A Discrete-Event Simulation Metamodel for Obtaining Simulation Models from Business Process Models

Organizations need to be agile and fl exible to meet the continuous changes. Business Process Management (BPM) is harnessing the continuous changes suffered by organizations in the value chain and, therefore, in their processes. Simulation models offer the ability to experience different decisions and analyze their results in systems where the cost or risk of actual experimentation are prohibitive. BPMN models are not directly executable nor is it possible to simulate their behavior in various input parameters. This paper proposes the application of model-driven engineering (MDE) to integrate the defi nition of business processes with Discrete- Event Simulation (DES) as a tool to support decision-making. We propose a platform independent DES metamodel and a set of rules, to automatically generate the simulation model from BPMN 2.0 based business process in accordance with previous metamodel.Ministerio de Economía y competitividad TIN2010- 20057- C03-02Ministerio de Economía y Competitividad TIN2010-20057-C03-03Junta de Andalucía TIC-5789Junta de Andalucía TIC-19

Computer-aided design for building multipurpose routing processes in discrete event simulation models

Good domain-modeling enables an appropriate separation of concerns that improves quality properties in the simulation models, such as modifiability and maintainability. In this paper, the interplay of abstraction and concreteness in advancing the theory and practice of Modelling and Simulation is improved using the Model-Driven Engineering levels for building simulation models devoted to routing processes. The definition of this type of processes is detailed as a domain-model conceived as an abstraction defined in a graph model. Such abstraction turns into a set of formal simulation models that are (later) translated into an executable implementation. The final simulation models are specified using Routed DEVS formalism. The methodological proposal is accomplished with the development of a Modelling and Simulation graphical software tool that uses the set of models (defined in terms of the Model-Driven Engineering approach) as the core of its operation. This graphical software tool is developed as a plug-in for Eclipse Integrated Development Environment with aims to take advantage of existent Modeling and Simulation software. Therefore, the usefulness of graphical modeling for supporting the development of the simulation models is empowered with a Model-Driven Engineering process. The main benefit obtained when the Model-Driven Engineering approach is used for modeling an abstraction of the final simulation model is a clear reduction of formalization and implementation times.Fil: Blas, María Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Gonnet, Silvio Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Robust Optimization in Simulation: Taguchi and Response Surface Methodology

Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a 'robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by Response Surface Methodology (RSM). George Box originated RSM, and Douglas Montgomery recently extended RSM to robust optimization of real (non-simulated) systems. We combine Taguchi's view with RSM for simulated systems, and apply the resulting methodology to classic Economic Order Quantity (EOQ) inventory models. Our results demonstrate that in general robust optimization requires order quantities that differ from the classic EOQ.Pareto frontier;bootstrap;Latin hypercube sampling

Robust optimization in simulation: Taguchi and response surface methodology

Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a `robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by Response Surface Methodology (RSM). George Box originated RSM, and Douglas Montgomery recently extended RSM to robust optimization of real (non-simulated) systems. We combine Taguchi's view with RSM for simulated systems. We illustrate the resulting methodology through classic Economic Order Quantity (EOQ) inventory models, which demonstrate that robust optimization may require order quantities that differ from the classic EOQ

Monotonicity-Preserving Bootstrapped Kriging Metamodels for Expensive Simulations

Kriging (Gaussian process, spatial correlation) metamodels approximate the Input/Output (I/O) functions implied by the underlying simulation models; such metamodels serve sensitivity analysis and optimization, especially for computationally expensive simulations. In practice, simulation analysts often know that the I/O function is monotonic. To obtain a Kriging metamodel that preserves this known shape, this article uses bootstrapping (or resampling). Parametric bootstrapping assuming normality may be used in deterministic simulation, but this article focuses on stochastic simulation (including discrete-event simulation) using distribution-free bootstrapping. In stochastic simulation, the analysts should simulate each input combination several times to obtain a more reliable average output per input combination. Nevertheless, this average still shows sampling variation, so the Kriging metamodel does not need to interpolate the average outputs. Bootstrapping provides a simple method for computing a noninterpolating Kriging model. This method may use standard Kriging software, such as the free Matlab toolbox called DACE. The method is illustrated through the M/M/1 simulation model with as outputs either the estimated mean or the estimated 90% quantile; both outputs are monotonic functions of the traffic rate, and have nonnormal distributions. The empirical results demonstrate that monotonicity-preserving bootstrapped Kriging may give higher probability of covering the true simulation output, without lengthening the confidence interval.Queues

