Finding the optimal solution for the call-center scheduling problem can be done by using Microsoft Excel with an integer programming software add-in. Utilizing VBA, we are able to vary start, break, and lunch times as well as number of employees. By creating a list of all possible schedules that follow these requirements, we use the optimization engine to solve for the best possible combination of individual schedules. Custom programs for optimization such as this are becoming a vital part of the world today as decisions need to be made quickly. This flexible and easy to use scheduling tool saves time and effort while creating peace of mind knowing that the best possible solution has been found. Using this tool, we are able to decrease the amount of time to create schedules from approximately 15 hours of manual work to 25.2 seconds. Additionally, we are able to improve the accuracy of meeting the forecast – guaranteeing all manpower demand is met with an efficient and reliable tool. Accuracy, efficiency, and reliability are traits that anyone could wish for, and this tool makes that possible.

M.Comm. / Globalization initiates rapid change and innovation that is: “… no longer an option, but it has become a business imperative” (Grulke, 2002, p. 18). Innovative organizations have developed the ability to satisfy both the shareholders’ demand for wealth (Hamel, 2000) and the customers’ demand for more creative and innovative products that facilitate ease of use (Kelley, 2001) while at the same time ensuring business sustainability (Skarzynski & Gibson, 2008). The development of creative and innovative thinking and problem-solving skills are crucial for the survival of organisations in the 21st century. Creative problem-solving training was generally found to be the most effective when organizations wanted to equip their employees with creative and innovative thinking and problem-solving skills. A specific financial services organisation in South Africa realised that they had to join the innovation revolution in order to remain commercially competitive in the twentyfirst century. With retailers and other competitors such as the telecommunication role players entering the traditional financial services domain, the organisation recognised that they required a novel approach to conduct their business. The highly regulated and to some extent conformist environment of the financial services organization constitute the sphere within which the research problem is situated. The organisation commissioned the researcher to design a Creativity and Innovation Workshop with the intent to improve the creative and innovative thinking and problem-solving skills of their employees. The evaluation question that the study purports to address therefore is whether employees in a corporate context such as a financial services organisation can develop appropriate creative and innovative thinking and problemsolving skills through an intervention such as a workshop and can a benefit for the business unit and organisation be identified. The unit of analysis is a niche business unit in a South African financial services organization. The sample used in this study comprises of managers (employees) and senior or executive management of those employees who attended the Creativity and Innovation Workshop.

Providing automated support for model selection is a significant research challenge in model management. Organizations maintain vast growing repositories of analytical models, typically in the form of spreadsheets. Effective reuse of these models could result in significant cost savings and improvements in productivity. However, in practice, model reuse is severely limited by two main challenges: (1) lack of relevant information about the models maintained in the repository, and (2) lack of end user knowledge that prevents them from selecting appropriate models for a given problem solving task. This study built on the existing model management literature to address these research challenges. First, this research captured the relevant meta-information about the models. Next, it identified the features based on which models are selected. Finally, it used Analytic Hierarchy Process (AHP) to select the most appropriate model for any specified problem. AHP is an established method for multi-criteria decision-making that is suitable for the model selection task. To evaluate the proposed method for automated model selection, this study developed a simulated prototype system that implemented this method and tested it in two realistic end-user model selection scenarios based on previously benchmarked test problems.

