Thesis (MScEng)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: The expected peak water demand in a water distribution system (WDS) is an important consideration for WDS design purposes. In South Africa the most common method of estimating peak demand is by multiplying the average demand by a dimensionless peak factor. A peak factor is the ratio between the maximum flow rate (which refers to the largest volume of flow to be received during a relatively short time period, say , expressed as the average volume per unit time), and the average flow rate over an extended time period. The magnitude of the peak factor will vary, for a given daily water demand pattern, depending on the chosen value of . The design guidelines available give no clear indication of the time intervals most appropriate for different peak factor applications. It is therefore important to gain a better understanding regarding the effect of on the derived peak factor. A probability based end-use model was constructed as part of this study to derive diurnal residential indoor water demand patterns on a temporal scale of one second. These stochastically derived water demand patterns were subsequently used to calculate peak factors for different values of , varying from one second to one hour. The end-use model derived the water demand patterns by aggregating the synthesised end-use events of six residential indoor end-uses of water in terms of the water volume required, duration and the time of occurrence of each event. The probability distributions describing the end-use model parameters were derived from actual end-use measurements that had previously been collected in a noteworthy North-American end-use project (Mayer et al., 1999). The original comprehensive database, which included water measurements from both indoor and outdoor end-uses, was purchased for use in this project. A single execution of the end-use model resulted in the synthesised diurnal water demand pattern for a single household. The estimated water demand pattern for simultaneous water demand by groups of households was obtained by adding individual iterations of the end-use model, considering group sizes of between one and 2 000 households in the process. A total of 99 500 model executions were performed, which were statistically aggregated by applying the Monte Carlo method and forming 4 950 unique water demand scenarios representing 29 different household group sizes. For each of the 4 950 water demand scenarios, a set of peak factors was derived for eight selected values. The end-use model presented in this study yielded realistic indoor water demand estimations when compared to publications from literature. In agreement with existing knowledge, as expected, an inverse relationship was evident between the magnitude of the peak factors and . The peak factors across all time intervals were also found to be inversely related to the number of households, which agreed with other publications from literature. As the number of households increased, the degree to which the peak factor was affected by the time intervals decreased. This study explicitly demonstrated the effect of time intervals on peak factors. The results of this study could act as the basis for the derivation of a practical design guideline for estimating peak indoor flows in a WDS, and the work could be extended in future to include outdoor water demand and sensitivity to WDS pressure. / AFRIKAANSE OPSOMMING: Die verwagte water spitsaanvraag is ‘n belangrike oorweging in die ontwerp van ‘n waterverspreidingsnetwerk. Die mees algemene metode in Suid Afrika om spitsaanvraag te bereken is deur die gemiddelde wateraanvraag te vermeningvuldig met ‘n dimensielose spitsfaktor. ‘n Spitsfaktor is die verhouding tussen die maksimum watervloei tempo (wat verwys na die grootste volume water wat ontvang sal word tydens ‘n relatiewe kort tydsinterval, , uitgedruk as die gemiddelde volume per tyd eenheid), en die gemiddelde watervloei tempo gedurende ‘n verlengde tydsinterval. Die grootte van die spitsfaktor sal varieer vir ‘n gegewe daaglikse vloeipatroon, afhangende van die verkose waarde. Die beskikbare ontwerpsriglyne is onduidelik oor watter tydsintervalle meer geskik is vir die verskillende spitsfaktor toepassings. Daarom is dit belangrik om ‘n beter begrip te verkry ten opsigte van die effek van op die verkrygde spitsfaktor. ‘n Waarskynliksheidsgebaseerde eindverbruik model is opgestel om deel te vorm van hierdie studie, om daaglikse residensiële binnenshuise wateraanvraag patrone af te lei op ‘n temporale skaal van een sekonde. Die stogasties afgeleide wateraanvraag patrone is daarna gebruik om die verskeie spitsfaktore te bereken vir verskillende waardes van , wat varieer vanaf een sekonde tot een uur. Die eindverbruik model stel die daaglikse vloeipatroon van een huis saam deur die eindeverbruik gebeure van ses residensiële binnenshuise eindverbruike saam te voeg in terme van the vereiste water volume en die tyd van voorkoms van elke gebeurtenis. Die waarskynliksheids distribusie wat die eindverbruik model parameters omskryf is verkry van werklike gemete eindverbruik waardes, wat voorheen in ‘n beduidende