Mathematical modeling and simulation of the earth's magnetic field: A comparative study of the models on the spacecraft attitude control application

In this paper, the Earth's magnetic field models which are widely used in spacecraft attitude control applications are modeled and extensively compared with a reference model. The reference model is obtained utilizing coefficients from the last generation of International Geomagnetic Reference Field (IGRF-12). The validity of this model is verified with the World Magnetic Model (WMM) in terms of intensity and direction of the field. The reference model is then used to evaluate lower-order and approximating models while the influence of effective parameters such as expansion order of modeling, orbit height, inclination, latitude and longitude on accuracy of modeling is investigated. The simulation results for several scenarios are presented and discussed. The linear and nonlinear transformations of the models from orbital frame to spacecraft body frame are compared for a wide range of attitude angles in order to investigate the sensibility and validity of linear transformation. Simulation of a spacecraft attitude control maneuver is performed to demonstrate the importance of the accuracy of the magnetic field model which is implemented in the attitude control system. The results indicated a meaningful increase in control effort when a simplified model was used. This research was aimed to investigate the borders of different geomagnetic field models and transformations for spacecraft attitude control applications. The presented results may lead to a proper choice of the Earth's magnetic field model based on the space mission requirements.     

Combining Data Mining and Discrete Event Simulation for a value-added view of a hospital emergency department

While simulation models have furthered understanding of the operations of emergency departments (EDs) and the dynamics of the ED within the healthcare system, they only model patient treatment implicitly, tracing the paths patients follow through the ED. By identifying the core patient treatments provided by the ED and incorporating them into a Discrete Event Simulation model, this paper provides insight into the complex relationship between patient urgency, treatment and disposal, and the occurrence of queues for treatment. The essential characteristics of the presented model are used to indicate a generally applicable methodology for identifying bottlenecks in the interface between an ED and a hospital ward.     

Key performance indicators for successful simulation projects

There are many factors that may contribute to the successful delivery of a simulation project. To provide a structured approach to assessing the impact various factors have on project success, we propose a top-down framework whereby 15 Key Performance Indicators (KPI) are developed that represent the level of successfulness of simulation projects from various perspectives. They are linked to a set of Critical Success Factors (CSF) as reported in the simulation literature. A single measure called Project’s Success Measure (PSM), which represents the project’s total success level, is proposed. The framework is tested against 9 simulation exemplar cases in healthcare and this provides support for its reliability. The results suggest that responsiveness to the customer’s needs and expectations, when compared with other factors, holds the strongest association with the overall success of simulation projects. The findings highlight some patterns about the significance of individual CSFs, and how the KPIs are used to identify problem areas in simulation projects.     

A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling

This paper considers the outpatient no-show problem faced by a rural free clinic located in the south-eastern United States. Using data mining and simulation techniques, we develop sequencing schemes for patients, in order to optimize a combination of performance measures used at the clinic. We utilize association rule mining (ARM) to build a model for predicting patient no-shows; and then use a set covering optimization method to derive three manageable sets of rules for patient sequencing. Simulation is used to determine the optimal number of patients and to evaluate the models. The ARM technique presented here results in significant improvements over models that do not employ rules, supporting the conjecture that, when dealing with noisy data such as in an outpatient clinic, extracting partial patterns, as is done by ARM, can be of significant value for simulation modelling.     

Small world network models of the dynamics of HIV infection

It has long been recognised that the structure of social networks plays an important role in the dynamics of disease propagation. The spread of HIV results from a complex network of social interactions and other factors related to culture, sexual behaviour, demography, geography and disease characteristics, as well as the availability, accessibility and delivery of healthcare. The small world phenomenon has recently been used for representing social network interactions. It states that, given some random connections, the degrees of separation between any two individuals within a population can be very small. In this paper we present a discrete event simulation model which uses a variant of the small world network model to represent social interactions and the sexual transmission of HIV within a population. We use the model to demonstrate the importance of the choice of topology and initial distribution of infection, and capture the direct and non-linear relationship between the probability of a casual partnership (small world randomness parameter) and the spread of HIV. Finally, we illustrate the use of our model for the evaluation of interventions such as the promotion of safer sex and introduction of a vaccine.     