In this thesis, methods for using computer-based models as support tools for assessing Transport Telematic Services (TTSs) are studied. Such assessments provide one way to understand how TTSs can address problems caused by transportation, such as accidents, emissions, and energy consumption. TTSs are services based on telematic systems which are Intelligent Transport Systems (ITS) involving the integrated use of information and communication technologies in transport. The focus is on TTSs that are relevant for road freight transport, even though the suggested methods can easily be adapted for TTSs in other areas. We characterize TTSs, e.g., in terms of their functionalities, and apply computer-based modeling for pre-deployment assessment of various TTSs (from an ex-ante perspective). By analyzing information provided by the suggested computer-based models, it is possible to make an informed decision whether to (or not to) deploy a given TTS. A review of previous studies reveals information about relevant TTSs for freight transport in areas such as driver support, administration, safety, traffic management, parking, and goods handling. A hierarchical clustering algorithm and a k-minimum spanning tree algorithm were employed to analyze synergies of TTSs. Synergies can enable identification of sets of TTSs that can lead to cost savings if deployed on a common platform (cf. Multi-Service Architectures). An analytical model inspired by the net present value concept is used to estimate quantified societal benefits of TTSs. An optimization model is formulated and solved using a branch and bound method to determine an optimal combination of TTSs taking into consideration societal benefits, costs, dependencies, and synergies. The optimization model also addresses possible system architectures for achieving multiple TTSs. Dominance rough set approach is used to assess and compare benefit areas for TTSs specific to truck parking. The benefit areas are suggested with the help of conceptual modeling, which describes functional models of a system in terms of states, transitions among states, and actions performed in states. The main scientific contributions of the thesis are in suggesting new quantitative models, extending and applying existing models in the assessments of TTSs, and obtaining results that can help decision-makers select TTSs for medium-to long-term investments. Researchers can employ and build on the proposed methods when addressing different scenarios (geographic or organizational) involving similar TTSs. By studying a range of TTSs and possible Multi-Service Architecture concepts for such TTSs, the thesis contributes to achieving convergence of TTSs in a Multi-Service Architecture environment that will improve cost efficiency, minimize redundancies, and encourage the establishment of standards in the deployment of TTSs in road freight transport. TTSs implemented in such an environment can contribute to optimizing available capacity, accuracy, speed, and efficiency of road freight transport systems.

A importância de um bom sistema de medição de desempenho (SMD) para o sucesso de uma empresa é uma questão bastante estudada e discutida, tanto no meio científico quanto no próprio meio empresarial. No entanto, grande parte das empresas não possui um sistema de medição de desempenho que possa lhes trazer uma vantagem competitiva, sendo ele muitas vezes pontual e desligado da estratégia. Por isso, este trabalho é mais uma tentativa de aproximar a teoria e a prática dentro das empresas no que se refere à medição de desempenho organizacional. O objetivo da pesquisa é desenvolver um sistema informatizado para a gestão do desempenho e apresentar um método para sua implantação. Para a definição das características deste método e dos requisitos do sistema, empreendeu-se uma revisão bibliográfica e um estudo de caso em uma empresa de grande porte que já possui um SMD formalizado. O método foi aplicado em duas empresas de Sertãozinho para identificar seus objetivos estratégicos e indicadores de desempenho, o que resultou em uma primeira versão de um SMD formal para elas. Uma aplicação piloto do software foi feita em uma destas empresas para verificar seu funcionamento na prática. Após estes estudos, pôde-se concluir que o método e o sistema são úteis para implantar e acompanhar indicadores de desempenho nas empresas pesquisadas. No entanto, a implantação encontrou alguns obstáculos como a falta de tempo das empresas para se dedicarem à medição de desempenho e a resistência de alguns funcionários em usar o software. / The importance of a well-developed performance measurement system (PMS) for the success of a company is a subject which is widely studied and discussed either in the academic and business enviroments. However, many companies still do not have a performance measurement system able to bring them competitive advantage, because they are all too often ad-hoc solutions not concerned about the strategy. Hence, this work is yet another attempt to bring theory closer to practice inside the companies in the regard of business performance measurement. This research\'s objective is to develop an information system for performance measurement and present a method for its deployment. In order to define the characteristics of both the method and the information system, a literature review and a case study in a company which had a formal PMS were done. The method was then applied to two companies from Sertãozinho aiming to identify their strategic objectives and performance indicators, which resulted in a first version of their formal PMS. A pilot deployment of the software was made in one of these companies to verify how it would work in practice. After these studies, it was possible to conclude that the method and the information system were useful for deploying and keeping track of performance indicators in the researched companies. However, some dificulties were found during the deployment, such as the lack of time available for performance management and the resistance of some employees to the use of the software.