Noord-Amerikaanse eindverbruik projek (Mayer et al. 1999) versamel is. Die oorspronklike en omvattende databasis, wat gemete waardes van binnenshuis en buite water verbruik ingesluit het, is aangekoop vir gebruik gedurende hierdie projek. ‘n Enkele uitvoering van die eindverbruik model stel gevolglik ‘n daaglikse wateraanvraag patroon saam vir ‘n elkele huishouding. Die wateraanvraag patroon vir gelyktydige water verbruik deur groepe huishoudings is verkry deur individuele iterasies van die eindverbruik model statisties bymekaar te tel met die Monte Carlo metode, terwyl groep groottes van tussen een en 2 000 huishoudings in die proses oorweeg is. ‘n Totaal van 99 500 model uitvoerings is gedoen, wat saamgevoeg is om 4 950 unieke watervraag scenarios voor te stel, wat verteenwoordigend is van 29 verskillende huishouding groep groottes. Vir elkeen van die 4 950 watervraag senarios, is ‘n stel spitsfaktore afgelei vir agt verkose waardes. Die eindverbruik model aangebied in hierdie studie lewer ‘n realistiese binnenshuise wateraanvraag skatting, wanneer dit vergelyk word met verslae in die literatuur. Ooreenkomstig met bestaande kennis is ‘n sterk inverse verhouding sigbaar tussen die grootte van die spitsfaktore en . Dit is ook gevind dat die spitsfaktore oor al die tydsintervalle ‘n inverse verband toon tot die aantal huishoudings, wat ooreenstemmend is met ander publikasies in die literatuur. Soos die aantal huishoudings toeneem, het die mate waartoe die spitsfaktor geaffekteer is deur die tydsintervalle afgeneem. Hierdie studie toon duidelik die effek van tydsintervalle op spitsfaktore. Die resultaat van hierdie studie kan dien as basis om praktiese ontwerpsriglyne te verkry in die skatting van binnenshuise spitsvloei in ‘n waterverspreidingsnetwerk, gegewe dat die werk in die toekoms uitgebrei kan word om ook buitenshuise waterverbruik in te sluit, asook sensitiwiteit tot druk in die waterverspreidingsnetwerk.

Purpose: This research aims to enhance the current understanding and knowledge of the demand-supply chain management (DSCM) concept by determining its elements, benefits, and requirements, as well as by analyzing key elements of the concept. Methodology: This research has utilized the case study strategy and the survey strategy, however, the case study strategy dominates. The case study research has involved five companies originating from Sweden and the collection of empirical data mainly from in-depth interviews with key persons representing senior and middle management. The survey research targeted the largest firms in Sweden and Finland and empirical data was collected through an online questionnaire. Findings: This research has established that the main elements of DSCM include market orientation, coordination of the demand and supply processes, viewing the demand and supply processes as being equally important, as well as value creation, differentiation, innovativeness, responsiveness, and cost-efficiency in the demand and supply processes. It has also been revealed that the main benefits of DSCM include enhanced competiveness, enhanced demand chain performance, as well as enhanced supply chain performance, while the main requirements of DSCM include organizational competences, company established principles, demand-supply chain collaboration, and information technology support. A key element of DSCM further investigated is differentiation focused supply chain design. It has been shown that these efforts can be organized into a process of five stages. In addition, it is important that this process is addressed in parallel with the new product development (NPD) process, that information is exchanged between them, and that they are directed on the basis of the same segmentation model. Another key element of DSCM further investigated is coordination between NPD and SCM. This research has identified several significant linkages between these management directions, which motivate the use of an integrative NPD process where the NPD functions are aligned with the main supply functions in the company and other sales-related functions supporting the commercialization. A final key element of DSCM further investigated is the significance of regarding the demand processes and the supply processes as being equally important. This research has revealed that logistics outsourcing can be risky, if it results in the supply processes being considered less important. Nevertheless, if senior management regards the outsourced processes as equally important as the in-house processes, the effect of logistics outsourcing on company strategies and direction in SCM could be reduced and logistics outsourcing could instead provide an opportunity to improve the design and differentiation of the supply chain. Research limitations/implications: This research has proposed, described, and further analyzed a demand-supply oriented management approach. Such a management approach stresses that the demand processes and the supply processes have to be coordinated and directed at an overlying level, in order to gain and sustain a competitive advantage in competitive and fragmented markets. This research is mainly explorative in nature, and more