SIMULATION AND OPTIMIZATION IN PRODUCTION PLANNING - A CASE-STUDY

This paper reports on a practical decision support system (DSS) for production planning, developed for a Dutch company. To evaluate this DSS, a simulation model is built. Moreover, the DSS has 15 control variables which are to be optimized. The effects of these 15 variables are investigated, using a sequence of 2k-p experimental designs. Originally 28 response variables were distinguished. These 28 variables, however, can be reduced to one criterion variable, namely productive machine hours, which is to be maximized, and one commercial variable measuring lead times, which must satisfy a certain side-condition. For this optimization problem the Steepest Ascent technique is applied to the experimental design outcomes. The resulting Response Surface Methodology is developed theoretically. In practice a number of complications arise.