Дисертації з теми "Resource on Demand"

This dissertation investigates two related aspects of firms’ choice of HRM practices. The first is why some firms expend a great deal of resources on HRM practices for each employee while others spend very little; the second is the extent to which firms’ bundles of HRM practices sort into general discrete employment systems. In order to empirically address these issues, this dissertation uses an economics-based theoretical approach. The key theoretical link to economics is to treat HRM as a separate factor input in the production process, which allows me to derive an HRM input demand function. This function expresses the firm’s per employee expenditures on HRM and their choice of HRM system as a function of prices and internal and external firm characteristics. Ordinary least squares, two-stage least squares and linear quantile analysis are used to empirically estimate the HRM demand function using a unique dataset of several hundred firms collected by the Bureau of National Affairs (BNA). The regression equation is found to be statistically significant, implying firms do have an identifiable demand for HRM practices. Second, there are nine independent variables which are found to be stable determinants of the demand for per employee expenditures on HRM practices. Regarding the existence of discrete employment systems, cluster analysis is used to determine if the sets of HRM practices adopted by these firms sort into identifiable types of HRM systems. The results show that there is a discrete set of four HRM systems; however, the HRM demand function does not predict which system a firm will choose.

Thesis (Ph. D.)--Georgia State University, 2008.
Title from file title page. Bruce E. Kaufman, committee chair; Barry T. Hirsch, Klara S. Peter, Hyeon J. Park, committee members. Description based on contents viewed Sept. 22, 2009. Includes bibliographical references (p. 159-165).

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2003.
Includes bibliographical references (leaves 127-128).
by Andrew J. Muench.
S.M.

2012 - 2013
The power grid is an electric system capable of performing electricity generation, transmission, distribution and control. Nowadays it has been subjected to a deep transformation, which will reshape it completely. In fact, growing electricity demand and consequent increase of power losses in transmission and distribution grids, the increase in prices of fossil fuels and the diffusion of renewable resources, the need for a more effective and efficient grid management and use of energy, the availability of new technologies to be integrated into the grid, they all push for a modernization of the power grid. Integrating technology and approaches typical of different areas (i.e. power systems, ICT, measurements, automatic controls), the aim is to build a grid capable of engulfing all types of sources and loads, capable of efficiently deliver electricity automatically adapting to changes in generation and demand, ultimately empowering customers with new and advanced services. This paradigm is known as Smart Grid. In this context, the role of measurement theories, techniques and instrumentation is a fundamental one: the automatic management and control of the grid is a completely unfeasible goal without a timely and reliable picture of the state of the electric network. For this reason, a metering infrastructure (including sensors, data acquisition and process system and communication devices and protocols) is needed to the development of a smarter grid. Among the features of such an infrastructure are the ability to execute accurate and real‐time measurements, the evaluation of power supply quality and the collection of measured data and its communication to the system operator. Moreover, a so defined architecture can be extended to all kinds of energy consumption, not only the electricity ones. With the development of an open energy market, an independent entity could be put in charge of the execution of measurements on the grid and the management of the metering infrastructure: in this way, “certified” measurements will be guaranteed, ensuring an equal treatment of all grid and market users. In the thesis, different aspects relative to measurement applications in the context of a Smart Grid have been covered. A smart meter prototype to be installed in customers’ premises has been realized: it is an electricity meter also capable of interfacing with gas and hot water meters, acting as a hub for monitoring the overall energy consumption. The realized prototype is based on an ARM Cortex M3 microcontroller architecture (precisely, the ST STM32F103), which guarantees a good compromise among cost, performance and availability of internal peripherals. Advanced measurement algorithms to ensure accurate bidirectional measurements even in non‐sinusoidal conditions have been implemented in the meter software. Apart from voltage and current transducer, the meter embeds also a proportional and three binary actuators: through them is possible to intervene directly on the monitored network, allowing for load management policies implementation. Naturally the smart meter is only functional if being a part of a metering and communication infrastructure: this allows not only the collection of measured data and its transmission to a Management Unit, which can so build an image of the state of the network, but also to provide users with relevant information regarding their consumptions and to realize load management policies. In fact, the realized prototype architecture manages load curtailments in Demand Response programs relying on the price of energy and on a cost threshold that can be set up by the user. Using a web interface, the user can verify his own energy consumptions, manage contracts with the utility companies and eventually his participation in DR programs, and also manually intervene on his loads. In the thesis storage systems, of fundamental importance in a Smart Grid Context for the chance they offer of decoupling generation and consumption, have been studied. They represent a key driver towards an effective and more efficient use of renewable energy sources and can provide the grid with additional services (such as down and up regulation). In this context, the focus has been on li‐ion batteries: measurement techniques for the estimation of their state of life have been realized. Since batteries are becoming increasingly important in grid operation and management, knowing the degradation they are subjected has a relevant impact not only on grid resource planning (i.e. substitution of worn off devices and its scheduling) but also on the reliability in the services based on batteries. The implemented techniques, based on Fuzzy logic and neural networks, allow to estimate the State of Life of li‐ion batteries even for variation of the external factors influencing battery life (temperature, discharge current, DoD). Among the requisites a Smart Grid architecture has, is the integration into the grid of Electric Vehicles. EVs include both All Electric Vehicles and Plug‐in Hybrid Electric Vehicles and have been considered by governments and industry as sustainable means of transportation and, therefore, have been the object of intensive study and development in recent years. Their number is forecasted to increase considerably in the next future, with alleged consequences on the power grid: while charging, they represent a consistent additional load that, if not properly managed, could be unbearable for the grid. Nonetheless, EVs can be also a resource, providing their locally stored energy to the power grid, thus realizing useful ancillary services. The paradigm just described is usually referred to as Vehicle‐to‐Grid (V2G). Being the storage systems onboard the EVs based on li‐ion batteries, starting from the measurement and estimation techniques precedently introduced, aim of the thesis work will be the realization of a management systems for EV fleets for the provision of V2G services. Assuming the system model in which the aggregator not only manages such services, but can also be the owner of the batteries, the goal is to manage the fleets so to maximize battery life, and guarantee equal treatment to all the users participating in the V2G program. [edited by author]
XII n.s.

We study the benefits of resource flexibility, considering two different flexibility structures. First, we want to understand the impact of the firm's pricing strategy on its resource investment decision, considering a partially flexible resource. Secondly, we study the benefits of a flexible resource strategic approach, considering a resource flexibility structure that has not been studied in the previous literature. First, we study the capacity investment decision faced by a firm that offers two products/services and that is a price-setter for both products/services. The products offered by the firm are of varying levels (complexities), such that the resources that can be used to produce the higher level product can also be used to produce the lower level one. Although the firm needs to make its capacity investment decision under high demand uncertainty, it can utilize this limited (downward) resource flexibility, in addition to pricing, to more effectively match its supply with demand. Sample applications include a service company, whose technicians are of different capabilities, such that a higher level technician can perform all tasks performed by a lower level technician; a firm that owns a main plant, satisfying both end-product and intermediate-product demand, and a subsidiary, satisfying the intermediate-product demand only. We formulate this decision problem as a two-stage stochastic programming problem with recourse, and characterize the structural properties of the firm's optimal resource investment strategy when resource flexibility and pricing flexibility are considered in the investment decision. We show that the firm's optimal resource investment strategy follows a threshold policy. This structure allows us to understand the impact of coordinated decision-making, when the resource flexibility is taken into account in the investment decision, on the firm's optimal investment strategy, and establish the conditions under which the firm invests in the flexible resource. We also study the impact of demand correlation on the firm's optimal resource investment strategy, and show that it may be optimal for the firm to invest in both flexible and dedicated resources when product demand patterns are perfectly positively correlated. Our results offer managerial principles and insights on the firm's optimal resource investment strategy as well as extend the newsvendor problem with pricing, by allowing for multiple resources (suppliers), multiple products, and resource pooling. Secondly, we study the benefits of a delayed decision making strategy under demand uncertainty, considering a system that satisfies two demand streams with two capacitated and flexible resources. Resource flexibility allows the firm to delay its resource allocation decision to a time when partial information on demands is obtained and demand uncertainty is reduced. We characterize the structure of the firm's optimal delayed resource allocation strategy. This characterization allows us to study how the revenue benefits of the delayed resource allocation strategy depend on demand and capacity parameters, and the length of the selling season. Our study shows that the revenue benefits of this strategy can be significant, especially when demand rates of the different types are close, while resource capacities are much different. Based on our analysis, we provide guidelines on the utilization of such strategies. Finally, we incorporate the uncertainty in demand parameters into our models and study the effectiveness of several delayed capacity allocation mechanisms that utilize the resource flexibility. In particular, we consider that demand forecasts are uncertain at the start of the selling season and are updated using a Bayesian framework as early demand figures are observed. We propose several heuristic capacity allocation policies that are easy to implement as well as a heuristic procedure that relies on a stochastic dynamic programming formulation and perform a numerical study. Our study determines the conditions under which each policy is effective.
Ph. D.

