Hybrid Agent-Based Modeling (HABM)—A Framework for Combining Agent-Based Modeling and Simulation, Discrete Event Simulation, and System Dynamics

2018 ◽  
pp. 603-608
Author(s):  
Joachim Block
Keyword(s):  
System Dynamics ◽  
Modeling And Simulation ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Event Simulation ◽  
Hybrid Agent

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.

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 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.

Agent-Based Simulation Model Representation Using BPMN

2013 ◽  
pp. 378-400 ◽  
Author(s):  
Bhakti S. S. Onggo
Keyword(s):  
System Dynamics ◽  
Conceptual Model ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Model Representation ◽  
Good Representation ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Event Simulation ◽  
Business Applications

Conceptual modelling is the process of abstracting a model from a real or proposed system into a conceptual model. An explicit conceptual model representation allows the model to be communicated and analysed by the stakeholders involved in a simulation project. A good representation that can be understood by all stakeholders is especially essential when the project involves different stakeholders. The three commonly used paradigms in business applications are discrete-event simulation, agent-based simulation, and system dynamics. While the conceptual model representations in discrete-event simulation and system dynamics have been dominated by process-flow and stock-and-flow diagrams, respectively, research into the conceptual model representation in agent-based simulation is relatively new. Many existing representation methods for agent-based simulation models are less friendly to business users. This chapter advocates the use of Business Process Model and Notation (BPMN) diagrams for the agent-based simulation conceptual model representation in the context of business applications. This chapter also demonstrates how the proposed BPMN representation and other methods such as Petri Nets, DEVS, and UML are used to represent the well-known SugarScape model.

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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