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ABSTRACT
This study dwells upon the electric milk collection problem. The problem extends electric vehicle routing problem with time windows, considering multi-depot, multi-product, split deliveries, multi-compartment, fast chargers and fleet composition. The problem aims to create an effective routing decision-making system for electric trucks in transporting milk of different quality from producers (or milk collection points) in different locations to the factories. The objective of the electric milk collection problem is to minimize the sum of the total energy costs of electric trucks. We develop an adaptive general variable neighborhood search algorithm that involves several procedures having been tailored to handle specific features of the problem. We perform extensive computational experiments on real-life data to investigate the performance of the heuristics and offer particular insight. The results indicate that our algorithm successfully solves small- and large-scale instances in terms of solution quality and computational time. Our results also quantify the benefits of using fast charger types and fleet composition on the total energy costs.
Disclosure statement
No potential conflict of interest was reported by the author(s).