Cardiac auscultation, an important part of the physical examination, is difficult for many primary care providers. As a result, diagnoses are missed or auscultatory signs misinterpreted. A reliable, automated means of interpreting cardiac auscultation should be of benefit to both the primary care provider and to patients. This paper explores a novel approach to this problem and develops an algorithm that can be expanded to include all the necessary electronics and programming to develop such a device. The algorithm is explained and its shortcomings exposed. The potential for further development is also expounded. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Personalized advertising represents an emerging trend in online advertising. Using enhanced data collection techniques, marketers can craft seemingly made to order advertisements tailored to specific individuals. In turn, this should lead to advertisements that are more relevant for consumers and more effective for marketers. Therefore, personalized advertising has the potential to benefit both consumers and firms alike. However, consumer acceptance of the technique remains a huge hurdle, as many consumers seem uncomfortable with the practice due in part to privacy concerns over the vast amounts of data collected and analyzed when generating personalized advertisements. Therefore, it is critical to garner a better understanding of consumers’ attitudes towards personalized advertising in order to be able to use those insights to alleviate consumer privacy concerns. The purpose of this research is to work towards developing a more thorough understanding of consumers’ attitudes towards personalized advertising by exploring the antecedents and outcomes of those attitudes. In particular, we examine what factors determine whether personalized advertising is perceived favorably vs. invasively by consumers and what effects those perceptions have on consumers’ attitudes and intentions. The research lends contributions to academicians, marketing practitioners, and consumers by helping to achieve an increased understanding of personalized advertising’s impact on consumers’ perceptions. The empirical study employed in this research utilizes a conceptual framework that integrates privacy calculus theory with previous research on invasiveness, advertising acceptance, and innovation adoption. In addition, this research contributes to the marketing and information privacy literatures by making a theoretical connection between perceived invasiveness and its relationship with privacy concerns, as well as its impact on consumers’ attitudes and behavioral intentions. The results from the empirical research reveal that a number of constructs, such as perceived invasiveness, privacy concerns, perceived usefulness, and consumer innovativeness demonstrate significant relationships with consumers attitudes and behavioral intentions in the context of personalized advertising. Implications for managers, researchers, and consumers are discussed. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Este trabalho apresenta a construção de um sistema especialista aplicado ao diagnóstico de doenças cardíacas, usando como ferramenta computacional redes Bayesianas. O trabalho envolveu a interação entre diferentes áreas do conhecimento, engenharia e medicina, com maior foco na metodologia da construção de sistemas especialistas. São apresentados os processos de definição do problema, modelagem qualitativa e quantitativa, e avaliação. Neste trabalho, os processos de modelagem e avaliação foram realizados com o auxílio de um especialista médico e de dados bibliográficos. São apresentados como resultados a rede Bayesiana construída e um software para manipulação de redes Bayesianas denominado iBNetz. / This work presents the construction of an expert system applied to the diagnosis of heart diseases, using Bayesian networks as a modeling tool. The work involved interactions between two different fields, engineering and medicine, with special emphasis on the methodology of building expert systems. The processes of problem definition, qualitative and quantitative modeling, and evaluation are presented here. In this work, the modeling and evaluation processes have been conducted with the aid of a medical expert and bibliographic sources. The work has produced a Bayesian network for diagnosis and a software, called iBNetz, for creating and manipulating Bayesian networks.

Motivated by IT evaluation problems identified in a large public sector organization, we propose how evaluation requirements can be supported by a framework combining different models and methods from IS evaluation theory. The article extends the content, context, process (CCP) perspectives of organizational change with operations research techniques and demonstrates the approach in practice for an Enterprise Resource Planning evaluation.

Os avanços na Tecnologia da Informação e a proliferação de itens de consumo, entre outros aspectos, mudaram o cenário e o desempenho das previsões. Os processos de previsão devem ser reexaminados, estabelecendo mecanismos de comunicação formais que compartilhem a informação entre os diferentes níveis hierárquicos dentro da organização, eliminando ou reduzindo o desconforto das previsões paralelas e desconexas oriundas de níveis hierárquicos diferentes. O objetivo deste trabalho é propor um sistema de apoio à decisão baseado em métodos matemáticos e sistemas de informação, capaz de integrar as previsões de vários níveis hierárquicos de uma empresa por um repositório de dados (Data Warehouse ou DW) e um Sistema de Apoio à Decisão (SAD) com sistema Business Intelligence (BI), onde os níveis hierárquicos acessem as informações com o nível de detalhe apropriado dentro do processo de decisão, alinhado às expectativas corporativas de crescimento. Assim, a modelagem realizada neste trabalho teve como foco a geração de cenários para criar um sistema de apoio à decisão, prevendo demandas agregadas e individuais, gerando uma estrutura de integração entre as previsões feitas em diferentes níveis e alinhando valores oriundos de métodos quantitativos e julgamento humano. Uma das maiores preocupações foi verificar qual método (séries temporais, métodos causais) teria destaque em um processo integrado de previsão. Entre os diferentes testes efetuados, pode-se destacar os seguintes resultados: (1) a suavização exponencial tripla proporcionou melhor ajuste (dos dados passados) de séries históricas de demandas mais agregadas e proporcionou previsões mais precisas de representatividades agregadas. Para séries históricas de demanda individual e representatividade individual, os outros métodos comparados apresentaram desempenho muito próximo; (2) a criação de diferentes cenários de previsão, fazendo uso de um repositório de dados e sistema de apoio à decisão, permitiu análise de uma gama de diferentes valores futuros. Uma forma de simulação para apoiar a formulação das expectativas da diretoria foi adaptada da literatura e sugerida; (3) os erros de previsão nas abordagens top-down ou bottom-up são estatisticamente iguais no contexto desta pesquisa. Conclui-se que o método de suavização exponencial tripla traz menos erros às previsões de séries mais agregadas, se comparado com outros métodos abordados no trabalho. Esse fato está de acordo com asserções encontradas na literatura pesquisada de que o método de suavização exponencial é cada vez mais utilizado na previsão, em detrimento dos métodos causais como a regressão múltipla. Conclui-se, principalmente, que os sistemas SAD e BI propostos deram suporte aos vários níveis hierárquicos, proporcionando variedades de estilos de decisão e que podem diminuir o hiato entre o raciocínio qualitativo adotado em nível estratégico e o aspecto quantitativo mais comum em níveis operacionais em qualquer empresa. / Advances in Information Technology (IT), and the increase of consumption items, among other things, changed the performance in the forecasts predictions. It is not uncommon that organizations will perform parallel forecasts within the various hierarchical levels without communicating with each other. The objective of this work is to build an integrated \"infrastructure\" for forecasting through a repository of data (Data Warehouse or DW) and a Decision Support System (DSS) with Business Intelligence (BI) where the hierarchical levels have access to the information with the appropriate level of detail within the process, aligned to the corporate growth expectations. The modeling in this work focused in the generation of scenarios to create a decision support system, predicting individual and aggregate demand, create a structure for integrating and aligning the estimated forecast generated by quantitative and qualitative methods. After a series of experimental tests, main results found were: (1) triple exponential smoothing provided the best fit using historical aggregated demand, and provided a more precise estimate of aggregate representation. For historical series of individual demand and individual representation, the other methods used for comparison performed similarly; (2) the creation of different scenarios for prediction, using data repository and decision support system, allowed for analysis of a range of different future values. The simulation to support management expectations has been adapted from the literature; (3) the prediction errors in the top-down and bottom-up approaches are statistically the same in the context of this research. In conclusion, the method of triple exponential smoothing has fewer errors in the forecasts of aggregated series when compared to other methods discussed in this work. Moreover, the DSS and BI systems provided decision-making support to the various hierarchical levels, reducing the gap between qualitative and quantitative decision processes thus bridging the strategic and operational decision making processes.