Agent-based Simulation Method Research Articles

Wireless sequential dual light delivery for programmed PDT in vivo

Using photodynamic therapy (PDT) to treat deep-seated cancers is limited due to inefficient delivery of photosensitizers and low tissue penetration of light. Polymeric nanocarriers are widely used for photosensitizer delivery, while the self-quenching of the encapsulated photosensitizers would impair the PDT efficacy. Furthermore, the generated short-lived reactive oxygen spieces (ROS) can hardly diffuse out of nanocarriers, resulting in low PDT efficacy. Therefore, a smart nanocarrier system which can be degraded by light, followed by photosensitizer activation can potentially overcome these limitations and enhance the PDT efficacy. A light-sensitive polymer nanocarrier encapsulating photosensitizer (RB-M) was synthesized. An implantable wireless dual wavelength microLED device which delivers the two light wavelengths sequentially was developed to programmatically control the release and activation of the loaded photosensitizer. Two transmitter coils with matching resonant frequencies allow activation of the connected LEDs to emit different wavelengths independently. Optimal irradiation time, dose, and RB-M concentration were determined using an agent-based digital simulation method. In vitro and in vivo validation experiments in an orthotopic rat liver hepatocellular carcinoma disease model confirmed that the nanocarrier rupture and sequential low dose light irradiation strategy resulted in successful PDT at reduced photosensitizer and irradiation dose, which is a clinically significant event that enhances treatment safety.

Simulation Modeling and Optimization of Traffic Flows in Local Sections of the Street Road Network Using AnyLogic

An approach to simulation modeling and optimization of traffic flows in local sections of the street road network using AnyLogic and heuristic algorithms is presented. A feature of this approach is the use of agent-based (ABM) and discrete-event simulation methods supported in the AnyLogic system. An original swarm optimization algorithm (PSO) is proposed, modified for the problem of optimizing road traffic on a real section of the road (near the Yugo-Zapadnaya metro station in Moscow). Numerical experiments were performed to confirm the possibility of optimizing the duration of traffic light phases in order to minimize the average travel time of vehicles on a simulated section of the street road network.

A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infections.

A model that jointly simulates infectious diseases with common modes of transmission can serve as a decision-analytic tool to identify optimal intervention combinations for overall disease prevention. In the United States, sexually transmitted infections (STIs) are a huge economic burden, with a large fraction of the burden attributed to HIV. Data also show interactions between HIV and other sexually transmitted infections (STIs), such as higher risk of acquisition and progression of co-infections among persons with HIV compared to persons without. However, given the wide range in prevalence and incidence burdens of STIs, current compartmental or agent-based network simulation methods alone are insufficient or computationally burdensome for joint disease modeling. Further, causal factors for higher risk of coinfection could be both behavioral (i.e., compounding effects of individual behaviors, network structures, and care behaviors) and biological (i.e., presence of one disease can biologically increase the risk of another). However, the data on the fraction attributed to each are limited. We present a new mixed agent-based compartmental (MAC) framework for jointly modeling STIs. It uses a combination of a new agent-based evolving network modeling (ABENM) technique for lower-prevalence diseases and compartmental modeling for higher-prevalence diseases. As a demonstration, we applied MAC to simulate lower-prevalence HIV in the United States and a higher-prevalence hypothetical Disease 2, using a range of transmission and progression rates to generate burdens replicative of the wide range of STIs. We simulated sexual transmissions among heterosexual males, heterosexual females, and men who have sex with men (men only and men and women). Setting the biological risk of co-infection to zero, we conducted numerical analyses to evaluate the influence of behavioral factors alone on disease dynamics. The contribution of behavioral factors to risk of coinfection was sensitive to disease burden, care access, and population heterogeneity and mixing. The contribution of behavioral factors was generally lower than observed risk of coinfections for the range of hypothetical prevalence studied here, suggesting potential role of biological factors, that should be investigated further specific to an STI. The purpose of this study is to present a new simulation technique for jointly modeling infectious diseases that have common modes of transmission but varying epidemiological features. The numerical analysis serves as proof-of-concept for the application to STIs. Interactions between diseases are influenced by behavioral factors, are sensitive to care access and population features, and are likely exacerbated by biological factors. Social and economic conditions are among key drivers of behaviors that increase STI transmission, and thus, structural interventions are a key part of behavioral interventions. Joint modeling of diseases helps comprehensively simulate behavioral and biological factors of disease interactions to evaluate the true impact of common structural interventions on overall disease prevention. The new simulation framework is especially suited to simulate behavior as a function of social determinants, and further, to identify optimal combinations of common structural and disease-specific interventions.