Thesis (M.A.)--University of Cincinnati, 2005.
Title from electronic thesis title page (viewed Sept. 6, 2007). Includes abstract. Keywords: dual-task; dual task; transition; transitions; demand transition; demand transitions; workload; mental workload; workload transition; workload transitions; task difficulty; tracking; tracking task; vigilance; compensatory tracking; performance; human performance; tracking performance; resource depletion; resource-depletion; mental resources; resource capacity; effort; effort-regulation; effort regulation; stress; transition stress; resource allocation; resource-allocation; human factors. Includes bibliographical references.

Manufacturing industries used to develop their operation strategies focusing on the cost of manufacturing, high volume production, and stabilizing the customer demand. But due to advancements in technology and evolving customer needs, the market demand became highly volatile, dynamic and customers expected customization, low volume products, and faster deliveries. This evolution in customer needs has pushed the companies to improve their operating systems to be more flexible, agile, and adaptable to the market’s dynamic character. In order to effectively evolves themselves and achieve more flexibility, manufacturing companies had to implement effective manufacturing, planning, and control systems.  The first breakthrough in planning systems came in the year 1975 where a systemic approach called material requirements planning was introduced by Orlicky. MRP has become the global for production planning and inventory management in manufacturing industries. Later, over the years, research on the planning systems has brought modifications in MRP and it was evolved into closed-loop MRP. Further into the late 1980’s availability of technology led to an introduction of a newly evolved system called the Manufacturing resource planning which resulted in a holistic approach in material planning involving, financial and accounting functions which improved the planning efficiency. Further advancement in technology resulted in advanced planning systems like Enterprise resource planning and Advanced planning and scheduling.  On the contrary, though there has been a lot of advancement in technology and effective production planning methods, there are still discrepancies in obtained results when compared to theory. This is because the existing systems were based either solely on push production or pull production strategy. There is a lack of a hybrid system that includes the positives of both production strategies and negates the MRP conflict. However, in the year 2011, a new concept called demand-driven material requirements planning was introduced by Ptak & Smith, which was a fusion of the core MRP, theory of constraints, and Lean principles. Since the introduction DDMRP has seen an increase in implementation across industries which claimed a significant improvement in performance, on-time delivery, reduction in inventory, and reduced stock-outs. DDMRP has received very little attention in academia due to a lack of awareness among researchers and industries. A literature review approach was used to collect and analyze the data on DDMRP and its advantages. The objective of this thesis was to shed light on the process of DDMRP, its pros, and cons in implementing the new material planning system.

La modernisation des réseaux électriques via ce que l'appelle aujourd'hui les réseaux intelligents (ou smart grids) promet des avancées pour permettre de faire face à une augmentation de la demande mondiale ainsi que pour faciliter l'intégration des ressources décentralisées. Grâce à des moyens de communication et de calcul avancés, les smart grids offrent de nouvelles possibilités pour la gestion des ressources des consommateurs finaux, y compris pour de petits éléments comme de l'électroménager. Cependant, ce type de gestion basée sur des décisions prises indépendamment peuvent causer des perturbations tels qu'un rebond de consommation, ou des instabilités sur le réseau. La prise en compte des interactions entre les décisions de gestion énergétique de différentes maisons intelligentes est donc une problématique naissante dans les smart grids. Cette thèse vise à évaluer l'impact potentiel de mécanismes de coordination entre consommateurs résidentiels au niveau de quartiers, et ce à travers trois études complémentaires. Tout d'abord, une première stratégie pour la gestion coordonnée de maisons est proposée avec l'objectif d'augmenter l'utilisation locale d'énergie renouvelable à travers la mise en place d'échanges d'énergie électrique entre voisins. Les participants reçoivent en échange une compensation financière. L'algorithme de gestion est étudié dans une configuration centralisée et une configuration décentralisée en faisant appel au concept de système multi-agents, chaque maison étant représentée par un agent. Les résultats de simulation montrent que les deux approches sont efficaces pour augmenter la consommation locale d'énergie renouvelable et réduire les coûts énergétiques journaliers des consommateurs. Bien que l'approche décentralisée retourne des résultats plus rapidement, l'approche centralisée a une meilleure performance concernant les coûts. Dans une seconde étude, deux algorithmes de gestion énergétiques à J-1 sont proposés pour un quartier résidentiel. Un modèle de tarification dynamique est utilisé, où le prix dépend de la consommation agrégée du quartier ainsi que d'une forme de tarification heures creuses-heures pleines. L'objectif est ici de concevoir un mécanisme de coordination plus avancé (par rapport au précédent), en permettant des échanges d'énergie renouvelable résiduelle au sein du quartier. La performance des algorithmes est étudiée sur une période d'une journée puis d'une année, en prenant ou non en compte les erreurs de prévision. D'après les résultats de simulation, les deux algorithmes proposés montrent de meilleurs performances que les méthodes de référence (sans contrôle, et algorithme égoïste), même en considérant les erreurs de prévision. Enfin, dans une troisième étude, l'impact de l'introduction de production photovoltaïque résidentielle sur la performance d'un agrégateur est évaluée, dans une configuration centralisée. L'agrégateur interagit avec le marché spot et le gestionnaire de réseau, de façon à proposer un nouveau modèle de tarification permettant d'influencer les consommateurs à agir sur leur consommation. Les résultats de simulation montrent quand le taux de pénétration de photovoltaïque résidentiel augmente, le profit de l'agrégateur diminue, du fait de l'autoconsommation dans le quartier
Grid modernization through philosophies as the Smart Grid has the potential to help meet the expected world increasing demand and integrate new distributed generation resources at the same time. Using advanced communication and computing capabilities, the Smart Grid offers a new avenue of controlling end-user assets, including small units such as home appliances. However, with such strategies, decisions taken independently can cause undesired effects such as rebound peaks, contingencies, and instabilities in the network. Therefore, the interaction between the energy management actions of multiple smart homes is a challenging issue in the Smart Grid. Under this purpose, in this work, the potential of coordination mechanisms established among residential customers at the neighborhood level is evaluated through three studies. Firstly, coordinative home energy management is presented, with the aim to increase local renewable energy usage in the neighborhood area by establishing energy trading among smart homes, which are compensated by incentives. The control algorithm is realized in both centralized and decentralized manners by deploying a multi-agent system, where neighborhood entities are modeled as agents. Simulations results show that both methods are effective on increasing local renewable energy usage and decreasing the daily electricity bills of customers. However, while the decentralized approach gives results in shorter time, the centralized approach shows a better performance regarding costs. Secondly, two decentralized energy management algorithms are proposed for day-ahead energy management in the neighborhood area. A dynamic pricing model is used, where price is associated to the aggregated consumption and grid time-of-use scheme. The objective of the study is to establish a more advanced coordination mechanism (compared to previous work) with residual renewable energy is shared among smart homes. In this study, the performance of the algorithms is investigated with daily and annual analyses, with and without considering forecasting errors. According to simulations results, both coordinative control models show better performance compared to baseline and selfish (no coordination) control cases, even when considering forecasting errors. Lastly, the impact of photovoltaic systems on a residential aggregator performance (in a centralized approach) is investigated in a neighborhood area. In the proposed model, the aggregator interacts with the spot market and the utility, and proposes a novel pricing scheme to influence customers to control their loads. Simulation results show that when the penetration level of residential photovoltaics (PV) is increased, the aggregator profit decreases due to self-consumption ability with PV in the neighborhood

