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A self-adaptive agent-based simulation modelling framework for dynamic processes

Agent-based simulation (ABS) modelling had been a prevalent approach for simulation of dynamic processes of various domains. Construction of ABS models are usually in the form of domain-specific for dynamic process simulation objectives. This methodology conveniently enable the construction AB...

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Main Author: Ling, Loo Yim, Dr.
Format: text::Thesis
Language:English
Published: 2023
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spelling my.uniten.dspace-196052023-05-05T05:45:21Z A self-adaptive agent-based simulation modelling framework for dynamic processes Ling, Loo Yim, Dr. Agent-based simulation (ABS) modelling had been a prevalent approach for simulation of dynamic processes of various domains. Construction of ABS models are usually in the form of domain-specific for dynamic process simulation objectives. This methodology conveniently enable the construction ABS models to meet specific simulation objectives without the need of reference of standard protocols or languages. Proprietary issues led to inability of customization or inextensibility of the models and lack of validation and verification which further to lack of replication of models and results. Inextensible simulation model led to manual construction of new ABS model for every new simulation objective. Hence there are large gaps of time and cost wastage which were not successfully addressed by previous research efforts. Lack of validation and verification of ABS model and results against broader dataset or domains, led to non-robust and unreliable simulation model and results. Previous research efforts attempted with generic ABS modelling framework that create simulation models from scratch to meet domain-specific simulation objectives. This research propose a self-adaptive ABS modelling framework addressing the gaps through adaptive capability of simulation model construction at runtime according to input domains. Key parameters for dynamic processes of different domains were formulated for the construction of self-adaptive simulation algorithms and modelling. Self-adaptive simulation algorithms were formulated to enable the model’s adaptive capability. The research work was made feasible by prudent judgement of experimenting on three (3) case studies of different domains but inherit key parameters namely; time, workflow, resources, number of dynamic processes, dynamic process size, dynamic process type, agent attributes, agent behaviours and agent capacity. Case studies for simulating dynamic processes of new student registration (administrative domain), transport request (transportation domain) and crime investigation (security domain) with real data collected through interview and document reviews to test the proposed idea. The successful construction of ABS models at runtime and phenomena-accurate simulation results implied the ability of proposed self-adaptive ABS modelling framework to bridge the aforementioned gaps as well as meeting the core objectives of this research. 2023-05-03T13:40:41Z 2023-05-03T13:40:41Z 2021-08 Resource Types::text::Thesis https://irepository.uniten.edu.my/handle/123456789/19605 en application/pdf
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description Agent-based simulation (ABS) modelling had been a prevalent approach for simulation of dynamic processes of various domains. Construction of ABS models are usually in the form of domain-specific for dynamic process simulation objectives. This methodology conveniently enable the construction ABS models to meet specific simulation objectives without the need of reference of standard protocols or languages. Proprietary issues led to inability of customization or inextensibility of the models and lack of validation and verification which further to lack of replication of models and results. Inextensible simulation model led to manual construction of new ABS model for every new simulation objective. Hence there are large gaps of time and cost wastage which were not successfully addressed by previous research efforts. Lack of validation and verification of ABS model and results against broader dataset or domains, led to non-robust and unreliable simulation model and results. Previous research efforts attempted with generic ABS modelling framework that create simulation models from scratch to meet domain-specific simulation objectives. This research propose a self-adaptive ABS modelling framework addressing the gaps through adaptive capability of simulation model construction at runtime according to input domains. Key parameters for dynamic processes of different domains were formulated for the construction of self-adaptive simulation algorithms and modelling. Self-adaptive simulation algorithms were formulated to enable the model’s adaptive capability. The research work was made feasible by prudent judgement of experimenting on three (3) case studies of different domains but inherit key parameters namely; time, workflow, resources, number of dynamic processes, dynamic process size, dynamic process type, agent attributes, agent behaviours and agent capacity. Case studies for simulating dynamic processes of new student registration (administrative domain), transport request (transportation domain) and crime investigation (security domain) with real data collected through interview and document reviews to test the proposed idea. The successful construction of ABS models at runtime and phenomena-accurate simulation results implied the ability of proposed self-adaptive ABS modelling framework to bridge the aforementioned gaps as well as meeting the core objectives of this research.
format Resource Types::text::Thesis
author Ling, Loo Yim, Dr.
spellingShingle Ling, Loo Yim, Dr.
A self-adaptive agent-based simulation modelling framework for dynamic processes
author_facet Ling, Loo Yim, Dr.
author_sort Ling, Loo Yim, Dr.
title A self-adaptive agent-based simulation modelling framework for dynamic processes
title_short A self-adaptive agent-based simulation modelling framework for dynamic processes
title_full A self-adaptive agent-based simulation modelling framework for dynamic processes
title_fullStr A self-adaptive agent-based simulation modelling framework for dynamic processes
title_full_unstemmed A self-adaptive agent-based simulation modelling framework for dynamic processes
title_sort self-adaptive agent-based simulation modelling framework for dynamic processes
publishDate 2023
_version_ 1806427647773769728
score 13.222552

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