scholarly journals Overview of construction simulation approaches to model construction processes

2019 ◽  
Vol 11 (1) ◽  
pp. 1853-1861 ◽  
Author(s):  
Orsolya Bokor ◽  
Laura Florez ◽  
Allan Osborne ◽  
Barry J. Gledson
Keyword(s):  
Discrete Event ◽  
Simulation Method ◽  
Agent Based Modeling ◽  
Model Construction ◽  
Construction Simulation ◽  
Dynamic Nature ◽  
Agent Based ◽  
Event Simulation ◽  
Hybrid Approaches ◽  
Construction Works

Abstract Construction simulation is a versatile tech­nique with numerous applications. The basic simulation methods are discrete-event simulation (DES), agent-based modeling (ABM), and system dynamics (SD). Depending on the complexity of the problem, using a basic simulation method might not be enough to model construction works appropriately; hybrid approaches are needed. These are combinations of basic methods, or pairings with other techniques, such as fuzzy logic (FL) and neural networks (NNs). This paper presents a framework for applying sim­ulation for problems within the field of construction. It describes DES, SD, and ABM, in addition to presenting how hybrid approaches are most useful in being able to reflect the dynamic nature of construction processes and capture complicated behavior, uncertainties, and depend­encies. The examples show the application of the frame­work for masonry works and how it could be used for obtaining better productivity estimates. Several structures of hybrid simulation are presented alongside their inputs, outputs, and interaction points, which provide a practical reference for researchers on how to implement simulation to model construction systems of labor-intensive activities and lays the groundwork for applications in other con­struction-related activities.

Multi-Methodology Approach

2021 ◽  
pp. 98-128
Keyword(s):  
System Dynamics ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Multiple Methods ◽  
Soft Systems ◽  
Agent Based ◽  
Event Simulation ◽  
Pluralistic Approach ◽  
The Difference

The pluralistic approach in today's world needs combining multiple methods, whether hard or soft, into a multi-methodology intervention. The methodologies can be combined, sometimes from several different paradigms, including hard and soft, in the form of a multi-methodology so that the hard paradigms are positivistic and see the organizational environment as objective, while the nature of soft paradigms is interpretive. In this chapter, the combination of methodologies has been examined using soft systems methodologies (SSM) and simulation methodologies including discrete event simulation (DES), system dynamics (SD), and agent-based modeling (ABM). Also, using the ontological, epistemological, and methodological assumptions underlying the respective paradigms, the difference between SD, ABM, SSM; a synthesis of SSM and SD generally known as soft system dynamics methodology (SSDM); and a promising integration of SSM and ABM referred to as soft systems agent-based methodology (SSABM) have been proven.

scholarly journals Methods for Weighting Decisions to Assist Modelers and Decision Analysists: A Review of Ratio Assignment and Approximate Techniques

2021 ◽  
Vol 11 (21) ◽  
pp. 10397
Author(s):  
Barry Ezell ◽  
Christopher J. Lynch ◽  
Patrick T. Hester
Keyword(s):  
Decision Making ◽  
Discrete Event Simulation ◽  
Computational Models ◽  
State Of The Art ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Decision Makers ◽  
Agent Based ◽  
Event Simulation ◽  
Decision Making Processes

Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions.

Discrete-event simulation and agent-based modeling in the tasks of the school informatics course

2020 ◽  
Vol 1 (9) ◽  
pp. 4-13
Author(s):  
N. V. Klimina ◽  
I. A. Morozov
Keyword(s):  
Extracurricular Activities ◽  
Discrete Event ◽  
Simulation Models ◽  
Agent Based Modeling ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Event Simulation ◽  
Cellular Environment ◽  
Scope Of Application ◽  
Biological Simulation

In order to study processes and phenomena, it is necessary to create their models. The most interesting and valuable for the analysis of current situations are models that describe processes as if they were happening in reality. These are the so-called simulation models. The scope of application of simulation models is extensive. The most in demand are economic, sociological and biological simulation models. The relevance of simulation is obvious, and it is natural that the theme of simulation is present in the content of the school informatics course. The article provides guidelines for studying the basics of modeling in a school informatics course related to the creation and research of discrete-event and agent-based simulation models. Examples of tasks recommended for consideration in the 9–11th grades at informatics lessons or in extracurricular activities are given, these are task based on the information-cybernetic approach, implemented in the PascalABC environment, and the task using a cellular automaton, implemented in the Cellular environment.

Simulation of management systems of forest infrastructure

2015 ◽  
Vol 5 (2) ◽  
pp. 177-187
Author(s):  
Васильев ◽  
Oleg Vasilyev ◽  
Корныльева ◽  
Yuliya Kornylyeva
Keyword(s):  
System Dynamics ◽  
Discrete Event Simulation ◽  
Input Data ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Management Systems ◽  
Center Problem ◽  
Management Tools ◽  
Agent Based ◽  
Event Simulation

The article considers the possibility of a modern and rapidly developing management tools – simulation, allows to obtain detailed statistics on various aspects of the system, depending on the input data. The basic modeling approaches: system dynamics, discrete-event simulation, and agent-based modeling. Forestry breeding and seed center problem to be solved with the help of simulation are substantiated. Examples of problems in other areas of forestry, which can be solved with the help of this tool, are given.

scholarly journals Comparing Three Simulation Model Using Taxonomy: System Dynamic Simulation, Discrete Event Simulation and Agent Based Simulation

2013 ◽  
Vol 1 (3) ◽  
pp. 54-59
Author(s):  
Suliza Sumari ◽  
Roliana Ibrahim ◽  
Nor Hawaniah Zakaria ◽  
Amy Hamijah Ab Hamid
Keyword(s):  
Simulation Model ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Simulation Method ◽  
System Dynamic ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Event Simulation ◽  
System Dynamic Simulation

Simulation model is one of the methods commonly used in Operational Research in order to represent the real situation that occurs in a system as well as to test the scenario based on different behavior.  In this paper we discuss about three different models used in simulation: system dynamic, agent based simulation and discrete event simulation.  The aim of this paper is to compare all these three methods in context of features, advantages, disadvantages and tools being used in each simulation method.  The comparison of this paper also includes the classification of simulation model using taxonomy.  Throughout this paper, we view a few software tools usually being used in a simulation like Vensim, ProModel and AnyLogic.

scholarly journals DVCompute Simulator for discrete event simulation

2021 ◽  
Vol 16 (93) ◽  
pp. 93-108
Author(s):  
David E. Sorokin ◽  
Keyword(s):  
Discrete Event Simulation ◽  
Large Scale ◽  
Distributed Simulation ◽  
Discrete Event ◽  
Simulation Models ◽  
General Purpose ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Event Simulation ◽  
Programming Techniques

The author of this article represents his own work DVCompute Simulator, which is a collection of general-purpose programming libraries for discrete event simulation. The aim of the research was to create a set of simulators in the Rust language, efficient in terms of speed of execution, based on a unified approach and destined for different simulation modes. The simulators implement such modes as ordinary sequential simulation, nested simulation and distributed simulation. The article describes that nested simulation is related to Theory of Games, while distributed simulation can be used for running large-scale simulation models on supercomputers. It is shown how these different simulation modes can be implemented based on the single approach that combines many paradigms: the event-oriented paradigm, the process-oriented one, blocks similar to the GPSS language and even partially agent-based modeling. The author's approach is based on using the functional programming techniques, where the simulation model is defined as a composition of computations. The results of testing two modules are provided, where the modules support both the optimistic and conservative methods of distributed simulation.

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