Simulation Software: Anylogic and Vensim

https://scholarworks.moreheadstate.edu/student_scholarship_posters/1254/thumbnail.jp

Simulation of evacuation with multi-agents on georeferenced layers with GAMA

This work deals with agent-based modeling withina geographical environment, it reviews concepts on Multi-agentsand Geographic Information Systems (GIS). It is oriented toperform a simulation that considers aspects of human behavior(through agents) during an evacuation, considering real restrictionsof the environment in which they are developed (GIS layers).This simulation is done in the GAMA1 platform, which allowsus to easily implement models based on agents and geographiclayers. It allows us to work with the attributes of the layers andto define constraints based on them. &nbsp

Place-Based Simulation Modeling: Agent-Based Modeling and Virtual Environments

Since the earliest geographical explorations of criminal phenomena, scientists have come to the realization that crime occurrences can often be best explained by analysis at local scales. For example, the works of Guerry and Quetelet—which are often credited as being the first spatial studies of crime—analyzed data that had been aggregated to regions approximately similar to US states. The next major seminal work on spatial crime patterns was from the Chicago School in the 20th century and increased the spatial resolution of analysis to the census tract (an American administrative area that is designed to contain approximately 4,000 individual inhabitants). With the availability of higher-quality spatial data, as well as improvements in the computing infrastructure (particularly with respect to spatial analysis and mapping), more recent empirical spatial criminology work can operate at even higher resolutions; the “crime at places” literature regularly highlights the importance of analyzing crime at the street segment or at even finer scales. These empirical realizations—that crime patterns vary substantially at micro places—are well grounded in the core environmental criminology theories of routine activity theory, the geometric theory of crime, and the rational choice perspective. Each theory focuses on the individual-level nature of crime, the behavior and motivations of individual people, and the importance of the immediate surroundings. For example, routine activities theory stipulates that a crime is possible when an offender and a potential victim meet at the same time and place in the absence of a capable guardian. The geometric theory of crime suggests that individuals build up an awareness of their surroundings as they undertake their routine activities, and it is where these areas overlap with crime opportunities that crimes are most likely to occur. Finally, the rational choice perspective suggests that the decision to commit a crime is partially a cost-benefit analysis of the risks and rewards. To properly understand or model these three decisions it is important to capture the motivations, awareness, rationality, immediate surroundings, etc., of the individual and include a highly disaggregate representation of space (i.e. “micro-places”). Unfortunately one of the most common methods for modeling crime, regression, is somewhat poorly suited capturing these dynamics. As with most traditional modeling approaches, regression models represent the underlying system through mathematical aggregations. The resulting models are therefore well suited to systems that behave in a linear fashion (e.g., where a change in model input leads to a predictable change in the model output) and where low-level heterogeneity is not important (i.e., we can assume that everyone in a particular group of people will behave in the same way). However, as alluded to earlier, the crime system does not necessarily meet these assumptions. To really understand the dynamics of crime patterns, and to be able to properly represent the underlying theories, it is necessary to represent the behavior of the individual system components (i.e. people) directly. For this reason, many scientists from a variety of different disciplines are turning to individual-level modeling techniques such as agent-based modeling

Introductory Chapter: Multi-Agent Systems

International audienc

ACoPla: a Multiagent Simulator to Study Individual Strategies in Dynamic Situations

One important issue in multi-agent systems is how to define agents’ interaction strategies in dynamic open environments. Generally, agents’ behaviors, such as being cooperative/altruistic or competitive/adversarial, are defined a priori by their creators. However, this is a weak premise when considering interaction among anonymous self-interested agents. Whenever agents meet, there is always a decision to be made: what is the best group interaction strategy? We argue that the answer depends on the amount of information required to make a decision and on the deadline proximity for accomplishing the task in hand. In certain situations, it is to the agents’ advantage to exchange information with others, while in other situations there are no incentives for them to spend time doing so. Understanding effective behaviors according to the decision- making scenario is still an open issue in multi-agent systems. In this paper, we present a multi-agent simulator (ACoPla) to understand the correlations between agents’ interaction strategy, decision-making context and successful task accomplishment rate. Additionally, we develop a case study in the domain of site evacuation to exemplify our findings. Through this study, we detect the types of conditions under which cooperation becomes the preferred strategy, as the environment changes

A Decision Support System for Efficient Last-Mile Distribution of Fresh Fruits and Vegetables as Part of E-Grocery Operations

