Editorial

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5 pages, 299 KiB

Open AccessEditorial

Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems

by Philippe Mathieu, Juan Manuel Corchado, Alfonso González-Briones and Fernando De la Prieta

Systems 2023, 11(10), 525; https://doi.org/10.3390/systems11100525 - 21 Oct 2023

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Abstract

Introduction [...] Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

Research

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31 pages, 1007 KiB

Open AccessArticle

Conflicting Bundle Allocation with Preferences in Weighted Directed Acyclic Graphs: Application to Orbit Slot Allocation Problems

by Stéphanie Roussel, Gauthier Picard, Cédric Pralet and Sara Maqrot

Systems 2023, 11(6), 297; https://doi.org/10.3390/systems11060297 - 09 Jun 2023

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Abstract

We introduce resource allocation techniques for problems where (i) the agents express requests for obtaining item bundles as compact edge-weighted directed acyclic graphs (each path in such a graph is a bundle whose valuation is the sum of the weights of the traversed [...] Read more.

We introduce resource allocation techniques for problems where (i) the agents express requests for obtaining item bundles as compact edge-weighted directed acyclic graphs (each path in such a graph is a bundle whose valuation is the sum of the weights of the traversed edges), and (ii) the agents do not bid on the exact same items but may bid on conflicting items that cannot be both assigned or that require accessing a specific resource with limited capacity. This setting is motivated by real applications such as Earth observation slot allocation, virtual network functions, or multi-agent path finding. We model several directed path allocation problems (vertex-constrained and resource-constrained), investigate several solution methods (qualified as exact or approximate, and utilitarian or fair), and analyze their performances on an orbit slot ownership problem, for realistic requests and constellation configurations. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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22 pages, 1377 KiB

Open AccessArticle

Agent-Based Collaborative Random Search for Hyperparameter Tuning and Global Function Optimization

by Ahmad Esmaeili, Zahra Ghorrati and Eric T. Matson

Systems 2023, 11(5), 228; https://doi.org/10.3390/systems11050228 - 05 May 2023

Cited by 5 | Viewed by 1476

Abstract

Hyperparameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles to the utilization of general purpose black-box optimization [...] Read more.

Hyperparameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles to the utilization of general purpose black-box optimization techniques. This paper proposes an agent-based collaborative technique for finding near-optimal values for any arbitrary set of hyperparameters (or decision variables) in a machine learning model (or a black-box function optimization problem). The developed method forms a hierarchical agent-based architecture for the distribution of the searching operations at different dimensions and employs a cooperative searching procedure based on an adaptive width-based random sampling technique to locate the optima. The behavior of the presented model, specifically against changes in its design parameters, is investigated in both machine learning and global function optimization applications, and its performance is compared with that of two randomized tuning strategies that are commonly used in practice. Moreover, we have compared the performance of the proposed approach against particle swarm optimization (PSO) and simulated annealing (SA) methods in function optimization to provide additional insights into its exploration in the search space. According to the empirical results, the proposed model outperformed the compared random-based methods in almost all tasks conducted, notably in a higher number of dimensions and in the presence of limited on-device computational resources. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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14 pages, 435 KiB

Open AccessArticle

An Infrastructure Cost and Benefits Evaluation Framework for Blockchain-Based Applications

by Miguel Pincheira, Elena Donini, Massimo Vecchio and Raffaele Giaffreda

Systems 2023, 11(4), 184; https://doi.org/10.3390/systems11040184 - 05 Apr 2023

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Abstract

Blockchain is currently a core technology for developing new types of decentralized applications. With the unique properties of blockchain, unique challenges and characteristics are introduced to the system. Among these characteristics, the infrastructure costs and benefits of the system are critical to evaluate [...] Read more.