Economic affordability of services and infrastructures has rapidly become one of the key issues in the evaluation and design of wireless access systems. The provisioning of high data rates, at an ``affordable'' price, constitutes a serious challenge to the structure and management of current and future wireless networks.

The management of radio resources, Radio Resource Management or RRM for short, has traditionally been benchmarked mostly by technical merits such as throughput (data delivery capability) and Quality of Service (QoS). When comparing different RRM schemes, the scheme that can deliver more bits per Hertz (unit of bandwidth) or per Euro is often assumed the more efficient. From an economic point of view, however, cost efficiency is not equivalent to profitability.

We conjecture that the economic efficiency and profitability can be improved both by better technical efficiency and by better accounting for users' service appreciation and willingness to pay. While we shall, primarily treat the operator's benefit of improved RRM, we will try to improve the RRM by means of being more responsive to the demands of the users. In eight conference and journal papers, we investigate: Provisioning of support for asymmetric traffic, Quality and pricing aware resource management and Creation of forwarding incentive in multihop cellular networks.

We show that implementing support for asymmetric links can improve the efficiency of (service) production in Time Division Duplexing (TDD) mode wireless networks with asymmetric traffic. That is, more traffic can be handled with the same system resources. Compared to Frequency Division Duplexing (FDD), TDD offers more flexible use of spectrum resources. The benefits of TDD and support for asymmetric links are readily available for systems providing high-rate spotty coverage. For systems aiming at full coverage and tight reuse, however, proper measures must be taken to control inter-mobile- and inter-base-station-interference.

We present the MEDUSA model framework for taking users' service appreciation and willingness to pay into account in performance evaluations of wireless networks with elastic traffic. Assuming that user satisfaction depends on both the quality and the price of the service, numerical experiments show that the economic efficiency of an RRM scheme is affected by the pricing scheme. We also introduce the concepts of speculative resource management to exploit traffic elasticity and improve resource utilisation. With speculative admission control, users with good propagation conditions may be admitted to a full system at the expense of a slight degradation of the QoS of some or all users, if the expected total revenue would thereby increase. Results indicate significant revenue gain with speculative admission control. Service perception aware scheduling was evaluated as a means to improve resource utilisation, but yielded only marginal gain compared to a weighted proportional fair scheduler.

For the third area studied in this Thesis, i.e. multihopping in cellular networks, economic efficiency was both the goal and one of the means to achieve it. By means of a resource re-distribution scheme called Resource Delegation we eliminated the bandwidth bottle neck of the relays. We combined Resource Delegation with economic compensation for the energy expenditures of the relays and were able to achieve significantly increased operator revenue with maintained or improved user utility. Assuming that the added complexity of keeping track of reward transactions is negligible, profitability was correspondingly improved.

This thesis studies intelligent on-demand radio resource provisioning involving sleep mode operation in ultra Small Cell Networks (SCNs). Sleep modes are low power states of base stations. The purpose of the research is to investigate how appropriate traffic information can be adopted in sleep mode operation schemes for SCNs with different architectures. A novel protocol-friendly sleep mode operation algorithm based on Adaptive Traffic Perception is proposed for distributed SCN architectures. It is proved robust to different SCN layouts with the reduction in the average power consumption of base stations being more than 35% while maintaining the Quality of Service. The Traffic-aware Cell Management scheme adopting Direction of Arrival information is particularly designed to eliminate the necessity of computation for sleeping base stations. This scheme is shown to significantly reduce the side effects associated with the sleep mode operation, including system overheads and the increasing user transmission power. For SCNs using centralised architectures, such as Cloud Radio Access Networks, Hotspot-oriented Green Frameworks are proposed for different information availabilities, which achieve almost 80% reduction in power consumption of Remote Radio Heads at low traffic levels. A clustering technique is utilised for the optimisation of the placement of active Remote Radio Heads, lowering the average user transmission power. The amount of reduction depends on the completeness of the information and can exceed 70% compared with the state-of-the-art. A type II Matern Hard-core Point Process is used for modelling SCNs. The derivation and approximation of its distance distributions are also proposed. The distance distributions are used for the probabilistic theoretical analysis of some metrics of the sleep mode operation.

We study the optimal resource portfolio for a price-setter firm under a consumer choice model with supply and demand risks. The firm sells two products that are vertically differentiated, and has the option to invest in both dedicated and flexible resources. Our objective is to understand the effectiveness of the two hedging mechanisms, resource flexibility and demand management through production differentiation, under demand and supply risks. We show that the presence of consumer-driven substitution does not always reduce the need for the firm to offer differentiated products. In particular, when the firm faces demand risk and differential production costs, it might invest in the flexible resource and offer differentiated products for a wider range of parameters. Interestingly, more uncertainty (in the form of additional supply risk) does not always make the firm more eager to adopt a hedging mechanism. This depends on the relationship between resource risks, product attributes, and resource investment costs. On the other hand, when the firm invests in the flexible resource, this never completely replaces the dedicated resources, and always results in a "diverse" resource portfolio. While this happens in the supply risk setting mainly due to resource diversification advantage, it also happens in the demand risk setting due to the vertical differentiation between the products. Finally, in the absence of differential production costs, demand management by itself (without resource flexibility) becomes powerful enough to hedge against the demand risk, but not the supply risk, due to the additional resource diversification benefit of the flexible resource in the latter setting.
Ph. D.

Background: In technology based industries, firms which originate from advanced economies have traditionally occupied the leading position. In order to keep that position, they relied on strong efforts devoted to research and development, striving for being at the cutting edge of technology. During the last decade, we observed that some change happened amongst the top ranking of firms within the telecommunication industry with the apparition of Huawei, which originates from China, an emerging economy, in second position. Aim: This research is aimed to investigate the influence of firm’s human resource and market demand on firm’s innovation strategy. Definitions: Human resource represents the employees working for a company. In our study, when we mention human resource we deal with employees working in research and development. Market demand refers to the products which are demanded on a specific market. For our research we divided it in three categories: less advanced, advanced and most advanced products. Innovation strategy deals with the way a firm organizes its research and development in order to innovate according to either, in our research, new-tofirm or new-to-world innovation strategy. Completion and results: This study revealed that Huawei managed to adjust its innovation strategy in accordance with its human resources and market demand. Thanks to an efficient and adapted innovation strategy, Huawei now manages to perform better than its main competitor Ericsson, not only on developing or emerging markets, but also in advanced markets. We also observed the role of rules and regulations and cooperation, without which Huawei would not have been able to lead an efficient new-to-firm strategy. We created a model which is designed to give a better understanding of the situation and illustrate the interrelation between human resource, market demand, innovation strategy and performance.

