Agent-based Simulation System Research Articles

Agent-based simulation and child protection systems: Rationale, implementation, and verification

Background and objectiveSimulation models are an important tool used in health care and other disciplines to support operational research and decision-making. In the child protection literature, simulation models are an under-utilized source of research evidence. Participants and settingIn this paper, we describe the rationale for and the development of an agent-based simulation of a child protection system in the US. Using the investigation, prevention service, and placement histories of 600,000 children served in an urban child welfare system, we walk the reader through the development of a prototype known as OSPEDALE. MethodsThe governing equations built into OSPEDALE probabilistically simulate the onset of investigations. Then, drawing from empirical survival distributions, the governing equations trace the probability of subsequent interactions with the system (recurrence of maltreatment, service referrals, and placement) conditional on the characteristics of children, their assessed risk level, and prior child protection system involvement. ResultsAs an initial test of OSPEDALE's utility, we compare empirical admission counts with counts generated from OSPEDALE. Though the verification step is admittedly simple, the comparison shows that OSPEDALE replicates the empirical count of new admissions closely enough to justify further investment in OSPEDALE. ConclusionsManagement of public child protection systems is increasingly research evidence-dependent. The emphasis on research evidence as a decision-support tool has elevated evidence acquired through randomized clinical trials. Though important, the evidence from clinical trials represents only one type of research evidence. Properly specified, simulation models are another source of evidence with real-world relevance.

Part One (Describing Criminality): The Society Agent Simulating (SAS) & Usage of Oral and Dental Diseases to Predict Criminals Earlier

Agent Based Modeling and Simulation & Usage of Oral and Dental Diseases to predict criminals & crimes: in part one (this paper) of our research description we only try to describe the knowledge of Cognitive Science, Agent-Based Simulation, Social Simulation, Artificial Intelligence and Multi Agent Systems. In the other hand in the second part (next paper) we will show how we can use the oral & dental diseases as a most important data for the Agent-Based Social Simulation, to early predict the criminals and crimes. In social simulating to create social behavioral modeling we need to apply the cognitive abilities. The cognitive ability is the most important property for the social agent. But the agent architecture is not included this ability. The agent with the cognitive ability will be able to improve the models of the behaviors in the society. This ability not only will be useful in the area of the society security and immigrant’s behavior in the target society, but also it is very useful in business and management fields. In this paper we are going to indicate different fields that can be used to predict the behavior of human. To know the different behaviors can make us to be able to predict criminals earlier by applying the comparison target’s internal harmony of life with the external harmony of world. So we will be able to predict the criminals before doing the crimes. It will help us to have secure life. Besides it will be useful in any other fields of life such as business, banking, political aspects, human resources and etc.

Agent-based simulation and optimization of hybrid flow shop considering multi-skilled workers and fatigue factors

In the past few decades, more and more studies have begun to consider the impact of human factors on manufacturing systems. This paper studies a hybrid flow shop scheduling problem considering multi-skilled workers and fatigue factors. An agent-based simulation system is established to cope with the uncertainties in the worker fatigue model. Furthermore, this paper proposes a novel simulation-based optimization (SBO) framework, which combines genetic algorithm (GA) and reinforcement learning (RL) to address the hybrid flow shop scheduling problem. Numerical experiments are conducted on several instances with different production configurations. In particular, a pharmaceutical production facility is modeled as a hybrid flow shop to demonstrate the feasibility and effectiveness of the proposed SBO method.

Desire-Driven Reasoning Considering Personalized Care Preferences

Care robots have been developed to address the shortage of caregivers in hospitals and homes. However, providing care services considering abstract physiological desires (e.g., hunger, thirst, hot, and cold) rather than specific commands (e.g., serve a cup of water) has not been fully investigated. The key to such services that satisfy abstract requests, e.g., thirst, is to evaluate the available options (e.g., milk, tea, biscuit, and Coke) with the consideration of the care recipient’s preferences. In this study, we argue that both general taste (long-term care preferences) and sequential influence [short-term care preferences (STCPs)] should be considered for a robot to provide satisfying services. In this article, a long STCP model (LSTCPM) is introduced. The model can be initialized and updated incorporating the feedback from the care recipients. Then, a virtual agent-based simulation system is presented, which provides a way to evaluate and fine-tune the LSTCPM prior to its deployment to actual care robots. Finally, experiments were conducted in a real household domain using our care robot KUT-PCR to demonstrate the feasibility of the proposed approach.