Blockchain is currently a core technology for developing new types of decentralized applications. With the unique properties of blockchain, unique challenges and characteristics are introduced to the system. Among these characteristics, the infrastructure costs and benefits of the system are critical to evaluate the feasibility of any system and have yet to be addressed in the current literature. This work presents a framework for evaluating blockchain applications’ infrastructure costs and benefits. The framework includes a taxonomy to classify the related transactions, a model to evaluate the infrastructure costs and benefits in applications using public or private blockchains, and a methodology to guide the use of the model. The model is based on simple parameters that describe the systems, and the methodology helps to identify and estimate these parameters at any stage of the application life cycle. We quantitatively analyze three real use cases to demonstrate the framework’s merit. The analyses highlight the model’s accuracy by achieving the same results presented in the use cases. Furthermore, the use-case analyses emphasize the framework’s potential to evaluate different scenarios across the entire life cycle of blockchain-based applications. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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16 pages, 405 KiB

Open AccessArticle

It’s All about Reward: Contrasting Joint Rewards and Individual Reward in Centralized Learning Decentralized Execution Algorithms

by Peter Atrazhev and Petr Musilek

Systems 2023, 11(4), 180; https://doi.org/10.3390/systems11040180 - 30 Mar 2023

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Abstract

This paper addresses the issue of choosing an appropriate reward function in multi-agent reinforcement learning. The traditional approach of using joint rewards for team performance is questioned due to a lack of theoretical backing. The authors explore the impact of changing the reward [...] Read more.

This paper addresses the issue of choosing an appropriate reward function in multi-agent reinforcement learning. The traditional approach of using joint rewards for team performance is questioned due to a lack of theoretical backing. The authors explore the impact of changing the reward function from joint to individual on learning centralized decentralized execution algorithms in a Level-Based Foraging environment. Empirical results reveal that individual rewards contain more variance, but may have less bias compared to joint rewards. The findings show that different algorithms are affected differently, with value factorization methods and PPO-based methods taking advantage of the increased variance to achieve better performance. This study sheds light on the importance of considering the choice of a reward function and its impact on multi-agent reinforcement learning systems. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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21 pages, 1717 KiB

Open AccessArticle

Enhancing Local Decisions in Agent-Based Cartesian Genetic Programming by CMA-ES

by Jörg Bremer and Sebastian Lehnhoff

Systems 2023, 11(4), 177; https://doi.org/10.3390/systems11040177 - 28 Mar 2023

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Abstract

Cartesian genetic programming is a popular version of classical genetic programming, and it has now demonstrated a very good performance in solving various use cases. Originally, programs evolved by using a centralized optimization approach. Recently, an algorithmic level decomposition of program evolution has [...] Read more.

Cartesian genetic programming is a popular version of classical genetic programming, and it has now demonstrated a very good performance in solving various use cases. Originally, programs evolved by using a centralized optimization approach. Recently, an algorithmic level decomposition of program evolution has been introduced that can be solved by a multi-agent system in a fully distributed manner. A heuristic for distributed combinatorial problem-solving was adapted to evolve these programs. The applicability of the approach and the effectiveness of the used multi-agent protocol as well as of the evolved genetic programs for the case of full enumeration in local agent decisions has already been successfully demonstrated. Symbolic regression, n-parity, and classification problems were used for this purpose. As is typical of decentralized systems, agents have to solve local sub-problems for decision-making and for determining the best local contribution to solving program evolution. So far, only a full enumeration of the solution candidates has been used, which is not sufficient for larger problem sizes. We extend this approach by using CMA-ES as an algorithm for local decisions. The superior performance of CMA-ES is demonstrated using Koza’s computational effort statistic when compared with the original approach. In addition, the distributed modality of the local optimization is scrutinized by a fitness landscape analysis. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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28 pages, 1125 KiB

Open AccessArticle

Engineering IoT-Based Open MAS for Large-Scale V2G/G2V

by Nikolaos I. Spanoudakis, Charilaos Akasiadis, Georgios Iatrakis and Georgios Chalkiadakis

Systems 2023, 11(3), 157; https://doi.org/10.3390/systems11030157 - 19 Mar 2023

Cited by 1 | Viewed by 1962

Abstract

In this paper, we aimed to demonstrate how to engineer Internet of Things (IoT)-based open multiagent systems (MASs). Specifically, we put forward an IoT/MAS architectural framework, along with a case study within the important and challenging-to-engineer vehicle-to-grid (V2G) and grid-to-vehicle (G2V) energy transfer [...] Read more.