On the Moments and the Distribution of the Cost of a Semi Markov Model for Healthcare Systems

In this paper we extend our previous semi-Markov reward model which attached costs to duration in states, by including costs of making a transition from one state to another. Theoretical results concerning the moments and consequently the distribution of interval costs for every member and of the total cost per unit period at any time and also through time intervals are obtained and provided in analytic form for the semi Markov reward model with discounting. The results are applied to an open healthcare system. In the healthcare domain such transition costs allow us to evaluate the overall costs of therapy or clinical intervention where an operation or other treatment may be an option. This model can be used for strategic approaches to planning and evaluating long-term patient care. The results demonstrate the potential of the model to demonstrate differential costs of different therapeutic strategies and explore optimal solutions.     

Applications of simulation within the healthcare context

A large number of studies have applied simulation to a multitude of issues relating to healthcare. These studies have been published in a number of unrelated publishing outlets, which may hamper the widespread reference and use of such resources. In this paper, we analyse existing research in healthcare simulation in order to categorise and synthesise it in a meaningful manner. Hence, the aim of this paper is to conduct a review of the literature pertaining to simulation research within healthcare in order to ascertain its current development. A review of approximately 250 high-quality journal papers published between 1970 and 2007 on healthcare-related simulation research was conducted. The results present a classification of the healthcare publications according to the simulation techniques they employ; the impact of published literature in healthcare simulation; a report on demonstration and implementation of the studies’ results; the sources of funding; and the software used. Healthcare planners and researchers will benefit from this study by having ready access to an indicative article collection of simulation techniques applied to healthcare problems that are clustered under meaningful headings. This study facilitates the understanding of the potential of different simulation techniques in solving diverse healthcare problems.     

Causal study of low stakeholder engagement in healthcare simulation projects

Stakeholder engagement plays a fundamental role in the success of ‘operational research’ initiatives including simulation projects. However, there is little empirical evidence of real engagement in the context of healthcare simulation. This paper principally examines this issue and aims to provide insights into the possible causes. The paper reports on the results of a literature review and 10 field studies within the UK healthcare settings, supplemented with the authors’ experience in order to arrive at an initial list of the causes, which will then be tested through a survey of expert opinions. Twelve primary and 26 secondary causal factors, which received statistically significant level of agreement from the experts, are presented in a fish-bone diagram. The findings indicate that communication gap between simulation and stakeholder groups is the top primary factor contributing the most to the poor stakeholder engagement in healthcare simulation projects, followed by ‘poor management support’, ‘clinician’s high workload’ and ‘failure in producing tangible and quick results’.     

A participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare

Existing approaches to conceptual modelling (CM) in discrete-event simulation do not formally support the participation of a group of stakeholders. Simulation in healthcare can benefit from stakeholder participation as it makes possible to share multiple views and tacit knowledge from different parts of the system. We put forward a framework tailored to healthcare that supports the interaction of simulation modellers with a group of stakeholders to arrive at a common conceptual model. The framework incorporates two facilitated workshops. It consists of a package including: three key stages and sub-stages; activities and guidance; tools and prescribed outputs. The CM framework is tested in a real case study of an obesity system. The benefits of using this framework in healthcare studies and more widely in simulation are discussed. The paper also considers how the framework meets the CM requirements.     

