Reoptimization Heuristic for the Capacitated Vehicle Routing Problem
The solution to a dynamic context of the Capacitated Vehicle Routing Problem (CVRP) is challenging. Routing and replenishment decisions are necessary by considering the assignment of customers to vehicles when the information is gradually revealed over horizon time. The procedure to solve this type...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/3743710 |
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| author | Rodrigo Linfati John Willmer Escobar |
| author_facet | Rodrigo Linfati John Willmer Escobar |
| author_sort | Rodrigo Linfati |
| collection | DOAJ |
| description | The solution to a dynamic context of the Capacitated Vehicle Routing Problem (CVRP) is challenging. Routing and replenishment decisions are necessary by considering the assignment of customers to vehicles when the information is gradually revealed over horizon time. The procedure to solve this type of problems is referred to as route reoptimization, which is the best option for minimizing expected transportation cost without incurring failures of unsatisfied demand on a route. This paper proposes a heuristic algorithm for the reoptimization of CVRP in which the number of customers increases. The algorithm uses proposed performance metrics to reduce route dispersion and minimize length. The initial solution is generated using the savings algorithm and then enhanced using the Record-to-Record travel metaheuristic. By including or reducing new customers in the system, a reoptimization is performed which considers the visited nodes and edges as fixed. The optimization of the algorithm is implemented hierarchically by first minimizing dispersion and then minimizing distance. Next, the local search procedure is executed to improve the solution. A classic optimization is performed on all instances using the original and new customers’ information for later comparison to minimize distance. The efficiency of the proposed algorithm was validated using real-world cases from the literature. The results are promising and show the effectiveness of the proposed method for solving the considered problem by using reoptimization procedures in order to achieve good approximation ratios within short computing times. |
| format | Article |
| id | doaj-art-aed343ea94e2499c9a49fdf9a97aa53d |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-aed343ea94e2499c9a49fdf9a97aa53d2025-08-20T03:39:04ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/37437103743710Reoptimization Heuristic for the Capacitated Vehicle Routing ProblemRodrigo Linfati0John Willmer Escobar1Department of Industrial Engineering, Universidad del Bío-Bío, Concepción 4030000, ChileDepartment of Accounting and Finance, Universidad del Valle, Cali 760001, ColombiaThe solution to a dynamic context of the Capacitated Vehicle Routing Problem (CVRP) is challenging. Routing and replenishment decisions are necessary by considering the assignment of customers to vehicles when the information is gradually revealed over horizon time. The procedure to solve this type of problems is referred to as route reoptimization, which is the best option for minimizing expected transportation cost without incurring failures of unsatisfied demand on a route. This paper proposes a heuristic algorithm for the reoptimization of CVRP in which the number of customers increases. The algorithm uses proposed performance metrics to reduce route dispersion and minimize length. The initial solution is generated using the savings algorithm and then enhanced using the Record-to-Record travel metaheuristic. By including or reducing new customers in the system, a reoptimization is performed which considers the visited nodes and edges as fixed. The optimization of the algorithm is implemented hierarchically by first minimizing dispersion and then minimizing distance. Next, the local search procedure is executed to improve the solution. A classic optimization is performed on all instances using the original and new customers’ information for later comparison to minimize distance. The efficiency of the proposed algorithm was validated using real-world cases from the literature. The results are promising and show the effectiveness of the proposed method for solving the considered problem by using reoptimization procedures in order to achieve good approximation ratios within short computing times.http://dx.doi.org/10.1155/2018/3743710 |
| spellingShingle | Rodrigo Linfati John Willmer Escobar Reoptimization Heuristic for the Capacitated Vehicle Routing Problem Journal of Advanced Transportation |
| title | Reoptimization Heuristic for the Capacitated Vehicle Routing Problem |
| title_full | Reoptimization Heuristic for the Capacitated Vehicle Routing Problem |
| title_fullStr | Reoptimization Heuristic for the Capacitated Vehicle Routing Problem |
| title_full_unstemmed | Reoptimization Heuristic for the Capacitated Vehicle Routing Problem |
| title_short | Reoptimization Heuristic for the Capacitated Vehicle Routing Problem |
| title_sort | reoptimization heuristic for the capacitated vehicle routing problem |
| url | http://dx.doi.org/10.1155/2018/3743710 |
| work_keys_str_mv | AT rodrigolinfati reoptimizationheuristicforthecapacitatedvehicleroutingproblem AT johnwillmerescobar reoptimizationheuristicforthecapacitatedvehicleroutingproblem |