Efficient last-mile distribution of fresh fruits and vegetables is a major challenge within e-grocery operations. This work presents a decision support system to jointly investigate the impact of various service offers on customer preferences and logistics operations. Results from a conjoint analysis surveying 531 end consumer are incorporated within an agent-based simulation. Delivery days, fees, time windows and discounts as well as guaranteed remaining shelf life of products at delivery are considered. To model shelf life and schedule deliveries, food quality models and vehicle routing procedures are further integrated within the system. Based on an e-grocery provider operating in Vienna, Austria, computational experiments investigate the impact of the offered delivery service on fulfilled demand, order volume and customer utility. Results indicate the importance of incorporating shelf life data within e-grocery operations and various potentials of considering customer preferences in logistics decision support systems

Remarks on the Behavior of an Agent-Based Model of Spatial Distribution of Species

Agent-based models have gained considerable notoriety in ecological modeling as well as in several other fields yearning for the ability to capture the emergent behavior of a complex system in which individuals interact with each other and with their environment. These models are implemented by applying a bottom-up approach, where the entire behavior of the system emerges from the local interaction between their components (agents or individuals). Usually, these interactions between individuals and their enclosing environment are modeled by very simple local rules. From the conceptual point of view, another appealing characteristic of this simulation approach is that it is well aligned with the reality whenever the system is composed of a multitude of individuals (behavioral units) that can be flexibly combined and placed in the environment. Due to their inherent flexibility, and despite of their simplicity, it is necessary to pay attention to the adjustments in their parameters which may result in unforeseen changes on the overall behavior of these models. In this paper we study the behavior of an agent-based model of spatial distribution of species, by analyzing the effects of the model parameters and the implications of the environment variables (that compose the environment where the species lives) on the models’ output. The presented experiments show that the behavior of the model depends mainly on the conditions of the environment where the species live, and the main parameters presented in life cycle of the species.This work was supported by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competências em Cloud Computing, co-financed by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica - Programas Integrados de IC\&DT.info:eu-repo/semantics/publishedVersio

Support to Design for Air Traffic Management: An Approach with Agent-Based Modelling and Evolutionary Search

To enhance Air Traffic Management (ATM) and meet the future traffic demand and environmental requirements, present ATM system is going to be modified (SESAR Joint Undertaking, 2017), designing new services to be integrated in future architecture considering the evolution of present fragmented structure of the airspace and the entanglement of air routes. Such a change process is complicated due to the nature of ATM, which is a large-scale Socio-Technical System (STS), typically involving a complex interaction between humans, machines and the environment. In such kind of systems, managing their evolution is a complex and difficult task since the social and technical implications of any proposed concept should be fully assessed before a choice is made whether or not to proceed with the related development. Often, simulation tools are also used to support the design of the concept itself by enabling what-if-analyses. However, these may be too effort and time consuming due to the exponential growth of the required analysis cases. A quite common mismatch between the performance evaluations in simulated conditions and those achieved in real life is represented by the partial assessment of human aspects that can be performed throughout the new concept lifecycle from its lowest maturity level up to “ready to market”. The proposed work defines an approach to support the design of new ATM solutions, including the evaluation on human behaviour. The approach adopts a combined paradigm, which involves Agent-Based Modelling and Simulation (ABMS) to specify and analyse the ATM models, and Agent-based Evolutionary Search (AES) to optimize the design of the new solutions. A specific case study is used to demonstrate the effectiveness of the proposed approach. Transition from Direct Routing Airspace (DRA) to Free Routing Airspace (FRA), respectively described by Solution #32 and Solution #33 in the SESAR solutions catalogue (SESAR Joint Undertaking, 2017), is used for both validation and experimentation activities. In detail, the proposed experimentation case regards the design of sector collapsing/decollapsing configuration to optimize controller workloads. The achieved results are presented and discussed

Support to Design for Air Traffic Management: An Approach with Agent-Based Modelling and Evolutionary Search

This paper presents a methodology to manage the support to design in ATM operations. We propose a workflow for the design of ATM solutions in a performance-based setting. The methodology includes the evaluation of the impact on human behaviour and exploits a combination of different paradigms, such as Agent-Based Modelling and Simulation, and Agent-Based Evolutionary Search. We prove the soundness of the methodology by carrying out a real case study, which is the transition from Direct Routing to Free Routing in the Italian airspace. The validation results exhibit limited errors for the assessment of the performance metrics under evaluation. Furthermore, the optimization of sector collapsing/decollapsing configuration is discussed to demonstrate the effectiveness of the implemented engines