Abstract
This paper considers the vehicle routing problem with multiple time windows. It introduces a general framework for three evolutionary heuristics that use three global multi-start strategies: ruin and recreate, genetic cross-over of best parents, and random restart. The proposed heuristics make use of information extracted from routes to guide customized data-driven local search operators. The paper reports comparative computational results for the three heuristics on benchmark instances and identifies the best one. It also shows more than 16% of average cost improvement over current practice on a set of real-life instances, with some solution costs improved by more than 30%.
| Original language | English |
|---|---|
| Pages (from-to) | 485-515 |
| Number of pages | 31 |
| Journal | Journal of Heuristics |
| Volume | 25 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Jun 2019 |
Keywords
- Evolutionary search
- Genetic algorithm
- Local search
- Vehicle routing problem with multiple time windows
ASJC Scopus subject areas
- Software
- Information Systems
- Computer Networks and Communications
- Control and Optimization
- Management Science and Operations Research
- Artificial Intelligence