Robust Optimization of Customized Electric Bus Routes in Village-town Areas
Village-town passenger transportation resources have a low utilization rate, and most passengers cannot achieve their riding purposes within a reasonable time period. Considering the impact of fuel vehicles on the environment and cost, electric buses responsive to urban and rural demand are presente...
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| Veröffentlicht in: | Engineering letters 2023-05, Vol.31 (2), p.743 |
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| creator | Jiao, Yuduan Zhao, Yongpeng Ma, Changxi |
| description | Village-town passenger transportation resources have a low utilization rate, and most passengers cannot achieve their riding purposes within a reasonable time period. Considering the impact of fuel vehicles on the environment and cost, electric buses responsive to urban and rural demand are presented as a solution to these problems in this study: passenger demand information is collected and used to formulate a realistic optimization problem in which both the total cost of the bus company and the total travel time of passengers, are minimized. That is, a robust optimization model for the routes of urban and rural customized electric buses is constructed to meet passenger ride demand using the vehicle travel time on the road section as an uncertainty value. Passenger stations are numbered by using the improved NSGA-II algorithm; natural number coding, a simulated annealing fitness function, the order crossover method, and a specially designed mutation operation are also used. Considering practical scenarios, the times for getting on and off the bus are added to the total travel time of passengers to verify the rationality of the model and algorithm. The results show that using the proposed improved NSGA-II algorithm (improved fast non-dominated multi-objective optimization algorithm with elite retention strategy) reduces the average travel time was by 14.47% and the computing time by 39.81% compared to using the multi-objective genetic algorithm NPGA. For route optimization of passenger demand-responsive electric buses in village-town areas, the proposed improved NSGA-II algorithm produces more accurate solutions with a higher efficiency and performance than the multi-objective genetic algorithm NPGA. To minimize the cost of passenger buses, the total travel time of passengers should be reduced to the largest possible extent. This consideration is extremely important for optimizing long-distance passenger routes of demand-responsive electric buses in village-town areas. |
| format | Article |
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Considering the impact of fuel vehicles on the environment and cost, electric buses responsive to urban and rural demand are presented as a solution to these problems in this study: passenger demand information is collected and used to formulate a realistic optimization problem in which both the total cost of the bus company and the total travel time of passengers, are minimized. That is, a robust optimization model for the routes of urban and rural customized electric buses is constructed to meet passenger ride demand using the vehicle travel time on the road section as an uncertainty value. Passenger stations are numbered by using the improved NSGA-II algorithm; natural number coding, a simulated annealing fitness function, the order crossover method, and a specially designed mutation operation are also used. Considering practical scenarios, the times for getting on and off the bus are added to the total travel time of passengers to verify the rationality of the model and algorithm. The results show that using the proposed improved NSGA-II algorithm (improved fast non-dominated multi-objective optimization algorithm with elite retention strategy) reduces the average travel time was by 14.47% and the computing time by 39.81% compared to using the multi-objective genetic algorithm NPGA. For route optimization of passenger demand-responsive electric buses in village-town areas, the proposed improved NSGA-II algorithm produces more accurate solutions with a higher efficiency and performance than the multi-objective genetic algorithm NPGA. To minimize the cost of passenger buses, the total travel time of passengers should be reduced to the largest possible extent. This consideration is extremely important for optimizing long-distance passenger routes of demand-responsive electric buses in village-town areas.</description><identifier>ISSN: 1816-093X</identifier><identifier>EISSN: 1816-0948</identifier><language>eng</language><publisher>Hong Kong: International Association of Engineers</publisher><subject>Buses ; Buses (vehicles) ; Computing time ; Customization ; Genetic algorithms ; Multiple objective analysis ; Optimization models ; Passengers ; Robustness ; Route optimization ; Simulated annealing ; Towns ; Travel demand ; Travel time ; Villages</subject><ispartof>Engineering letters, 2023-05, Vol.31 (2), p.743</ispartof><rights>Copyright International Association of Engineers May 23, 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781</link.rule.ids></links><search><creatorcontrib>Jiao, Yuduan</creatorcontrib><creatorcontrib>Zhao, Yongpeng</creatorcontrib><creatorcontrib>Ma, Changxi</creatorcontrib><title>Robust Optimization of Customized Electric Bus Routes in Village-town Areas</title><title>Engineering letters</title><description>Village-town passenger transportation resources have a low utilization rate, and most passengers cannot achieve their riding purposes within a reasonable time period. Considering the impact of fuel vehicles on the environment and cost, electric buses responsive to urban and rural demand are presented as a solution to these problems in this study: passenger demand information is collected and used to formulate a realistic optimization problem in which both the total cost of the bus company and the total travel time of passengers, are minimized. That is, a robust optimization model for the routes of urban and rural customized electric buses is constructed to meet passenger ride demand using the vehicle travel time on the road section as an uncertainty value. Passenger stations are numbered by using the improved NSGA-II algorithm; natural number coding, a simulated annealing fitness function, the order crossover method, and