Academic articles

Particle Filtering Using Agent-based Transmission Models


Dynamic models are used to describe the spatio-temporal evolution of complex systems. It is frequently difficult to construct a useful model, especially for emerging situations such as the 2003 SARS outbreak.Here we describe the application of a modern predictor-corrector method – particle filtering – that could enable relatively quick model construction and support on-the-fly correction as empirical data arrives.

An Agent-based Approach for Modeling the Effect of Learning Curve on Labor Productivity


The labor-intensive nature of construction projects requires proper management and efficient utilization of labor resources. Improvement of labor productivity can enhance project performance and thereby lead to substantial time and cost savings. Several studies focused on identifying the effect of different factors on labor productivity, whereby the learning curve factor proved of paramount importance. Although previous research efforts developed models to represent the learning curve effect using traditional simulation approaches such as System Dynamics (SD) and Discrete Event Simulation (DES), none of these studies used Agent-Based Modeling (ABM) techniques. This study takes the initial steps and presents work targeted at analyzing the effect of learning on labor productivity using ABM.

Use of agent-based modelling to predict benefits of cleaner fish in controlling sea lice infestations on farmed Atlantic salmon


Sea lice, Lepeophtheirus salmonis, are ectoparasites of farmed and wild salmonids. Infestations can result in significant morbidity and mortality of hosts in addition to being costly to control. Integrated PEST management programmes have been developed to manage infestations, and in some salmon farming areas, these programmes include the use of wrasse. To explore at what densities wrasse should be stocked in order to meet specific control targets, an individual-based model was built to simulate sea lice infestation patterns on a representative salmonid host. It was found that the wrasse can effectively control sea lice, and the densities of wrasse needed for effective control depend upon the source of the infestation and the targeted level of control.

Major City Evacuation Planning Using Simulation Modeling


Disaster, whether manmade or natural, can have a catastrophic impact on a populated area. Sometimes, the disaster is so devastating that it requires a large-scale evacuation. As a result, evacuation plans have become a necessity. One such evacuation plan is the regional hub reception center (RHRC), which will help evacuate the careless population when an evacuation is needed. Using AnyLogic as a simulation modeling software, an RHRC model was developed to test the efficiency of the proposed plan.

Simulation Software Comparison: Discrete Event Simulation Competitors


The comparison of discrete event simulation software alternatives is based on ORMS Today Simulation Software Survey published in October, 2017. “ORMS Today” journal, published by a global institute of operational management and analytics “INFORMS”, completed a detailed comparison of popular competitor tools for discrete event modelling. In 2017 it was supported by 38 developers with 45 tools. Data used in the research was kindly presented by the developers themselves.

Agent-based population model used to identify and evaluate dog population management strategies


Developing countries are faced with finding novel and humane ways to permanently reduce and control their dog population. Agent-based models developed to describe dog populations represent a unique, platform for using computer based simulation to identify control strategies with the greatest potential for success, aid in the design of more effective control measures, and provide a means to evaluate the success of different interventions.

A Plant-Level, Spatial, Bioeconomic Model Of Plant Disease Diffusion And Control: Grapevine Leafroll Disease


Grapevine leafroll disease threatens the economic sustainability of the grape and wine industry in the United States and around the world. This viral disease reduces yield, delays fruit ripening, and affects wine quality. Although there is new information on the disease spatial-dynamic diffusion, little is known about profit-maximizing control strategies. Using cellular automata, we model the disease spatial-dynamic diffusion for individual plants in a vineyard, evaluate nonspatial and spatial control strategies, and rank them based on vineyard expected net present values.

A heuristic approach taking operators’ fatigue into account for the dynamic assignment of workforce to reduce the mean flowtime


In order to cope with the frequent unpredictable changes that may occur in manufacturing systems, and to optimize given performance criteria, the assignment of workers can be decided online in a dynamic manner, whenever the worker is released. Several studies, in the ergonomics literature, have shown that individuals' performances decrease because of their fatigue in work. In manufacturing context, the workers’ fatigue impacts the task durations. Therefore, we propose to solve the online workers assignment problem through a heuristic, which takes this workers' fatigue into consideration, so as to minimize the mean flowtime of jobs.

The Effect of Cellular Interactions on Cancer Cell Growth Using Evolutionary Game Theory


In this experiment, game theory was used to assess the interactions between three cell phenotypes usually found in cancer. The three defined cells were autonomous growth cells, invasive and motile malignant cells, and cells that performed anaerobic glycolysis. Based on preset variables in the payoff matrix, analytical equations were deduced that allowed for the analysis of the proportion of autonomous growth and malignant cells in a tumor. AnyLogic was also used to simulate the interactions between cancerous and normal cells.