AnyLogic Tool Research Articles

Crowd Emergency Evacuation Simulation Time Analysis via Obstacle Optimization Strategy

The crowd evacuation simulation is essential to provide important results for occupants, especially in the large capacity building compared to the human fire drill exercise. The strategy of evacuation such as the use of obstacles may need to be adapted by many organizations as an aid to help in visualizing and estimating the evacuation time during an emergency. During certain crowd events, they may consider the various setting of the object to ensure smoothness and effective crowd evacuation flow. In this paper, it aims to provide the simulation with 100-1000 agents and testing with obstacle using Anylogic tool and analysis of evacuation time validated using SPSS. The results show that the placement of obstacles near the exit way indeed can reduce the evacuation time and complies with the anti-arching phenomenon during evacuation.

Simplification of deep reinforcement learning in traffic control using the Bonsai Platform

The paper deals with the problem of traffic light control of road intersection. The authors use a model of a real road junction created in the AnyLogic modelling tool. For two scenarios, there are three simulation experiments performed ? fixed time control, fixed time control after AnyLogic-based optimizations, and dynamic control obtained through the cooperation of the AnyLogic tool and the Bonsai platform, utilizing benefits of deep reinforcement learning. At present, there are trends to simplify machine learning processes as much as possible to make them accessible to practitioners with no artificial intelligence background and without the need to become data scientists. Project Bonsai represents an easy-to-use connector, that allows to use AnyLogic models connected to the Bonsai platform - a novel approach to machine learning without the need to set any hyper-parameters. Due to unavailability of real operational data, the model uses simulation data only, with presence and movement of vehicles only (no pedestrians). The optimization problem consists in minimizing the average time that agents (vehicles) must spend in the model, passing the modelled intersection. Another observed parameter is the maximum time of individual vehicles spent in the model. The authors share their practical, mainly methodological, experiences with the simulation process and indicate economic cost needed for training as well.

Parallel multi-agent real-coded genetic algorithm for large-scale black-box single-objective optimisation

There is an ongoing evolution involving a new approach to large-scale optimisations based on co-evolutionary searches using interacting heterogeneous agent-processes via the implementation of synchronised genetic algorithms with local populations. The individualisation of heuristic operators at the level of agent-processes that implement independent evolutionary searches facilitate the improved likelihood of obtaining the best solutions in the fastest time. Based on this property, a parallel multi-agent single-objective real-coded genetic algorithm for large-scale constrained black-box single-objective optimisations (LSOPs) is proposed. This facilitates the effective frequency exchange of the best potential decisions between interacting agent-processes with individual parameters, such as types of crossover and mutation operators with their own characteristics. We have improved the quality of both solutions and the time-efficiency of a multi-agent real-coded genetic algorithm (MA−RCGA). A novel framework was developed that represents the aggregation of MA−RCGA with simulation models by implementing a set of objective functions for real-world large-scale optimisation problems such as the simulation model of the ecological-economics system implemented in the AnyLogic tool.

Toward a Simulation-based Approach for Emergency Evacuation Route Planning in Metro Stations

This paper presents a systematic simulation-based approach with detailed step-by-step procedures for route planning in emergency evacuation in metro stations. In accordance with emergency evacuation mechanism analysis, the length of evacuation route (L), the time of evacuation (T) and the density of pedestrian flow (D) are identified as critical factors that affect the performance of emergency evacuation. With all the critical factors, such as L, T and D, taken into account, a comprehensive multi-attribute decision algorithm is developed in order to optimize the selection of evacuation route under emergency. Taking the Hongshan Station in Wuhan metro systems as an example, the Anylogic tool is used to simulate the scenario of evacuation route planning in case of a fire. The simulation results regarding the evacuation performance indicators are analyzed and compared between the traditional and proposed approaches. Results indicate that the proposed approach can reduce the evacuation time without increasing the route length, and improve greatly the crowded conditions of pedestrian flow. The developed approach can provide guidelines and support for the optimization of evacuation route planning under emergency conditions.

소부대 전투시나리오 기반의 UGV 효과분석 실험방안 연구

Purpose: The purpose of this study is to design an experimental simulation model for evaluating the UGV(Unmanned Ground Vehicle) effectiveness in a small unit combat scenario. Methods: We design and build a simulation model to evaluate the combat effectiveness of UGV in a small unit combat scenario. In order to build a simulation model, we used AnyLogic software tool which has functional advantages to describe agent-based simulation model. As for the combat scenario, we applied the typical engagement of mechanized unit equal or lower than battalion level. Analysis process follows the three phases. 1) Design an agent based conceptual medel in a small unit combat scenario. 2) Build a simulation medel using AnyLogic tool. 3) Analyze the simulation results and evaluate the UGV effectiveness. Results: The UGV effectiveness was measured and presented as a numeric values. Those numeric values were represented as a MOE(Measure of Effectiveness) which was the blue survival ratio. Conclusion: We developed an agent based simulation model which can provide a pattern of change how UGV effectiveness varied depending upon the number of UGV in a small unit combat scenario. We also found that the UGV effectiveness grows in the given scenario as the number of UGV increases.

Distributed simulation of hybrid systems with AnyLogic and HLA

In the paper we discuss one of the approaches to modeling and distributed simulation of hybrid systems. We use hybrid state machines to model complex interdependencies between discrete and continuous time behaviors. This framework is fully supported by UML-RT/Java tool AnyLogic developed at Experimental Object Technologies ( http://www.xjtek.com). We use high level architecture (HLA) standard for distributed simulation, as a communication and synchronization media for distributed hybrid simulation components. We present a new technique (remote predicate evaluation (RPE)) for distributed modeling of hybrid systems that is applicable to a wide class of simulations. RPE integration with AnyLogic tool and HLA is also considered.