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In a job shop production system, assigning exact due dates is an important task for scheduling performance. On the other hand, predicting due dates accurately is very difficult because of several decision factors should be considered. Many due date assignment models proposed in the literature but none of them is perfect and can produce the best due dates for all jobs arrived to the dynamic job shop environment. So using more than one due date assignment model for all job set may be reasonable. In this study a dynamic solution methodology for determining the most appropriate due date assignment models for the jobs arrived to the shop to optimize the aimed objective function by considering the related decision factors in a dynamic job shop environment is presented.

A Dynamic Methodology on Determining the Most Appropriate Due Date Assignment Models for Job Shop Scheduling