Abstract
This paper presents a new framework for the optimization of real-life multiple-objective vehicle routing problems with multiple timewindows.This framework uses a hybrid variable neighborhood tabu search heuristic that chooses Pareto nondominated solutions from the search space of solutions that satisfy a set of Nash equilibrium conditions for a multiple-agent game theory model. Even though the framework is general and can tackle different classes of vehicle routing problems, it is herein tested on three objectives: minimizing the total travel cost (expressed in time units), maximizing the minimal customers' utility, and maximizing the minimal drivers' utility. The results on real-life instances provided by a Canadian transportation company highlight the benefits of the multiple-criteria model; an important motivation to the transportation industry for its real-life implementation.
| Original language | English |
|---|---|
| Pages (from-to) | 1251-1262 |
| Number of pages | 12 |
| Journal | IEEE Systems Journal |
| Volume | 12 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 2018 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Extreme Nash equilibrium
- Heterogeneous vehicle routing with multiple time windows
- Multiple criteria
- Multiple-agent noncooperative game
- Tabu search
- Variable neighborhood search (VNS)
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Computer Networks and Communications
- Electrical and Electronic Engineering