An optimal construction of smart aged homes based on SDLC using smart sensors and agent networks

With the increased growth of the added population group and advancements in health informatics, the smart aged home needs to be equipped with intelligent systems and various electronic devices. Industry 5.0 is yet another revolution that bought about the transformation in the practices concerning the use of intelligent technology with smart sensors and agent networks. Smart sensors enabled industry 5.0 is equipped with automation and self-monitoring ability that assists with analyzing the issues without the intervention of human beings. We, in this paper, proposed an iterative development process based on the smart sensor and agent networks of the SDLC software development models. The proposed model is developed by synchronizing the smart aged home with the cyber-physical system of industry 5.0. The quality value matrix is introduced as a tool to identify the requirements of the residents and the stakeholders. The feasibility of the proposed work is assessed with the help of an agent-based simulation system.

Terrain visualization information integration in agent-based military industrial logistics simulation

Agent-based simulation has become a useful tool in describing military industrial logistics organization behavior to implement military industrial information integration. Considering the specific demands of virtual engagement terrain design, this study present a framework for terrain visualization information integration in agent-based military industrial logistics simulation, which consists of perception access layer, data layer, service layer, and application layer. Focusing on its data source and the data analysis, processing, use, and validation, we propose a novel design approach on an engagement terrain database system for agent-based military industrial logistics simulation, and provide resolutions to actual problems, such as real-time efficient accession of the agent-based simulation system to high-resolution terrain data, management and maintenance of large-scale terrain data, and integrated visual simulation of terrain data. Such an initiative is achieved by presenting specific methods on the data and model designs for terrain relation, terrain analysis application, and terrain texture in terrain visualization application. A case study of the agent-based military industrial logistics simulation verifies the feasibility and effectiveness of the approach.

Spatial-temporal pricing for ride-sourcing platform with reinforcement learning

Ever since the emergence of ride-sourcing services, the spatial–temporal pricing problem has been a hot research topic in both the transportation and management fields. The difficulty lies in simultaneously obtaining the optimal multivariable solution for spatial pricing and sequential solution for dynamic pricing, considering the heterogeneity, dynamics, and imbalance of on-demand ride supply/demand. Due to this problem's complexity, most studies have simplified the modeling setting and omitted the complicated matching and waiting process between drivers and passengers. To go beyond the existing models, this paper proposes a reinforcement learning enhanced agent-based modeling and simulation (RL-ABMS) system to reveal the complex mechanism in the ride-sourcing system and tackle the problem of spatial–temporal pricing for a ride-sourcing platform. The reinforcement learning approach proximal policy optimization (PPO) is implemented in the RL-ABMS system, where two feed-forward neural networks are built as critic and actor. The critic judges the goodness of the current state, and the actor generates the optimal pricing strategy.Compared with the fixed pricing strategy, the experimental results on a real-world urban network show that dynamic pricing raises the platform's profit to 1.25 times, and spatial–temporal pricing even raises it to 1.85 times. Besides, the number of idle drivers/vehicles has significantly dropped under the spatial–temporal pricing strategy, which indicates that our proposed strategy has a remarkable effect on coordinating supply and demand in the ride-sourcing market.

An agent based simulation system for analyzing stress regulation policies at the workplace

Workplace stress has a significant impact on productivity, since keeping workers’ stress on an adequate level results a key factor for companies to increase their performance. While a high stress level may conduct to anxiety or absenteeism, a low level may also have undesirable consequences, such as lack of motivation. To identify and understand all the elements which interfere on workers’ stress results a key factor in order to improve workers’ performance. However, the complexity of human behavior increases the difficulty of recognizing the influence of these stressors and finding a way to regulate workers’ stress. This paper proposes the use of agent-based simulation techniques for addressing the challenge of analyzing workers’ behavior and stress regulation policies. The main contributions of the paper are: (i) the definition of a stress model that takes into account work and ambient conditions to calculate the stress and the productivity of workers; (ii) the implementation of this model in an agent-based simulation system, enabling the analysis of workplace stress and productivity for different stress regulation policies; (iii) the analysis of four different stress regulation policies; and (iv) the validation of the model with a sensitivity analysis and with its application to a living lab.

