Job shop scheduling for missed due-date performance

Relatively little job shop scheduling research has focused on the missed due-date performance. In this paper, we investigate how two important decision factors, namely dispatching rules and due-date assignment methods, affect the missed due-date performance in job shop scheduling. A new dispatching rule and two new due-date setting models are developed in order to improve the performance criteria based on missed due dates. The new due-date models are capable of adjusting dynamically the flowtime estimation by using feedback information about current shop load conditions. Simulation results show that the proposed dynamic due-date models are significantly better than their static counterparts. The best missed due-date performance is observed when a combination of a dynamic due-date setting model and an appropriate due-date-dependent dispatching rule is employed. In addition, the new models are simple and easy to implement without preliminary runs for parameter estimation.