Discrete event analysis

The other approach is to use discrete event analysis. This approach is able to handle the real-world complexity of most systems. Referring to Example 1 in Chapter 1, it may be found that the availability of Pumps, P-101 A/B is not a simple number but varies according to many time-dependent factors such as when they were most recently inspected, the availability of spare parts and the operating conditions. Factors such as these can materially affect the reliability and maintainability of the pumps, often in ways that are impossible to model deterministically. 

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... 

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. 

For consequence analysis, we have developed a dynamic simulation model of the refinery SC, called Integrated Refinery In-Silico (IRIS) (Pitty et al., 2007). It is implemented in Matlab/Simulink (MathWorks, 1996). Four types of entities are incorporated in the model external SC entities (e.g. suppliers), refinery functional departments (e.g. procurement), refinery units (e.g. crude distillation), and refinery economics. Some of these entities, such as the refinery units, operate continuously while others embody discrete events such as arrival of a VLCC, delivery of products, etc. Both are considered here using a unified discrete-time model. The model explicitly considers the various SC activities such as crude oil supply and transportation, along with intra-refinery SC activities such as procurement planning, scheduling, and operations management. Stochastic variations in transportation, yields, prices, and operational problems are considered. The economics of the refinery SC includes consideration of different crude slates, product prices, operation costs, transportation, etc. The impact of any disruptions or risks such as demand uncertainties on the profit and customer satisfaction level of the refinery can be simulated through IRIS. 

During an analysis of the semi-formal work process model (5), the work process engineer may identify problems and shortcomings of the design process and make suggestions for their improvement. Some examples for a manual analysis are given in [99, 10.5]. The analysis can also be supported by tools like discrete-event simulation systems (cf. Sect. 5.2). 

WOMS only provides limited means to analyze the work process. Often a qualitative analysis resulting from a careful inspection of the model by the stakeholders involved is not sufficient. Rather, quantitative performance measures are of interest. Such measures can be deduced from Monte-Carlo simulation of the work process models, which can be cast into a discrete-event... 

One of the primary applications of stochastic models is in discrete-event simulation of engineering systems that are subject to randomness. The first step in tins methodology is to develop a stochastic model of the system in question. Simulation is used to analyze the model because models of real systems are usually too complex for direct mathematical analysis. Simulation is treated thoroughly... 

Chong, E. K. R, and Ramadge, P. J. (1992), Convergence of Recursive Optimization Algorithms Using Infinitesimal Perturbation Analysis Estimates, Discrete Event Dynamic Systems Theory and Applications, Vol. 1, pp. 339-372. 

Rubinstein, R. Y., and Shapiro, A. (1993), Discrete Event Systems Sensitivity Analysis and Stochastic Optimization by the Score Function Method, John Wiley Sons, Chichester. 

Pure simulation approaches are proposed by Pitty et al. (2008) and Adhitya and Srini-vasan (2010). Pitty et al. (2008) propose a discrete-event simulation model for a refinery supply chain. Operational decisions such as unloading schedules and production planning are made based on simple priority rules. Various configurations of the modelled SC are studied and compared to reveal optimization potentials. This approach explicitly considers some details of ship and pipeline transports. Adhitya and Srinivasan (2010) describe a discrete-event simulation model for an SC producing and distributing lubricant additives. Here, batch production is modelled. Again, operational production decisions are made by priority rules and a scenario analysis is conducted to evaluate the effects of other priority... 

A number of quantitative analysis techniques have been presented in literature for FMS analysis (Matta and Semeraro 2005) and Groover (2007). FMS analysis techniques include deterministic and queuing models, discrete events simulation, and other approaches including heuristics. 

Perrone G, Zinno A, Noto La Diega S (2001) Fuzzy discrete event simulation anew tool for rapid analysis of production systems under vague inftumation. J Intell Manuf 12(3) 309-326 Petrovic PB, Milacic VR (1998) A eoneept of an inlelli-genee fuzzy eontrol for assembly robot CIRP Ann 47(1) 9... 

The analysis of the expected potential losses associated with the separate components revealed that the separator (1) is responsible for approximately 24.5% of the total system losses. This component needs attention and its lehahihty should he improved if the overall system losses are to he reduced. RehahUity can for example he improved hy providing redundancy for this component. The developed discrete-event solver has heen successfidly appUed for determining the production avadahihty of oil and gas production systems. 

Huseby, A. B., Eide, K. A., Isaksen, S. L., Natvig B. Gasemyr, J. 2008. Advanced discrete event simulation methods with application to importance measure estimation. Safety, Reliability and Risk Analysis. Theory, Methods and Applications, volume 3. London CRC Press. 1747-1753. 

The Petri Nets are a powerfirl method to approach various kinds of discrete event systems. They allow expressing efficiently a variety of phenomena such as sequences, parallelism, synchronized start and stop, etc. They get the advantage to be able to be used both for the modelling of a static structure and the dynamic behaviour. They allow in this way to examine not only the system architecture but also its temporal evolution andreactions to stimuli. This makes them very suitable for the dependability, safety and performance evaluation. CPN can be employed throughout the complete process development cycle one can thus preserve the same formalism to imderstand the architecture and the behaviour of the process (as well as the lunctional analysis). The driver model and various test scenarios can be also implemented in this formalism. 

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