Safety perception and pedestrian dynamics: Experimental results towards affective agents modeling

The modeling of a new generation of agent-based simulation systems supporting pedestrian and crowd management taking into account affective states represents a new research frontier. Pedestrian behaviour involves human perception processes, based on subjective and psychological aspects. Following the concept of pedestrian environmental awareness, each walker adapts his/her crossing behaviour according to environmental conditions and his/her perception of safety. Different pedestrian behaviours can be related to subjective mobility and readiness to respond, and these factors are strongly dependent on the subjective interaction with the environment. Having additional inputs about pedestrian behaviour related to their perception processes could be useful in order to develop a more representative pedestrian dynamic model. In particular, the subjective perception of the safeness of crossing should be taken into consideration. In order to focus on the pedestrians’ perception of safe road crossing and walking, an experiment in an uncontrolled urban scenario has been carried out. Besides more conventional self-assessment questionnaires, physiological responses have been considered to evaluate the affective state of pedestrians during the interaction with the urban environment. Results from the analysis of the collected data show that physiological responses are reliable indicators of safety perception while road crossing and interacting with real urban environment, suggesting the design of agent-based models for pedestrian dynamics simulations taking in account the representation of affective states.

An improved approach on the model checking for an agent-based simulation system

Model checking is an effective way to verify behaviours of an agent-based simulation system. Three behaviours are analysed: operational, control, and global behaviours. Global behaviours of a system emerge from operational behaviours of local components regulated by control behaviours of the system. The previous works principally focus on verifying the system from the operational point of view (operational behaviour). The satisfaction of the global behaviour of the system conforming to the control behaviour has not been investigated. Thus, in this paper, we propose a more complete approach for verifying global and operational behaviours of systems. To do so, these three behaviours are firstly formalized by automata-based techniques. The meta-transformation between automata theories and Kripke structure is then provided, in order to illustrate the feasibility for the model transformation between the agent-based simulation model and Kripke structure-based model. Then, a mapping between the models is proposed. Subsequently, the global behaviour of the system is verified by the properties extracted from the control behaviour and the operational behaviour is checked by general system performance properties (e.g. safety, deadlock freedom). Finally, a case study on the simulation system for aircraft maintenance has been carried out. A counterexample of signals sending between Flight agent and Plane agent has been produced by NuSMV model checker. Modifications for the NuSMV model and agent-based simulation model have been performed. The experiment results show that 9% out of 19% of flights have been changed to be serviceable.

Assessment of Different Parking Pricing Strategies: A Simulation-based Analysis

Parking demand exceeding parking supply and uneven parking demand distribution are the existing conflicts in city centers. Parking pricing is frequently utilized to manage parking resources. This study aims to assess different parking pricing strategies through simulations for providing operational suggestions for urban parking managers. Two widely used parking pricing strategies in China combined with an optimized parking pricing strategy are proposed and compared. We introduce an agent-based simulation system to describe the parking and traffic conditions. Various measures of effectiveness under different parking pricing strategies can be obtained via agent-based simulations. We then construct a comprehensive benefit combining average cost and failure rate. Results show that the second strategy with charging different parking fees by considering locations and third optimized strategy can effectively improve traffic efficiency. However, the second strategy may lead to higher average cost than that of the third one. Thus, the third optimized strategy performs the best and can be used to optimize the parking policy of parking managers in the future. The entire assessment through simulations can provide evaluation suggestions for parking managers to adjust parking policies.

Investigating Impacts of Telemedicine on Emergency Department Through Decreasing Non-Urgent Patients in Spain

In this paper, a new method is presented to study the impacts of telemedicine on the performance of an emergency department in Spain. Spain's Demographics indicate that this country is experiencing population aging, resulting in overcrowding of emergency departments and significant demand on the healthcare system. However, it has been reported that most patients visiting emergency departments are not in an urgent clinical condition, thus they causing hospital overcrowding, high medical expenses, delays in clinical service delivery and low service efficiency for urgent patients who truly need emergency care. Telemedicine and e-health are considered as solutions for remote delivery of health services to care seekers in order to decrease hospital visits for patients who are in less of an emergency condition. In this study, by using detailed computational modeling and clinical data, we have investigated the impacts of telemedicine on the performance of an emergency department through estimations of Length of Stay as a quantitative index for evaluation of quality of service in the emergency department. Specifically, an agent-based modeling and simulation system was developed and used to study the behavior of the emergency department by taking detailed modeling parameters, including varying the number of non-urgent arrivals as a result of telemedicine, into account as inputs of the model. The inputs were provided through collection and analysis of clinical data that enabled us to predict how telemedicine changes emergency department visits. Our results indicated that emergency departments would experience decreases equal to 41.14% in total Length of Stay if eliminating all non-urgent visits and decreases of up to 10.48% if restricting the non-urgent visits. The developed computational tool in this study and the corresponding results obtained can provide decision makers and health care providers with objective information on the impacts of e-health services on the efficiency of emergency department and they can have also implications for care delivery, optimizing resources, planning, and improving the quality of care.

