Discrete-event simulation

In Section 42.2 we have discussed that queuing theory may provide a good qualitative picture of the behaviour of queues in an analytical laboratory. However the analytical process is too complex to obtain good quantitative predictions. As this was also true for queuing problems in other fields, another branch of Operations Research, called Discrete Event Simulation emerged. The basic principle of discrete event simulation is to generate sample arrivals. Each sample is characterized by a number of descriptors, e.g. one of those descriptors is the analysis time. In the jargon of simulation software, a sample is an object, with a number of attributes (e.g. analysis time) and associated values (e.g. 30 min). Other objects are e.g. instruments and analysts. A possible attribute is a list of the analytical 

An event takes place when the state of the laboratory changes. Examples of events are  

With each event a number of actions is associated. For example when a sample arrives, the following actions are taken  

In Fig. 42.9 we show the simulation results obtained by Janse [8] for a municipal laboratory for the quality assurance of drinking water. Simulated delays are in good agreement with the real delays in the laboratory. Unfortunately, the development of this simulation model took several man years which is prohibitive for a widespread application. Therefore one needs a simulator (or empty shell) with predefined objects and rules by which a laboratory manager would be capable to develop a specific model of his laboratory. Ideally such a simulator should be linked to or be integrated with the laboratory information management system in order to extract directly the attribute values. 

Klaessens [14-17] developed a laboratory simulator , written in SIMULA, which by a question-answering session assembles the simulation model. SIMULA [18] is a programming environment dedicated to the simulation of queuing systems. KEE [ 19] offers a graphics-driven discrete event simulator, in which the objects are represented by icons which can be connected into a logical network (e.g. a production line for the manufacturing of electronic devices). Although KEE has proven its potential in many areas, no examples are known of analytical laboratories simulated in KEE. 

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Radhesh Nair for contributions on discrete event simulation. 

A condition for discrete event simulation to become a relevant tool for the laboratory manager is the availability of an easy to use simulator with a user friendly user interface. 

Duran CL (2004) Logistics for world-wide cmde oil transportation using discrete event simulation and optimal control. Comput Chem Eng 28 897-911... 

A discrete-event simulation tool considers - nomen est omen - discrete events at discrete points in time. Typically, in a discrete-event simulator items such as parts are moving through the modeled system changing their state, e.g., when they enter or leave a machine. A reactor in the process industry continuously produces a certain output. This is something a discrete-event simulator is not really made... 

It finally may be stated that the use of discrete-event simulation on different decision levels even though state-of-the-art is still slightly underrepresented in the process industry. However, since the technology has proven itself in an... 

Watson, E.F. (1997) An application of discrete-event simulation for batch-process chemical plant design. Interfaces, 27 (6), 35-50. 

The simulation module simulates the basic operation(s) which are processed by a combination of a vessel and a station using a discrete event simulator. All necessary data (basic operation(s), equipment parameters, recipe scaling percentage, etc.) is provided by the scheduling-module. The simulator calculates the processing times and the state changes of the contents of the vessels (mass, temperature, concentrations, etc.) that are relevant for logistic considerations. 

Azzaro-Pantel, C. L. Bemal-Haro P. Baudet S. Demenech, et al. A Two-Stage Methodology for Short-term Batch Plant Scheduling Discrete-Event Simulation and Genetic Algorithm Comput Chem Eng 22 1461-1481 (1998). 

Stock/flow structure combine xmcertainty wi cause-and-effect relationships which can be quantified through discrete event simulation. 

Screening for the Important Factors in Large Discrete-Event Simulation Models Sequential Bifurcation and Its Applications... 

We give only a short description of the three supply chain configurations and their simulation models for details we refer to Persson and Olhager (2002). At the start of our sequential bifurcation, we have three simulation models programmed in the Taylor II simulation software for discrete event simulations see Incontrol (2003). We conduct our sequential bifurcation via Microsoft Excel, using the batch run mode in Taylor II. We store input-output data in Excel worksheets. This set-up facilitates the analysis of the simulation input-output data, but it constrains the setup of the experiment. For instance, we cannot control the pseudorandom numbers in the batch mode of Taylor II. Hence, we cannot apply common pseudorandom numbers nor can we guarantee absence of overlap in the pseudorandom numbers we conjecture that the probability of overlap is negligible in practice. 

Persson, F. (2003). Discrete event simulation of supply chains Modelling, validation, and analysis. Doctoral dissertation. Profll 20, Linkoping Institute of Technology. 

Wan, H., Ankenman, B. E., and Nelson, B. L. (2004). Controlled sequential bifurcation A new factor-Screening method for discrete-event simulation. IEMS Technical Report 04-011, Northwestern University, http //www.iems.northwestern.edu/content/papers.asp... 

If physical experiments are not feasible or cost-effective, use a computer simulation such as discrete event simulation (see Technique 41) to determine which inputs result in the optimal output. 

Not all systems can be modeled with discrete event simulation. Some events are continuous, such as the rate of evaporation. These occurrences can be modeled, but they require a different approach. For more information see Theory of Modeling and Sinnulation Integrating Discrete Event and Continuous Complex Dynamic Systems, second edition, by B. Zeigler, H. Praehofer, and T. C. Kim, New York Academic Press, 2000. 

Discrete event simulation is performed often in the banking and call center industries—or any industry that processes resources through a system at unpredictable flow rates with a large variation in volume. 

Discrete event simulation is based on queuing theory, which is the mathematical study of waiting in lines or queues. 

Each process has a finite amount of capacity. If this becomes full, new entities are blocked from entering or proceeding through the process. Understanding how to optimize the capacity for a particular process is one of the benefits of discrete event simulation. 

It may take several iterations before you get the model just right, especially if you re new to this technique. Don t give up Once you get the hang of it, you ll find that discrete event simulation can be an invaluable approach. 

EXHIBIT 41.1 This is a sample, stylized output of the SigmaFlow Simulator Process Analyzer. In addition to performing discrete event simulations, this product also performs value stream mapping, improvement score-carding, and other analytics. 

The advantages of discrete event simulation are speed, flexibility, and cost. However, poor modeling or failure to verify your findings in the real world can lead to processes that are unexpectedly impacted by small fluctuations in demand or resources. 

SigmaFlow s Simulator Process Analyzer software (www.sigmaflow.com). This is an affordable way to conduct discrete event simulations. For a f 4-day trial, go to the web site and select VSM/Simulator on the Contact Us page. To extend the typical 14-day trial to three months, use license ID 23392 and password v sms3-65095. 

Prototyping is typically the precursor to a product pilot, while discrete event simulation (Technique 41) can be conducted before or instead oi a service pilot. 

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