Evolutionary digital twin model with an agent-based discrete-event simulation method

Evolutionary digital twin model with an agent-based discrete-event simulation method

Моделирование последствий ядерного удара

The risks of increasing international tension have sharply exacerbated the possibility of a nuclear conflict. The main geopolitical players in the international arena have actually recognized the possibility and even expediency of using nuclear weapons. In this context, the need to simulate the consequences of a nuclear strike in order to prepare for emergencies of a critical nature has become extremely urgent. The present article analyzes foreign experience of using digital simulators to this end. The authors outline Russian views on applying agent-based simulation methods for this purpose in the analysis of non-military (civilian) aspects of a nuclear blow effects.

Evolution of micro and small enterprises’ work safety behavior in high-risk industries

Due to external regulations and limited resources, micro and small enterprises’ (MSEs) work safety behavior in China’s high-risk industries is passive behavior under constraints. Based on stakeholder theory, this paper describes the attributes and behavior rules of MSEs, the government safety supervision department and work safety service agencies using the agent-based simulation methods. We construct an evolution model to study this behavior. The software platform Netlogo is used for simulation exercises. Under the four factors of enterprise work safety resource strength, government supervision, government subsidies, and organization service level, the evolution of MSEs’ work safety behavior in high-risk industries is simulated and the results are discussed. It further reveals internal and external drivers of work safety of MSEs in high-risk industries, strengthens the scientific supervision and effective support of government safety supervision departments, and improves work safety service quality of service agencies multi-dimensionally.

Research on Modeling and Simulation System of Economic Factors Based on Swarm Platform

Based on the VAR model, this paper uses the stepwise regression analysis method to analyze the main influencing factors of the economy, and empirically analyzes the impact of Nanning's fixed asset investment on urban and rural economic development in the past ten years. Use eviews statistical analysis software to perform regression analysis on the data to obtain a regression model, and build a VAR model according to investment and income, and finally find out the correlation of variables. On the basis of the existing theory (genetic algorithm, cellular automata and classification system), according to the agent-based modeling and simulation method Swarm, according to the steps of agent-based distributed simulation of complex system, modeling and simulation are carried out. The abstraction level of simulation is established on the big data computing level, and the final simulation results are briefly explained and explained.

The Application of Supply Chain Digital Twin to Measure Optimal Inventory Policy

The present heterogeneous behaviour of the competitive market poses significant problems at regional as well as global scales in terms of inconsistent tracking, ripple effects, dashboard collaboration, and complex product portfolios along with stringent criteria. Meanwhile, it is also counterproductive to the visibility of the supply chain besides that adversely responsible for optimal inventory policies (OIP). Henceforth, this paper considers the supply chain digital twin (SCDT) for OIP within an integrated physical and digital world fusion vision. Consequently, the agent-based simulation method is applied to create administrative asset shells for small and medium-scale factories. The proposed model incorporates an OIP trade-off for retailers, wholesalers, customers, and factories. The results underlined the benefits and characterized OIP tactics that help estimate system performance. Finally, the managerial implications provide insides for predictive and reactive decisions to ensure the optimal supply chain running cost and customer satisfaction by utilizing the advantages of SCDT. Furthermore, this paper discusses the OIP optimization method and essential measures for developing the SCDT distribution strategy within the trade network.

