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F. Yan
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7 records found
1
In the operation process of wet ball mill, there are often multi-modal and multi-condition problems. In this paper, a multi-view based domain adaptive extreme learning machine (MVDAELM) was used to measure the mill load. Firstly, the correlation relationship between the load parameters and the two views (vibration and acoustic signals of the ball mill) was obtained by Canonical Correlation Analysis (CCA) respectively. Secondly, a small number of labeled data from the target domain were introduced to construct a Domain Adaptation Extreme Learning Machine (DAELM) model under manifold constraints, which solve the mismatch problem caused by the change of working conditions in the multi-condition grinding process. Finally, based on the correlation coefficient obtained before, the two views domain adaptive load parameter soft sensor model was integrated to solve the uncertainty problem in single-modal data modeling. The experimental results show that the proposed method can effectively improve the learning accuracy of the soft sensor model under multi-modal conditions.
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In the operation process of wet ball mill, there are often multi-modal and multi-condition problems. In this paper, a multi-view based domain adaptive extreme learning machine (MVDAELM) was used to measure the mill load. Firstly, the correlation relationship between the load parameters and the two views (vibration and acoustic signals of the ball mill) was obtained by Canonical Correlation Analysis (CCA) respectively. Secondly, a small number of labeled data from the target domain were introduced to construct a Domain Adaptation Extreme Learning Machine (DAELM) model under manifold constraints, which solve the mismatch problem caused by the change of working conditions in the multi-condition grinding process. Finally, based on the correlation coefficient obtained before, the two views domain adaptive load parameter soft sensor model was integrated to solve the uncertainty problem in single-modal data modeling. The experimental results show that the proposed method can effectively improve the learning accuracy of the soft sensor model under multi-modal conditions.
Journal article
(2019)
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Fei Yan, Nikola Bešinović, Rob Goverde
This paper proposes a new multi-objective periodic railway timetabling (MOPRT) problem with four objectives to be minimized: train journey time, timetable regularity deviation, timetable vulnerability and the number of overtakings. The aim is to find an efficient, regular and robust timetable that utilizes the infrastructure capacity as good as possible. Based on the Periodic Event Scheduling Problem, we formulate the MOPRT problem as a Mixed Integer Linear Program (MILP). The ε-constraint method is applied to deal with the multi-objective property, and algorithms are designed to efficiently create the Pareto frontier. By solving the problem for different values of ε, the four-dimensional Pareto frontier is explored to uncover the trade-offs among the four objectives. The optimal solution is obtained from the Pareto-optimal set by using standardized Euclidean distance, while capacity utilization is used as an additional indicator to chose between close solutions. Computational experiments are performed on a theoretical instance and a real instance in one direction of a Dutch railway corridor, demonstrating the efficiency of the model and approach.
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This paper proposes a new multi-objective periodic railway timetabling (MOPRT) problem with four objectives to be minimized: train journey time, timetable regularity deviation, timetable vulnerability and the number of overtakings. The aim is to find an efficient, regular and robust timetable that utilizes the infrastructure capacity as good as possible. Based on the Periodic Event Scheduling Problem, we formulate the MOPRT problem as a Mixed Integer Linear Program (MILP). The ε-constraint method is applied to deal with the multi-objective property, and algorithms are designed to efficiently create the Pareto frontier. By solving the problem for different values of ε, the four-dimensional Pareto frontier is explored to uncover the trade-offs among the four objectives. The optimal solution is obtained from the Pareto-optimal set by using standardized Euclidean distance, while capacity utilization is used as an additional indicator to chose between close solutions. Computational experiments are performed on a theoretical instance and a real instance in one direction of a Dutch railway corridor, demonstrating the efficiency of the model and approach.
