algorithm co-evolutionary co-evolutionary scheduling scheduling suspension shifts suspension shifts uncertain processing times uncertain processing times
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| GB/T 7714 | Huang, Zhijie , Huang, Lin , Li, Debiao . Co-Evolutionary Algorithm for Two-Stage Hybrid Flow Shop Scheduling Problem with Suspension Shifts [J]. | MATHEMATICS , 2024 , 12 (16) . |
| MLA | Huang, Zhijie et al. "Co-Evolutionary Algorithm for Two-Stage Hybrid Flow Shop Scheduling Problem with Suspension Shifts" . | MATHEMATICS 12 . 16 (2024) . |
| APA | Huang, Zhijie , Huang, Lin , Li, Debiao . Co-Evolutionary Algorithm for Two-Stage Hybrid Flow Shop Scheduling Problem with Suspension Shifts . | MATHEMATICS , 2024 , 12 (16) . |
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Fabric dyeing is the most time-consuming and energy-intensive process in textile production with some batch processing machines (BPMs) and uncertainty. In this study, a fuzzy energy-efficient parallel BPMs scheduling problem (FEPBSP) with machine eligibility and sequence-dependent setup time (SDST) in fabric dyeing process is investigated, and a dynamical teaching-learning-based optimization algorithm (DTLBO) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and total fuzzy energy consumption. In DTLBO, multiple classes are constructed by non-dominated sorting. Dynamical class evolution is designed, which incorporates diversified search among students and adaptive self-learning of teachers. The former is implemented using various combinations of the teacher phase and the learner phase, and the latter is achieved through teacher quality and an adaptive threshold. Additionally, a reinforcement local search based on neighborhood structure dynamic selection is also applied. Extensive experiments are conducted, and the computational results demonstrated that the new strategies of DTLBO are effective, and it is highly competitive in solving the considered problem.
Keyword ：
Batch processing machine Batch processing machine Fabric dyeing process Fabric dyeing process Fuzzy energy-efficient scheduling Fuzzy energy-efficient scheduling Teaching-learning-based optimization Teaching-learning-based optimization
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| GB/T 7714 | Wang, Jing , Li, Debiao , Tang, Hongtao et al. A dynamical teaching-learning-based optimization algorithm for fuzzy energy-efficient parallel batch processing machines scheduling in fabric dyeing process [J]. | APPLIED SOFT COMPUTING , 2024 , 167 . |
| MLA | Wang, Jing et al. "A dynamical teaching-learning-based optimization algorithm for fuzzy energy-efficient parallel batch processing machines scheduling in fabric dyeing process" . | APPLIED SOFT COMPUTING 167 (2024) . |
| APA | Wang, Jing , Li, Debiao , Tang, Hongtao , Li, Xixing , Lei, Deming . A dynamical teaching-learning-based optimization algorithm for fuzzy energy-efficient parallel batch processing machines scheduling in fabric dyeing process . | APPLIED SOFT COMPUTING , 2024 , 167 . |
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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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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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It is challenging for a patient without medical knowledge to select a `capable' physician based on nontransparent medical information. The situation becomes pervasive in specialty care. Motivated by this existing patient-physician matching problem, we propose a novel physician matching index (PMI) obtained by an analytical framework integrated with an improved multi-disease pre-diagnosing Bayesian network (BN) model. The pre-diagnosis BN structure learning is critical since it provides the causal map among diseases and symptoms, but it has been proved to be NP-hard. To improve the computational tractability of the BN structure learning, we propose a dynamic programming based cache calculation algorithm integrated with expert knowledge. The optimal BN structure is obtained through an improved branch-and-bound algorithm. Given patients' symptoms and physicians' specialty information, we apply the trained pre-diagnosis BN model to obtain PMI, which can be extended to the weighted matching index by considering patient preferences. A case study of the patient-physician matching problem in the ear, nose, and throat (ENT) department is conducted. The branchand-bound algorithm with the proposed cache calculation algorithm learns the optimal BN structure with high pre-diagnosing accuracy and time efficiency. We disclose that the proposed PMI can rectify the misdiagnosis since the highly related diseases usually belong to one specialty. Moreover, we demonstrate the significance of the consistency between the physicians' specialty and the patients' disease distribution. We also highlight that the proposed PMI guides the patients in choosing physicians more appropriately under independent patient preferences.
