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By considering dynamic environment features to construct the heuristic rule and using multiple scenarios for training, the proposed methodology aims to find generic and efficient heuristic rules that perform well on different scenarios. Then, an evolutionary framework based on the Cartesian Genetic Programming algorithm and an agent-based crowd simulation model is developed to search for the optimal heuristic rule. The key idea is to divide the region into a set of sub-regions and use a heuristic rule to dynamically recommend an exit to agents in each sub-region. In the proposed methodology, a heuristic-based evacuation scheme is firstly introduced. This paper proposes a new evolutionary algorithm-based methodology for optimal crowd evacuation planning.

#Anylogic tutorial inject how to#

This paper presents how to parallelize MATSim using the MASS library and demonstrates the library's portability and execution performance in practical transport simulations. To support distributed-memory parallelization for multi-agent models, we have developed the MASS (multi-agent spatial simulation) library.

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Multithreading can partially contribute to parallelization by utilizing multi-cores, but cannot give full scalability of both CPU power and memory space.

However, as the simulations scale up, their computational requirements could get increased beyond the capability of a single CPU and thus should be fulfilled with parallelization. They are viewed as an emergent and collective behavior of agents, (i.e., vehicles). Multi-agent models such as MATSim (multi-agent transport simulation) have been highlighted for recent years as a solution to address these complex and microscopic simulation requirements. One of the major trends in traffic simulations is to take into account microscopic aspects of traffic flows at the street level. Since the computational offload from the workstation to a remote computing entity also allows the use of novel user interfaces (design and devices), through the use of RESTful interfaces, use-case applicable interfaces for simulations can also be created. In this tutorial we will present an approach of how to work with an entirely cloud-based solution for modeling and simulation, with an exemplary implementation of an urban traffic simulation cloud service. This leads to the conclusion of moving the computational demand to the cloud, where the on-demand culture for resources has been expanding. The vendors have not sufficiently addressed this.

The access to HPC resources is not available. Users of such simulations are domain experts with little knowledge in computer science and optimization of such simulations. The move to High Performance Computing clusters is a viable solution. pdfįor large-scale urban system simulations the computing power of traditional workstations is not sufficient. We will discuss how to use this framework to incrementally develop embedded applications, and to seamlessly integrate simulation models with hardware components. This approach combines the advantages of a simulation-based approach with the rigor of a formal methodology. We present a Model-driven framework to develop cyber-physical systems based on the DEVS (Discrete Event systems Specification) formalism. M&S let users experiment with “virtual” systems, allowing them to explore changes, and test dynamic conditions in a risk-free environment. Instead, construction of system models and their analysis through simulation reduces both end costs and risks, while enhancing system capabilities and improving the quality of the final products.

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Most of the methods used for developing embedded applications are either hard to scale up for large systems, or require a difficult testing effort with no guarantee for bug-free software products. Embedded systems development has interesting challenges due to the complexity of the tasks they execute.