The main theme of the diploma thesis is the senior's demand after the tourist services. The diploma thesis defines the main characteristics of the senior segment, its individual attributes and travel behaviour. It also presents the actual offer in tourism for this segment and its possible progress. The diploma thesis is divided into six chapters.

Le contexte de la thèse porte sur le contrôle des réseaux de distribution de contenu à la demande. La performance des systèmes distribués interactifs dépend essentiellement sur la prévision du comportement de l'utilisateur et la bande passante en tant que ressource de réseau critique. Le préchargement est une approche prédictive bien connu dans le World Wide Web ce qui évite les délais de réponse en exploitant un temps d'arrêt que permet d'anticiper les futures demandes de l'utilisateur et prend avantage des ressources réseau disponibles. Le contrôle de préchargement est une opération vitale pour les systèmes à la demande interactifs où la réponse instantanée est le facteur crucial pour la réussite du système. Le contrôleur en ce type de système interactif fonctionne dans un environnement incertain et rend séquences de décisions à court et long terme effets stochastique. La difficulté est alors de déterminer à chaque état du système les contenus préchargés dans le cache. Le plan de préchargement pendant une session en flux continu interactif peut être modélisé comme un problème de décision séquentielle par les processus de décision de Markov (MDP). Nous nous concentrons sur le problème de contrôle de préchargement, dans lequel le contrôleur cherche à atteindre l'état du système à coût zéro aussi vite que possible. Nous modélisons ce problème de contrôle comme un problème de programmation dynamique stochastique négatif dans lequel nous minimisons le coût total prévu. Dans ce contexte, nous avons abordé les questions de recherche suivantes: 1) Comment fournir un politique de préchargement optimale/ approximative optimale qui maximise l'utilisation de la bande passante tout en minimisant les coûts de blocage et de la latence de l'utilisateur engagés sur le chemin? 2) Comment exploiter la structure du modèle de contrôle de préchargement pour aider efficacement calculer la politique de contrôle de préchargement avec la réduction des efforts de calcul et la mémoire de stockage? 3) Comment mener une étude d'évaluation pour évaluer le préchargement de différents algorithmes heuristiques basée sur le contexte de l'optimisation au lieu du cadre de l'empirique / simulation. Pour l'étude de notre problème de recherche, nous avons développé notre modèle MDP de préchargement, PREF-CT, nous avons établi ses propriétés théoriques et nous avons résolu par l'algorithme Value Iteration comme algorithme MDP pour calculer la politique de préchargement optimale. Pour calcul de la politique de préchargement optimale efficace, nous avons détecté une structure spéciale qui réalise un modèle de contrôle plus compact. Cette structure spéciale permet de développer deux algorithmes différents stratégiquement qui améliorent la complexité du calcul de la politique de préchargement optimale: - la première est « ONE-PASS » le second est « TREE-DEC ». Pour surmonter le problème de la dimensionnalité résultant du calcul de la politique de préchargement optimale, nous avons proposé l'algorithme de préchargement heuristique: « Relevant Blocks Prefetching » (RBP). Pour évaluer et comparer le préchargement politiques calculés par des algorithmes de préchargement heuristiques différents, nous avons présenté un cadre fondé sur des différentes mesures de performance. Nous avons appliqué le cadre proposé sous différentes configurations de coûts et différents comportements des utilisateurs pour évaluer les politiques de préchargement calculées par notre algorithme de préchargement proposé; RBP. Par rapport aux politiques de préchargement optimales, l'analyse expérimentale a prouvé des performances significatives des politiques de préchargement de l'heuristique du RBP algorithme. En outre, l'algorithme heuristique de préchargement; RBP se distingue par une propriété de clustériser qui est important pour réduire considérablement la mémoire nécessaire pour stocker la politique de préchargement. / The thesis context is concerned with the control of theOn-demand contents distribution networks. The performance of suchinteractive distributed systems basically depends on the prediction ofthe user behavior and the bandwidth as a critical network resource.Prefetching is a well-known predictive approach in the World Wide Webwhich avoids the response delays by exploiting some downtime thatpermits to anticipate the user future requests and takes advantage ofthe available network resources. Prefetching control is a vitaloperation for the On-demand interactive systems where the instantaneousresponse is the crucial factor for the system success. The controller insuch type of interactive system operates in an uncertain environment andmakes sequences of decisions with long and short term stochasticeffects. The difficulty, then, is to determine at every system statewhich contents to prefetch into the cache. The prefetching plan duringan interactive streaming session can be modeled as a sequential decisionmaking problem by a Markov Decision Process (MDP). We focus on theprefetching control problem in which the controller seeks to reach aZero-Cost system state as quickly as possible. We model this controlproblem as a Negative Stochastic Dynamic Programming problem in which weminimize the undiscounted total expected cost. Within this context, weaddressed the following research questions: 1) How to provide anoptimal/approximate-optimal prefetching policy that, maximizes thebandwidth utilization while minimizes the user's blocking and latencycosts incurred along the way? 2) How to exploit structure in theprefetching control model to help efficiently compute such prefetchingcontrol policy with both computational efforts and storage memoryreduction? 3) How to conduct a performance evaluation study to evaluatedifferent prefetching heuristic algorithms based on the context of thecontrol optimization rather than the context of theempirical/simulation. For studying our research problem, we developedour MDP prefetching control model, PREF-CT, we established itstheoretical properties and we solved it by the Value Iteration algorithmas MDP algorithm for computing the optimal prefetching policy. Forcomputing the optimal prefetching policy efficiently, we detected aspecial structure that achieves more compact control model. This specialstructure permits to develop two strategically different algorithmswhich improve the complexities of computing the optimal prefetchingpolicy: - the first one is the ONE-PASS which is based mainly on solvinga system of linear equations simultaneously in only one iteration,whereas the second is the TREE-DEC which is based on Markov decisiontree decomposition in which sequential sets of systems of equations aresolved. For overcoming the problem of the curse of dimensionalityresulting from the computation of the optimal prefetching policy, weproposed the prefetching heuristic algorithm: the Relevant BlocksPrefetching algorithm (RBP). For evaluating and comparing prefetchingpolicies computed by different prefetching heuristic algorithms, wepresented a framework based on different performance measures. Weapplied the suggested framework under different costs configurations anddifferent user behaviors to evaluate the prefetching policies computedby our proposed prefetching heuristic algorithm; the RBP. Compared tothe optimal prefetching policies, the experimental analysis provedsignificant performance of the prefetching policies of the RBP heuristicalgorithm. In addition, the RBP prefetching heuristic algorithm isdistinguished by a clustering property which is of importance to reducesignificantly the memory necessary to store the prefetching policy tothe controller.

