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564. Solving a VRP in the energy sector through tabu search with a double neighborhood
Invited abstract in session MB-25: Discrete, continuous or stochastic optimization and control in networks, transportation and design II, stream Combinatorial Optimization.
Monday, 10:30-12:00Room: 011 (building: 208)
Authors (first author is the speaker)
| 1. | Paula Amaral
|
| Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa | |
| 2. | Ana Mendes
|
| Data Science, Closer | |
| 3. | Miguel Espinosa
|
| IT & Digital Department, GALP |
Abstract
This presentation deals with the distribution of products in the context of an energy company. This problem can be modeled as a Vehicle Routing Problem (VRP) with several complexities like: time windows, different vehicles, several products, among others. The urgency of optimizing energy transportation cannot be overstated, given the rising global demand for energy resources. Tabu Search is a powerful meta-heuristi algorithm commonly used for solving combinatorial optimization problems. Its adaptability and efficiency make it well-suited for a variety of applications, as the VRP, for identifying good vehicle routes. However, like any optimization algorithm, Tabu Search faces challenges, and one of the main issues is dealing with local optima where the algorithm might get stuck. In scenarios with numerous local optima, Tabu Search may struggle to escape these regions of attraction efficiently, leading to slow convergence and potentially sub-optima solutions. In response to this challenge, we propose a novel double neighborhood strategy employing opposite optimization directions (minimization and maximization). The maximization component is strategically employed to facilitate the escape from local minima. We showcase the application of this approach to a challenging and intricate VRP problem. Our results demonstrate the effectiveness of the double neighborhood strategy in enhancing the performance of Tabu Search, particularly in scenarios where traditional implementations ma
Keywords
- Combinatorial Optimization
- Metaheuristics
- Vehicle Routing
Status: accepted
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