Abstract
The Vehicle Routing Problem (VRP) is a known optimization problems falling under the category of NP-Hard set of problems. VRP, along with its variations, continue to be extensively explored by the research community due to their large domain of application (environment, agriculture, industry, etc.) and economic impact on improving the overall performance, Quality of Services and reducing the operational cost. In this paper, we focus on VRPMTW; a variant of VRP with Multiple Time Windows constraints. We introduce a novel Hybrid Genetic Variable Neighborhood Search (HGVNS) based heuristic for the optimization of VRPMTW. The proposed framework uses genetic cross-over operators on a list of best parents and new implementations of local search operators. Computational results on benchmark data show substantial performance improvement when using the newly introduced heuristic.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1319-1326 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781509046010 |
| DOIs | |
| State | Published - 5 Jul 2017 |
Publication series
| Name | 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings |
|---|
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Genetic Algorithm
- VRP with Multiple Time Windows
- Variable Neighborhood Search
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Signal Processing