A Hybrid Genetic Algorithm for the Inventory Routing Problem

This study considers the application of a hybrid genetic algorithm (HGA) to the Inventory Routing Problem (IRP), that aims to minimize the cost of the total distance traveled over a time horizon discretized in periods, while guaranteeing that the customers do not incur a stock-out event. The proposed algorithm is tested using the instances proposed by Archetti et al. [1] and [2] where we consider only one vehicle available at the supplier. This instance is the biggest one solved so far. In this paper, computational results are given by an HGA, showing that this approach is competitive with other search and simulated annealing in terms of solution time and quality.

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