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Selection of Vehicle Routing Heuristic Using Neural Networks
Author(s) -
Tuzun D.,
Magent M.A.,
Burke L.I.
Publication year - 1997
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/j.1475-3995.1997.tb00077.x
Subject(s) - heuristics , heuristic , vehicle routing problem , benchmark (surveying) , selection (genetic algorithm) , computer science , set (abstract data type) , artificial neural network , simple (philosophy) , mathematical optimization , routing (electronic design automation) , variety (cybernetics) , artificial intelligence , machine learning , mathematics , computer network , philosophy , geodesy , epistemology , programming language , geography
Due to its combinatorial structure, the vehicle routing problem has been attacked by many heuristic solution approaches. Given the large number of available heuristics, selecting the best heuristic for a particular problem poses its own kind of difficulty. This study uses a simple neural network approach to select the best heuristic for a VRP instance according to its basic characterstics. The approach has been trained and tested on a large test bed which covers problems with a wide variety of characterstics. It was also tested on a set of benchmark problems from the literature. For these problems, a simple procedure was used to extract the problem characterstics from the problem data. Statistical analysis reveals that the performance of each heuristic is affected differently by the problem characterstics. Neural network results for both test sets show that our approach is capable of selecting the best algorithm for a given VRP instance.