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学者姓名：杨立熙
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Abstract ：
The recovery of a departure flight is an important part of airline disruption management. The commonly used optimization objectives for assessing airport slot-scheduling efficiency are delay cost and delay time. However, the current literature does not consider the preferences of airlines and passengers in the recovery process simultaneously. The objective of this paper is to focus on the departure slot reassignment problem and develop a bi-objective optimization model. We introduce a metric for the price of fairness and formulate the airport slot scheduling problem as a bi-objective optimization model that considers the trade-off between the total airline delay cost and total passenger delay time. To quantify the trade-off between total airline delay costs and total passenger delay time, we apply the metric of the price of fairness in the bi-objective model, treating the total airline delay cost as an efficiency metric and the total passenger delay time as a fairness metric, calculating the price of fairness for each Pareto solution. An adaptive non-dominated sorting genetic algorithm-II based on dominant strengths (ANSGA2-DS) is developed to solve this problem. More precisely, three improved operations are presented: the improved fast dominant strength sorting method, new crowding distance improvement, and an adaptive elitist retention strategy. Three scenarios derived from a Chinese airline's operation data are applied to the proposed bi-objective model and algorithm. The experimental findings demonstrate that the proposed model and method can effectively and efficiently address the problem. This may provide a basis for airline operation controllers to achieve a generally acceptable solution.
Keyword ：
airfield and airspace capacity and delay airfield and airspace capacity and delay airport operations airport operations air traffic management air traffic management aviation aviation optimization optimization
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| GB/T 7714 | Chen, Kejia , Wu, Juntao , Yang, Lixi . Reassigning Departure Slots with Preferences of the Airline and Passengers [J]. | TRANSPORTATION RESEARCH RECORD , 2023 , 2678 (3) : 786-804 . |
| MLA | Chen, Kejia 等. "Reassigning Departure Slots with Preferences of the Airline and Passengers" . | TRANSPORTATION RESEARCH RECORD 2678 . 3 (2023) : 786-804 . |
| APA | Chen, Kejia , Wu, Juntao , Yang, Lixi . Reassigning Departure Slots with Preferences of the Airline and Passengers . | TRANSPORTATION RESEARCH RECORD , 2023 , 2678 (3) , 786-804 . |
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Purpose: Flights are often delayed owing to emergencies. This paper proposes a cooperative slot secondary assignment (CSSA) model based on a collaborative decision-making (CDM) mechanism, and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time. Design/methodology/approach: Taking passenger delays, transfer delays and flight cancellation delays into account comprehensively, the total delay time is minimized as the objective function. The model is verified by a linear solver and compared with the first come first service (FCFS) method to prove the effectiveness of the method. An improved adaptive partheno-genetic algorithm (IAPGA) using hierarchical serial number coding was designed, combining elite and roulette strategies to find pareto solutions. Findings: Comparing and analyzing the experimental results of various scale examples, the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time, and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality. Originality/value: Based on the actual situation, this paper considers the operation mode of flight waves. In addition, the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness, which can provide airlines with reasonable decision-making opinions when reassigning slot resources. © 2022, Emerald Publishing Limited.
Keyword ：
Collaborative decision-making Collaborative decision-making Flight delay Flight delay Flight wave Flight wave Improved adaptive partheno-genetic algorithm Improved adaptive partheno-genetic algorithm Slot secondary assignment Slot secondary assignment
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| GB/T 7714 | Chen, K. , Chen, J. , Yang, L. et al. Collaborative slot secondary allocation based on flight wave operation [J]. | International Journal of Intelligent Computing and Cybernetics , 2023 , 16 (2) : 364-395 . |
| MLA | Chen, K. et al. "Collaborative slot secondary allocation based on flight wave operation" . | International Journal of Intelligent Computing and Cybernetics 16 . 2 (2023) : 364-395 . |
| APA | Chen, K. , Chen, J. , Yang, L. , Yang, X. . Collaborative slot secondary allocation based on flight wave operation . | International Journal of Intelligent Computing and Cybernetics , 2023 , 16 (2) , 364-395 . |
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价值流图是一种实施精益生产的工具,但是因价值流图不能详细描述生产系统的动态行为,导致其在精益生产应用方面存在局限性,通过结合仿真技术能够克服价值流图的静态局限性。针对D公司车载面板车间,首先运用价值流图识别车间存在的问题,并制定改善设计方案;接着,建立改善后的车间仿真模型,并以基本存储定量在制品控制策略(Base Stock CONWIP control strategy,BS-CONWIP)为生产控制策略,应用遗传算法对缓存区容量进行优化;最后,依据仿真运行数据与改善设计方案绘制出了未来状态价值流图,对比现在和未来状态价值流图,发现改善方案在简化车间生产流程的同时,生产周期、在制品库存等绩效...
