A genetic algorithm with exact dynamic programming for the green vehicle routing & scheduling problem
Traffic congestion significantly increases CO2 (a well-known greenhouse gas) emissions of vehicles in road transportation and causes other environmental costs as well. A road-based delivery company can reduce its CO2 emissions through operational decisions such as efficient vehicle routes and delive...
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| Published in | Journal of cleaner production Vol. 167; pp. 1450 - 1463 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
20.11.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0959-6526 1879-1786 |
| DOI | 10.1016/j.jclepro.2016.11.115 |
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| Abstract | Traffic congestion significantly increases CO2 (a well-known greenhouse gas) emissions of vehicles in road transportation and causes other environmental costs as well. A road-based delivery company can reduce its CO2 emissions through operational decisions such as efficient vehicle routes and delivery schedules by considering time-varying traffic congestion in its service area. In this paper, we study the time-dependent vehicle routing & scheduling problem with CO2 emissions optimization (TD-VRSP-CO2) and develop an exact dynamic programming algorithm to determine the optimal vehicle schedules for given vehicle routes. A hybrid solution approach that combines a genetic algorithm with the exact dynamic programming procedure (GA-DP) is proposed as an efficient solution approach for the TD-VRSP-CO2. Computational experiments on 30 small-sized instances and 14 large-sized instances are used to study the efficiency and effectiveness of the proposed hybrid optimization approach with promising results. Contributions of this study can help road-based delivery companies be ready for a low-carbon economy and also help individual vehicle drivers make better vehicle scheduling plans with lower CO2 emissions and fuel consumption. |
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| AbstractList | Traffic congestion significantly increases CO2 (a well-known greenhouse gas) emissions of vehicles in road transportation and causes other environmental costs as well. A road-based delivery company can reduce its CO2 emissions through operational decisions such as efficient vehicle routes and delivery schedules by considering time-varying traffic congestion in its service area. In this paper, we study the time-dependent vehicle routing & scheduling problem with CO2 emissions optimization (TD-VRSP-CO2) and develop an exact dynamic programming algorithm to determine the optimal vehicle schedules for given vehicle routes. A hybrid solution approach that combines a genetic algorithm with the exact dynamic programming procedure (GA-DP) is proposed as an efficient solution approach for the TD-VRSP-CO2. Computational experiments on 30 small-sized instances and 14 large-sized instances are used to study the efficiency and effectiveness of the proposed hybrid optimization approach with promising results. Contributions of this study can help road-based delivery companies be ready for a low-carbon economy and also help individual vehicle drivers make better vehicle scheduling plans with lower CO2 emissions and fuel consumption. Traffic congestion significantly increases CO₂ (a well-known greenhouse gas) emissions of vehicles in road transportation and causes other environmental costs as well. A road-based delivery company can reduce its CO₂ emissions through operational decisions such as efficient vehicle routes and delivery schedules by considering time-varying traffic congestion in its service area. In this paper, we study the time-dependent vehicle routing & scheduling problem with CO₂ emissions optimization (TD-VRSP-CO₂) and develop an exact dynamic programming algorithm to determine the optimal vehicle schedules for given vehicle routes. A hybrid solution approach that combines a genetic algorithm with the exact dynamic programming procedure (GA-DP) is proposed as an efficient solution approach for the TD-VRSP-CO₂. Computational experiments on 30 small-sized instances and 14 large-sized instances are used to study the efficiency and effectiveness of the proposed hybrid optimization approach with promising results. Contributions of this study can help road-based delivery companies be ready for a low-carbon economy and also help individual vehicle drivers make better vehicle scheduling plans with lower CO₂ emissions and fuel consumption. |
| Author | Xiao, Yiyong Konak, Abdullah |
| Author_xml | – sequence: 1 givenname: Yiyong surname: Xiao fullname: Xiao, Yiyong email: xiaoyiyong@buaa.edu.cn organization: School of Reliability and System Engineering, Beihang University, Beijing, 100191, China – sequence: 2 givenname: Abdullah surname: Konak fullname: Konak, Abdullah organization: Information Sciences and Technology, Penn State Berks, Tulpehocken Road, P.O. Box 7009, Reading, PA, 19610-6009, United States |
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| SubjectTerms | algorithms business enterprises carbon dioxide CO2 emissions Dynamic programming energy use and consumption Green logistics greenhouse gas emissions greenhouse gases Hybrid optimization road transportation Sustainability traffic |
| Title | A genetic algorithm with exact dynamic programming for the green vehicle routing & scheduling problem |
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