Keyword ：
Bayesian network Bayesian network branch and bound algorithm branch and bound algorithm expert knowledge expert knowledge Patient-physician matching Patient-physician matching
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| GB/T 7714 | Li, Debiao , Chen, Xiaoqiang , Chen, Siping . Learning and Optimization of Patient-Physician Matching Index in Specialty Care [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2023 . |
| MLA | Li, Debiao et al. "Learning and Optimization of Patient-Physician Matching Index in Specialty Care" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2023) . |
| APA | Li, Debiao , Chen, Xiaoqiang , Chen, Siping . Learning and Optimization of Patient-Physician Matching Index in Specialty Care . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2023 . |
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In open-pit mining, a trade-off between determination of appropriate sizes of mining jobs and optimisation of allocating and sequencing mining equipment units at each operational stage is one of critical decisions for mining practitioners. To simultaneously optimise the above data-driven interplay between planning and scheduling decisions in multi-stage mine production timetabling, we introduce a novel integrated-planning-scheduling problem for considering the disturbances and variability of jobs' sizes based on the theory of parallel-machine flow shop scheduling with lot streaming. This new problem is called the "Multi-stage Mine Production Timetabling with Optimising the Sizes of Mining Operations " and abbreviated as the MMPT-OSMO, in which the sizes of mining jobs (i.e., the number of block units to be aggregated on different working benches) are considered as planning-type variables and integrated with scheduling-type variables in a parallel-machine flow shop scheduling system. Due to considerable complexity, an innovative math-heuristic approach embodied as a hybridisation of decomposed mixed integer programming models and heuristic algorithms under a three-level divide-&-conquer scheme is devised to efficiently solve the MMPT-OSMO. By integrating both planning and scheduling decision variables in such a solitary problem, the MMPT-OSMO intrinsically characterises the potential to significantly improve mining productivity, which is validated by theoretical analysis and extensive computational experiments. In real-world implementation, replacing the current labour-intensive manual way, the proposed MMPT-OSMO methodology provides an intelligent decision-making tool to mathematically optimise the interactive decisions between mine planning and scheduling engineers. The proposed MMPT-OSMO methodology would make a breakthrough in the field of mining optimisation, as it contributes to extend mathematical modelling boundary by applying continuous-time machine scheduling theory to operational-level mining optimisation in theory and to help mining practitioners improve the production throughput using lot-streaming techniques in practice.
Keyword ：
Integrated-planning-scheduling Integrated-planning-scheduling Lot streaming Lot streaming Math-heuristic approach Math-heuristic approach Mining optimisation Mining optimisation Parallel-machine flow shop scheduling Parallel-machine flow shop scheduling
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| GB/T 7714 | Liu, Shi Qiang , Kozan, Erhan , Masoud, Mahmoud et al. Multi-stage mine production timetabling with optimising the sizes of mining operations: an application of parallel-machine flow shop scheduling with lot streaming [J]. | ANNALS OF OPERATIONS RESEARCH , 2022 . |
| MLA | Liu, Shi Qiang et al. "Multi-stage mine production timetabling with optimising the sizes of mining operations: an application of parallel-machine flow shop scheduling with lot streaming" . | ANNALS OF OPERATIONS RESEARCH (2022) . |
| APA | Liu, Shi Qiang , Kozan, Erhan , Masoud, Mahmoud , Li, Debiao , Luo, Kai . Multi-stage mine production timetabling with optimising the sizes of mining operations: an application of parallel-machine flow shop scheduling with lot streaming . | ANNALS OF OPERATIONS RESEARCH , 2022 . |
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