Knowledge-based approach for dimensionality reduction solving repetitive combinatorial optimization problems

Abstract

This paper proposes a knowledge-based approach that combines case-based reasoning and operational research methodologies for solving repetitive combinatorial optimization problems. The novel approach makes use of past experience which is generally neglected in solving current optimization problems, and introduces this knowledge into operational research techniques for problem-solving, especially when the dimension of the problem is large and conventional schemes cannot solve it within a reasonable time limit. It greatly reduces the dimension of the problem and provides near-optimal solutions.