On the application of explanation-based learning to acquire control knowledge for branch and bound algorithms

Matthew J. Realff, George Stephanopoulos

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The goal of this article is to present a methodology for the automatic acquisition of new control knowledge in the form of dominance and equivalence conditions, using an explanation-based learning algorithm from artificial intelligence. The acquisition of new control knowledge proceeds through the following stages: 1) Analysis of the problem-solving activity, generated from the application of a branch and bound algorithm on specific instance(s) of a class of problems. 2) Interpretation of the problem-solving activity and synthesis of new control knowledge, which can lead to more efficient solution of future problems. The generated control knowledge is provably correct within the theory of the given class of problems and the employed branch and bound strategy. Consequently, the number of nodes evaluated by a specific branch and bound algorithm is guaranteed to be reduced, as new control knowledge is continuously acquired from the solution of specific problems. The article presents the theoretical foundations for the handling of the above two tasks. It concludes with an application of the proposed approach to a mixed integer linear programming formulation of a batch scheduling problem.

Original languageEnglish (US)
Pages (from-to)56-71
Number of pages16
JournalINFORMS Journal on Computing
Volume10
Issue number1
DOIs
StatePublished - Jan 1 1998
Externally publishedYes

Fingerprint

Linear programming
Learning algorithms
Artificial intelligence
Scheduling
Branch and bound algorithm
Problem solving
Learning algorithm
Branch-and-bound
Batch
Node
Equivalence
Mixed integer linear programming
Methodology

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

Cite this

On the application of explanation-based learning to acquire control knowledge for branch and bound algorithms. / Realff, Matthew J.; Stephanopoulos, George.

In: INFORMS Journal on Computing, Vol. 10, No. 1, 01.01.1998, p. 56-71.

Research output: Contribution to journalArticle

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