Orienteering problem modeling for electric vehicle-based tour

Abstract

This paper presents the design and analyzes the performance of a tour planner for electric vehicles, aiming at overcoming their long charging time by computational intelligence. This service basically finds the maximal subset out of the whole user-selected tour spots and their visiting sequence not inducing waiting time for battery charging. For the schedule search belonging to the orienteering problem category, genetic algorithms are employed. It includes encoding a visiting sequence based on omission probability, defining a fitness function to count the number of visitable destinations, and tailoring genetic operators. For constraint processing, the waiting time estimator prohibits those schedules having non-permissible waiting time to be included in the population. The performance measurement result obtained from a prototype implementation discovers that the proposed service can include 95 % of selected spots in the final schedule on the typical tour scenario for the given inter-destination and stay time distribution. © 2013 Springer-Verlag.