Comparing conventional and distributed approaches to simulation in a complex supply-chain health system

Decision making in modern supply chains can be extremely daunting due to their complex nature. Discrete-event simulation is a technique that can support decision making by providing what-if analysis and evaluation of quantitative data. However, modelling supply chain systems can result in massively large and complicated models that can take a very long time to run even with today's powerful desktop computers. Distributed simulation has been suggested as a possible solution to this problem, by enabling the use of multiple computers to run models. To investigate this claim, this paper presents experiences in implementing a simulation model with a ‘conventional’ approach and with a distributed approach. This study takes place in a healthcare setting, the supply chain of blood from donor to recipient. The study compares conventional and distributed model execution times of a supply chain model simulated in the simulation package Simul8. The results show that the execution time of the conventional approach increases almost linearly with the size of the system and also the simulation run period. However, the distributed approach to this problem follows a more linear distribution of the execution time in terms of system size and run time and appears to offer a practical alternative. On the basis of this, the paper concludes that distributed simulation can be successfully applied in certain situations.     

Cross-validation using the t statistic

One application area of regression analysis is simulation where the regression model may explain the relationship between the simulation model's inputs and outputs.However, whether or not the regression model is used in a simulation context, its validity can be tested by comparing the model's forecast to one or more new observations not used in the estimation of the model's parameters. The familiar Student or t statistic is proposed for this comparison, combined with a Bonferroni approach accounting for the presence of multiple, dependent validation observations.A ‘trick’ is used to obtain as many validation observations as possible. This trick is also known as cross-validation.Several Monte Carlo experiments are performed to study the α and β errors of the proposed validation procedure. The experimental results suggest that the procedure is worthwhile.     

The Metamodel in Simulation Analysis: Can It Be Trusted?

The use of a mathematical metamodel such as a regression model, constructed from simulation data and used to aid in the analysis of the simulated system, has been studied in recent years. For practitioners, the vast benefits of establishing a functional relationship among the variables in an unfamiliar and complex simulated system may be largely overshadowed by the concern that the metamodel, being a strongly data-based technique, may be valid only for the one particular set of simulation-generated data that went into it, which is to say not valid at all. Based on a study of 30 simulation experiments using three different simulation models, the authors conclude that the simulation metamodel is a reliable and valid technique to use in post-simulation analysis, and is probably just as good as the simulation model on which it is based.     

The Importance of Representing Microterrain in Studies of Air Defence against Helicopters

Simulation of air-defence engagements between ground forces and aircraft or helicopters requires complex models to examine the many-on-many interactions. One of the critical factors determining the outcome of a low-altitude engagement is the terrain over which it is fought. The terrain determines the aircraft's flight path as well as the firing opportunities for the ground forces. Methods of terrain representation that work well for aircraft engagements may not be suitable for helicopters. The different flight characteristics of helicopters cause them to interact more closely with microterrain features and so modify their flight paths. Representation of microterrain features on the terrain database leads to significantly different exposure statistics compared with those obtained when such features are absent. A bias is also present in battle-simulation results, leading to the conclusion that microterrain must be included to obtain realistic battle-model outputs when studying air defence against helicopters.     

Functionality defense through diversity: a design framework to multitier systems

Diversification is one of the most effective approaches to defend multitier systems against attacks, failure, and accidents. However, designing such a system with effective diversification is a challenging task because of stochastic user and attacker behaviors, combinatorial-explosive solution space, and multiple conflicting design objectives. In this study, we present a systematic framework for exploring the solution space, and consequently help the designer select a satisfactory system solution. A simulation model is employed to evaluate design solutions, and an artificial neural network is trained to approximate the behavior of the system based on simulation output. Guided by a trained neural network, a multi-objective evolutionary algorithm (MOEA) is proposed to search the solution space and identify potentially good solutions. Our MOEA incorporates the concept of Herbert Simon??s satisficing. It uses the decision maker??s aspiration levels for system performance metrics as its search direction to identity potentially good solutions. Such solutions are then evaluated via simulation. The newly-obtained simulation results are used to refine the neural network. The exploration process stops when the result converges or a satisfactory solution is found. We demonstrate and validate our framework using a design case of a three-tier web system.     

Quantile regression metamodeling: Toward improved responsiveness in the high-tech electronics manufacturing industry

Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input–output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment “on demand” to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.     

