The application of genetic algorithms to operation sequencing for use in computer-aided process planning

Abstract

Operation sequencing has long been a difficult problem in process planning. As part complexity increases, the number of potential solutions increases exponentially. This paper presents an approach to operation sequence coding that permits the application of genetic algorithms for quickly determining optimal, or near-optimal, operation sequences for parts of varying complexity. This approach improves on existing techniques by utilizing common sequencing constraints to guide the coding process resulting in a further reduction in the size of the solution search space. These improvements permit the determination of near-optimal operation sequences for complex parts within a time frame necessary for real-time dynamic planning. Application of this strategy is illustrated using three parts of varying complexity as well as comparing the genetic algorithm's performance using the improved constrained coding strategy with that of an unconstrained strategy.