by Kam-Hon Chu. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1987. / Bibliography: leaves 152-158.

O incremento nos padrões de consumo de energia elétrica e o fácil acesso a diversas tecnologias eletroeletrônicas têm contribuído para a superação anual dos índices de consumo de eletricidade. Tendo em vista que esse insumo ainda não é economicamente armazenável em larga escala, se faz necessário manter o equilíbrio em tempo real entre a oferta/demanda mais perdas. Entretanto, a maioria dos consumidores brasileiros atendidos em baixa tensão paga tarifas baseadas nos custos médios, os quais ocultam os efeitos da alta concentração de consumo de eletricidade em determinados horários do dia. Nesse contexto, o presente estudo analisa o impacto que Programas de Resposta à Demanda (PRD), baseado em tarifas com diferenciação horária, exercem sobre o desempenho dos sistemas de distribuição. A metodologia proposta utiliza o conceito de elasticidade-preço da demanda de energia elétrica, por meio de uma abordagem matricial e permite representar diferentes tipos de consumidores. A partir de uma análise de sensibilidade dos estudos de casos, verificou-se a influência dos parâmetros que compõem as equações do PRD proposto. Para avaliar o desempenho do modelo, foram feitos estudos numéricos usando uma versão modificada do sistema teste IEEE de 34 nós. A análise de sensibilidade entre os estudos de caso apresentou uma avaliação do percentual de adesão dos consumidores, bem como o comportamento das perdas ativas mensais e do perfil de tensão. Os resultados obtidos no presente estudo evidenciam a validade da abordagem proposta, a partir de uma formulação simplificada, além de demonstrar a potencial aplicabilidade a casos reais. / Power consumption behavior increase and easy access to electroelectronics technologies had contributed to annual power consumption rates surpass. As there is not yet an economically sustainable way to store electric power it is necessary to maintain the balance between offer and power demand (considering losses). Brazilian customers majority supplied in low voltage are charged by its mean power consumption masking peak consumption in certain periods of the day. Within this reality the present work analysis the impact of Demand Response Programs (DRP) using Time-Of-Use tariff (TOU) over the power distribution system’s performance. The proposed methodology applies the concept of Price Elasticity demand and uses the representation of different consumers’ types in a matrix approach. The DRP parameters’ variation impact was checked using a sensitivity analysis. In order to evaluate the performance of the proposed model numerical studies were done using the IEEE 34 modified node test feeder. A sensitivity analysis among the case studies presents the customers adherence percentage and the monthly active power losses and voltage profile. The methodology's results besides supporting the proposal approach from a simplified formulation show the potential use on real cases.

