An Evolutionary Algorithm with Practitioner’s-Knowledge-Based Operators for the Inventory Routing Problem

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10782)

Abstract

This paper concerns the Inventory Routing Problem (IRP) which is an optimization problem addressing the optimization of transportation routes and the inventory levels at the same time. The IRP is notable for its difficulty - even finding feasible initial solutions poses a significant problem.

In this paper an evolutionary algorithm is proposed that uses approaches to solution construction and modification utilized by practitioners in the field. The population for the EA is initialized starting from a base solution which in this paper is generated by a heuristic, but can as well be a solution provided by a domain expert. Subsequently, feasibility-preserving moves are used to generate the initial population. In the paper dedicated recombination and mutation operators are proposed which aim at generating new solutions without loosing feasibility. In order to reduce the search space, solutions in the presented EA are encoded as lists of routes with the quantities to be delivered determined by a supplying policy.

The presented work is a step towards utilizing domain knowledge in evolutionary computation. The EA presented in this paper employs mechanisms of solution initialization capable of generating a set of feasible initial solutions of the IRP in a reasonable time. Presented operators generate new feasible solutions effectively without requiring a repair mechanism.

Keywords

Inventory Routing Problem Dedicated genetic operators Knowledge-based optimization Constrained optimization 

Notes

Acknowledgment

This work was supported by the Polish National Science Centre (NCN) under grant no. 2015/19/D/HS4/02574. Calculations have been carried out using resources provided by Wroclaw Centre for Networking and Supercomputing (http://wcss.pl), grant no. 405.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computational Intelligence Research Group, Institute of Computer ScienceUniversity of WroclawWroclawPoland
  2. 2.Department of Information Technologies, Institute of Business InformaticsWroclaw University of EconomicsWroclawPoland

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