Modularization Approaches in the Context of Monolithic Simulations

Qualitätsmerkmale eines Software-Systems wie Zuverlässigkeit oder Performanz können über dessen Erfolg oder Scheitern entscheiden. Diese Qualitätsmerkmale können im klassischen Software-Ingenieurswesen erst bestimmt werden, wenn der Entwurfsprozess bereits vollendet ist und Teile des Software-Systems implementiert sind. Computer-Simulationen erlauben es jedoch Schätzungen dieser Werte schon während des Software-Entwurfs zu bestimmen. Simulationen werden erstellt um bestimmte Aspekte eines Systems zu analysieren. Die Repräsentation des Systems ist auf diese Analyse spezialisiert. Diese Spezialisierung resultiert oft in einer monolithischen Struktur der Simulation. Solch eine Struktur kann jedoch die Wartbarkeit der Simulation negativ beeinflussen und das Verständnis und die Wiederverwendbarkeit der Repräsentation des Systems verschlechtern. Die Nachteile einer monolithischen Struktur können durch das Konzept der Modularisierung reduziert werden. In diesem Ansatz wird ein Problem in kleinere Teilprobleme zerlegt. Diese Zerlegung ermöglicht ein besseres Veständnis und eine bessere Handhabung der Teilprobleme. In dieser Arbeit wird ein Ansatz präsentiert, um die Kopplung von neu entwickelten oder bereits existierenden Simulationen zu einer modularen Simulation zu beschreiben. Dieser Ansatz besteht aus einer Domänenspezifischen Sprache (DSL), die mit modellgetriebenen Technologien entwickelt wird. Die DSL wird in einer Fallstudie angewendet, um die Kopplung von zwei Simulationen zu beschreiben. Weiterhin wird die Kopplung dieser Simulationen mit einem existierenden Kopplungsansatz gemäß der erzeugten Beschreibung manuell implementiert. In dieser Fallstudie wird die Vollständigkeit der Fähigkeit der DSL untersucht, die Kopplung von mehreren Simulation zu einer modularen Simulation zu beschreiben. Weiterhin wird die Genauigkeit des Modularisierungsansatzes bezüglich der Verhaltensbewahrung der modularen Simulation gegenüber der monolithischen Version evaluiert. Hierfür werden die Resultate der modularen Simulation mit denen der monolithischen Version verglichen. Zudem wird die Skalierbarkeit des Ansatzes durch die Betrachtung der Ausführungszeiten untersucht, wenn mehrere Simulationen gekoppelt werden. Außerdem wird der Effekt der Modularisierung auf die Ausführungszeit in Relation zur monolithischen Simulation betrachtet. Die erhaltenen Resultate zeigen, dass die Kopplung der beiden Simulationen der Fallstudie, mit der DSL beschrieben werden kann. Die Resultate bezüglich der Evaluation der Genauigkeit weisen Probleme bei der Interaktion der Simulationen mit dem Kopplungsansatz auf. Nichts desto trotz bleibt das Verhalten der monolithischen Simulation in der modularen Version insgesamt erhalten. Die Evaluation zeigt, dass die modulare Simulation eine Erhöhung der Ausführungszeit im Vergleich zur monolithischen Version erfährt. Zudem deutet die Analyse der Skalierbarkeit darauf hin, dass die Ausführungszeit der modularen Simulation nicht exponentiell mit der Anzahl der gekoppelten Simulationen wächst

Software Process Simulation Modeling: Systematic literature review

Changes and continuous progress in logistics and productive systems make the realization of improvements in decision making necessary. Simulation is a good support tool for this type of decisions because it allows reproducing processes virtually to study their behavior, to analyze the impact of possible changes or to compare different design alternatives without the high cost of scale experiments. Although process simulation is usually focused on industrial processes, over the last two decades, new proposals have emerged to bring simulation techniques into software engineering. This paper describes a Systematic Literature Review (SLR) which returned 8070 papers (published from 2013 to 2019) by a systematic search in 4 digital libraries. After conducting this SLR, 36 Software Process Simulation Modeling (SPSM) works were selected as primary studies and were documented following a specific characterization scheme. This scheme allows characterizing each proposal according to the paradigm used and its technology base as well as its future line of work. Our purpose is to identify trends and directions for future research on SPSM after identifying and studying which proposals in this topic have been defined and the relationships and dependencies between these proposals in the last five years. After finishing this review, it is possible to conclude that SPSM continues to be a topic that is very much addressed by the scientific community, but each contribution has been proposed with particular goals. This review also concludes that Agent-Based Simulation and System Dynamics paradigm is increasing and decreasing, respectively, its trend among SPSM proposals in the last five years. Regarding Discrete-Event Simulation paradigm, it seems that it is strengthening its position among research community in recent years to design new approaches.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-

Optimal design of simulation experiments with nearly saturated queues

Simulation Models;Interpolation;Queueing Network;Extrapolation

The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose