Importância da Programação Linear na Determinação de Informações de Gestão - Caso Moave, Sa

A Investigação Operacional vem demonstrando ser uma valiosa ferramenta de gestão nos dias de hoje em que se vive num mercado cada vez mais competitivo. Através da Programação Linear pode-se reproduzir matematicamente um problema de maximização dos resultados ou minimização dos custos de produção com o propósito de auxiliar os gestores na tomada de decisão. A Programação Linear é um método matemático em que a função objectivo e as restrições assumem características lineares, com diversas aplicações no controlo de gestão, envolvendo normalmente problemas de utilização dos recursos disponíveis sujeitos a limitações impostas pelo processo produtivo ou pelo mercado. O objectivo geral deste trabalho é o de propor um modelo de Programação Linear para a programação ou produção e alocação de recursos necessários. Optimizar uma quantidade física designada função objectivo, tendo em conta um conjunto de condicionalismos endógenas às actividades em gestão. O objectivo crucial é dispor um modelo de apoio à gestão contribuindo assim para afectação eficiente de recursos escassos à disposição da unidade económica. Com o trabalho desenvolvido ficou patente a importância da abordagem quantitativa como recurso imprescindível de apoio ao processo de decisão. The operational research has proven to be a valuable management tool today we live in an increasingly competitive market. Through Linear Programming can be mathematically reproduce a problem of maximizing performance or minimizing production costs in order to assist managers in decision making. The Linear Programming is a mathematical method in which the objective function and constraints are linear features, with several applications in the control of management, usually involving problems of resource use are available subject to limitations imposed by the production process or the market. The overall objective of this work is to propose a Linear Programming model for scheduling or production and allocation of necessary resources. Optimizing a physical quantity called the objective function, given a set of endogenous constraints on management thus contributing to efficient allocation of scarce resources available to the economic unit. With the work has demonstrated the importance of the quantitative approach as essential resource to support the decision process.

Perfil dos Turistas que Visitam a Ilha de São Vicente

O presente trabalho tem por objectivo definir o perfil do turista que visitou a ilha de São Vicente e avaliar o grau de satisfação do turista enquanto consumidor de diferentes bens e serviços, nos meses de Maio e Junho do decorrente ano. Para se fazer uma abordagem qualitativa falou-se da problemática do mercado turístico - procura e oferta turística – e a importância da segmentação desse sistema turístico juntamente com algumas teorias do comportamento do consumidor. Por sua vez, para o estudo do perfil e satisfação dos turistas utilizou-se uma abordagem quantitativa, através da aplicação de questionários no Aeroporto Internacional Cesária Évora e entrevistas aos estabelecimentos hoteleiros, 150 e 7 respectivamente. Após a análise explicativa, com base na bibliografia seleccionada, nas entrevistas e questionários realizados, os resultados da pesquisa indicam que estudar o perfil e a satisfação dos turistas permite fazer um levantamento de diversas variáveis importantes como, por exemplo, a nacionalidade do turista, a principal razão e objectivo da viagem e o que gostaram, ou desejavam ver no destino. E assim, entender-se o perfil do visitante e propor-se estratégias, que contribuam para um planeamento sustentável do desenvolvimento turístico na região pretendida. A complexidade do processo de decisão do turista por um ou outro destino é que verdadeiramente motiva esta pesquisa. The objective of the following work is to define the profile of a tourist that visited São Vicente, and evaluate the levels of satisfaction of the tourist as a consumer of different items and services, on May and June of the current year. For a quality methodological approach, the problematic of the touristic industry (demand and touristic offer) – the essential of subdivision of these touristic systems alongside with the principles of the consumer behavior were discussed. In continuity, to define the profile and satisfaction of the tourists, a quantity approach was also used, through the application of questionnaires at the International Airport Cesária Évora and interviews throughout hotel establishments, 150 and 7 respectively. After an explanatory analyses, based on a selected bibliography, the applied interviews and questionnaires, the outcome of this research shows that by studying the profile and the satisfaction of the tourists allows to have variable important results, for example, the tourist´s nationality, the main reason and objective of the trip and what they enjoyed or would like to see on their destination. Thus, understanding the visitor´s profile and suggesting strategies, that contributes for a sustainable planning of a touristic development on the intended region. The process complexity of the decision making of the tourist for one destination is truly the main motivation for this research.