Synchronization Model of Sea Transport Scheduling of Pioneer Ship and Sea Toll: Case Study at Maluku

Sea transportation is the only vessels connected to islands in Indonesia, especially Pioneer Shipping that has an important role to connect the smaller and isolated islands to reduce price disparity. Pioneer shipping becomes a Feeder Vessel from Sea Toll and Pelni Passenger Ship that must be maintained reliability of scheduling accuracy in accordance with ministerial regulation number 37 of 2015. The aim of this research is to create a model of synchronization ship scheduling that considers to the arrival of the main ship (sea toll and pelni passenger ship) with feeder vessel (pioneer shipping) that can determine the impact financially for users (passengers and shipper) and ship operators. Agent-based simulation methods are used in this research to obtain scheduled arrivals and departures of ships using AnyLogic Software 8.7.4 (Personal Learning Edition). Therefore, schedule is considering the synchronization between the departure and arrival of the main ship (sea toll transportation and passenger transport) with the feeder (pioneering transport) ship.

Multi-method simulation modelling of circular manufacturing systems for enhanced decision-making

Circular manufacturing systems (CMS) constitute complex value networks comprising a large and diverse set of stakeholders that collaborate to close the loop of products through multiple lifecycles. Complex systems modelling and simulation play a crucial role in providing quantitative and qualitative insights into the behaviour of such systems. In particular, multi-method simulation modelling that combines agent-based, discrete-event, and system dynamics simulation methods is considered more suitable to model and simulate CMS as it allows to capture their complex and dynamic nature. This paper provides a step-by-step approach on how to build a CMS multi-method simulation model in order to assess their economic, environmental, and technical performance for enhanced decision-making. To model and simulate CMS three main elements need to be considered:•A multi-method model architecture where the CMS stakeholders with heterogeneous characteristics are modelled individually as autonomous agents using agent-based, discrete-event, and system dynamics.•An agent environment defined by a Geographic Information System (GIS) to establish connections based on agents’ geographic location.•The product journey resulting from the product's interaction with various CMS stakeholders in the circular value network is traced throughout its multiple lifecycles.

Agent-Based Modeling and Simulation of Tourism Market Recovery Strategy after COVID-19 in Yunnan, China

The tourism industry hit severely by COVID-19 faces the challenge of developing effective market recovery strategies. Nonetheless, the existing literature is still limited regarding the dynamic evolution process and management practice. Hence, this study chose several famous spots in the Yunnan Province of China as the focus for a case study and utilized an agent-based simulation method for the decision-making process of tourists’ destination selection and the dynamic recovery process of the destinations under different price and information strategies. The study found that the recovery effects of information strategies are positive, negative, or have no effect in different destinations. In contrast, price strategies can significantly stimulate an increase in the market share of destinations. When price strategy and information strategy are applied simultaneously, the interaction effects are inconsistent in different destinations. The findings contribute to the prediction of the recovery effect of strategies, can reduce trial and error costs, and can improve the scientific understanding of tourism market recovery.

Hybrid simulation models for spare parts supply chain considering 3D printing capabilities

In the era of Industry 4.0, 3D printing unlocks a wide array of solutions to rapidly-produce spare parts for maintenance operations. In this research, we propose a hybrid simulation approach, combining agent-based and discrete event simulation methods, to investigate how the adoption of 3D printing technologies to manufacture spare parts for maintenance operations will improve operational efficiency and effectiveness. Specifically, our framework is applied to the United States Navy’s fighter jet maintenance operations to study various network configurations, where 3D printing facilities may be centralized, decentralized, or hub configured. System performance in terms of the total cost, timeliness of delivery, and vulnerability under disruptions such as cyber-attacks and emergencies are evaluated. Lastly, the impact of 3D printing technological advancements on operational performance is investigated to obtain managerial insights.

