Abstract
This paper studies a method of generating hard instances of the Inventory Routing Problem (IRP) using evolutionary algorithms. The difficulty of the generated instances is measured by the time used by the CPLEX solver to solve a given instance. In order to ensure that feasible problem instances are generated, the Infeasibility Driven Evolutionary Algorithm (IDEA) is used along with seeding of the population with perturbed copies of an instance taken from a well-known set of benchmark instances. In the experiments a significant increase in problem solving time was observed with respect to IRP instances proposed in the literature. Generated IRP instances do not show any unusual properties that would make them unrealistic and they are made available as benchmarks for optimization algorithms. In the paper an attempt was made to compare generated problem instances and to draw conclusions regarding the structure of these instances, however, this has proven to be a difficult task.
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