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Logistic Characteristic Curves (LCCs) or Logistic Operating Curves (LOCs) describe relationships between various Key Performance Indicators (KPIs) of production and logistics systems. These relationships can be qualitatively or quantitatively visualized by charts to illustrate the performance of these systems. Discrete Event Simulation (DES) allows a detailed investigation of the dynamic behavior of production and logistics systems under consideration of uncertainties and thus contributes to their planning reliability. Using simulation models and the data generated by the experiments, KPIs of the modeled systems are measured. Of course, different production and logistics systems also have several target systems whereby the individual target variables interact with each other and can, therefore, conflict. In this paper, a methodology is presented that combines DES and a statistical technique for empirical model building, namely the response surface model, to predict the behavior of production and logistics systems by using LOCs and thereby decrease the effort for experimentation by reducing the number of simulation runs.

Generating Logistic Characteristic Curves using Discrete Event Simulation and Response Surface Models