In this paper, we aimed to demonstrate how to engineer Internet of Things (IoT)-based open multiagent systems (MASs). Specifically, we put forward an IoT/MAS architectural framework, along with a case study within the important and challenging-to-engineer vehicle-to-grid (V2G) and grid-to-vehicle (G2V) energy transfer problem domain. The proposed solution addresses the important non-functional requirement of scalability. To this end, we employed an open multiagent systems architecture, arranging agents as modular microservices that were interconnected via a multi-protocol Internet of Things platform. Our approach allows agents to view, offer, interconnect, and re-use their various strategies, mechanisms, or other algorithms as modular smart grid services, thus enabling their seamless integration into our MAS architecture, and enabling the solution of the challenging V2G/G2V problem. At the same time, our IoT-based implementation offers both direct applicability in real-world settings and advanced analytics capabilities via enabling digital twin models for smart grid ecosystems. We have described our MAS/IoT-based architecture in detail; validated its applicability via simulation experiments involving large numbers of heterogeneous agents, operating and interacting towards effective V2G/G2V; and studied the performance of various electric vehicle charging scheduling and V2G/G2V-incentivising electricity pricing algorithms. To engineer our solution, we used ASEME, a state-of-the-art methodology for multiagent systems using the Internet of Things. Our solution can be employed for the implementation of real-world prototypes to deliver large-scale V2G/G2V services, as well as for the testing of various schemes in simulation mode. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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28 pages, 1198 KiB

Open AccessArticle

Deep Reinforcement Learning Reward Function Design for Autonomous Driving in Lane-Free Traffic

by Athanasia Karalakou, Dimitrios Troullinos, Georgios Chalkiadakis and Markos Papageorgiou

Systems 2023, 11(3), 134; https://doi.org/10.3390/systems11030134 - 02 Mar 2023

Cited by 3 | Viewed by 2906

Abstract

Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the notion of lanes, and consider the whole lateral space within the road boundaries. This constitutes an entirely different problem domain for autonomous driving compared to lane-based traffic, as [...] Read more.

Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the notion of lanes, and consider the whole lateral space within the road boundaries. This constitutes an entirely different problem domain for autonomous driving compared to lane-based traffic, as there is no leader vehicle or lane-changing operation. Therefore, the observations of the vehicles need to properly accommodate the lane-free environment without carrying over bias from lane-based approaches. The recent successes of deep reinforcement learning (DRL) for lane-based approaches, along with emerging work for lane-free traffic environments, render DRL for lane-free traffic an interesting endeavor to investigate. In this paper, we provide an extensive look at the DRL formulation, focusing on the reward function of a lane-free autonomous driving agent. Our main interest is designing an effective reward function, as the reward model is crucial in determining the overall efficiency of the resulting policy. Specifically, we construct different components of reward functions tied to the environment at various levels of information. Then, we combine and collate the aforementioned components, and focus on attaining a reward function that results in a policy that manages to both reduce the collisions among vehicles and address their requirement of maintaining a desired speed. Additionally, we employ two popular DRL algorithms—namely, deep Q-networks (enhanced with some commonly used extensions), and deep deterministic policy gradient (DDPG), which results in better policies. Our experiments provide a thorough investigative study on the effectiveness of different combinations among the various reward components we propose, and confirm that our DRL-employing autonomous vehicle is able to gradually learn effective policies in environments with varying levels of difficulty, especially when all of the proposed rewards components are properly combined. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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17 pages, 1898 KiB

Open AccessArticle

The Hidden Side of Electro-Mobility: Modelling Agents’ Financial Statements and Their Interactions with a European Focus

by Jonatan J. Gómez Vilchez and Roberto Pasqualino

Systems 2023, 11(3), 132; https://doi.org/10.3390/systems11030132 - 01 Mar 2023

Viewed by 1245

Abstract

While much attention has been given, to date, to subsidies and taxes, the literature on the topic is yet to address less visible aspects of electro-mobility. These include the interactions among players, including money exchanges, and balance sheet issues. Analysing these is needed, [...] Read more.

