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

Nussbaum M.; Sepúlveda M.; Laval, E; Singer, M.

Keywords: systems, models, search, research, intelligence, planning, algorithms, approximation, theory, allocation, representation, operations, resource, expert, artificial, annealing, methods, method, statistical, heuristic, mathematical, problem, solving, Strategic, Simulated, Declarative, Tabu

Abstract

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.

Más información

Título de la Revista: JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
Volumen: 49
Número: 1
Editorial: TAYLOR & FRANCIS LTD
Fecha de publicación: 1998
Página de inicio: 52
Página final: 65
URL: http://www.scopus.com/inward/record.url?eid=2-s2.0-0031673246&partnerID=q2rCbXpz