Energy consumption models in dialysis clinics for agent-based decision support

This paper describes an approach to predict the consumptions of water and power in dialysis clinics by simulating the internal processes. Better understanding is essential to prevent and mitigate adverse health impact on patients. Our methods are based on literature about dialysis processes and dialysis clinics, which are first-hand technical reports. The nonlinear model comprises the energy consumption of the technical systems together with their working schedules and the time schedule of the number of patients to be treated. Our model will be used as sub-model in an agent-based simulation system aiming to optimise the use of resources in an emergency with power outages or brownouts. The results presented in this study can be treated as a benchmark for decision makers to estimate the performance of a dialysis clinic or centre based on the amount of electricity and water needed and available.

Energy consumption models in dialysis clinics for agent-based decision support

This paper describes an approach to predict the consumptions of water and power in dialysis clinics by simulating the internal processes. Better understanding is essential to prevent and mitigate adverse health impact on patients. Our methods are based on literature about dialysis processes and dialysis clinics, which are first-hand technical reports. The nonlinear model comprises the energy consumption of the technical systems together with their working schedules and the time schedule of the number of patients to be treated. Our model will be used as sub-model in an agent-based simulation system aiming to optimise the use of resources in an emergency with power outages or brownouts. The results presented in this study can be treated as a benchmark for decision makers to estimate the performance of a dialysis clinic or centre based on the amount of electricity and water needed and available.

Simulation-based fuzzy-rough nearest neighbour fault classification and prediction for aircraft maintenance

ABSTRACT This paper addresses the problem of Fault Classification and Prediction (FCP) without sufficient data in aircraft maintenance. An innovative approach based on Agent-Based Simulation System (ABSS) and the Fuzzy-Rough Nearest Neighbour (FRNN) method is proposed. To do so, a framework for integrating the FRNN approach into ABSS is firstly provided. The concept and architecture models of FRNN-ABSS are then used to describe the FRNN-ABSS system. Subsequently, random and sequence strategies are designed for the FCP of the engine. An algorithm for integrating the FRNN method into ABSS is also developed to automate FCP. Finally, the experiments analysing the impact of different strategies on maintenance costs and service level have been conducted. The results show that the approach proposed has achieved success for random and sequence strategies: 9.3% and 2.5% of maintenance costs have been saved; 4.17% and 12.5% of delayed flights have been changed into on-time flights.

Effectiveness of Ecosystem Strategies for the Sustainability of Marketplace Platform Ecosystems

Physical intermediary firms, such as logistics firms, are the foundation of marketplace platform ecosystems. This study introduces the case of a delivery crisis caused by the withdrawal of major logistics firms from the Japanese marketplace platform. To address such a problem, this study considers the application of an “ecosystem strategy”. We define an ecosystem strategy in this situation, as “the strategy by which the platform owner cooperates with logistics firms to standardize logistics services and provides a platform system to improve cooperation among them”. We constructed an agent-based simulation system customized by a dataset of Japanese platform-based markets to test the effectiveness of the proposed strategy. The results indicate that the introduction of the ecosystem strategy postponed the start of the collapse. It also increased the number of platform users by roughly 1.10 times and increased the total profits of logistics firms about 1.22 times. Additionally, it removed the trade-off relationship between platform users and the profits of logistics firms and allowed the maximization of both. This study contributed to the research stream of platform ecosystems by defining an ecosystem strategy, including physical intermediary firms, and verifying the effectiveness of the strategy for ecosystem evolution and sustainability.