This thesis explores Centralised Demand Information Sharing (CDIS) in supply chains. CDIS is an information sharing approach where supply chain members forecast based on the downstream member’s demand. The Bullwhip Effect is a demand variance amplification phenomenon: as the demand moves upstream in supply chains, its variability increases. Many papers in the literature show that, if supply chain members forecast using the less variable downstream member’s demand, this amplification can be reduced leading to a reduction in inventory cost. These papers, using strict model assumptions, discuss three demand information sharing approaches: No Information Sharing (NIS), Downstream Demand Inference (DDI) and Demand Information Sharing (DIS). The mathematical analysis in this stream of research is restricted to the Minimum Mean Squared Error (MMSE) forecasting method. A major motivation for this PhD research is to improve the above approaches, and assess those using less restrictive supply chain assumptions. In this research, apart from using the MMSE forecasting method, we also utilise two non-optimal forecasting methods, Simple Moving Averages (SMA) and Single Exponential Smoothing (SES). The reason for their inclusion is the empirical evidence of their high usage, familiarity and satisfaction in practice. We first fill some gaps in the literature by extending results on upstream demand translation for ARMA (p, q) processes to SMA and SES. Then, by using less restrictive assumptions, we show that the DDI approach is not feasible, while the NIS and DIS approaches can be improved. The two new improved approaches are No Information Sharing – Estimation (NIS-Est) and Centralised Demand Information Sharing (CDIS). It is argued in this thesis that if the supply chain strategy is not to share demand information, NIS-Est results in less inventory cost than NIS for an Order Up To policy. On the other hand, if the strategy is to share demand information, the CDIS approach may be used, resulting in lower inventory cost than DIS. These new approaches are then compared to the traditional approaches on theoretically generated data. NIS-Est improves on NIS, while CDIS improves on the DIS approach in terms of the bullwhip ratio, forecast error (as measured by Mean Squared Error), inventory holding and inventory cost. The results of simulation show that the performance of CDIS is the best among all four approaches in terms of these performance metrics. Finally, the empirical validity of the new approaches is assessed on weekly sales data of a European superstore. Empirical findings and theoretical results are consistent regarding the performance of CDIS. Thus, this research concludes that the inventory cost of an upstream member is reduced when their forecasts are based on a Centralised Demand Information Sharing (CDIS) approach.

Thesis (M. Com. (Economics)) -- University of Limpopo, 2017 / The study intends to investigate the determinants of employment in the platinum mining industry in South Africa. Employment levels decreased dramatically in the platinum mining industry in South Africa. This is due to decrease in export demand for platinum, high operating cost, labour unrest, low levels of production and other determinants of employment. The specific objective of the study is to determine the nexus between employment, output, domestic demand and export demand. Annual time series data covering the period between 1992-2013 was used. The study employed the Vector Error Correction Model approach. Johansen Cointegration test results confirmed the existence of a long run relationship amongst variables under investigation. Export demand and output are found to be positively related with employment. The speed of adjustment to equilibrium is -0.283202. Impulse response functions and variance decomposition are also generated to explain the response to shock amongst variables. The results of the study vindicate that the platinum mining industry should implement policies and strategies to increase output which will lead to higher levels of employment as well as economic growth. In addition, government should also create a conducive environment to enable the industry to expand and the industry should also intensify its export drive, these findings are envisaged to contribute significantly to the existing but limited literature on the subject under investigation. / National Research Foundation