Microscopic modeling of cyclists on off-street paths: a stochastic imitation learning approach

Accurate modeling of bicycles in microsimulation tools is challenging due to the limited availability of detailed data, complexity of cyclist decision-making, and heterogeneity in cycling behavior. This paper proposes an agent-based bicycle simulation method in which generative adversarial imitation learning (GAIL) is used to infer the uncertain intentions and heterogeneous preferences of cyclists from observational data. The model is tested on video-derived data of cyclists on a unidirectional path in Vancouver, Canada. In cross-validation, multivariate distributions of movement variables such as speed, direction, and spacing are similar between observed and simulated cyclist trajectories. The model also performs well in comparison to two other cyclist simulation models from the literature. The proposed approach to agent-based microsimulation is a significant advancement, with continuous, non-linear, and stochastic representation of cyclist states, decisions, and actions. The enhanced consideration of cyclist diversity is necessary for developing bicycle networks for all ages and abilities of riders.

Cross-docking versus traditional warehouse in a chain store: a deductive study based on agent-based simulation

The purpose of this paper is to conduct a deductive study to choose the best distribution strategy for a chain store. A cross-docking system was proposed for a chain store, which used a traditional warehouse, to enhance the supply chain performance and reduce distribution costs. The simulation is an approach to study the feasibility of cross-docking before its actual implementation. Among various simulation methods, the agent-based simulation is used in this paper. By applying agent-based simulation method and some performance metrics, the cost reduction and performance improvement are more common in the cross-docking system in comparison with a traditional warehouse. The present paper contributed to utilising an agent-based simulation approach in cross-docking that was less used than classical approaches in the literature. In addition, the agent-based simulation of cross-docking in the literature was mainly done for cross-docking optimisation and not for managerial level decisions which have been done in this research.

Development of a multimodal (railroad-water) chain of grain supply by the agent-based simulation method

The results of the simulation of a multi-element chain of grain supply by the rail and water multimodal route were shown. Mathematical substantiation of the optimization problem was presented. The minimum cargo delivery time was selected as the optimization criterion. The limits for the admissible use (loading) of fleets of transport units of railroad and water transport were selected as optimization constraints. The optimization model is a multi-parametric problem of stochastic programming. The objective function of the model was represented in implicit expression. The search for the solution of the optimization model was performed using experiments with the developed simulation model.The simulation model is based on the discrete-event and agent-based principles, it simulates the interaction of two railroad and one sea transport and technological lines, as well as terminal points of accumulation, storage, and reloading of cargo batches. One ton of wheat grain acts as a part of the cargo module.The simulation model was developed in AnyLogic RE (USA) and Java SE (USA) environments. The algorithm of the simulation model involves the interaction of populations of agents of transport junction points; agents of transport and technological lines; populations of agents of fleets of transport units; agents of information orders for transportation. The model was implemented using the example of the actual process of grain supply from Ukraine to Egypt.The model was studied using the integer optimization method. As a result of experiments, the optimal values of the required stock of cars, locomotives, and naval vessels were established. In addition, the required capacity of granaries at the shipping stations and seaports’ terminals, as well as the necessary capacity of track development of railroad stations, were found. The established average delivery time was within 185 hours

Modeling and simulation of firm-leading self-organization evolution process in open source product design project

ABSTRACTOpen source product design is characterized by dynamic evolution, autonomous task control, loose organization structure, and frequent collaboration, which shows the process is full of high uncertainty. Firms have used this innovation pattern to obtain ideas. To describe the firm-leading self-organization evolution process, an agent-based modeling and simulation method is applied. We have proposed an agent-based model including designers and firms in light of Complex Adaptive System theory firstly; Secondly, their behaviors, especially the two typical behaviors: task selection and plan screening are illustrated; Finally, the simulation results show that:1) evolution process of agent, product, and community can be described clearly by simulation. Agent-based simulation can support the analysis, evaluation and optimization of the evolution process;2) in task selection phase: firm’s recommendation can improve the evolution efficiency;3)in plan screening phase, firms’ “control” also plays important role in the evolution process, but the impact of designer agents’ self-organization cannot be ignored.

