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What makes a solution good? The generation of problem-specific knowledge for heuristics

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  • ARNOLD, Florian
  • SÖRENSEN, Kenneth

Abstract

Heuristics are the weapon of choice when it comes to solving complex combinatorial optimization problems. Even though some research focuses on tuning a heuristic with respect to a certain problem, little research has been done to investigate structural characteristics of the problem itself. In this paper we argue that knowledge about a problem is highly valuable when it comes to designing effi cient heuristics, and we show how it can be generated. With knowledge we hereby mean that we can defi?ne desirable structural characteristics of good solutions. Our knowledge generation approach is based on data mining and we demonstrate its concept with the help of the most prominent combinatorial problem in Operations Research, the Vehicle Routing Problem. We de?fine metrics to measure a solution and an instance, and generate and classify 192.000 solutions for various instances. With these metrics we are able to distinguish between optimal and non-optimal solutions with an accuracy of up to 93%. Furthermore, we reveal the most distinguishing characteristics of good VRP solutions, and use them to improve an existing heuristic.

Suggested Citation

  • ARNOLD, Florian & SÖRENSEN, Kenneth, 2017. "What makes a solution good? The generation of problem-specific knowledge for heuristics," Working Papers 2017003, University of Antwerp, Faculty of Business and Economics.
    • Handle: RePEc:ant:wpaper:2017003
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    Cited by:

    1. ARNOLD, Florian & SÖRENSEN, Kenneth, 2017. "A simple, deterministic, and efficient knowledge-driven heuristic for the vehicle routing problem," Working Papers 2017012, University of Antwerp, Faculty of Business and Economics.

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    Keywords

    Multi-depot vehicle routing problem; Multi-product; Inventory management; Inventory allocation; Metaheuristics;
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