A simulation analysis of due date assignment rules

In the job shop scheduling literature, due dates, when considered, are treated in one of two ways: (1) externally imposed or (2) internally set. Externally imposed due dates are outside the control of the scheduler and are assumed to be set by an order of entry or marketing department in agreement with the customer. In this situation, current shop status information is not normally considered in setting the date. Research in this setting has focused on the identification of those priority (dispatching) rules which yield good ‘due date performance.’ Due date performance is usually measured by job lateness, job tardiness, and/or proportion of jobs tardy. Internally set due dates are established by the scheduler as each job arrives. Using job characteristics and/or shop status information, the scheduler estimates the job flow time for each job and sets the due date accordingly. The early research in this setting did not focus explicitly on predicting individual job flow times, but rather on identifying simple due date assignment heuristics which provide good due date performance in concert with a variety of priority rules. More recent researchers have included shop status information in setting due dates. This article reports on a study of eight different rules for specifying due dates in a job shop and evaluation of their performance. A series of tests is conducted with a computer simulation model utilizing three different dispatching rules to control the sequencing of work. Mean tardiness, mean absolute missed due dates, and standard deviation of lateness data are collected to measure each rule’s performance. This study reports the first comparative analysis of various due date assignment rules that have been mentioned in the literature by different authors. The findings reported here provide some useful insight into past and future research. First, both job characteristic and shop status information should be used to develop due date assignment rules. Second, the dispatching rule used to sequence jobs at work centers influences shop performance. Third, information about work center congestion along a job’s routing is more useful information than general shop conditions. And fourth, the use of more detailed information in predicting flow time provides only marginal improvement in performance over other rules that use more aggregate information.