The general aim of this research is to investigate approaches to: •improve small area market demand (i.e. SAMD) prediction accuracy for the purchase of automobiles at the level of each Census Collection District (i.e. CCD); and •enhance understanding of meso-level marketing phenomena (i.e. geographically aggregated phenomena) relating to SAMD. Given the importance of SAMD prediction, and the limitations posed by current methods, four research questions are addressed: •What are the key challenges in meso-level SAMD prediction? •What variables affect SAMD prediction? •What techniques can be used to improve SAMD prediction? •What is the value of integrating these techniques to improve SAMD prediction? To answer these questions, possible solutions from two broad areas are examined: spatial analysis and data mining. The research is divided into two main studies. In the first study, a seven-step modelling process is developed for SAMD prediction. Several sets of models are analysed to examine the modelling techniques’ effectiveness in improving the accuracy of SAMD prediction. The second study involves two cases to: 1) explore the integration of these techniques and their advantages in SAMD prediction; and 2) gain insights into spatial marketing issues. The case study of Peugeot in the Sydney metropolitan area shows that urbanisation and geo-marketing factors can have a more important role in SAMD prediction than socio-demographic factors. Furthermore, results show that modelling spatial effects is the most important aspect of this prediction exercise. The value of the integration of techniques is in compensating for the weaknesses of conventional techniques, and in providing complementary and supplementary information for meso-level marketing analyses. Substantively, significant spatial variation and continuous patterns are found with the influence of key studied variables. The substantive implications of these findings have a bearing on both academic and managerial understanding. Also, the innovative methods (e.g. the SAMD modelling process and the model cube based technique comparison) developed from this research make significant contributions to marketing research methodology.

<p>A study has been conducted to assess the possibilities to introduce dead cattle organs as the raw material for biogas generation at the rural household level in Bangladesh. At the same time, the present energy situation in Bangladesh is highlighted. The actors in the energy sector have been identified. The energy policy of Bangladesh is not transparent and there seems to be no energy strategy for the country. Possibilities of other renewable sources of energy are also discussed. Biomass fuels comprise the main source of energy for the rural people and the major share of energy use is consumed after cooking and household lightning. Enormous amount of labor is spent in gathering and collecting of fuel wood and agricultural residues that reduces productivity among women and young children. Besides, biogas is generated from agricultural residues and animal excreta in Bangladesh. Tremendous pressure on rural forests for fuel wood is increasing and environmental degradation is occurring. Agricultural lands are losing vital nutrients as people are using crop residues and animal excreta for energy. Under present condition, the possibilities of adopting biogas technology and dead cattle organs as the raw materials to generate biogas is analyzed in terms of availability of the raw material. Sustainable development using biogas is also considered. And lastly, some recommendation is suggested, based on the current energy situation of Bangladesh.</p>

A study has been conducted to assess the possibilities to introduce dead cattle organs as the raw material for biogas generation at the rural household level in Bangladesh. At the same time, the present energy situation in Bangladesh is highlighted. The actors in the energy sector have been identified. The energy policy of Bangladesh is not transparent and there seems to be no energy strategy for the country. Possibilities of other renewable sources of energy are also discussed. Biomass fuels comprise the main source of energy for the rural people and the major share of energy use is consumed after cooking and household lightning. Enormous amount of labor is spent in gathering and collecting of fuel wood and agricultural residues that reduces productivity among women and young children. Besides, biogas is generated from agricultural residues and animal excreta in Bangladesh. Tremendous pressure on rural forests for fuel wood is increasing and environmental degradation is occurring. Agricultural lands are losing vital nutrients as people are using crop residues and animal excreta for energy. Under present condition, the possibilities of adopting biogas technology and dead cattle organs as the raw materials to generate biogas is analyzed in terms of availability of the raw material. Sustainable development using biogas is also considered. And lastly, some recommendation is suggested, based on the current energy situation of Bangladesh.

This dissertation aims to contribute to the field of economics of competition policy by analyzing the demand structure and the market power in the Turkish beverage industry and in the cola market in particular. First, a demand system for the beverage products has been estimated by using a multi-stage linearized Almost Ideal Demand System (AIDS). Using the own-price elasticity of cola in a SSNIP test (Small but Significant Non-Transitory Increase in Price), it is shown that cola market consists of a distinct relevant product market. Then, the demand elasticities of cola products at brand and package level have been estimated by the simple and nested logit models. Finally, the estimated demand elasticities of cola products have been used in measuring the degree of market power and predicting the effects of a hypothetical merger between Pepsi and Cola Turca by using a merger simulation technique. The results show that all cola suppliers have large price-cost margins for most of their products. Prices of the merging parties increase in average by 15 - 21% after the merger. The merger also causes the market price to increase by 16- 22% and consumer surplus to decrease by nearly 5% in average. Finally, depending on these results, the thesis recommends a stricter merger control criterion than dominance criterion for competition policy in Turkey.