Keyword ：
价值流图 价值流图 仿真技术 仿真技术 生产控制策略 生产控制策略 车载面板车间 车载面板车间
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| GB/T 7714 | 黄夏宝 , 龚丽云 , 杨立熙 . 基于价值流图的车载面板车间优化 [J]. | 现代制造工程 , 2021 , (02) : 8-15 . |
| MLA | 黄夏宝 et al. "基于价值流图的车载面板车间优化" . | 现代制造工程 02 (2021) : 8-15 . |
| APA | 黄夏宝 , 龚丽云 , 杨立熙 . 基于价值流图的车载面板车间优化 . | 现代制造工程 , 2021 , (02) , 8-15 . |
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为解决企业在不同生产类型下柔性作业车间分批调度的实际问题,对带有重入生产特性的印刷电路板组装(PCBA)调度进行研究.首先,构建以完工时间最小为目标的PCBA等量分批柔性作业车间调度优化模型.其次,提出并设计了一种遗传模拟退火算法来对分批调度模型进行求解,将整个求解过程分为订单批量分批、子批排程这两个阶段,并引入了Metropolis接受准则,以保证算法迭代初期种群的多样性.最后,以某企业PCBA车间的实例展开,运用MATLAB编程求解,验证了遗传模拟退火算法和等量分批的可行性和有效性,同时确定了各个批量生产类型的最优分批数.
Keyword ：
PCBA车间 PCBA车间 柔性作业车间分批调度 柔性作业车间分批调度 模拟退火算法 模拟退火算法 遗传算法 遗传算法
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| GB/T 7714 | 黄夏宝 , 杨立熙 , 傅光炎 . PCBA柔性作业车间等量分批调度问题研究 [J]. | 制造业自动化 , 2020 , 42 (9) : 89-94 . |
| MLA | 黄夏宝 et al. "PCBA柔性作业车间等量分批调度问题研究" . | 制造业自动化 42 . 9 (2020) : 89-94 . |
| APA | 黄夏宝 , 杨立熙 , 傅光炎 . PCBA柔性作业车间等量分批调度问题研究 . | 制造业自动化 , 2020 , 42 (9) , 89-94 . |
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Purpose: Flexible job-shop scheduling is significant for different manufacturing industries nowadays. Moreover, consideration of transportation time during scheduling makes it more practical and useful. The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem (MOFJSP) considering transportation time. Design/methodology/approach: A hybrid genetic algorithm (GA) approach is integrated with simulated annealing to solve the MOFJSP considering transportation time, and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance. Findings: The performance of the proposed algorithm is tested on different MOFJSP taken from literature. Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution, especially when the number of jobs and the flexibility of the machine increase. Originality/value: Most of existing studies have not considered the transportation time during scheduling of jobs. The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs. Meanwhile, GA is one of primary algorithms extensively used to address MOFJSP in literature. However, to solve the MOFJSP, the original GA has a possibility to get a premature convergence and it has a slow convergence speed. To overcome these problems, a new hybrid GA is developed in this paper. © 2019, Emerald Publishing Limited.