While much attention has been given, to date, to subsidies and taxes, the literature on the topic is yet to address less visible aspects of electro-mobility. These include the interactions among players, including money exchanges, and balance sheet issues. Analysing these is needed, as it helps identify additional mechanisms that may affect electro-mobility. This paper reports a modelling exercise that applies the system dynamics method, with its focus on stock and flow variables. The resulting simulation model captures the financial statements of several macro agents. The results show that the objective of the study is met: the model remains ‘stock-flow consistent’, meaning that assets and equity and liabilities balance out. By attaining this, the model serves as a coherent framework that makes the “hidden” side of electro-mobility visible, for the first time, based on current state-of-the-art, with the implication that it facilitates the analysis of potential financial factors that may either jeopardise or be conducive to faster road electrification. We conclude that the incorporation of the financial statements of key electro-mobility agents and their interlinkages in a simulation model is both a feasible and desired property for policy-relevant models. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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24 pages, 7670 KiB

Open AccessArticle

Multi-Category Innovation and Encroachment Strategy Evolution of Composite E-Commerce Platform Based on Multi-Agent Simulation

by Ziyan Wang and Tianjian Yang

Systems 2022, 10(6), 215; https://doi.org/10.3390/systems10060215 - 11 Nov 2022

Cited by 1 | Viewed by 1490

Abstract

An in-depth study of the product encroachment behavior on the composite e-commerce platform is of great significance to standardize the platform economy. This paper studies product encroachment behavior of composite e-commerce platforms with double-differentiated multi-product competition and constructs a game model of product [...] Read more.

An in-depth study of the product encroachment behavior on the composite e-commerce platform is of great significance to standardize the platform economy. This paper studies product encroachment behavior of composite e-commerce platforms with double-differentiated multi-product competition and constructs a game model of product innovation by an independent seller and product encroachment by the platform owner. Using multi-agent simulation, we simulate the bounded rational decision-making and interaction process of multiple agents in multiple periods and analyze the main parameters’ influence. Results indicate the following: (1) In dual-differentiated multi-product competition, the third-party seller is more willing to invest in innovating high-quality category P, and the profit-driven platform owner only encroaches on the new variants of category P. (2) The larger consumers’ platform owner preference can encourage the third-party seller to innovate high-quality new products. The increase in vertical differentiation of categories can enhance the third-party seller’s innovation motivation for the traffic-attracting category. (3) A reasonable commission rate set by the platform owner can ensure the variety of variants of various categories, thereby expanding the sales scope of the composite e-commerce platform. Diseconomies of scale of category diversity management costs hinder the growth of product variety in the online marketplace. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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18 pages, 1498 KiB

Open AccessArticle

Agent-Based Modeling of Consensus Group Formation with Complex Webs of Beliefs

by Ismo T. Koponen

Systems 2022, 10(6), 212; https://doi.org/10.3390/systems10060212 - 09 Nov 2022

Cited by 2 | Viewed by 2258

Abstract

Formation of consensus groups with shared opinions or views is a common feature of human social life and also a well-known phenomenon in cases when views are complex, as in the case of the formation of scholarly disciplines. In such cases, shared views [...] Read more.

Formation of consensus groups with shared opinions or views is a common feature of human social life and also a well-known phenomenon in cases when views are complex, as in the case of the formation of scholarly disciplines. In such cases, shared views are not simple sets of opinions but rather complex webs of beliefs (WoBs). Here, we approach such consensus group formation through the agent-based model (ABM). Agents’ views are described as complex, extensive web-like structures resembling semantic networks, i.e., webs of beliefs. In the ABM introduced here, the agents’ interactions and participation in sharing their views are dependent on the similarity of the agents’ webs of beliefs; the greater the similarity, the more likely the interaction and sharing of elements of WoBs. In interactions, the WoBs are altered when agents seek consensus and consensus groups are formed. The consensus group formation depends on the agents’ sensitivity to the similarity of their WoBs. If their sensitivity is low, only one large and diffuse group is formed, while with high sensitivity, many separated and segregated consensus groups emerge. To conclude, we discuss how such results resemble the formation of disciplinary, scholarly consensus groups. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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15 pages, 842 KiB

