Optimizing control dynamic complexity and production schedule

Abstract

Over the last few decades sophisticated line balancing methodologies have been developed in response to the emerging trend of lean production in order to more effectively streamline production sequence scheduling. Newer approaches now demonstrate the potential to achieve lean production while at the same time simplifying what have become increasingly complex analytical methodologies and production sequences. Unlike past studies on line balancing for mixed-model production lines, in this paper the impacts of line balance on Control Dynamic Complexity are investigated and their deployment illustrated with a case study. Precedence matrices have been studied and a combined precedence matrix computed. The mathematical programming language Matlab is employed to illustrate state diagrams representing the relationships between the tasks and terminals. It is found that control dynamic complexities are higher for some particular sequences, and in such cases further explanations are offered. Furthermore, this study reveals that it is possible to minimize the complexities by better exploiting some aspects of line scheduling guidelines. Since line balancing and line scheduling are NP hard issues, this paper has also demonstrated ways in which computational analysis of multiple iterations can be significantly accelerated in future by deploying ant colony optimization techniques. Analysis of complexity level highlights what may otherwise be intuitive, that excessive sophistication of line balance techniques is very likely to be detrimental to an overall organization if resulting scheduling becomes difficult to practically monitor.