INTEGRATING COMPREHENSIVE INDUSTRIAL RAW MATERIAL DELIVERY PLANNING AND PRODUCT-SERVICE SYSTEM INVENTORY CONTROL

Product Service Systems (PSS) are integrated product and service offerings that provide superior customer value in industrial applications for planning, inventory control, delivery planning and a use jointly defined. Delivery planning is a particular challenge in providing personal security services. A good delivery planning is can minimize problem from great complexity subject to various constraints within a large solution space. Organizations offering PSS services or industrial services suffer from decision support to put in place robust capacity planning strategies in highly dynamic and uncertain environments. This paper presents a simulation-based approach to capacity planning. The goal of this project to identify potential routes of integration between two factors deliveries planning and PSS inventory control that could improve the delivery performance based on the raw material industry. Emphasis is placed on capacity planning for PSS inventory control and delivery planning. By using IDEFØ (Integration Definition for Function) generic modelling and Delmia Quest simulation software in this research able to achieve the time required. The advantage of IDEFØ includes: a well-tested language and a comprehensive systems modelling technique. The resulting IDEFØ models are well-defined, easy to understand, well-structured, easy to change and use, and can be extended to any depth of detail. Meanwhile, Delmia Quest software will provide real time simulation for production process and generated report for actual operational situation. After a few general considerations on robust capacity planning for the control and delivery of technical support inventory using scenario simulations, the main elements of the agent-based simulation method are presented. The most important parameters given by company A, the control parameters and the performance indicators are discussed and the scenario planning process based on the simulation is described.

Highlighting the benefits of Industry 4.0 for production: an agent-based simulation approach

Abstract: Advanced Manufacturing or Industry 4.0 concepts bring new advances and challenges to current industrial processes. Such concepts are not always well understood and their results in terms of production performance may not be clear. This work proposes a comparison between a traditional manufacturing process and an advanced manufacturing process, both modelled by a multiagent society. In the traditional manufacturing simulation, the agents follow the defined times of each process, including the maintenance times. In the advanced manufacturing simulation, the decision about when to stop a piece of equipment for maintenance is defined by the agent according to data received from sensors and the definitions of the process. The results indicate a significant improvement in equipment usage and consequently higher production in the same time interval. The process simulation clearly indicates that the application of advanced manufacturing concepts in industry is relevant in order to increase the efficiency of production processes. Among the main concepts introduced in advanced manufacturing models are the Internet of Things (IoT), Cyber-Physical Systems (CPSs), and Artificial Intelligence (AI). The models generated are computationally simulated using an agent-based simulation method from the software AnyLogic. The results obtained should contribute to encouraging small and medium sized enterprises to adopt the concepts of Industry 4.0 in their businesses.

Energy-saving behavior of urban residents in China: A multi-agent simulation

Abstract With recent improvements in residents’ quality of life and the implementation of the two-child policy, guiding the energy-saving behavior of urban residents has become a focus for achieving the national goals of the sustainable development strategy in China. Considering the subjective initiatives of individuals in a realistic environment is the key to studying energy-saving behavior and guiding policy making. This study builds a simulation model of the energy-saving behavior of urban residents using agent-based modeling and simulation (ABMS) methods, which are based on complex adaptive theory. By means of artificial neural networks and the Netlogo simulation platform, the subsequent effect of behavioral outcomes due to the short- and long-term influence of energy-saving behavior and intentions is analyzed in different policy situation. The results show that energy-saving intentions and behavior are poorly matched in the absence of an external policy framework. In the optimal policy situation, residents’ energy-saving intentions and behavior have improved significantly. Policies can significantly encourage energy-saving intentions to become behavior. Different kinds of situational factors have different effects on intentions and the four types of energy-saving behavior. Finally, relevant policy implications are proposed based on analysis of the simulation results.

Agent-Based Simulation of the Two-Dimensional Patlak-Keller-Segel Model

The Patlak-Keller-Segel equation describes the chemotactic interactions of small organisms in the continuum limit, and a singular peak appears through spontaneous aggregation when the total mass of the organisms exceeds a critical value. To deal with this singular behavior numerically, we propose an agent-based simulation method in which both the organisms and the chemicals are represented as particles. Our numerical estimates for the threshold behavior are consistent with the analytic predictions.