SIMULATION

Regardless of the coordination of its activities, a healthcare system is composed of a large number of distributed components that are interrelated by complex processes. Understanding the behavior of the overall system is becoming a major concern among healthcare managers and decision-makers. This paper presents a modeling and simulation framework to support a holistic analysis of healthcare systems through a stratification of the levels of abstraction into multiple perspectives and their integration in a common simulation framework. In each of the perspectives, models of different components of a healthcare system can be developed and coupled together. Concerns from other perspectives are abstracted as parameters, that is, we reflect the parameter values of other perspectives through explicit assumptions and simplifications in such models. Consequently, the resulting top model within each perspective can be coupled with its experimental frame to run simulations and derive results. Components of the various perspectives are integrated to provide a holistic view of the healthcare problem and system under study. The resulting global model can be coupled with a holistic experimental frame to derive results that cannot be accurately addressed in any of the perspectives taken alone. Furthermore, as we endeavored to allow perspective-specific experts to contribute to the modeling process, we took benefit of results originating from research efforts that Norbert Giambiasi initiated in the 2000s, which his PhD students further developed with their own PhD students.

This paper exposes and explains the synthesis of a mechanism, the Eclipses Abacus, by which the date of solar and lunar eclipses can be predicted and determined sufficiently accurately. Moreover, a three-dimensional (3D) model of this mechanical device is created by using Solid Works application and the simulation of its operation is accomplished by which the accuracy of its predictions can be demonstrated and inspected. This work is significant mainly for education in the field of 3D modeling, mechanism synthesis, and simulation. Moreover, the Eclipses Abacus is interesting for teaching courses in astronomy since it can demonstrate solar and lunar eclipses simply and clearly.

This study focused on the uncertain flexible flow shop scheduling problem with limited buffers when preventive maintenance is applied at fixed intervals. This issue has not been addressed in spite of widespread applications, due to complexity arising in solving such a stochastic decision making problem. To this aim, a novel optimization model is presented along with two types of solving methods using metaheuristic algorithms with and without a computer simulation model. The proposed hybrid method, named HSIM-META, integrates the computer simulation model along with the three most common metaheuristic algorithms, i.e., genetic algorithm (GA), simulated annealing (SA) algorithm, and particle swarm optimization (PSO), which offer better solution quality according to the literature. For this purpose, the simulation outputs are applied as an initial population for the tuned metaheuristic parameters to look for the next improved solution by investigating different approaches. Different numerical examples are discussed to examine the performance of the proposed method. The computational results of the proposed method, including hybrid simulation with GA (HSIM-GA), SA (HSIM-SA), and PSO (HSIM-PSO), are compared with the just applying GA, SA, and PSO. The results reveal that the suggested method acts more efficiently in terms of accuracy and speed in solving the problem.

A mobile manipulator is typically an assembly of a mobile robot base and an on-board manipulator arm. As the manipulator arm is mounted over the mobile robot base, the controller has the additional task of taking care of the disturbances of the mobile robot due to the dynamic interactions between the mobile robot base and manipulator arm. In the present work, dynamic models for the manipulator arm and an omni-wheeled mobile robot base were developed separately and then both were combined. Two control strategies, namely only manipulator arm control (OMAC) and simultaneous manipulator and base control (SMBC) were developed for the effective control of tip trajectory. In both strategies, an amnesia recovery coupled with classical proportional integral and derivative (PID) control was used. The bond graph methodology was used for the development of the dynamic model and control for the mobile manipulator. Simulation results are presented to illustrate the efficacy of the two control strategies.

The need to increase productivity has led to innovations in highly mechanized equipment such as the continuous miner (CM). However, often times the CM is underutilized, resulting in loss of productivity and increased operating cost. The objective of this study is to apply a simulation tool to evaluate if optimizing support systems, such as cut-out distance, fleet size, and panel dimensions, can maximize CM utilization and productivity. Discrete event simulation is used to analyze different cut-out distances for a room and pillar mining system. The results show that an 11-entry panel width with a fleet size of three shuttle cars is optimum for the variable cut-out distances examined. The utilization and production rate also increases by 6% at the maximum cut-out distance.

Radial Basis Functions are a modern way of creating a regression model of a multivariate function when sampled data points are not uniformly distributed in a perfect grid. Radial Basis Functions are well suited to atmospheric characterization when unmanned aerial vehicles (UAVs) are used to sample the given space. Multiple UAVs reduce the time for the Radial Basis Functions to yield a suitable solution to the measured data while data from all aircraft are aggregated and sent to Radial Basis Functions to fit the data. The research presented here focuses on the requirements for a high correlation value between the sampled data and the actual data. It is found that the number of centers is a large driver of the goodness of fit in the Radial Basis Function routine, much like aliasing is an issue in sampling a sinusoidal function. These centers act like a sampling rate for the spatially varying wind field. If the centers are dense enough to fully capture the spatial frequency of the wind field, the Radial Basis Functions will produce a suitable fit. This also requires the number of data points to be larger than the number of centers. The ratio between the number of centers and number of sampled data points declines as the number of centers increases. The results presented here are revealed using a two-dimensional Fourier series analysis coupled to a spatially varying atmospheric wind model and a Radial Basis Function regression model.

The proposed observation model provides for calculating the probability of detection of different types of unmanned aerial vehicle (UAV) at a certain range with regard to their tactical and technical characteristics and security equipment capabilities. The comparison of the obtained values of generalized indicators of security equipment use efficiency is based on a specified criterion. To take into account factors that significantly affect a modeling object, calculations are carried out under specified conditions and restrictions. UAVs should be detected until a covering object gets in a swath width given the time required for countermeasures. Based on the software implementation of the algorithm we have evaluated the efficiency of use of hypothetical security equipment for detecting certain types of UAVs, and defined means of further use or improvement.