Abstract
We argue that the selective vehicle routing problem is more appropriate than the conventional VRP in handling uncertainty with limited resources. However, previous formulations of selective VRPs have all been deterministic. Three new formulations are proposed to account for different optimization strategies under uncertain demand (or utility) level: reliable, robust, and fuzzy selective vehicle routing problems. Three parallel genetic algorithms (PGAs) and a classic genetic algorithm are developed and compared to the deterministic solution. PGAs differ based on their communication strategies and diversity in sub-populations. Results show that a PGA, wherein communication between demes, or subpopulations, occurs in every generation and does not eliminate repeated chromosomes, outperforms other algorithms at the cost of higher computation time. A faster variation of PGA is used to solve the non-convex reliable selective VRP, robust selective VRP and the large-scale fuzzy selective VRP, consisting of 200 nodes. Large scale application demonstrates the value of fuzzy selective vehicle routing problem FSVRP in humanitarian logistics.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Transportation Research Part E: Logistics and Transportation Review
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
