Knowledge-based successive linear programming for linearly constrained optimization

Abstract

Combining knowledge engineering technology with some operations research algorithms will get novel efficient optimization methods. As an example, this paper discusses a knowledge-based successive linear programming method for linearly constrained optimization problems. In this new method we use both traditional successive linear programming algorithm in operations research and the knowledge base which is constructed with the expertise of an optimization expert and valuable experience data, so that this knowledge-based program can solve optimization problems somewhat like a human expert who is great at operations research and has a lot of practical experience of problem-solving. The improvement of efficiency in problem-solving depends mainly on the skilful use of plausible reasoning based on incomplete experience knowledge. In addition, man-machine interaction during the computation procedures is also used. Finally, two numerical examples illustrate that the proposed method is much more flexible and efficient than the traditional operations research algorithms concerned.