Keyword ：
Genetic algorithms Genetic algorithms Job shop scheduling Job shop scheduling Multiobjective optimization Multiobjective optimization Scheduling Scheduling Simulated annealing Simulated annealing
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| GB/T 7714 | Huang, Xiabao , Yang, Lixi . A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time [J]. | International Journal of Intelligent Computing and Cybernetics , 2019 , 12 (2) : 154-174 . |
| MLA | Huang, Xiabao et al. "A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time" . | International Journal of Intelligent Computing and Cybernetics 12 . 2 (2019) : 154-174 . |
| APA | Huang, Xiabao , Yang, Lixi . A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time . | International Journal of Intelligent Computing and Cybernetics , 2019 , 12 (2) , 154-174 . |
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Purpose: The purpose of this paper is to propose a grey clustering evaluation model based on analytic hierarchy process (AHP) and interval grey number (IGN) to solve the clustering evaluation problem with IGNs. Design/methodology/approach: First, the centre-point triangular whitenisation weight function with real numbers is built, and then by using interval mean function, the whitenisation weight function is extended to IGNs. The weights of evaluation indexes are determined by AHP. Finally, this model is used to evaluate the flight safety of a Chinese airline. The results indicate that the model is effective and reasonable. Findings: When IGN meets certain conditions, the centre-point triangular whitenisation weight function based on IGN is not multiple-cross and it is normative. It provides a certain standard and basis for obtaining the effective evaluation indexes and determining the scientific evaluation of the grey class. Originality/value: The traditional grey clustering model is extended to the field of IGN. It can make full use of all the information of the IGN, so the result of the evaluation is more objective and reasonable, which provides supports for solving practical problems. © 2019, Emerald Publishing Limited.
Keyword ：
Analytic hierarchy process Analytic hierarchy process Function evaluation Function evaluation Hierarchical systems Hierarchical systems
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| GB/T 7714 | Chen, Kejia , Chen, Ping , Yang, Lixi et al. Grey clustering evaluation based on AHP and interval grey number [J]. | International Journal of Intelligent Computing and Cybernetics , 2019 , 12 (1) : 127-137 . |
| MLA | Chen, Kejia et al. "Grey clustering evaluation based on AHP and interval grey number" . | International Journal of Intelligent Computing and Cybernetics 12 . 1 (2019) : 127-137 . |
| APA | Chen, Kejia , Chen, Ping , Yang, Lixi , Jin, Lian . Grey clustering evaluation based on AHP and interval grey number . | International Journal of Intelligent Computing and Cybernetics , 2019 , 12 (1) , 127-137 . |
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Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into genetic algorithm, and therefore, during the process of evolution, the offspring in genetic algorithm can learn the characteristics of elite chromosomes from the bi-memory learning. For solving multi-objective flexible job-shop scheduling problem, this study proposes a discrete particle swarm optimization algorithm. The population is partitioned into two subpopulations for genetic algorithm module and particle swarm optimization module. These two algorithms simultaneously search for solutions in their own subpopulations and exchange the information between these two subpopulations, such that both algorithms can complement each other with advantages. The proposed algorithm is evaluated on some instances, and experimental results demonstrate that the proposed algorithm is an effective method for multi-objective flexible job-shop scheduling problem.
Keyword ：
Flexible job-shop scheduling problem Flexible job-shop scheduling problem genetic algorithm genetic algorithm hybrid algorithm hybrid algorithm multi-objective optimization multi-objective optimization particle swarm optimization particle swarm optimization
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| GB/T 7714 | Huang, Xiabao , Guan, Zailin , Yang, Lixi . An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem [J]. | ADVANCES IN MECHANICAL ENGINEERING , 2018 , 10 (9) . |
| MLA | Huang, Xiabao et al. "An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem" . | ADVANCES IN MECHANICAL ENGINEERING 10 . 9 (2018) . |
| APA | Huang, Xiabao , Guan, Zailin , Yang, Lixi . An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem . | ADVANCES IN MECHANICAL ENGINEERING , 2018 , 10 (9) . |
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针对面向绿色制造的车间调度问题,将低碳因素作为影响因子考虑到模型中,构建低碳生产下的多目标柔性作业车间调度模型,并提出改进的免疫遗传算法求解模型.算法改进初始种群的形成机制,以提高收敛速度和改善求解质量,采用合理的选择策略机制、交叉和变异方式,结合记忆库设计熵值移除法筛选Pareto解集,以提高算法的搜索能力和避免算法陷入早熟.运用MATLAB编程运算实例,实验结果表明,该方法能够有效地解决了绿色制造中低碳的多目标调度优化问题.
