Mixing static and dynamic flowtime estimates for due-date assignment

Prediction of job flowtimes is important from the perspectives of both providing internal control and customer satisfaction. More specifically, accurate and precise flowtime prediction can facilitate proper timing for the release of materials and resources, improved accuracy of delivery dates to customers, and identification of jobs that require expediting. The objective of this paper is to introduce a new approach to flowtime prediction which improves performance quality over that found in existing dynamic flowtime estimation models. This new approach, mixed flowtime estimation, incorporates both static and dynamic flowtime estimates into a single, flowtime prediction model. In this investigation, mixed and dynamic models are compared experimentally using computer simulation of a job shop under various congestion conditions and dispatching heuristics. The results of this investigation reveal that the mixed flowtime prediction models provide significant improvements in job shop due-date estimation performance. Statistically significant performance improvements are obtained in both the average lateness and fraction tardy jobs for mixed estimates. Specifically, average lateness is reduced by 25% in the balanced shop and 40% in the bottleneck shop. This improvement enables a corresponding increase in the accuracy of job flowtime estimates, and, hence, due-date assignment accuracy. Due-date performance improvements are observed for each scheduling heuristic investigated.