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FUTURE PATHS FOR INTEGER PROGRAMMING AND LINKS TO Artificial Intelligence (1986)
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@MISC{Glover86futurepaths,
author = {Fred Glover},
title = { FUTURE PATHS FOR INTEGER PROGRAMMING AND LINKS TO Artificial Intelligence},
year = {1986}
}
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Abstract
Scope and Purpose-A summary is provided of some of the recent (and a few not-so-recent) developments that otTer promise for enhancing our ability to solve combinatorial optimization problems. These developments may be usefully viewed as a synthesis of the perspectives of operations research and artificial intelligence. Although compatible with the use of algorithmic subroutines, the frameworks examined are primarily heuristic, based on the supposition that etTective solution of complex combinatorial structures in some cases may require a level of flexibility beyond that attainable by methods with formally demonstrable convergence properties. Abstract-Integer programming has benefited from many innovations in models and methods. Some of the promising directions for elaborating these innovations in the future may be viewed from a framework that links the perspectives of artificial intelligence and operations research. To demonstrate this, four key areas are examined: (1) controlled randomization, (2) learning strategies, (3) induced decomposition and (4) tabu search. Each of these is shown to have characteristics that appear usefully relevant to developments on the horizon.
Keyphrases
operation research artificial intelligence many innovation promising direction algorithmic subroutine complex combinatorial structure purpose-a summary abstract-integer programming combinatorial optimization problem key area demonstrable convergence property ettective solution learning strategy tabu search