Keyword ：
低碳 低碳 免疫算法 免疫算法 柔性作业车间调度 柔性作业车间调度 遗传算法 遗传算法
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| GB/T 7714 | 杨立熙 , 王秀萍 . 考虑低碳的柔性作业车间调度问题研究 [J]. | 组合机床与自动化加工技术 , 2018 , (6) : 168-171,176 . |
| MLA | 杨立熙 et al. "考虑低碳的柔性作业车间调度问题研究" . | 组合机床与自动化加工技术 6 (2018) : 168-171,176 . |
| APA | 杨立熙 , 王秀萍 . 考虑低碳的柔性作业车间调度问题研究 . | 组合机床与自动化加工技术 , 2018 , (6) , 168-171,176 . |
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针对柔性作业车间调度问题,提出一种改进的免疫遗传混合算法.该算法使用新的初始化方式产生初始解,然后设计合理的自适应交叉变异机制产生新抗体,再通过免疫接种和免疫记忆等操作优化新的抗体,将免疫算法和遗传算法相结合可以提高算法的全局搜索能力,避免遗传算法陷入早熟收敛的问题.最后使用MATLAB编程求解基准算例来验证所提算法的有效性,再将算法应用于其他文献中实际生产企业的车间调度问题,得到更多的最优解且结果更优,证明了该算法的实用性.
Keyword ：
免疫 免疫 柔性作业车间调度 柔性作业车间调度 自适应 自适应 遗传 遗传
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| GB/T 7714 | 杨立熙 , 王秀萍 . 基于免疫遗传算法求解多目标柔性作业调度问题 [J]. | 武汉理工大学学报（信息与管理工程版） , 2018 , 40 (1) : 69-74 . |
| MLA | 杨立熙 et al. "基于免疫遗传算法求解多目标柔性作业调度问题" . | 武汉理工大学学报（信息与管理工程版） 40 . 1 (2018) : 69-74 . |
| APA | 杨立熙 , 王秀萍 . 基于免疫遗传算法求解多目标柔性作业调度问题 . | 武汉理工大学学报（信息与管理工程版） , 2018 , 40 (1) , 69-74 . |
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在实际加工生产中,工序间的运输时间占整个加工时间的比例很大.为了更合理地研究柔性作业车间调度问题,将运输时间作为独立影响因子考虑到模型中.针对模型的特殊性与传统遗传算法易早熟的缺陷,运用小生境的思想和自适应距离变量划分种群并协同进化,进而平衡种群选择压力,避免算法过早收敛至局部最优.同时提出最短工作时间方法优化初始种群,改善算法的求解效率.运用Matlab对国际上通用的算例进行实验并与经典遗传算法对比,结果表明,新提出的算法能够获得更优的调度方案.
Keyword ：
小生境 小生境 柔性作业车间调度 柔性作业车间调度 运输时间 运输时间 选择压力 选择压力 遗传算法 遗传算法
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| GB/T 7714 | 杨立熙 , 余慧慧 . 考虑运输时间的柔性作业车间调度问题研究 [J]. | 武汉理工大学学报（信息与管理工程版） , 2017 , 39 (1) : 104-109 . |
| MLA | 杨立熙 et al. "考虑运输时间的柔性作业车间调度问题研究" . | 武汉理工大学学报（信息与管理工程版） 39 . 1 (2017) : 104-109 . |
| APA | 杨立熙 , 余慧慧 . 考虑运输时间的柔性作业车间调度问题研究 . | 武汉理工大学学报（信息与管理工程版） , 2017 , 39 (1) , 104-109 . |
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