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2918. Hybrid Genetic Algorithm a new Perspective to Solving Capable Vehicle Routing Problem (CPVPR)
Invited abstract in session MB-58: Heuristics for Vehicle Routing 3, stream VeRoLog - Vehicle Routing and Logistics.
Monday, 10:30-12:00Room: S07 (building: 101)
Authors (first author is the speaker)
| 1. | Lucas Contreras Rada
|
| Industrial Engineer, University of cordoba | |
| 2. | Helman Enrique Hernandez Riaño
|
| Industrial Engineer, Universidad de Córdoba | |
| 3. | Jorge Mario López Pereira
|
| Industrial Engineer, Universidad de Córdoba |
Abstract
This study focuses on solving a capable vehicle routing problem (CVRP)
using a genetic algorithm (GA). The CVRP is a complex optimization problem that often arises in supply chains when routing vehicles with different populations. The study proposes a new solution that uses a metaheuristic to optimize the implementation between crossover and mutation of new operators. These operators use different strategies to search for better solutions generated from an initial population. The CVRP is formulated as a mixed integer programming (MIP)problem, where the genes represent decisions about which customers to visit and
which vehicles to use for the route.
The existing literature provides ample support for this issue, with many articles discussing the indicators and comparisons of experimental tests post-realizations that show the efficacy of the proposed algorithm. Through significant advances in the solution problem, the study obtained a GA-modified solution to the CVRP by adjusting different parameters to identify the most adaptable solution. The results were then compared with other solution methods as solvers focus on the same problem.
Keywords
- Algorithms
- Metaheuristics
- Vehicle Routing
Status: accepted
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