Multi-Objective Stochastic Synchronous Timetable Optimization Model Based on a Chance-Constrained Programming Method Combined with Augmented Epsilon Constraint Algorithm

The design of the timetable is essential to improve the service quality of the public transport system. A lot of random factors in the actual operation environment will affect the implementation of the synchronous timetable, and adjusting timetables to improve synchronization will break the order of...

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Published inMathematical problems in engineering Vol. 2022; pp. 1 - 18
Main Authors Yuan, Yu, Wang, Pengcheng, Wang, Minghui
Format Journal Article
LanguageEnglish
Published New York Hindawi 28.08.2022
John Wiley & Sons, Inc
Subjects
Algorithms
Buses (vehicles)
Collaboration
Commuting
Constraint modelling
Engineering
Integer programming
Linear programming
Multiple objective analysis
Normal distribution
Operating costs
Optimization
Optimization models
Pareto optimization
Passengers
Planning
Public transportation
Quality of service
Random variables
Randomness
Scheduling
Sensitivity analysis
Stochastic programming
Synchronism
Timetables
Transportation services
Transportation systems
Travel time
Online AccessGet full text
ISSN1024-123X
1026-7077
1563-5147
1563-5147
DOI10.1155/2022/9222636

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Abstract The design of the timetable is essential to improve the service quality of the public transport system. A lot of random factors in the actual operation environment will affect the implementation of the synchronous timetable, and adjusting timetables to improve synchronization will break the order of normal service and increase the cost of operation. A multi-objective bus timetable optimization problem is characterized by considering the randomness of vehicle travel time and passenger transfer demand. A multi-objective optimization model is proposed, aiming at minimizing the total waiting time of passengers in the whole bus network and the inconsistency between the timetable after synchronous optimization and the original timetable. Through large sample analysis, it is found that the random variables in the model obey normal distribution, so the stochastic programming problem is transformed into the traditional deterministic programming problem by the chance-constrained programming method. A model solving method based on the augmented epsilon-constraint algorithm is designed. Examples show that when the random variables are considered, the proposed algorithm can obtain multiple high-quality Pareto optimal solutions in a short time, which can provide more practical benefits for decisionmakers. Ignoring the random influence will reduce the effectiveness of the schedule optimization scheme. Finally, sensitivity analysis of random variables and constraint confidence in the model is made.
AbstractList The design of the timetable is essential to improve the service quality of the public transport system. A lot of random factors in the actual operation environment will affect the implementation of the synchronous timetable, and adjusting timetables to improve synchronization will break the order of normal service and increase the cost of operation. A multi-objective bus timetable optimization problem is characterized by considering the randomness of vehicle travel time and passenger transfer demand. A multi-objective optimization model is proposed, aiming at minimizing the total waiting time of passengers in the whole bus network and the inconsistency between the timetable after synchronous optimization and the original timetable. Through large sample analysis, it is found that the random variables in the model obey normal distribution, so the stochastic programming problem is transformed into the traditional deterministic programming problem by the chance-constrained programming method. A model solving method based on the augmented epsilon-constraint algorithm is designed. Examples show that when the random variables are considered, the proposed algorithm can obtain multiple high-quality Pareto optimal solutions in a short time, which can provide more practical benefits for decisionmakers. Ignoring the random influence will reduce the effectiveness of the schedule optimization scheme. Finally, sensitivity analysis of random variables and constraint confidence in the model is made.
Author Wang, Minghui
Yuan, Yu
Wang, Pengcheng
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ContentType Journal Article
Copyright Copyright © 2022 Yu Yuan et al.
Copyright © 2022 Yu Yuan et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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SubjectTerms Algorithms
Buses (vehicles)
Collaboration
Commuting
Constraint modelling
Engineering
Integer programming
Linear programming
Multiple objective analysis
Normal distribution
Operating costs
Optimization
Optimization models
Pareto optimization
Passengers
Planning
Public transportation
Quality of service
Random variables
Randomness
Scheduling
Sensitivity analysis
Stochastic programming
Synchronism
Timetables
Transportation services
Transportation systems
Travel time
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Title Multi-Objective Stochastic Synchronous Timetable Optimization Model Based on a Chance-Constrained Programming Method Combined with Augmented Epsilon Constraint Algorithm
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