Open AccessArticle

Practical Formalism-Based Approaches for Multi-Resolution Modeling and Simulation

by Jang Won Bae and Il-Chul Moon

Systems 2022, 10(5), 174; https://doi.org/10.3390/systems10050174 - 29 Sep 2022

Viewed by 1387

Abstract

Multi-resolution modeling (MRM) has been considered as an ideal form of simulation to acquire low-resolution scalability as well as high-resolution modeled details. Although both practical and theoretical interests exist in MRM, actual implementations were quite different in terms of cases and methods. Specifically, [...] Read more.

Multi-resolution modeling (MRM) has been considered as an ideal form of simulation to acquire low-resolution scalability as well as high-resolution modeled details. Although both practical and theoretical interests exist in MRM, actual implementations were quite different in terms of cases and methods. Specifically, MRM implementations range from parameter-based interoperation to model exchanges with different resolutions, yet it is difficult to observe a method that focuses on both of these aspects. To this end, this paper introduces a formalism or multi-resolution translational Discrete Event System Specification (MRT-DEVS). Focusing on the practical perspective, MRT-DEVS intends to ease the implementation’s difficulty and reduce the simulation’s execution costs. Specifically, MRT-DEVS embeds state and event translation functions into the model’s specifications so that it enables MRM with less complex mechanisms in terms of operations. Using the provided case study and a reduction to other MRM methods, the theoretical soundness of the proposed method is supported. Moreover, we discussed the pros and the cons of the proposed method from various MRM perspectives. We expect that with all the provided information, MRMS users would consider the proposed method as a practical option to implement their models. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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23 pages, 3738 KiB

Open AccessArticle

Research on the Evolutionary Path of Eco-Conservation and High-Quality Development in the Yellow River Basin Based on an Agent-Based Model

by Aiwu Zhao, Jingyi Wang, Zhenzhen Sun and Hongjun Guan

Systems 2022, 10(4), 105; https://doi.org/10.3390/systems10040105 - 27 Jul 2022

Cited by 4 | Viewed by 1708

Abstract

The high-quality economic and social development of the Yellow River Basin is a combined system comprising the coordinated development of “economy–resources–environment–society”, with resources and the ecological environment bearing capacity as the constraints, and green innovative development as the driving force. Based on the [...] Read more.

The high-quality economic and social development of the Yellow River Basin is a combined system comprising the coordinated development of “economy–resources–environment–society”, with resources and the ecological environment bearing capacity as the constraints, and green innovative development as the driving force. Based on the systematic analysis of the structural dimensions of the composite system, this paper uses the balanced indicators and their coordinated development effectiveness to describe the development quality of the macro-composite system. In order to reveal the mechanism of the evolutionary path of the macro system, the resource- and environment-bearing capacity, regional high-quality development potential, regional innovation capacity, and high-quality development guarantee capacity are adopted as the main attributes and decision-making basis of the autonomous agents. The simulation results show that, under the existing development model, the economic development of all of the provinces in the Yellow River Basin will be constrained by resources and the environment. However, different policy scenarios significantly affect the evolutionary trends of economic development, resource consumption, and the environmental pollution situation. The mechanisms to overcome the bottleneck of the resource and ecological constraints are different for these policies, and the effects of the same policy in different provinces are also not the same. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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17 pages, 1924 KiB

Open AccessArticle

A Novel Public Opinion Polarization Model Based on BA Network

by Yuanjian Ye, Renjie Zhang, Yiqing Zhao, Yuanyuan Yu, Wenxin Du and Tinggui Chen

Systems 2022, 10(2), 46; https://doi.org/10.3390/systems10020046 - 09 Apr 2022

Cited by 2 | Viewed by 2605

Abstract

At present, the polarization of online public opinion is becoming more frequent, and individuals actively participate in attitude interactions more and more frequently. Thus, online views have become the dominant force in current public opinion. However, the rapid fermentation of polarized public opinion [...] Read more.