Electricity is the most preferred source of energy, because of its quality and convenience of usage. It is probably one of the most vital infrastructural inputs for economic development of a country. Indeed it is the fulcrum which can leverage the future pace of growth and development. These reasons have made the electric power industry one of the fastest growing sectors in most developing countries and particularly in India. Therefore it is not surprising to observe the economic growth of a country being related to the increase in electricity consumption. In India, the growth rate of demand for power is generally higher than that of Gross Domestic Product (GDP). However, to achieve this kind of growth in electricity supply, the capital investments required are very huge. Even though the electricity sector generally gels a major share in the budgetary allocations in India, this is inadequate to add the required quantum of new generation capacity to keep pace with the increase in demand for electricity. Additional constraints like capital scarcity in the public sector, lack of enthusiasm among the private and foreign investors, and strong opposition from the environmentalists have further contributed to this slow pace of new generating capacity addition. This has resulted in severely constrained systems in India. The main focus of the present research work is on the development of an integrated approach for electricity planning using a mathematical modeling framework in (he context of resource constrained systems. There are very few attempts in the literature to integrate short, medium and long term issues in electricity planning. This is understandable from the point of view of unconstrained electricity systems where this type of integration is unnecessary since such systems have a luxury of surplus capacity to meet the current demand and capacity additions are required only for meeting predicted future increase in demand. However, in the case of constrained electricity systems, which are characterized by shortages, this kind of integration is very essential. These systems have to manage with inadequate capacity in the present, plan capacity additions to bridge the existing gap and to meet future increase in demand, and always explore the possibility of adding capacity with short gestation period. The integrated approach is expected to achieve effective supply-demand matching on a continuous basis encompassing both the short term and long term horizons. To achieve this, we have considered three alternatives- existing supply, new supply and non-supply (rationing) of electricity. The electricity system of the state of Karnataka, which is severely constrained by both limited capital and energy resources, has been selected for this purpose. As a first step, the supply and demand situation has been studied in the context of resource constraints. In terms of supply, both existing and future additions are studied in detail with respect to the potential created, generation types, import potential, technical constraints, energy and power shortages, planned and proposed capacity additions by both public and private sectors, etc. The demand patterns have been studied by introducing a new concept of "Representative Load Curves (RLCs)". These RLCs are used to model the temporal and structural variations in demand for electricity. Also, appropriate non-supply options (rationing measures) for effective management of shortages are identified. Incorporating this information, an integrated mathematical model, which is expected to generate a target plan for a detailed generation scheduling exercises and a requirement plan for a regular generation expansion planning, has been developed. The other important alternative "Demand-Side-Management (DSM)", which could be considered as an effective option to achieve efficient supply-demand matching has not been included in the present research work. The major reason for not including the DSM alternatives is due to the difficulty in integrating these in the modelling approach adopted here. In the present approach we have used typical daily load curves (RLCs) to represent the demand for electricity. These are aggregate load curves and do not contain any sector-wise or end-use-wisc details. On the other hand, DSM alternatives are end-use focused. To incorporate DSM alternatives, we should have information on end-usc-wisc power demand (kW or MW), savings potential, time-of-use, etc. For this purpose it may be required to have end-use-wisc daily load curves. This information is not available and a separate detailed survey may be required to generate these load curves. This, we felt, is out of the scope of this present research work and a separate study may be required to do this. Therefore, we restricted our focus to supply planning alone. A detailed literature review is conducted to understand different types of modeling approaches to electricity planning. For the present study, however, the review of literature has been restricted to the methods of generation expansion planning and scheduling. In doing so, we attempted to bring out the differences in various approaches in terms of solution methods adopted, alternatives included and modifications suggested. Also, we briefly reviewed the literature on models for power and energy rationing, because management of shortages is an important aspect of the present study. Subsequently, a separate section is devoted to present an overview of the non-supply of electricity and its economic impacts on the consumers. We found that the low reliability of the electrical system is an indicator of the existence of severe shortages of power and energy, which cause non-supply of electricity to the consumers. The overview also presented a discussion on reasons for non-supply of electricity, and the types of non-supply options the utilities adopt to over come these shortages. We also attempted to explain what we mean by non-supply of electricity, what are its cost implications, and the methods available in the literature to estimate these costs. The first objective of the research pertains to the development of a new approach to model the varying demand for electricity. Using the concept of Representative Load Curves (RLCs) we model the hourly demand for a period of four years, 1993-94, 1994-95, 1995-96 and 1996-97, to understand the demand patterns of both unconstrained and constrained years. Multiple discriminant analysis has been used to cluster the 365 load curves into nine RLCs for each of the four years. The results show that these RLCs adequately model the variations in demand and bring out the distinctions in the demand patterns existed during the unconstrained and constrained years. The demand analysis using RLCs helped to study the differences in demand patterns with and without constraints, impacts of constraints on preferred pattern of electricity consumption, success of non-supply options in both reducing the demand levels and greatly disturbing the electricity usage patterns. Multifactor ANOVA analyses are performed to quantify the statistical significance of the ability of the logically obtained factors in explaining the overall variations in demand. The results of the ANOVA analysis clearly showed that the considered factors accounted for maximum variations in demand at very high significance levels. It also brought out the significant influence of rationing measures in explaining the variations in demand during the constrained years. Concerning the second objective, we explained in detail, the development of an integrated mixed integer-programming model, which we felt is appropriate for planning in the case of resource constrained electricity systems. Two types of integrations are attempted (i) existing supply, non-supply and new supply options for dynamically matching supply and demand, (ii) operational and strategic planning in terms of providing target plans for the former and requirement plans for the latter. Broadly, the approach addresses the effective management of existing capacity, optimal rationing plan to effectively manage shortages and rationally decide on the new capacity additions both to bridge the existing gap between supply and demand, and to meet the future increases in demand. There is also an attempt to arrive at an optimal mix of public and private capacity additions for a given situation. Finally, it has been attempted to verify the possibility of integration of captive generation capacity with the grid. Further, we discussed in detail about the data required for the model implementation. The model is validated through the development of a number of scenarios for the state of Karnataka. The base case scenario analyses are carried out for both the unconstrained and constrained years to compare the optimal allocations with actual allocations that were observed, and to find out how sensitive are the results for any change in the values of various parameters. For the constrained years, a few more scenarios are used to compare the optimal practice of managing shortages with to what has been actually followed by the utility. The optimal allocations of the predicted demand to various existing supply and non-supply options clearly showed that the actual practice, reflected by the actual RLCs, are highly ad hoc and sub-optimal. The unit cost comparisons among different scenarios show that the least cost choice of options by the utility does not necessarily lead to good choices from the consumers’ perspective. Further, a number of future scenarios are developed to verify the ability of the model to achieve the overall objective of supply-demand matching both in the short and long term. For this purpose both the short horizon annual scenarios (1997-98 to 2000-01) and long horizon terminal year scenarios (2005-06 and 2010-11) are developed assuming capacity additions from only public sector. Overall, the results indicated that with marginal contributions from non-supply options and if the public sector generates enough resources to add the required capacity, optimal matching of supply and demand could be achieved. The scenario analyses also showed that it is more economical to have some level of planned rationing compared to having a more reliable system. The quantum of new capacity additions required and the level of investments associated with it clearly indicated the urgent need of private sector participation in capacity additions. Finally, we made an attempt to verify the applicability of the integrated model to analyse the implications of private sector participation in capacity additions. First, a number of scenarios are developed to study the optimal allocations of predicted hourly demand to private capacity under different situations. Secondly, the impacts of privatisation on the public utility and consumers are analysed. Both short term and long term scenarios are developed for this purpose. The results showed the advantage of marginal non-supply of electricity both in terms of achieving overall effective supply-demand matching and economic benefits that could be generated through cost savings. The results also showed the negative impacts of high guarantees offered to the private sector in terms of the opportunity costs of reduced utilization of both the existing and new public capacity. The estimates of unit cost of supply and effective cost of supply facilitated the relative comparison among various scenarios as well as finding out the merits and demerits of guarantees to private sector and non-supply of electricity. The unit cost estimates are also found to be useful in studying the relative increase in electricity prices for consumers on account of privatization, guarantees and reliable supply of electricity. Using the results of scenario analyses, likely generation expansion plans till the year 2010-11 are generated. The analyses have been useful in providing insights into fixing the availability and plant load factors for the private sector capacity. Based on the analysis, the recommended range for plant utilization factor is 72.88 - 80.57%. The estimated generation losses and the associated economic impacts of backing down of existing and new public capacity on account of guarantees offered to private sector are found to be significantly high. The analyses also showed that the backing down might take place mainly during nights and low demand periods of monsoon and winter seasons. Other impacts of privatization that studied are in terms of increased number of alternatives for the utility to buy electricity for distribution and the associated increase in its cost of purchase. Regarding the consumers, the major impact could be in terms of significant increase in expected tariffs. The major contributions of this thesis are summarized as follows: i. An integrated approach to electricity planning that is reported here, is unique in the sense that it considers options available under various alternatives, namely, existing supply, non-supply and new supply. This approach is most suited for severely constrained systems having to manage with both energy and capital resource shortages. ii. The integration of operational and strategic planning with coherent target plans for the former and requirement plans for the latter bridges the prevailing gap in electricity planning approaches. iii. The concept of Representative Load Curves (RLCs), which is introduced here, captures the hourly, daily and seasonal variations in demand. Together, all the RLCs developed for a given year are expected to model the hourly demand patterns of that year. These RLCs are useful for planning in resource constrained electricity systems and in situations where it is required to know the time variations in demand (e.g. supply-demand matching, seasonal scheduling of hydro plants and maintenance scheduling). RLCs are also useful in identifying the factors influencing variations in demand. This approach will overcome the limitations of current method of representation in the form of static and aggregate annual load duration curves. iv. A new term, "non-supply of electricity" has been introduced in this thesis. A brief overview of non-supply presented here includes reasons for non-supply, type of non-supply, methods to estimate cost of non-supply and factors influencing these estimates. v. The integrated mixed integer programming model developed in the study has been demonstrated as a planning tool for- • Optimal hourly and seasonal scheduling of various existing supply, non-supply and new supply options • Estimation of supply shortages on a representative hourly basis using the information on resource constraints • Effectively planning non-supply of electricity through appropriate power/energy rationing methods • Estimation of the need for the new capacity additions both to bridge the existing gap and to take care of increase in future demand levels • Optimal filling of gaps between demand and supply on a representative hourly basis through new supply of electricity • Optimally arriving at the judicious mix of public and private capacity additions • Studying the impacts of private capacity on the existing and new public sector capacity, and on the consumers • Optimally verifying the feasibility of integrating the captive generation with the total system vi. The demand analysis using RLCs helped to bring out the differences in demand patterns with and without constraints, impacts of constraints on preferred pattern of electricity consumption, success of non-supply options in both reducing the demand levels and greatly disturbing the electricity usage patterns. Multifactor ANOVA analyses results showed that the logically obtained factors accounted for maximum variations in demand at very high significance levels. vii. A comparison of optimal (represented by optimal predicted RLCs) and actual (reflected by actual RLCs) practices facilitated by the model showed that the actual practice during constrained years is highly ad hoc and sub-optimal. viii. The results of the scenario analyses showed that it is more economical to have some amount of planned rationing compared to having a more reliable system, which does not allow non-supply of electricity. ix. The scenarios, which analysed the impacts of high guarantees offered to the private sector, showed the negative impacts of these in terms of reduced utilization of both the existing and new public capacity. x. Generation expansion plans till the year 2010-11 are developed using the results of various kinds of scenario analyses. Two groups of year-wise generation expansion plans are generated, one with only public sector capacity additions and the other with private sector participation. xi. The impacts of privatization of capacity additions are studied from the point of view of the utility and consumers in terms of expected increase in cost of purchase of electricity and tariffs. xii. The analyses are also made for developing some insights into fixing the availability and plant load factors for the private capacity. Based on the analysis, the recommended range for plant utilization factor is 72.88 - 80.57%. We believe that the integrated approach presented and the results obtained in this thesis would help utilities (both suppliers and distributors of electricity) and governments in making rational choices in the context of resource constrained systems. The results reported here may also be used towards rationalization of Government policies vis-a-vis tariff structures in the supply of electricity, planning new generation capacity additions and effective rationing of electricity. It is also hoped that the fresh approach adopted in this thesis would attract further investigations in future research on resource constrained systems.

