SIMULATION

Cellular automata are discrete dynamical systems that provide a mathematical framework for modelling, studying and predicting the behaviour and response of systems across many different disciplines and domains, ranging from physical and biological to computational and social models. Cell-DEVS is a formalism that provides a discrete event approach to define cellular models with timing delay constructions and using simple definition of complex timing. It has been shown that the application of the Cell-DEVS paradigm produces a significant reduction in the development times of cell-shaped models and a wide variety of complex models has been developed using this approach. In this work we present the definition of complex cellular automata models using the Cell-DEVS paradigm, we use the CD++ tool to obtain executable models and study their behaviour through computer simulation.

In this work, we provide an approach for Modeling and Simulation (M&S) of crowds using Cellular Discrete EVent System Specification (Cell-DEVS). We present many examples of Cellular Discrete EVent System Specification entity-based crowd models, and we show how to use Cellular Discrete EVent System Specification for entity-based modeling and simulation of crowds. We provide an approach for using Cellular Discrete EVent System Specification theory in modeling and simulation of crowds, and we propose Cellular Discrete EVent System Specification entity-based models for modeling and simulation of one-, two-, and three-dimensional movement of crowds. We extend the models above, and propose a more advanced model for crowd movement in multi-level building. Furthermore, we use this model for simulation of building evacuations. We propose another advanced model for crowd modeling, and deploy the model in occupancy analysis of buildings. Simulation results verify the usability of the proposed models.

This work aims at developing a general methodology to determine the spatial changes in the basic reproduction number for vector-borne diseases. This requires a spatially explicit modeling system which will be based on a cellular automata (CA) approach and applied to the Chagas disease for both homogeneous and heterogeneous landscapes. Using an extension of the so-called next-generation matrix, we obtained an expression for the basic reproduction number R 0 that characterizes the spatial heterogeneity of the risk of emergence of an outbreak. The performed simulation with the built CA model shows the effect of local vector dispersal in a heterogeneous landscape for Chagas disease transmission.

In this paper, we propose a distributed algorithm based on a generalization of the Cellular Automata concept called Graph Cellular Automata (GCA) to solve the Maximum Lifetime Coverage Problem (MLCP) in wireless sensor networks (WSNs). In GCA, we adapt life-like state transition functions inspired by Conway’s Game of Life in order to solve the problem. The goal of this paper is to study the quality of state transition functions for an objective provided by the MLCP in WSNs. The proposed algorithm possesses all the advantages of a localized algorithm, i.e., using only some knowledge about neighbors, a WSN is able to self-organize in such a way as to prolong its lifetime, at the same time preserving the required coverage ratio of the target field. Our experimental results show that certain rules are better solvers of the given problem than others. The paper also presents the results of an experimental study of the proposed algorithm and comparison with a centralized Genetic Algorithm.

The aim of the paper is to present a new approach based on the Cellular Automata technique for a specific class of scheduling problems with parallel machines (in which some important parameter values cannot be determined a priori). The problem domain is represented by an asynchronous non-homogeneous cellular automaton. In addition, the division of the method into three levels is introduced. Inseparable use of simulation, optimization and result levels, is proposed. To illustrate our proposition, the optimization problem of drilling tunnels in a given area is considered. A number of simulation experiments were performed involving different instances of the problem and the results are presented and discussed in the paper.

The problem of ‘humans and work’ in a model working group is investigated by means of the cellular automata technique. The attitude of members of a group towards work is measured by an indicator of loyalty to the group (the number of agents who carry out their tasks) and lack of loyalty (the number of agents who give their tasks to other agents). Initially, all agents realize scheduled tasks one by one. Agents with the number of scheduled tasks larger than a given threshold change their strategy to an unloyal one and begin to avoid completing tasks by passing them to their colleagues. Optionally, in some conditions, we allow agents to return to the loyal state; hence the rule is hysteretic. Results are presented on an influence of (i) the density of tasks, (ii) the threshold number of tasks assigned to the agent, forcing him/her to change strategy on the system efficiency. We show that a ‘black’ scenario of the system stacking in a ‘jammed phase’ (with all agents preferring the unloyal strategy and having plenty of tasks scheduled for realization) may be avoided when return to loyalty is allowed and either (i) the number of agents chosen for task realization, (ii) the number of assigned tasks, (iii) the threshold value of assigned tasks that forces the agent to conversion from a loyal strategy to an unloyal one, or (iv) the threshold value of tasks assigned to an unloyal agent that forces him/her to task redistribution among his/her neighbors, are smartly chosen.

The development of a parallel version of the fracture model dedicated for multi-phase materials based on a combination of the finite element model and random cellular automata approach is the overall goal of this study. Dual-phase (DP) steel, commonly used in the automotive industry, is selected as a case study for the present investigation. Firstly, various fracture modes that can occur during deformation in DP steel grade microstructures are presented from an experimental point of view. To consider explicitly microstructure features that play a significant role during initiation and subsequent failure propagation, the digital material representation concept is used. Then, details of the developed random cellular automata model, fully embedded within the finite element framework, are discussed. The cellular automata space definition, internal variables, state variables and transition rules replicating investigated fracture modes are presented in detail and discussed. The concept of data transfer and parallelization based on the Message Passing Interface methodology in such an innovative hybrid numerical model is also clearly presented. The final section of the paper is devoted to examples of obtained results highlighting model predictive capabilities.