empirical data, from similar and other research settings, is needed to further validate the findings. Another limitation of the research is that it is essentially limited to Swedish companies (even if some Finnish companies are involved in the survey), however, many of the case companies have a large international presence and are among the top three in their industries, facts which provide some grounds for generalization. Practical implications: This research provides researchers and practitioners with insights into how to develop a demand-supply oriented business. It shows that companies should organize themselves around understanding how customer value is created and delivered, as well as how these processes and management directions can be coordinated. In order for this to occur, the demand and supply processes must be considered as being equally important and the firm needs to be managed jointly and in a coordinated manner by the demand- and supply-side of the company. It is also important that value creation is considered in both the demand and supply processes. Originality/value: Despite strong arguments from both researchers and practitioners for a demand-supply oriented management approach only a minority of companies appear to have effectively coordinated the demand and supply processes. This might be influenced by the lack of research examining how the demand and supply processes can be coordinated, what benefits can be gained by coordinating them, and what requirements are necessary to succeed. This research contributes by investigating these types of aspects further.

The implementation of a low carbon electricity system within the GB requires a significant change to the generation mix with an increasing role for renewable generation. Much of this generation will be intermittent. To date system balancing has largely relied on predicting demand and ensuring provision. With substantial intermittency, continuation of this paradigm necessitates significant investment in peaking plant and/or storage. However, some of this investment can be avoided by harnessing the flexibility inherent in many electrical loads. Despite the attractiveness of such services, we do not see their large-scale implementation. The aim of this thesis is to consider why. A historical analysis reveals that both nationalisation and subsequent privatisation provide precedents for significant structural change as the integration of large-scale flexible demand might require. The need for political will is identified as a crucial enabling factor. Without an ideological driver, however, a perception of economic and/or technological risk can preclude the implementation of supportive policy. This perception is addressed through demonstration. An effective demonstration must show the ability to aggregate many small loads in a coordinated manner. A genetic algorithm that provides this core dispatch and optimisation capability is presented. This algorithm is shown to be effective in aggregating many small loads to provide a net effect that can be used as a balancing service and to do so in an optimal way considering both cost and reliability. Having demonstrated feasibility appropriate incentives must be created. An initial outline for a framework based on SysML is presented that can be used to identify where structural barriers to implementation are present to aid the design of appropriate policy incentives.

The basic design concept of most advanced traveler information systems (ATIS) is to present generic information to travelers, leaving travelers to react to the information in their own way. This passive way of managing traffic by providing generic traffic information makes it difficult to predict the outcome and may even incur an adverse effect, such as overreaction (also referred to as the herding effect). Active traffic and demand management (ATDM) is another approach that has received continual attention from both academic research and real-world practice, aiming to effectively influence people's travel demand, provide more travel options, coordinate between travelers, and reduce the need for travel. The research discussed in this article deals with how to provide users with a travel option that aims to minimize the marginal system impact that results from this routing. The goal of this research is to take better advantage of the available real-time traffic information provided by ATIS, to further improve the system level traffic condition from User Equilibrium (UE), or a real-world traffic system that is worse than UE, toward System Optimal (SO), and avoid passively managing traffic. A behaviorally induced, system optimal travel demand management model is presented to achieve this goal through incremental routing. Both analytical derivation and numerical analysis have been conducted on Tucson network in Arizona, as well as on the Capital Area Metropolitan Planning Organization (CAMPO) network in Austin, TX. The outcomes of both studies show that our proposed modeling framework is promising for improving network traffic conditions toward SO, and results in substantial economic savings.

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