a specially designed mutation operation are also used. Considering practical scenarios, the times for getting on and off the bus are added to the total travel time of passengers to verify the rationality of the model and algorithm. The results show that using the proposed improved NSGA-II algorithm (improved fast non-dominated multi-objective optimization algorithm with elite retention strategy) reduces the average travel time was by 14.47% and the computing time by 39.81% compared to using the multi-objective genetic algorithm NPGA. For route optimization of passenger demand-responsive electric buses in village-town areas, the proposed improved NSGA-II algorithm produces more accurate solutions with a higher efficiency and performance than the multi-objective genetic algorithm NPGA. To minimize the cost of passenger buses, the total travel time of passengers should be reduced to the largest possible extent. This consideration is extremely important for optimizing long-distance passenger routes of demand-responsive electric buses in village-town areas.</description><subject>Buses</subject><subject>Buses (vehicles)</subject><subject>Computing time</subject><subject>Customization</subject><subject>Genetic algorithms</subject><subject>Multiple objective analysis</subject><subject>Optimization models</subject><subject>Passengers</subject><subject>Robustness</subject><subject>Route optimization</subject><subject>Simulated annealing</subject><subject>Towns</subject><subject>Travel demand</subject><subject>Travel time</subject><subject>Villages</subject><issn>1816-093X</issn><issn>1816-0948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNo9jc1KxDAURoMoOIzzDgHXgZukaW-WYxl_cGBgUHE3pEkqkdrUJkHw6S0ors7HWXznjKw48pqBrvD8f8vXS7JJKXRQVY1UGtSKPB5jV1KmhymHj_BtcogjjT1tFxkX4R3dDd7mOVh6UxI9xpJ9omGkL2EYzJtnOX6NdDt7k67IRW-G5Dd_XJPn291Te8_2h7uHdrtnE0eZGaKsjVDKGcReOd1oK_te2ZrXyjrQ4K0WygNY7Y1rsEEA2ThuXdWZ2li5Jte_v9McP4tP-fQeyzwuyZNAjlpVIIT8AZAeS28</recordid><startdate>20230523</startdate><enddate>20230523</enddate><creator>Jiao, Yuduan</creator><creator>Zhao, Yongpeng</creator><creator>Ma, Changxi</creator><general>International Association of Engineers</general><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20230523</creationdate><title>Robust Optimization of Customized Electric Bus Routes in Village-town Areas</title><author>Jiao, Yuduan ; Zhao, Yongpeng ; Ma, Changxi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p183t-8836a255da88f5d979c3ff5c6165cd090ec925e00c9ead78780037d1cd4ba6ac3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Buses</topic><topic>Buses (vehicles)</topic><topic>Computing time</topic><topic>Customization</topic><topic>Genetic algorithms</topic><topic>Multiple objective analysis</topic><topic>Optimization models</topic><topic>Passengers</topic><topic>Robustness</topic><topic>Route optimization</topic><topic>Simulated annealing</topic><topic>Towns</topic><topic>Travel demand</topic><topic>Travel time</topic><topic>Villages</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiao, Yuduan</creatorcontrib><creatorcontrib>Zhao, Yongpeng</creatorcontrib><creatorcontrib>Ma, Changxi</creatorcontrib><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Engineering letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiao, Yuduan</au><au>Zhao, Yongpeng</au><au>Ma, Changxi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robust Optimization of Customized Electric Bus Routes in Village-town Areas</atitle><jtitle>Engineering letters</jtitle><date>2023-05-23</date><risdate>2023</risdate><volume>31</volume><issue>2</issue><spage>743</spage><pages>743-</pages><issn>1816-093X</issn><eissn>1816-0948</eissn><abstract>Village-town passenger transportation resources have a low utilization rate, and most passengers cannot achieve their riding purposes within a reasonable time period. Considering the impact of fuel vehicles on the environment and cost, electric buses responsive to urban and rural demand are presented as a solution to these problems in this study: passenger demand information is collected and used to formulate a realistic optimization problem in which both the total cost of the bus company and the total travel time of passengers, are minimized. That is, a robust optimization model for the routes of urban and rural customized electric buses is constructed to meet passenger ride demand using the vehicle travel time on the road section as an uncertainty value. Passenger stations are numbered by using the improved NSGA-II algorithm; natural number coding, a simulated annealing fitness function, the order crossover method, and a specially designed mutation operation are also used. Considering practical scenarios, the times for getting on and off the bus are added to the total travel time of passengers to verify the rationality of the model and algorithm. The results show that using the proposed improved NSGA-II algorithm (improved fast non-dominated multi-objective optimization algorithm with elite retention strategy) reduces the average travel time was by 14.47% and the computing time by 39.81% compared to using the multi-objective genetic algorithm NPGA. For route optimization of passenger demand-responsive electric buses in village-town areas, the proposed improved NSGA-II algorithm produces more accurate solutions with a higher efficiency and performance than the multi-objective genetic algorithm NPGA. To minimize the cost of passenger buses, the total travel time of passengers should be reduced to the largest possible extent. This consideration is extremely important for optimizing long-distance passenger routes of demand-responsive electric buses in village-town areas.</abstract><cop>Hong Kong</cop><pub>International Association of Engineers</pub></addata></record> |
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| ispartof | Engineering letters, 2023-05, Vol.31 (2), p.743 |
| issn | 1816-093X 1816-0948 |
| language | eng |
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| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Buses Buses (vehicles) Computing time Customization Genetic algorithms Multiple objective analysis Optimization models Passengers Robustness Route optimization Simulated annealing Towns Travel demand Travel time Villages |
| title | Robust Optimization of Customized Electric Bus Routes in Village-town Areas |
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