At present, the polarization of online public opinion is becoming more frequent, and individuals actively participate in attitude interactions more and more frequently. Thus, online views have become the dominant force in current public opinion. However, the rapid fermentation of polarized public opinion makes it very easy for actual topic views to go to extremes. Significantly, negative information seriously affects the healthy development of the social opinion ecology. Therefore, it is beneficial to maintain national credibility, social peace, and stability by exploring the communication structure of online public opinions, analyzing the logical model of extreme public attitudes, and guiding the communication of public opinions in a timely and reasonable manner. Starting from the J–A model and BA network, this paper explores the specific attributes of individuals and opinion network nodes. By incorporating parameters such as individual conformity and the strength of individual online relationships, we established a model of online group attitude polarization, then conducted simulation experiments on the phenomenon of online opinion polarization. Through simulations, we found that individual conformity and the difference in environmental attitude greatly influence the direction of opinion polarization events. In addition, crowd mentality makes individuals spontaneously choose the side of a particular, extreme view, which makes it easier for polarization to form and reach its peak. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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11 pages, 5998 KiB

Open AccessArticle

Disinformation in Social Networks and Bots: Simulated Scenarios of Its Spread from System Dynamics

by Alfredo Guzmán Rincón, Ruby Lorena Carrillo Barbosa, Nuria Segovia-García and David Ricardo Africano Franco

Systems 2022, 10(2), 34; https://doi.org/10.3390/systems10020034 - 10 Mar 2022

Cited by 2 | Viewed by 3530

Abstract

Social networks have become the scenario with the greatest potential for the circulation of disinformation, hence there is a growing interest in understanding how this type of information is spread, especially in relation to the mechanisms used by disinformation agents such as bots [...] Read more.

Social networks have become the scenario with the greatest potential for the circulation of disinformation, hence there is a growing interest in understanding how this type of information is spread, especially in relation to the mechanisms used by disinformation agents such as bots and trolls, among others. In this scenario, the potential of bots to facilitate the spread of disinformation is recognised, however, the analysis of how they do this is still in its initial stages. Taking into consideration what was previously stated, this paper aimed to model and simulate scenarios of disinformation propagation in social networks caused by bots based on the dynamics of this mechanism documented in the literature. For achieving the purpose, System dynamics was used as the main modelling technique. The results present a mathematical model, as far as disinformation by this mechanism is concerned, and the simulations carried out against the increase in the rate of activation and deactivation of bots. Thus, the preponderant role of social networks in controlling disinformation through this mechanism, and the potential of bots to affect citizens, is recognised. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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Other

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20 pages, 2169 KiB

Open AccessTechnical Note

Earned Value Management Agent-Based Simulation Model

by Manuel Castañón-Puga, Ricardo Fernando Rosales-Cisneros, Julio César Acosta-Prado, Alfredo Tirado-Ramos, Camilo Khatchikian and Elías Aburto-Camacllanqui

Systems 2023, 11(2), 86; https://doi.org/10.3390/systems11020086 - 07 Feb 2023

Viewed by 1850

Abstract

Agile project management (APM) can be defined as an iterative approach that promotes satisfying customer requirements, adjusts to change, and develops a working product in rapidly changing environments. Managers usually apply agile management as the project management approach in projects requiring extraordinary speed [...] Read more.

Agile project management (APM) can be defined as an iterative approach that promotes satisfying customer requirements, adjusts to change, and develops a working product in rapidly changing environments. Managers usually apply agile management as the project management approach in projects requiring extraordinary speed and flexibility in their processes. Earned value management (EVM) is a fundamental part of project management to establish practical measures. Often, managers use a task board to visually represent the work on a project and the path to completion. Still, managing an agile project can be a challenging endeavor. In this paper, we propose an agent-based model describing the management of tasks within a project using earned value assessment and a task board. Our model illustrates how EVM yields an efficient method to measure a project’s performance by comparing actual progress against planned activities, thus facilitating the formulation of more accurate predicted estimations. As proof of concept, we leverage our implementation to calculate EVM performance indexes according to a performance measurement baseline (PMB) in a task board fashion. Full article

(This article belongs to the Special Issue Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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