Integrating Fuzzy and Ant Colony System for Fuzzy Vehicle Routing Problem with Time Windows
This work addresses routing optimization under uncertainty for logistics and transportation industries, but it is incremental as it combines existing fuzzy and ant colony methods.
The paper tackles the fuzzy vehicle routing problem with time windows under uncertain travel times by integrating fuzzy credibility theory with an ant colony system evolutionary algorithm, achieving effective computational results on benchmark problems with both short and long time horizons.
In this paper fuzzy VRPTW with an uncertain travel time is considered. Credibility theory is used to model the problem and specifies a preference index at which it is desired that the travel times to reach the customers fall into their time windows. We propose the integration of fuzzy and ant colony system based evolutionary algorithm to solve the problem while preserving the constraints. Computational results for certain benchmark problems having short and long time horizons are presented to show the effectiveness of the algorithm. Comparison between different preferences indexes have been obtained to help the user in making suitable decisions.