This paper presents and illustrates a methodology for estimating minimum cost due-dates in a job shop production system. Simulation of a hypothetical dual-constrained job shop is used to derive response measures of shop performance for various dispatching, labor assignment and due-date assignment rules. Multiple linear and nonlinear regression is used to estimate the relationship between the response measures and the value of K, the multiple of total processing time employed in assigning due-dates, for various dispatching-labor assignment operating policies. Five response measures are used as dependent variables for the regression models. They are mean job flow-time cost, mean job lateness cost, mean job earliness cost, mean job due-date cost, and mean labor transfer cost. The coefficients of the regression analysis are employed to predict shop performance in terms of the five component cost measures for varying levels of K given the dispatching-labor assignment operating policy. The estimated relationships between the component cost measures and the decision roles are combined to form a total cost function. The total cost function is then searched to find the minimum cost due-date multiple for each of the various dispatching-labor assignment policies. Graphical results indicate that the minimum cost multiple K employed to assign due-dates and the sensitivity of costs to deviations from this minimum cost multiple may depend on the cost structure and dispatching-labor assignment operating policy considered. Further, the results suggest that regression analysis of simulation results can be used to estimate due-dates and select dispatching and labor assignment rules for desired performance effects.