Thesis (Ph. D.)--Missouri University of Science and Technology, 2009.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 15, 2009) Includes bibliographical references (p. 72-80).

Areas of California's Central Valley are sinking at rates up to 1 foot per year due to subsidence caused, in part, by the state's years-long drought, challenging growers to locate additional water sources for their crops. Supply and demand theory guided this correlational study. The purpose of the study was to examine the financial impact of drought on almond demand. This study included annualized historical almond industry data for the United States (N = 97), downloaded from a United States Department of Agriculture database. The results of multiple linear regression analysis indicated that the model was capable of predicting almond demand, F(3,92) = 483.579, p < .001, R2 = .940. Both supply and price were statistically significant in the final model, with supply (p < .001) accounting for a higher contribution to the model than price (p = .015). Fine effect's contribution (p = .267) to the model was not statistically significant. The results of this study could enable almond industry leaders to increase profit margins through market predictability understanding and mitigate fiscal risks associated with variable labor and groundwater pumping costs. The implications for positive social change include the potential to restore employment opportunities, stabilize migratory worker prospects, and reduce water utilization to preserve natural resources.

The United States is the leading producer and exporter of corn and soybeans in the world. The United States exports 20% of the world’s corn and 30% of soybeans in a typical year (USDA, ERS). The U.S., being the top producer and exporter of these commodities, is also confronting major rivals such as Argentina, Brazil, and Ukraine, which are increasing their exports and causing the U.S. to lose some of its market share. In order to stop this decline in market share, the U.S. can adopt and implement different policies to manage resources and employ advanced technology more effectively. In this study, we empirically estimate the export demand function of U.S. corn and soybeans to the top four export destinations: China, Japan, European Union, and Mexico in the current context of energy and agriculture linkages and production of ethanol from corn. A log-linear, panel data equation is used to estimate the U.S. corn and soybeans export demand function. Own price, cross price, income and exchange rate elasticities are estimated econometrically. Data for the U.S. and its top four importer countries were gathered for the 1980-2012 period. A Hausman test implies that a random effects estimator is better for the estimations. Elasticity analysis indicates that U.S. corn demand is elastic to own price, cross price, income and poultry inventory, while inelastic to real exchange rate and pig inventory. The positive cross price elasticity reveals that corn and soybeans are substitutes in these countries. Conversely elasticity analysis for the U.S. soybean demand shows elastic cross price, real exchange rate, and pig and poultry inventory effects, while inelastic own price and income effects. Consequently, for the U.S. to gain more international market share, U.S. corn and soybean producers need to take advantage of their advanced technology and high management skills to increase quality and have more competitive pricing compared to rivals. The U.S. can gain more market share by employing better regulation to increase the quality of products, and provide incentives to U.S. farmers and exporters that could help boost their advantages in a highly competitive international environment. Higher quality and more product differentiation could help in this regard. This could help U.S. farmers increase exports to currently existing foreign destinations and access new markets, to expand market shares.