Journal article
(2019)
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Fei Yan, Rob M.P. Goverde
Rail systems have been developing rapidly in recent years aiming at satisfying the growing passenger demand and shortening passenger travel time. The line planning problem (LPP) and train timetabling problem (TTP) are two key issues at the strategic level and tactical level, laying the foundation of a high-level service quality for railway operation. In this paper, a multi-frequency LPP (MF-LPP) model and a multi-period TTP (MP-TTP) model are introduced for direct connections, with consideration of both periodic and aperiodic nature to meet strongly heterogeneous train services and reduce the capacity loss of train operating companies. A combined LPP and TTP method is designed considering timetable robustness, timetable regularity, and passenger travel time. For a given line pool, a multi-objective mixed integer linear programming model for the MF-LPP is formulated to obtain a line plan with multiple line frequencies by minimizing travel time, empty-seat-hour and the number of lines. Using the acquired line plan from the previous step, a MP-TTP model is proposed to achieve the minimal travel time, the maximal timetable robustness and the minimal number of overtakings. The two models work iteratively with designed feedback constraints to find a better plan for the rail transport system. Numerical experiments are applied to verify the performance of the proposed model and solution approach.
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Rail systems have been developing rapidly in recent years aiming at satisfying the growing passenger demand and shortening passenger travel time. The line planning problem (LPP) and train timetabling problem (TTP) are two key issues at the strategic level and tactical level, laying the foundation of a high-level service quality for railway operation. In this paper, a multi-frequency LPP (MF-LPP) model and a multi-period TTP (MP-TTP) model are introduced for direct connections, with consideration of both periodic and aperiodic nature to meet strongly heterogeneous train services and reduce the capacity loss of train operating companies. A combined LPP and TTP method is designed considering timetable robustness, timetable regularity, and passenger travel time. For a given line pool, a multi-objective mixed integer linear programming model for the MF-LPP is formulated to obtain a line plan with multiple line frequencies by minimizing travel time, empty-seat-hour and the number of lines. Using the acquired line plan from the previous step, a MP-TTP model is proposed to achieve the minimal travel time, the maximal timetable robustness and the minimal number of overtakings. The two models work iteratively with designed feedback constraints to find a better plan for the rail transport system. Numerical experiments are applied to verify the performance of the proposed model and solution approach.
A perishable food supply chain problem considering demand uncertainty and time deadline constraints
Modeling and application to a high-speed railway catering service
Journal article
(2018)
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Xin Wu, Lei Nie, Meng Xu, Fei Yan
This paper attempts to optimize the flow patterns in a perishable food supply chain network for a high-speed rail catering service. The proposed variational inequality models describe the uncertain demand on trains using the Newsvendor model and impose time deadline constraints on paths considering flow-dependent lead time. The constraints are then reformulated based on the Dirac delta function so that they can be directly dualized. An Euler algorithm with an Augmented Lagrangian Dual algorithm is developed to solve the model. A case study using 246 trains in the Beijing-Shanghai high-speed corridor is applied to demonstrate the applicability of the method.
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This paper attempts to optimize the flow patterns in a perishable food supply chain network for a high-speed rail catering service. The proposed variational inequality models describe the uncertain demand on trains using the Newsvendor model and impose time deadline constraints on paths considering flow-dependent lead time. The constraints are then reformulated based on the Dirac delta function so that they can be directly dualized. An Euler algorithm with an Augmented Lagrangian Dual algorithm is developed to solve the model. A case study using 246 trains in the Beijing-Shanghai high-speed corridor is applied to demonstrate the applicability of the method.
Journal article
(2018)
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Nadjla Ghaemi, Aurelius Zilko, Fei Yan, Oded Cats, Dorota Kurowicka, Rob Goverde
Disruptions such as rolling stock breakdown, signal failures, and accidents are recurrent events during daily railway operation. Such events disrupt the deployment of resources and cause delay to passengers. Obtaining a reliable disruption length estimation can potentially reduce the negative impact caused by the disruption. Different factors such as the location, cause of disruption, traffic density, etc. can determine the disruption length. The uncertainty inherent to the variability of each factor and the unavailability of sufficient data results in a wide distribution of disruption lengths from which a certain value should be selected as the length prediction. The rescheduling measure considered in this research is short-turning the trains that are heading to the disrupted area. To investigate the impact of the disruption length estimates on the rescheduling strategy and the resulting passengers delays, this research presents a framework consisting of three models: a disruption length model, short-turning model and passenger assignment model. The framework is applied to a part of the Dutch railway network. The results show the effects of short (optimistic) and long (pessimistic) estimates on the number of affected passengers, generalized travel time and number of passengers rerouting and transferring.
