SIMULATION: Transactions of The Society for Modeling and Simulation International

Prakash Tripathi, Jay; Tripathi, Richa

doi: 10.1177/00375497221078947pmid: N/A

An early understanding of effective policy and transmission dynamics can help us prevent further outbreaks of the COVID-19. This article proposes a nonlinear adaptive control strategy based on effective policy to contain this epidemic. A mathematical model of nonlinear dynamics of the COVID-19 transmission in India is considered. The model also takes uncertainty into account. The control input calculated by the proposed controller prescribes the contact rate of the disease so that the number of infectious population converges to a desired zero value. The controller utilizes a Lyapunov theorem-based adaption law to be able to deal with modeling uncertainties, instability, and divergence. The controller performance on the nonlinear and uncertain model of the COVID-19 transmission has been investigated through a simulation study.

Mohd Ibrahim, Azhar; Venkat, Ibrahim; De Wilde, Philippe; Mohd Romlay, Muhammad Rabani; Bahamid, Alala

doi: 10.1177/00375497221075611pmid: N/A

Crowd dynamics have constituted a hotspot of research in recent times, particularly in areas where developmental progress has taken place in crowd evacuation for ensuring human safety. In high-density crowd events which happen frequently, panic or an emergency can lead to an increase in congestion which may cause disastrous incidents. Crowd control planning via simulation of people’s movement and behavior can promote safe departures from a space, despite threatening circumstances. Up until now, the evolution of distinctive types of crowd behavior towards cooperative flow remains unexplored. Hence, in this paper, we investigate the impact of potential crowd behavior, namely best-response, risk-seeking, risk-averse, and risk-neutral agents in achieving cooperation during evacuation and its connection with evacuation time using a game-theoretic evacuation simulation model. We analyze the crowd evacuation of a rectangular room with either a single-door or multiple exits in a continuous space. Simulation results show that mutual cooperation during evacuation can be realized when the agents’ population is dominated by risk-averse agents. The results also demonstrate that the risk-seeking agents tend toward aggressiveness by opting for a defector strategy regardless of the local crowd densities, while other crowd behavior shows cooperation under high local crowd density.

Bae, Ki-Hwan; Mustafee, Navonil; Lazarova-Molnar, Sanja; Zheng, Long

doi: 10.1177/00375497221075614pmid: N/A

The sharing economy is a peer-to-peer economic model characterized by people and organizations sharing resources. With the emergence of such economies, an increasing number of logistics providers seek to collaborate and derive benefit from the resultant economic efficiencies, sustainable operations, and network resilience. This study investigates the potential for collaborative planning enabled through a Physical Internet-enabled logistics system in an urban area that acts as a freight transport hub with several e-commerce warehouses. Our collaborative freight transportation planning approach is realized through a three-layer structured hybrid model that includes agent-based simulation, auction mechanism, and optimization. A multi-agent model simulates a complex transportation network, an auction mechanism facilitates allocating transport services to freight requests, and a simulation–optimization technique is used to analyze strategic transportation planning under different objectives. Furthermore, sensitivity analyses and Pareto efficiency experiments are conducted to draw insights regarding the effect of parameter settings and multi-objectives. The computational results demonstrate the efficacy of our developed model and solution approach, tested on a real urban freight transportation network in a major US city.

Singh, Mohit Kumar; Ramachandra Rao, K

doi: 10.1177/00375497221078936pmid: N/A

Cellular automata (CA) simulation models developed for traffic are either closed or open boundary type. The selection and difference of boundaries has been studied extensively for ideal and single-lane homogeneous traffic conditions. However, the effect of these on multi-lane-heterogeneous traffic still needs attention because most of the traffic observed in many parts of the world is not single-lane homogeneous traffic. It is evident from multiple studies that open and closed boundaries affect the simulation results. Moreover, these require different inputs for simulation. This study attempts to evaluate the difference in the results of open and closed boundary simulations in heterogeneous non-lane-based traffic. The methodology discussed in this study is relatable to the field conditions. The present study includes some of the common but often ignored features in the model such as seepage of small-sized vehicles. Furthermore, this study also includes some of the previously unnoticed features while modeling the non-lane-based traffic at intersections. The modeling of open boundaries simulation can be better and easy in most of the situations compared to the closed boundaries. Closed boundary simulation results for flow-density curve show a smooth trend, whereas open boundary simulation results are scattered as observed in the field. This study further concludes that the size of the vehicle does not change the fundamental diagrams except when other characteristics such as seepage, lane change, and different maximum speeds for different modes are considered. The study used field observed influence zone of intersections to decide the dimension of intersection in the simulation model.