National Adaptation Programme of Action on Climate Change - 2008 - 2012

The archipelago of Cape Verde is made up of ten islands and nine islets and is located between latitudes 14º 28' N and 17º 12' N and longitudes 22º 40' W and 25º 22' W. It is located approximately 500 km from the Senegal coast in West Africa (Figure 1). The islands are divided into two groups: Windward and Leeward. The Windward group is composed of the islands of Santo Antão, São Vicente, Santa Luzia, São Nicolau, Sal and Boavista; and the Leeward group is composed of the islands Maio, Santiago, Fogo and Brava. The archipelago has a total land surface of 4,033 km2 and an Economic Exclusive Zone (ZEE) that extends for approximately 734,000 km2. In general, the relief is very steep, culminating with high elevations (e.g. 2,829 m on Fogo and 1,979 m on Santo Antão). The surface area, geophysical configuration and geology vary greatly from one island to the next. Cape Verde, due to its geomorphology, has a dense and complex hydrographical network. However, there are no permanent water courses and temporary water courses run only during the rainy season. These temporary water courses drain quickly towards the main watersheds, where, unless captured by artificial means, continue rapidly to lower areas and to the sea. This applies equally to the flatter islands. The largest watershed is Rabil with an area of 199.2 km2. The watershed areas on other islands extend over less than 70 km2. Cape Verde is both a least developed country (LDC) and a small island development state (SIDS). In 2002, the population of Cape Verde was estimated at approximately 451,000, of whom 52% were women and 48% men. The population was growing at an average 2.4% per year, and the urban population was estimated at 53.7 %. Over the past 15 years, the Government has implemented a successful development strategy, leading to a sustained economic growth anchored on development of the private sector and the integration of Cape Verde into the world economy. During this period, the tertiary sector has become increasingly important, with strong growth in the tourism, transport, banking and trade sectors. Overall, the quality of life indicators show substantial improvements in almost all areas: housing conditions, access to drinking water and sanitation, use of modern energy in both lighting and cooking, access to health services and education. Despite these overall socio-economic successes, the primary sector has witnessed limited progress. Weak performance in the primary sector has had a severe negative impact on the incomes and poverty risks faced by rural workers1. Moreover, relative poverty has increased significantly during the past decade. The poverty profile shows that: (i) extreme poverty is mostly found in rural areas, although it has also increased in urban areas; (ii) poverty is more likely to occur when the head of the household is a woman; (iii) poverty increases with family size; (iv) education significantly affects poverty; (v) the predominantly agricultural islands of Santo Antão and Fogo have the highest poverty rates; (vi) unemployment affects the poor more than the nonpoor; (vii) agriculture and fisheries workers are more likely to be poor than those in other sectors. Therefore, the fight against poverty and income inequalities remains one of the greatest challenges for Cape Verde authorities. The various governments of Cape Verde over the last decade have demonstrated a commitment to improving governance, notably by encouraging a democratic culture that guarantees stability and democratic changes without conflicts. This democratic governance offers a space for a wider participation of citizens in public management and consolidates social cohesion. However, there are some remaining challenges related to democratic governance and the gains must be systematically monitored. Finally, it is worth emphasizing that the country’s insularity has stimulated a movement to decentralized governance, although social inequalities and contrasts from one island to the next constitute, at the same time, challenges and opportunities.

One-sided tolerance interval in a two-way balanced nested model with mixed effects

In many research areas (such as public health, environmental contamination, and others) one deals with the necessity of using data to infer whether some proportion (%) of a population of interest is (or one wants it to be) below and/or over some threshold, through the computation of tolerance interval. The idea is, once a threshold is given, one computes the tolerance interval or limit (which might be one or two - sided bounded) and then to check if it satisfies the given threshold. Since in this work we deal with the computation of one - sided tolerance interval, for the two-sided case we recomend, for instance, Krishnamoorthy and Mathew [5]. Krishnamoorthy and Mathew [4] performed the computation of upper tolerance limit in balanced and unbalanced one-way random effects models, whereas Fonseca et al [3] performed it based in a similar ideas but in a tow-way nested mixed or random effects model. In case of random effects model, Fonseca et al [3] performed the computation of such interval only for the balanced data, whereas in the mixed effects case they dit it only for the unbalanced data. For the computation of twosided tolerance interval in models with mixed and/or random effects we recomend, for instance, Sharma and Mathew [7]. The purpose of this paper is the computation of upper and lower tolerance interval in a two-way nested mixed effects models in balanced data. For the case of unbalanced data, as mentioned above, Fonseca et al [3] have already computed upper tolerance interval. Hence, using the notions persented in Fonseca et al [3] and Krishnamoorthy and Mathew [4], we present some results on the construction of one-sided tolerance interval for the balanced case. Thus, in order to do so at first instance we perform the construction for the upper case, and then the construction for the lower case.