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Disruptions such as rolling stock breakdown, signal failures, and accidents are recurrent events during daily railway operation. Such events disrupt the deployment of resources and cause delay to passengers. Obtaining a reliable disruption length estimation can potentially reduce the negative impact caused by the disruption. Different factors such as the location, cause of disruption, traffic density, etc. can determine the disruption length. The uncertainty inherent to the variability of each factor and the unavailability of sufficient data results in a wide distribution of disruption lengths from which a certain value should be selected as the length prediction. The rescheduling measure considered in this research is short-turning the trains that are heading to the disrupted area. To investigate the impact of the disruption length estimates on the rescheduling strategy and the resulting passengers delays, this research presents a framework consisting of three models: a disruption length model, short-turning model and passenger assignment model. The framework is applied to a part of the Dutch railway network. The results show the effects of short (optimistic) and long (pessimistic) estimates on the number of affected passengers, generalized travel time and number of passengers rerouting and transferring.
Conference paper
(2017)
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Fei Yan, Rob M.P. Goverde
This paper focuses on optimizing the robustness of a timetable with multiple train lines of different frequencies, where overtakings are also taken into account. An optimization model is considered of a cyclic railway timetable problem where dwell times and running times are variable and overtaking is allowed for relevant stations and each line. Based on the Periodic Event Scheduling Problem, train journey time, robustness and the number of dwell time stretches (which decides whether a train can have overtakings) are proposed as objectives, with corresponding constraints included in the model. This approach is studied in a small network with six stations and proved to be efficient. Six model variants from a different combination of objectives and constraints are compared on robustness, for which a number of robustness indicators are defined.
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This paper focuses on optimizing the robustness of a timetable with multiple train lines of different frequencies, where overtakings are also taken into account. An optimization model is considered of a cyclic railway timetable problem where dwell times and running times are variable and overtaking is allowed for relevant stations and each line. Based on the Periodic Event Scheduling Problem, train journey time, robustness and the number of dwell time stretches (which decides whether a train can have overtakings) are proposed as objectives, with corresponding constraints included in the model. This approach is studied in a small network with six stations and proved to be efficient. Six model variants from a different combination of objectives and constraints are compared on robustness, for which a number of robustness indicators are defined.
To satisfy the growing passenger transportation demands and improve the service quality in a railway system, a high-quality line plan needs to be designed. Line planning is an initial optimization problem in the process of railway transportation management, which includes the origin and destination (OD) of trains, routes, stop patterns and frequencies. Aiming to a optimal line plan for a dense high-speed railway corridor, this paper proposes a optimization model with objectives of minimizing passenger's total travel time and empty-seat-hour. Considering the problem is NP-hard, we introduce a novel matheuristic approach that combines metaheuristic and mathematical programming technique. Genetic algorithm (GA) is developed for providing possible combination of frequencies, and integer linear program (ILP) is applied for optimization of passenger assignment model. With integration of both, we
produce a optimal line plan with frequencies. Finally numerical experiments of Chinese case are applied to verify the proposed model and approach.
...
To satisfy the growing passenger transportation demands and improve the service quality in a railway system, a high-quality line plan needs to be designed. Line planning is an initial optimization problem in the process of railway transportation management, which includes the origin and destination (OD) of trains, routes, stop patterns and frequencies. Aiming to a optimal line plan for a dense high-speed railway corridor, this paper proposes a optimization model with objectives of minimizing passenger's total travel time and empty-seat-hour. Considering the problem is NP-hard, we introduce a novel matheuristic approach that combines metaheuristic and mathematical programming technique. Genetic algorithm (GA) is developed for providing possible combination of frequencies, and integer linear program (ILP) is applied for optimization of passenger assignment model. With integration of both, we
produce a optimal line plan with frequencies. Finally numerical experiments of Chinese case are applied to verify the proposed model and approach.