Discrete-Event Simulation Software: What the Users Want

Hlupic, Vlatka

doi:10.1177/003754979907300605

Cited by 22 publications

(15 citation statements)

References 6 publications

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“…It is not surprising that simulation has been an academic tool since the 1960s with no real success in construction industry [20]. A simulation survey presented by Hlupic [31] revealed many simulation platforms limitations, such as unsuitability for complex and nonstandard problems, much expensiveness, difficulty to learn, lack of integration/compatibility with other packages such as database management tools, and lack of flexibility. Similar problems were reported by Mohamed and AbouRizk [32], Centeno and Carrillo [3], and AbouRizk [2].…”

Section: Summary Of Construction Projects Modeling Challengesmentioning

confidence: 99%

Hybrid Simulation Environment for Construction Projects: Identification of System Design Criteria

Moussa

Ruwanpura

Jergeas

et al. 2014

Journal of Construction Engineering

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Large construction projects are complex, dynamic, and unpredictable. They are subject to external and uncontrollable events that affect their schedule and financial outcomes. Project managers take decisions along the lifecycle of the projects to align with projects objectives. These decisions are data dependent where data change over time. Simulation-based modeling and experimentation of such dynamic environment are a challenge. Modeling of large projects or multiprojects is difficult and impractical for standalone computers. This paper presents the criteria required in a simulation environment suitable for modeling large and complex systems such as construction projects to support their lifecycle management. Also presented is a platform that encompasses the identified criteria. The objective of the platform is to facilitate and simplify the simulation and modeling process and enable the inclusion of complexity in simulation models.

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Section: Summary Of Construction Projects Modeling Challengesmentioning

confidence: 99%

Hybrid Simulation Environment for Construction Projects: Identification of System Design Criteria

Moussa

Ruwanpura

Jergeas

et al. 2014

Journal of Construction Engineering

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“…4.2, p. 81) allows to leverage the strengths of a plug-in based approach for conducting scientifically sound simulation studies. The desire of users to have a flexible and thorough support for experimentation is not new (e.g., [137]) and has been implemented in many other simulation systems as well, e.g., Tornado, where a-priori experiments are used for (manual) algorithm selection [41, p. 145 et sqq.]. Few simulation systems, however, are able to integrate a comparable range of techniques from different fields, let alone allowing users to combine them flexibly.…”

Section: The Experimentation Layer Of James IImentioning

confidence: 99%

Automatic Algorithm Selection for Complex Simulation Problems

Ewald¹

2012

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To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments, without demanding expert knowledge on simulation.The first part of the thesis surveys existing approaches to solve the algorithm selection problem and discusses techniques to analyze simulation algorithm performance. A unified categorization of existing algorithm selection techniques is worked out, as these stem from various research domains (e.g., finance, artificial intelligence).The second part introduces a software framework for automatic simulation algorithm selection and describes its constituents, as well as their integration into the modeling and simulation framework JAMES II. The implemented selection mechanisms are able to cope with three situations: a) no prior knowledge is available, b) the impact of problem features on performance is unknown, and c) a relationship between problem features and algorithm performance can be established empirically.An experimental evaluation of the developed methods concludes the thesis. It is shown that an automated algorithm selection may significantly increase the overall performance of a simulation system. Some of the presented mechanisms also support the research on simulation methods, as they facilitate their development and evaluation. ZusammenfassungDie Auswahl des passendsten Simulationsalgorithmus für eine bestimmte Aufgabe ist oftmals schwierig. Dies liegt an der komplexen Interaktion zwischen Modelleigenschaften, Implementierungsdetails und der Laufzeitumgebung, welche die Gesamtleistung stark beeinflussen kann. Eine automatisierte Auswahl von Simulationsalgorithmen unterstützt die Anwender eines Simulationssystems, indem sie eine geeignete Konfiguration des Systems ohne zusätzliches Fachwissen aus dem Bereich Simulation ermöglicht.Der erste Teil der Arbeit befasst sich eingehend mit Vorarbeiten zur automatischen Algorithmenauswahl, sowie mit der Leistungsanalyse von Simulationsalgorithmen. Eine einheitliche Kategorisierung wird erarbeitet um die aus den verschiedensten Fachgebieten (z.B. Finanzwesen, Künstliche Intelligenz) stammenden Ansätze zur Algorithmenauswahl einzuordnen.Der zweite Teil der Arbeit stellt ein Rahmenwerk zur automatischen Auswahl von Simulationsalgorithmen vor und beschreibt dessen Bestandteile, einschließlich ihrer Integration ins Modellierungsund Simulationsrahmenwerk JAMES II. Die realisierten Auswahlmechanismen beziehen sich dabei auf drei Situationen: a) keinerlei Vorwissen ist vorhanden, b) der Einfluss von Problemeigenschaften auf die Leistung ist unbekannt, und c) ein Zusammenhang zwischen Problemeigenschaften und Algorithmenleistung konnte empirisch ermittelt werden.Eine experimentelle Untersuchung der entwickelten Methoden schließt die Arbeit ab. Es wird gezeigt, dass eine aut...

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“…In a survey of simulation users, Hlupic 107 identified better experimental support as the highest ranked requirement. This could either be addressed by developing software to guide simulation users or even automating aspects of the experimental process.…”

Section: Model Developmentmentioning

confidence: 99%

Discrete-event simulation: from the pioneers to the present, what next?

Robinson

2005

Journal of the Operational Research Society

173

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Discrete-event simulation is one of the most popular modelling techniques. It has developed significantly since the inception of computer simulation in the 1950s, most of this in line with developments in computing. The progress of simulation from its early days is charted with a particular focus on recent history. Specific developments in the past 15 years include visual interactive modelling, simulation optimisation, virtual reality, integration with other software, simulation in the service sector, distributed simulation and the use of the world wide web.The future is then speculated upon. Potential changes in model development, model use, the domain of application for simulation and integration with other simulation approaches are all discussed. The desirability of continuing to follow developments in computing, without significant developments in the wider methodology of simulation, is questioned.

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Discrete-Event Simulation Software: What the Users Want

Cited by 22 publications

References 6 publications

Hybrid Simulation Environment for Construction Projects: Identification of System Design Criteria

Hybrid Simulation Environment for Construction Projects: Identification of System Design Criteria

Automatic Algorithm Selection for Complex Simulation Problems

Discrete-event simulation: from the pioneers to the present, what next?

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