The design and simulation of an autonomous system for aircraft maintenance scheduling

Operational support is a key issue for aircraft maintenance, which aims to improve operational efficiency and reduce operating costs under the premise of ensuring flight safety. Although many works have emerged to achieve this aim, they mostly address the concept of maintenance systems, the relationship between stakeholders and the loop of maintenance information separately. Hence, the cooperation between stakeholders could be impeded especially when urgent decisions should be made, relying on historical data and real-time data. In this paper, we propose an innovative design of an autonomous system supporting the automatic decision-making for maintenance scheduling. The design starts from the proposition of the analysis framework, to concept formulation of the system, to information transitional level interface, and ends with an instance of system module interactions. The underlying architecture illustrates the high-level fusion of technical and business drives; optimizes strategies and plans with regard to maintenance costs, service level and reliability. An agent-based simulation system is developed as a proof to illustrate the feasibility of the system principle and algorithms. Furthermore, the simulation experiment analyzing the impact of maintenance sequence strategies on maintenance costs and service level has demonstrated the algorithm functionality and the feasibility of the proposed approach.

Knowledge-oriented modeling for influencing factors of battle damage in military industrial logistics: An integrated method

Modeling influencing factors of battle damage is one of essential works in implementing military industrial logistics simulation to explore battle damage laws knowledge. However, one of key challenges in designing the simulation system could be how to reasonably determine simulation model input and build a bridge to link battle damage model and battle damage laws knowledge. In this paper, we propose a novel knowledge-oriented modeling method for influencing factors of battle damage in military industrial logistics, integrating conceptual analysis, conceptual modeling, quantitative modeling and simulation implementation. We conceptualize influencing factors of battle damage by using the principle of hierarchical decomposition, thus classifying the related battle damage knowledge logically. Then, we construct the conceptual model of influencing factors of battle damage by using Entity-Relationship approach, thus describing their interactions reasonably. Subsequently, we extract the important influencing factors by using social network analysis, thus evaluating their importance quantitatively and further clarifying the elements of simulation. Finally, we develop an agent-based military industry logistics simulation system by taking the modeling results on influencing factors of battle damage as simulation model input, and obtain simulation model output, i.e., new battle damage laws knowledge, thus verifying feasibility and effectiveness of the proposed method. The results show that this method can be used to support human decision-making and action.

An agent-based simulation system for multi-project scheduling under uncertainty

This work proposes an agent-based simulation system to address challenges in resource constrained multi-project scheduling under uncertainty. The system aims at helping users develop robust schedules by taking care of intra- and inter-dependencies between projects, conflicting demands for limited resources, and the potential knock-on effect of unpredictable events. With the interactive graphical user interface embedded in the system, users are enabled to investigate project process, evaluate modified schedule, forecast impact resulted from unexpected disruptions, and conduct various what-if analysis. Reactive and proactive scheduling algorithms have been developed in the simulation system to resolve unforeseen disruptions and increase the robustness of the solutions. Experiments on a real-world case study of a technology-intensive industrial product development program have been conducted to evaluate the effectiveness of the proposed algorithms.

Developing an Agent-Based Simulation System for Post-Earthquake Operations in Uncertainty Conditions: A Proposed Method for Collaboration among Agents

Agent-based modeling is a promising approach for developing simulation tools for natural hazards in different areas, such as during urban search and rescue (USAR) operations. The present study aimed to develop a dynamic agent-based simulation model in post-earthquake USAR operations using geospatial information system and multi agent systems (GIS and MASs, respectively). We also propose an approach for dynamic task allocation and establishing collaboration among agents based on contract net protocol (CNP) and interval-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, which consider uncertainty in natural hazards information during agents’ decision-making. The decision-making weights were calculated by analytic hierarchy process (AHP). In order to implement the system, earthquake environment was simulated and the damage of the buildings and a number of injuries were calculated in Tehran’s District 3: 23%, 37%, 24% and 16% of buildings were in slight, moderate, extensive and completely vulnerable classes, respectively. The number of injured persons was calculated to be 17,238. Numerical results in 27 scenarios showed that the proposed method is more accurate than the CNP method in the terms of USAR operational time (at least 13% decrease) and the number of human fatalities (at least 9% decrease). In interval uncertainty analysis of our proposed simulated system, the lower and upper bounds of uncertain responses are evaluated. The overall results showed that considering uncertainty in task allocation can be a highly advantageous in the disaster environment. Such systems can be used to manage and prepare for natural hazards.