Although it remains a tea consuming nation, both production and consumption of coffee in China has been increasing at double-digit rates and is not expected to slow down (International Coffee Organization (ICO), 2015). With investments and upward trends in production and rapid increases in consumption of coffee in China it is important for producers and retailers of the bean1 in China to understand the new Chinese coffee consumer. Using survey data from Wuhan, China we help understand the Chinese coffee consumer by explaining their consumption using standard OLS regression. Results show that whether or not consumers make/brew their own coffee, how long consumers have been consuming coffee regularly, the size of the coffee cup most often purchased and individuals’ prediction of their coffee consumption in the following year are all important in explaining Chinese coffee consumption. We suggest for long-run success, that Chinese coffee producers and retailers in China focus on the quality of their coffee bean. 1 ‘bean’ refers to coffee

The Kingdom of Jordan is one of the world's most water poor countries suffering from physical water scarcity, a growing population, regional instability, poor water infrastructure, inefficient water sector management and high water misuse. Through conducting a content analysis of 73 USAID reports, focusing on water demand management, this paper aims to understand how water demand is managed in the Kingdom, in addition to understanding the role of international aid in transforming Jordan's water sector. A greater understanding of the crisis and actions taken to mitigate the impacts were revealed by examining specific water polices and laws, the role of government structures and water sectors, and implemented projects. It was found that Jordan has taken significant efforts to sustainably manage water resources and to address growing water demand. Reallocating water among various sectors, reducing non-revenue water, and decentralizing water provision are key priorities outlined in the Kingdom's water strategy.

This thesis endeavours to model a trip demand function for recreationalangling in Sweden, including the individual expected per-trip utility of regionalattributes. The analysis is conducted with the use of a Random Utility Model (RUM)for the estimation of ‘site-specific’ utility, and a negative binomial logit model fortrip demand. The site quality variable is stated as expected catch-rates (CR) definedin terms of three different specifications: sample mean CR, individually perfectlyforesighted CR, and an econometrically predicted individual CR. Results indicatethat the econometrically estimated individual catch-rate specification performs wellas an explanatory variable both when modelling discrete site choice and trip demand,while the sample mean and perfectly foresighted CR specifications provide withunintuitive and insignificant parameter values. The inclusive value of the regionchosen by the angler, estimated with the RUM, was subsequently found to be asignificant predicting variable for the number of recreational angling trips conductedby Swedish anglers.

In this thesis, we study three applications of dynamic resource allocation: the first two consider dynamic lead-time quotation in make-to-order (MTO) systems with substitutable products and order cancellations, respectively; and the third application is a manpower allocation problem with job-teaming constraints. Matching supply and demand for manufacturing and service industries has been a fundamental focus of operations management literature, which concentrated on optimizing or improving supply-side decisions since demand has generally been assumed to be exogenously determined. However, recent business trends and advances in consumer behavior modeling have shown that demand for goods and services can clearly be shaped by various decisions that a firm makes, such as price and lead-time. In fact, competition between companies is no longer mainly based on price or product features; lead-time is one of the strategic measures to evaluate suppliers. In MTO manufacturing or service environments that aim to satisfy the customers' unique needs, lead-time quotation impacts the actual demand of the products and the overall profitability of the firm. In the first two parts of the thesis, we study the dynamic lead-time quotation problem in pure MTO (or service) systems characterized by lead-time sensitive Poisson demand and exponentially distributed service times. We formulate the problem as an infinite horizon Markov decision process (MDP) with the objective of maximizing the long-run expected average profit per unit time, where profits are defined to specifically account for delays in delivery of the customer orders. We study dynamic lead-time quotation problem in two particular settings; one setting with the possibility of demand substitution and another setting with order cancellations. The fundamental trade-off in lead-time quotation is between quoting short lead-times and attaining them. In case of demand substitution, i.e., in presence of substitutable products and multiple customer classes with different requirements and margins, this trade-off also includes capacity allocation and order acceptance decisions. In particular, one needs to decide whether to allocate capacity to a low-margin order now, or whether to reserve capacity for potential future arrivals of high-margin orders by considering customer preferences, the current workload in the system, and the future arrivals. In the case of order cancellations, one needs to take into account the probability of cancellation of orders currently in the system and quote lead-times accordingly; otherwise quotation of a longer lead-time may result in the loss of customer order, lower utilization of resources, and, in turn, reduced in profits. In Chapter 2, we study a dynamic lead-time quotation problem in a MTO system with two (partially) substitutable products and two classes of customers. Customers decide to place an order on one of the products or not to place an order, based on the quoted lead-times. We analyze the optimal profit and the structure of the optimal lead-time policy. We also compare the lead-time quotes and profits for different quotation strategies (static vs. dynamic) with or without substitution. Numerical results show that substitution and dynamic quotation have synergetic effects, and higher benefits can be obtained by dynamic quotation and/or substitution when difference in product revenues or arrival rates, or total traffic intensity are higher. In Chapter 3, we study a dynamic lead-time quotation problem in a MTO system with single product considering the order cancellations. The order cancellations can take place during the period that the order is being processed (either waiting or undergoing processing), or after the processing is completed, at the delivery to the customer. We analyze the behavior of optimal profit in terms of cancellation parameters. We show that the optimal profit does not necessarily decrease as cancellation rate increases through a numerical study. When the profit from a cancelled order, arrival rate of customers, or lead-time sensitivity of customers are high, there is a higher probability that optimal profit increases as cancellation rate increases. We also compare the cancellation scenarios with the corresponding no-cancellation scenarios, and show that there exists a cancellation scenario that is at least as good in terms of profit than a no-cancellation scenario for most of the parameter settings. In Chapter 4, we study the Manpower Allocation Problem with Job-Teaming Constraints with the objective of minimizing the total completion time of all tasks. The problem arises in various contexts where tasks require cooperation between workers: a team of individuals with varied expertise required in different locations in a business environment, surgeries requiring different composition of doctors and nurses in a hospital, a combination of technicians with individual skills needed in a service company. A set of tasks at random locations require a set of capabilities to be accomplished, and workers have unique capabilities that are required by several tasks. Tasks require synchronization of workers to be accomplished, hence workers arriving early at a task have to wait for other required workers to arrive in order to start processing. We present a mixed integer programming formulation, strengthen it by adding cuts and propose heuristic approaches. Experimental results are reported for low and high coordination levels, i.e., number of workers that are required to work simultaneously on a given task.

The growing computational demand of scientific applications has greatly motivated the development of large-scale high-performance computing (HPC) systems in the past decade. To accommodate the increasing demand of applications, HPC systems have been going through dramatic architectural changes (e.g., introduction of many-core and multi-core systems, rapid growth of complex interconnection network for efficient communication between thousands of nodes), as well as significant increase in size (e.g., modern supercomputers consist of hundreds of thousands of nodes). With such changes in architecture and size, the energy consumption by these systems has increased significantly. With the advent of exascale supercomputers in the next few years, power consumption of the HPC systems will surely increase; some systems may even consume hundreds of megawatts of electricity. Demand response programs are designed to help the energy service providers to stabilize the power system by reducing the energy consumption of participating systems during the time periods of high demand power usage or temporary shortage in power supply. This dissertation focuses on developing energy-efficient demand-response models and algorithms to enable HPC system's demand response participation. In the first part, we present interconnection network models for performance prediction of large-scale HPC applications. They are based on interconnected topologies widely used in HPC systems: dragonfly, torus, and fat-tree. Our interconnect models are fully integrated with an implementation of message-passing interface (MPI) that can mimic most of its functions with packet-level accuracy. Extensive experiments show that our integrated models provide good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance. In the second part, we present an energy-efficient demand-response model to reduce HPC systems' energy consumption during demand response periods. We propose HPC job scheduling and resource provisioning schemes to enable HPC system's emergency demand response participation. In the final part, we propose an economic demand-response model to allow both HPC operator and HPC users to jointly reduce HPC system's energy cost. Our proposed model allows the participation of HPC systems in economic demand-response programs through a contract-based rewarding scheme that can incentivize HPC users to participate in demand response.