Simulation methodology — An introduction for queueing theorists

Simulation is a widely used methodology for queueing systems. Its superficial simplicity hides a number of pitfalls which are not all as well known as they should be. In particular simulation experiments need careful design and analysis as well as good presentations of the results. Even the elements of simulation such as the generation of arrival and service times have a chequered history with major problems lying undiscovered for 20 years. On the other hand, good simulation practice can offer much more than is commonly realized.     

A dynamical model for describing behavioural interventions for weight loss and body composition change

We present a dynamical model incorporating both physiological and psychological factors that predict changes in body mass and composition during the course of a behavioural intervention for weight loss. The model consists of a three-compartment energy balance integrated with a mechanistic psychological model inspired by the Theory of Planned Behaviour. This describes how important variables in a behavioural intervention can influence healthy eating habits and increased physical activity over time. The novelty of the approach lies in representing the behavioural intervention as a dynamical system and the integration of the psychological and energy balance models. Two simulation scenarios are presented that illustrate how the model can improve the understanding of how changes in intervention components and participant differences affect outcomes. Consequently, the model can be used to inform behavioural scientists in the design of optimized interventions for weight loss and body composition change.     

A calibrated model of local food system of Hawaii: What are the economic implications of the state's food goals and policies?

We develop a fully calibrated positive mathematical programming model for Hawaii's local food systems—which captures the production and the consumer sides of the market. Then we use the model to assess two proposed policies—a general excise tax (GET) exemption on locally produced foods, and an investment in agricultural infrastructure. For the GET exemption case, our results indicate an economic gain of $118 per $100 cost. On the other hand, an investment in 1,200 acres of land injected to support local production may generate an economic gain of up to $357 per $100 annual cost of the investment. However, these estimates should be considered preliminary, and thus viewed with caution. Although the model is used to capture Hawaii's local food systems, we believe that our model is generalizable and can be adopted by other economies to assess their respective food localization policies. Recommendations for Resource Managers

• Local food policies need to be based on quantitative terms instead of mere armchair speculation because often their potential outcomes may vary significantly.

• The current modeling framework demonstrates the potential of using positive mathematical programming (PMP) in capturing the intricacies of local food systems. However, this exploratory exercise should be viewed as preliminary in nature and the ensuing results were taken with caution because many important factors such as labor availability may have been left out.

• Thus, further model refinements are necessary to better capture the complexities of local food systems such as farm heterogeneity, availability of farm labor, water availability, and interisland transportation of farm products in the case of Hawaii.

     

Infinitesimal Perturbation Analysis: A Tool for Simulation

Perturbation analysis is a technique that expedites the process of performing experiments on discrete-event simulation models. This makes it possible to derive sensitivity estimates from one computer execution of a simulation model. Infinitesimal perturbation analysis (IPA) is one class of algorithms used in perturbation analysis. In this paper, the techniques and algorithms used in simulation to perform infinitesimal perturbation analysis are examined. Each algorithm is discussed in detail, with comments concerning implementation problems and examples with experimental results for serial transfer lines. The results of this paper show that for simple systems, IPA can be easily implemented in a general-purpose simulation language such as SIMAN. Unfortunately, for any given system, parameter or performance measure, the algorithm used to generate the gradient may vary. Additionally, algorithms for more complex classes of problems do not yet exist. This problem hampers the current possibility of incorporating IPA into general-purpose simulation languages.     

Behavioural modelling of career progression in the European Commission

Though manpower planning models have been part of OR for many years, and simulation has always been acknowledged as a potential approach, there are few reported applications of its use. In this case-based study we report on a micro-simulation model that exploits the structure of the European Commission’s appraisal and promotion rules, and includes regression-based sampling schemes which allow for non-Normal error terms to represent behavioural factors that led to the need for a new system. With a suitably parsimonious formulation the 20,000 person model runs very effectively, and the transparency associated with simulation proves an important factor in the successful use of the model as the basis for designing a promotion box system that was implemented across the Commission in 2009. The simulation modelling incorporates many Markov-type elements, and we reflect on important lessons learned from this combined use of micro-simulation and Markov-based approaches to manpower modelling.