VNS and PBIG as Optimization Cores in a Cooperative Optimization Approach for Distributing Service Points

Abstract

We present a cooperative optimization approach for distributing service points in a geographical area with the example of setting up charging stations for electric vehicles. Instead of estimating customer demands upfront, customers are incorporated directly into the optimization process. The method iteratively generates solution candidates that are presented to customers for evaluation. In order to reduce the number of solutions presented to the customers, a surrogate objective function is trained by the customers’ feedback. This surrogate function is then used by an optimization core for generating new improved solutions. In this paper we investigate two different metaheuristics, a variable neighborhood search (VNS) and a population based iterated greedy algorithm (PBIG) as core of the optimization. The metaheuristics are compared in experiments using artificial benchmark scenarios with idealized simulated user behavior.