Population growth, urbanization, degrading water quality, and climate change are making management of scarce water resources an increasingly difficult task for the domestic sector. It is recognized that in order to manage urban water resources demand management is requisite. Demand management has been experimented with in large cities of developing countries but continued focus on expanding supply overshadows its potential benefits and ultimate success. In order to manage demand, it must be measured and understood. Intermittent water services are prevalent in developing countries, but unmetered domestic water use under such conditions has not been carefully studied. This study conducted 1,149 household surveys in a small, growing, coastal city (population est. 35,645) in La Libertad, Peru. The objectives were to 1) characterize current household water use behaviors, perceptions and values as they vary among three user groups (two distinct unmetered intermittent water services and well users) and reveal the existing water use and potential household demand for water, and 2) propose demand management tactics applicable to conditions of the study site that may be generalizable to small, developing, cities. Survey results show daily per capita water use in the range of 35 to 90 L with more water being used by the group that receives water for a longer duration of time. The distribution of water was inequitable and, on average, households received water for less time than the service providers' reported duration. Demand is likely to grow due to increasing water-related infrastructure, established water behaviors, and a lack of understanding regarding regional scarcity and water conservation. Households are not satisfied with existing service conditions, particularly water quality, but due to an apparent distrust in their water providers are unwilling to pay for improvements. For domestic service to remain sustainable under the pressures of increasing water scarcity, demand management strategies, particularly education and awareness building, are likely achievable and should be adopted, complementary to supply-minded management.

Bibliography: leaves 40-42.
Namiba is the driest country in sub-Saharan Africa and has been projected to reach an overall water deficit by 2020. Southern Namibia is especially arid, and appropriate and holistic management of water resources is thus becoming increasingly essential

The ongoing increase of the world population entails huge challenges for all countries’ agro-food systems. Agriculture has to satisfy growing food requirements both in quantitative and qualitative terms, but the on hand natural resource stock is quickly depleting. Moreover, food production and energy production from biomass are competing for land. The thesis aims at measuring the environmental impact of nutrition in three agri-food systems. The study considers the amount of natural resources used in the production and consumption of food along its life-cycle. It is featured in two parts. In the first one the analysis is restricted to the supply chain of food, and provides a sustainability rating of thirty-seven products, grown through different agricultural practices. The second analysis takes into consideration all the value chain according to three paradigms of agri-food systems. They refers to different models of food production and consumption, observed in Italy. The first one is the dominant paradigm, including conventional and intensive farming practices, long and globalized food chains, retailing in supermarkets. The other models refer to Alternative Food Networks (AFN). The first is the GAS (Gruppi di Acquisto Solidali) initiative, i.e. solidarity purchasing groups, that manage collectively the purchasing of organic food and natural manufactured products mainly from local enterprises and farmers. They aim at accessing high quality and naturally grown food, avoiding the retailing passage and ensuring a fair price to the farmers. The third model refers to UPM (Un Punto Macrobiotico), an international association established in the Marche region in 1980. It manages and controls an entire food chain, since the agricultural production, based on the post-organic technique “Ma-Pi polyculture”, till the food purchasing and catering, in sixty-three restaurants spread in the Italian territory. UPM is a very consumer-oriented food chain, in which a very exigent demand (in terms of food naturality and healthiness) drives the agricultural production towards the minimization of the external inputs employment. A further model concerns the reduction of the “Food Miles”, i.e. the kilometres covered by food, since the dominant paradigm. Thus, in this model the distances are reduced by an average 90%, while the production practices do not change. The methodology used for assessing the sustainability of food is the Material Input Per Service unit (MIPS) indicator. It estimates the overall environmental pressure caused by products or services by indicating the life-cycle-wide consumption of natural resources in relation to the benefit provided. It includes five resource categories: abiotic and biotic materials (renewable resources), water, air and soil. Results of the first analysis provide a dataset of the material intensity of Italian agricultural products and foodstuffs, which can expedite future research on the topic of nutrition’s sustainability. The second analysis evaluates the natural resource demand for feeding one person during a week, in the paradigms under investigation. With respect to the dominant, the GAS model allows a 58% reduction in the abiotic resource demand, 53% in water and 71% in air. The UPM system, instead, permits a tenfold reduction in abiotic, biotic and water, and 82% decrease in the air category. The insight on the socio-economic features of the systems, together with the environmental sustainability measurement, allowed making some general conclusions and policy recommendations on the sustainability of the different food systems. The role of a low external input technology in agriculture is emphasized as a possible strategy for driving food system towards more sustainable patterns and empowering the small-scale farming

Smart metering residential demand management programs, such as consumption feedback, variable pricing regimes and the remote control of appliances, are being used to respond to the resource management problems of peak electricity demand, climate change and water shortages. Like other demand management programs, these strategies fail to account for (and respond to) the reasons why people consume resources in their homes, namely to carry out everyday practices such as bathing, laundering, heating and cooling. In particular, comfort and cleanliness practices together constitute most of Australia's potable water consumption in urban centres, and represent most of household energy consumption. In addition, new household cooling practices involving air-conditioning appliances are the major contributor to the nation's rising peak electricity demand, which overloads the electricity system on hot days, costing consumers millions of dollars each year. The oversight of comf ort and cleanliness practices in smart metering demand management programs is concerning because these practices are continuing to shift and change, often in more resource-consuming directions, potentially negating the resource savings achieved through demand management programs. This thesis aims to bridge the problematic divide between the policies and strategies of demand managers, and the day-to-day practices which constitute everyday life. Using the empirical 'hook' of smart metering demand management programs and the everyday practices of comfort and cleanliness, this thesis develops a practice-based conceptual framework to study, understand and analyse these practices and the ways in which smart metering demand management programs reconfigure or further entrench them. A series of qualitative methods were employed in studying 65 households across four research groups, focusing specifically on the household practices of heating, cooling, bathing, laundering, toilet flushing and house cleaning. In addition, 27 interviews were conducted with smart metering industry stakeholders involved or implicated in delivering demand management strategies. Together, these lines of inquiry are used to analyse householders' existing and changing comfort and cleanliness practices, the role of several smart metering demand management strategies in reconfiguring these practices, and potential avenues and opportunities for further practice change in less resource-intensive directions. In particular, this thesis highlights the inherent contradictions and problems in accounting for everyday practices within the dominant demand management paradigm, and offers an alternative paradigm termed the co-management of everyday practices. The thesis concludes by briefly identifying the ways in which smart metering could potentially constrain or catalyse a transition towards this new paradigm.

