An architecture for solving sequencing and resource allocation problems using approximation methods

In the search for better optimisation techniques, new methods that mix artificial intelligence and operations research have emerged. Search heuristics are integrated with optimisation algorithms. Approximation methods, like Hill Climbing, Simulated Annealing, and Tabu Search, that have been used wit...

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Bibliographic Details
Published in:The Journal of the Operational Research Society Vol. 49; no. 1; pp. 52 - 65
Main Authors: Nussbaum, M, Sepúlveda, M, Singer, M, Laval, E
Format: Journal Article
Language:English
Published: London Taylor & Francis 01.01.1998
Palgrave Macmillan UK
Subjects:
artificial intelligence
Business and Management
heuristics
knowledge-based systems
Management
Operations Research/Decision Theory
optimisation
Theoretical Paper
optimisation
heuristics
artificial intelligence
knowledge-based systems
ISSN:0160-5682, 1476-9360
Online Access:Get full text
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Summary:In the search for better optimisation techniques, new methods that mix artificial intelligence and operations research have emerged. Search heuristics are integrated with optimisation algorithms. Approximation methods, like Hill Climbing, Simulated Annealing, and Tabu Search, that have been used with success in combinatorial optimisation problems, are one of such research lines. This paper presents the key elements of approximation methods and combines them in a tool appropriate for solving sequencing and resource allocation problems. The system permits a clear division between problem specification and problem solving, allowing a declarative representation and therefore minimising developing costs. The key issues discussed in this work are a model for representing this class of problems in a standard form, a set of strategies for applying the approximation methodology, and an expert system that dynamically manipulates the strategies' parameters.
ISSN:0160-5682
1476-9360
DOI:10.1057/palgrave.jors.2600479