Building insightful simulation models using formal approaches - A case study on Petri Nets

In recent years development of formal approaches for modeling and simulation of manufacturing systems received significant attention. Approaches building on alternative Petri Nets formalisms show essential strengths in accurately capturing both a system's static structure and its dynamics, availability of mathematical analysis methods, and graphical representation. However, models of realistic systems are often perceived as too large and complex to understand by project stakeholders. This hinders their participation in modeling, and solution finding, and may influence their perception of model credibility. In this article we address this issue by considering a structured approach for embodying high-level manufacturing concepts. The approach aims at creating more insightful simulation models by building on sound and explicit conceptualization, i.e., the choice of manufacturing concepts, and clear rules for their formalization, i.e., their mapping on elementary model components. We adopted the Petri Nets based tool ExSpect (TM) to illustrate and evaluate our approach

Robust simulation-optimization using metamodels

Optimization of simulated systems is the goal of many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by either Response Surface Methodology or Kriging metamodeling. We illustrate the resulting methodology through the well-known Economic Order Quantity (EOQ) model

Generating, Benchmarking and Simulating Production Schedules – From Formalisation to Real Problems

Production scheduling has attracted the interest of production economics communities for decades, but there is still a gap between academic research, real -world problems, operations research and simulation. Genetic Algorithms (GA) represent a technique that has already been applied to a variety of combinatorial problems. Simulation can be used to find a solution to problems through repetitive simulation runs or to prove a solution computed by an optimization algorithm. We will explain the application of two special GAs for job -shop and resource- constrained project scheduling problems trying to bridge the gap between problem solving by algorithm and by simulation. Possible goals for scheduling problems are to minimize the makespan of a production program or to increase the due -date reliability of jobs or possibly any goal which can be described in a mathematical expression. The approach focuses on integrating a GA into a commercial software product and verifying the results with simulation

Cycle time distributions of semiconductor workstations using aggregate modeling

Recently an aggregate modeling method has been developed to predict cycle time distributions as a function of throughput for manufacturing workstations with dispatching. The aggregate model is a single-server representation of the workstation with a workload-dependent process time distribution, and a workload-dependent overtaking distribution. The process time and overtaking distribution can be determined from arrival and departure events measured from the workstation at the factory floor. In this paper, we validate the proposed method in the context of semiconductor manufacturing. In particular weconsider a lithography workstation. First, we present a simulation case that demonstrates the accuracy of the aggregate model to predict cycle time distributions. Second, we apply the aggregate modeling method to a case from semiconductor industry,and illustrate how the method performs using arrival and departure data obtained from the manufacturing execution system

An agent-based simulation model for the market diffusion of a second generation biofuel

Kiesling E, Günther M, Stummer C, Wakolbinger LM. An agent-based simulation model for the market diffusion of a second generation biofuel. In: Rossetti M, Hill RR, Johansson B, Dunkin A, Ingalls RG, eds. Proceedings of the Winter Simulation Conference (WSC 2009). Piscataway, NJ: IEEE; 2009: 1474-1481