Location-allocation problems can be described generically as follows: Given the location or distribution (perhaps, probabilistic) of a set of customers and their associated demands for a given product or service, determine the optimum location of a number of service facilities and the allocation of products or services from facilities to customers, so as to minimize total (expected) location and transportation costs. This study is concerned with a particular subclass of location-allocation problems involving capacitated facilities and a continuum of demand. Specifically, two minisum, network-based location-allocation problems are analyzed in which facilities having known finite capacities are to be located so as to optimally supply/serve a known continuum of demand. The first problem considered herein, is an absolute p-median problem in which p > l capacitated facilities are to be located on a chain graph having both nodal and link demands, the latter of which are defined by nonnegative, integrable demand functions. In addition, the problem is balanced, in that it is assumed the total demand equals the total supply. An exact solution procedure is developed, wherein the optimality of a certain location-allocation scheme (for any given ordering of the facilities) is used to effect a branch and bound approach by which one can identify an optimal solution to the problem. Results from the chain graph analysis are then used to develop an algorithm with which one can solve a dynamic, sequential location-allocation problem in which a single facility per period is required to be located on the chain. Finally, an exact solution procedure is developed for locating a capacitated, absolute 2-median on a tree graph having both nodal and link demands and for which the total demand is again equal to the total supply. This procedure utilizes an algorithm to construct two subtrees, each of whose ends constitute a set of candidate optimal locations for one of the two elements of an absolute 2-median. Additional localization results are used to further reduce the number of candidate pairs (of ends) that need to be considered, and then a post-localization analysis provides efficient methods of comparing the relative costs of the remaining pairs.
Ph. D.

Increased energy performance of the building stock of European Union is seen as an important measure towards mitigating climate change, increasing resource utilisation efficiency and energy supply security. Whether to improve the supply-side, the demand-side or both is an open issue. This conflict is even more apparent in countries such as Sweden with a high penetration of district heating (DH). Many Swedish DH systems have high share of secondary energy resources such as forest industry residuals, waste material incineration and waste heat; and resource efficient cogeneration of electricity in combined heat and power (CHP) plants. When implementing an energy conservation measure (ECM) in a DH connected building stock, it will affect the operation of the whole DH system. If there are CHP plants and the cogeneration of electricity decreases due to an ECM, and this electricity is valued higher than the fuel savings, the consequences of the ECM would be negative.  These complex relationships are investigated by conducting a case study on the Eskilstuna DH system, a renewable energy supply system with relatively high share of cogenerated electricity. Heat demand profiles of ECMs are determined by building energy simulation, using recently deep energy retrofitted multifamily buildings of the “Million Programme”-era in Eskilstuna as model basis. How implementing ECMs impact on the DH system’s heat and electricity production under different electricity revenue scenarios has been computed and evaluated in terms of resource efficiency and CO2 emissions.  The results show that different ECMs in the buildings impact differently on the DH system. Measures such as improved insulation level of the building’s envelope, that decrease the heat demand’s dependence to outdoor temperature, increase the amount of cogenerated electricity. While measures such as thermal solar panels, which save heat during summer, affects the absolute amount of cogenerated electricity negatively. Revenues from cogenerated electricity influence the amount of cost-effectively produced electricity much more than the impact from ECMs. Environmental benefits of the ECMs, measured in CO2 emissions and primary energy consumption, are quite small in DH systems that have high share of forest residual fuels and electricity cogeneration. The consequences can even be negative if ECMs lead to increased need of imported electricity that is produced resource inefficiently or/and by fossil fuels. However, all studied ECMs increase the relative amount of cogenerated electricity, the ratio between amount of cogenerated electricity and the heat load. This implied that all ECMs increase the overall efficiency of the Eskilstuna DH system.

Urban water use arises from a mix of scale-dependent biophysical and socioeconomic factors. In Portland, Oregon, single-family residential water use exhibits a tightly coupled relationship with summertime weather, although this relationship varies with land use patterns across households and neighborhoods. This thesis developed a multilevel regression model to evaluate the relative importance of weather variability, parcel land use characteristics, and neighborhood geographic context in explaining single-family residential water demand patterns in the Portland metropolitan area. The model drew on a high-resolution panel dataset of weekly mean summer water use over five years (2001-2005) for a sample of 460 single-family households spanning an urban-to-suburban gradient. Water use was found to be most elastic with respect to parcel-scale building size. Building age was negatively related to water use at both the parcel and neighborhood scale. Half the variation in water use can be attributed to between-household factors. Between-neighborhood variation exerted a modest but statistically significant effect. The analysis decomposed household temperature sensitivity into four components: a fixed effect common to all households, a household-specific deviation from the fixed effect, a separate extreme heat effect, and a land use effect, where lot size exaggerated the effect of temperature on water use. Results suggested that land use planning may be an effective non-price mechanism for long-range management of peak demand, as land use decisions have water use implications. The combined effects of population growth, urbanization, and climate change expose water providers to risk of water stress. Modeling fine-grain relationships among heat, land use, and water use across scales plays a role in long-range climate change planning and adaptation.

碩士
國立高雄師範大學
工業科技教育學系
94
This study aimed to discuss the current human resource structure and investigate the human resource demand of the optoelectronics industry, in an attempt to provide substantial references for human resource policies and educational development. Mainly based on questionnaire surveys, the study collected information about human resource demand and talent training in the optoelectronics industry. In addition to designing a questionnaire to investigate the structure of human resource demand, in order to enhance the accuracy of the research results, this study also gathered related data from the government and the current situations of talent training to assure the completeness of the analysis. The findings in this study were expected to offer substantial information for the establishment of human resources policies and the planning of an educational development project.

碩士
國立彰化師範大學
工業教育學系
86
The main purpose of the study is to explore the importance and the extent of execution of the human resource information system to the companies.In addition, it discusses the variance of two groups to the human resourcesystem. In so doing, I hope to benefit the domestic companies while setting up the human resource information system and provide reliable informaton for further study. Subjects are chosen from the human resource supervisors of the 200manufacturers in Taiwan for conducting the survey. 76 valuable data collectedfor analysis, including description analysis, Pearson correlative analysis and ANOVA. The main findings are as follows:1.The human resource information system in Taiwan has great influence upon the inner operation of the company. However, it reveals the insufficient operation of the human resource information system.2.There is low relationship between the two groups, the importance and the extent of execution in human resource information system.3. Extenal environmental factor and the supervisors'' personal point of view toward HRIS have much differences in affecting the variance of the importance extent in human resource information system.4.Managing policy, the number of persons in human resource department ,and the standard paper procedure in human resource department have much difference in affecting the variance of execution extent in human resource informaton system. Based on the above results, the study offers four suggestions.1.To strengthen the poor execution items in the human resource information system in the eyes of the supervisors the important ones.2.To promote the operation of the human resource information system which should first work on both system coordination and data analysis.3.To educate the supervisors the importance of the human resource information system and cultivate their support to the activities of the human resource management.4.To include the training of the human resouce information system in the human resource management program.

碩士
國立高雄師範大學
工業科技教育學系
93
The purposes of this study were to investigate the development in the past, and the current human resource condition in the construction industry, and to forecast the future demand of human resource in the comprehensive construction enterprises from 2005 to 2007. The population of this study was Taiwan construction industry. The method of document-analysis was adopted to review the development in the past and the current human resource condition in the construction industry. The methods of questionnaire-survey and grey prediction model were used to analyze and forecast the future demand of human resource in the comprehensive construction enterprises. The results of this study showed that the construction industry had negative growth in the past years and the numbers of human resource demand were declined as well. The forecast numbers of human resource demand in the grade A and B comprehensive construction enterprises are decreasing, but grade C comprehensive construction enterprises are increasing year by year. To sum up, the total numbers of human resource demand in the comprehensive construction enterprises from 2005 to 2007 are 174756, 173101, and 173711. Compared with those numbers in 2003, the numbers of human resource demand are decreasing with slight fluctuation.