Singh, Rajmeet; Bera, Tarun Kumar; Chatti, Nizar

doi: 10.1177/00375497221091592pmid: N/A

In this paper, an obstacle avoidance and target tracking method for both indoor and outdoor mobile robots in dynamic environment is presented, and it aims to enhance autonomous navigation capability of such robots. In the proposed method, image processing and machine-learning approaches are considered. Since obstacles have differences in color and texture and in order to identify non-navigable areas, a monocular onboard camera is used to capture the road lanes by dealing with an image processing technique. The position of the robot with respect to road lane center during navigation is calculated on the basis of a proposed fuzzy logic rules set. In order to provide fast and robust computation, the Haar cascade classifier-based machine-learning technique has been exploited to detect the different sizes and shapes of the obstacles faced by the robot during its movement from source to destination. The dynamic model of a four-wheel mobile robot is initially developed using bond graph theory and then, the proposed obstacle-avoidance strategy is applied. The effectiveness and performance of the proposed method are tested under various simulation and experimentation scenarios. The behavior of the mobile robot for detecting and avoiding static obstacle for single and double road lane change environment is analyzed and discussed.

Grigoryan, Gayane; Etemadidavan, Sheida; Collins, Andrew J

doi: 10.1177/00375497221093652pmid: N/A

One of the challenges for agent-based modeling is being able to incorporate human behavior. Human behavior is a multifaceted phenomenon, with strategic coalition formation being one form. A hybrid agent-based modeling approach, called ABMSCORE, has been derived to emulate strategic group formation. In this paper, we describe a simulation experiment to compare the ABMSCORE with actual human behavior. The comparison criterion is the respective rates of finding an ideal coalition. In our experimental design, we go to great lengths to ensure the similarity of the scenarios in the two trial types: trials with computerized agents only and trials involving human participants when one of the computerized agents is replaced by an actual human. We did this to limit the number of possible extraneous variables introduced into the experimental system. The scenario considered is the glove game, a standard cooperative game that has been previously used in human experiments. Our results indicate that the ABMSCORE model produces similar rates of finding the ideal coalition as the human players; however, there are some limitations. This research provides evidence for using the ABMSCORE modeling approach to model human strategic coalition formation in agent-based models.

Bush, Brian; Stright, Dana; Huggins, Jay; Newes, Emily

doi: 10.1177/00375497221093381pmid: N/A

This paper documents the workflow and supporting technologies that a large system dynamics model, the biomass scenario model, employs to streamline the data preparation, simulation, quality control, and analysis process at the National Renewable Energy Laboratory. The workflow centers on automation of routine aspects of the flow of data between data stores, simulations, and visualizations. It enforces quality checks on data, reproducibility of computations, and traceability of results, while maintaining complete archives of modeling and analysis artifacts. The resulting frictionless simulation/analysis environment supports large-scale sensitivity analysis, interactive creation of ensembles of simulations, and rapid visualization-based exploration of simulation results.

Rodríguez-Núñez, Jesús Manuel; de León, Aned; Molinar-Tabares, Martín E; Flores-Acosta, Mario; Castillo, SJ

doi: 10.1177/00375497221098417pmid: N/A

In this paper, we propose to use a computational method of chaos control to simulate complex experimental spectra. This computational chaos control technique is based on the Ott–Grebogi–York (OGY) method. We chose the logistic map as the base mathematical model for the development of our work. For the numeric part, we created arbitrary precision algorithms to generate the solutions. This way, we completely eliminated any degradation of chaos from our results. These algorithms were also necessary for the proper perturbation process that the computational chaos control method requires. We control the chaos of the logistic map in two cases of Period 1 and one case of Period 2 to demonstrate that our control method works. The behavior of a complex experimental spectrum was taken and numerically simulated. The simulated spectrum was obtained by controlling the chaos of the logistic map in a variable way with the methods proposed in this work. Our results show that it is possible to simulate very complicated experimental spectra by computationally controlling the chaos of an equation unrelated to the experimental system.