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Background: Green consumerism is on the rise. However, the individuals with budget constraints are unable to afford sustainable products due to their budget as well as the premium pricing of the products. This in turn impedes them from expanding their sustainability options. In addition, the sustainability motivated individuals seek other means to meet their sustainable consumption practices. Thus, this thesis explores how the individuals innovate in their sustainable consumption practices while facing budget constraints. Purpose: The purpose of this research is to explore in what ways budget constrained sustainability motivated consumers innovate in their sustainable consumption practices. Method: The method used to investigate this topic is a mixed method of focus groups and interviews. The reason why this method was chosen was because it allows us to obtain an in depth discussion and rich data. Conclusion: The findings of our research has demonstrated that there are many ways that the budget constrained, sustainability motivated students innovate. Nevertheless, premium pricing as well as budget constraints play a significant role in limiting the extent to which they can innovate in their sustainable consumption practices.

Background: Green consumerism is on the rise. However, the individuals with budget constraints are unable to afford sustainable products due to their budget as well as the premium pricing of the products. This in turn impedes them from expanding their sustainability options. In addition, the sustainability motivated individuals seek other means to meet their sustainable consumption practices. Thus, this thesis explores how the individuals innovate in their sustainable consumption practices while facing budget constraints. Purpose: The purpose of this research is to explore in what ways budget constrained sustainability motivated consumers innovate in their sustainable consumption practices. Method: The method used to investigate this topic is a mixed method of focus groups and interviews. The reason why this method was chosen was because it allows us to obtain an in depth discussion and rich data. Conclusion: The findings of our research has demonstrated that there are many ways that the budget constrained, sustainability motivated students innovate. Nevertheless, premium pricing as well as budget constraints play a significant role in limiting the extent to which they can innovate in their sustainable consumption practices.

We study task scheduling and offloading in a cloud computing system with multiple users where tasks have different processing times, release times, communication times, and weights. Each user may schedule a task locally or offload it to a shared cloud with heterogeneous processors by paying a price for the resource usage. We consider four different models in this paper: (i) zero task release and communication times, (ii) non-zero task release times and zero communication times, (iii) non-zero task release times and fixed communication times, and (iv) non-zero task release times and sequence-dependent communication times. Our work aims at identifying a task scheduling decision that minimizes the weighted sum completion time of all tasks, while satisfying the users' budget constraints. We propose an efficient solution framework for this NP-hard problem. As a first step, we use a relaxation and a rounding technique to obtain an integer solution that is a constant factor approximation to the minimum weighted sum completion time. This solution violates the budget constraints, but the average budget violation decreases as the number of users increases. Thus, we develop a scalable algorithm termed Single-Task Unload for Budget Resolution (STUBR), which resolves budget violations and orders the tasks to obtain robust solutions. IEEE, QC 20220916

Increased human life expectancy combined with declining birth rates around the globe has led to ageing populations, particularly in the developed world. This phenomenon brings about increased dependency ratios and calls for setting new policies for the elderly citizens. This comprises provision of a set of life-enhancing services in an accessible and equitable way. In this paper, we consider the multi-period problem of locating senior centres offering these services to the elderly population with budget constraints and capacity limitations. Both consistent and inconsistent versions of the problem are considered, aiming at identifying the set of facilities to operate in each region at each period, the service type(s) to be offered and the allocation of budget in each period to location and operation of facilities. A mixed integer mathematical programming model is presented, an efficient iterated local search procedure is proposed and managerial insights are provided.

Cloud Infrastructure as a Service (IaaS) has been known as a suitable platform for the execution of workflow applications. Quality of service (QoS) in such platforms is considered a challenging problem from both customers’ and service providers’ perspectives to perform workflow schedules. This paper proposes Budget Deadline Delicate Cloud (BDDC) and Budget Deadline Cloud (BDC) algorithms to consider both budget and deadline constraints for scheduling scientific workflows on cloud IaaS platforms. Methods for distribution of budget and deadlines under task leveling are proposed. Four metrics (success rate, time ratio, cost ratio, and utilization rate) are utilized to evaluate the proposed algorithms’ performance. Results of our proposed algorithms are compared with the BDHEFT, DBCS, and BDSD algorithms under various scenarios. Simulation results demonstrate that BDDC outperforms other algorithms in achieving cheaper costs while earning a higher success rate and utilization rate, and BDC accomplishes higher success rates and faster makespan. The performance of the proposed methods is confirmed using a real cloud environment. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

In 2018, the Army Futures Command (AFC) was established to modernize the Army and ensure the Army and its soldiers outmatch their adversaries in future conflicts. The Army’s modernization efforts include eight priorities encompassing 34 programs. The challenge Army senior leaders face is determining the overall modernization strategy that will maximize future total operational benefit when faced with budgetary constraints. The primary aim of this project was to determine the maximum total operational benefit subject to a fixed year budget constraint. First, we reviewed the DoD’s budget and acquisition process. From this analysis, we formed a methodology incorporating the Planning, Programming, Budgeting, and Execution (PPBE) process with the defense acquisition strategy timeline. We then generated a five-year fixed budget constraint derived from the Future Years Defense Program (FYDP) budget projection. Finally, we implemented a linear programming model to compute the maximum operational benefit subject to our fixed budget constraint. The model results outline the impact potential budgetary constraints have on the total operational benefit. By varying the range of budget constraints from 100% funded to 50% funded, we used sensitivity analysis to determine the impact on specific programs. Our model results will assist Army senior leaders to make more informed decisions regarding the allocation of resources to fund current and future modernization programs., Outstanding Thesis, Captain, United States Army, Approved for public release. Distribution is unlimited.

Increased human life expectancy combined with declining birth rates around the globe has led to ageing populations, particularly in the developed world. This phenomenon brings about increased dependency ratios and calls for setting new policies for the elderly citizens. This comprises provision of a set of life-enhancing services in an accessible and equitable way. In this paper, we consider the multi-period problem of locating senior centres offering these services to the elderly population with budget constraints and capacity limitations. Both consistent and inconsistent versions of the problem are considered, aiming at identifying the set of facilities to operate in each region at each period, the service type(s) to be offered and the allocation of budget in each period to location and operation of facilities. A mixed integer mathematical programming model is presented, an efficient iterated local search procedure is proposed and managerial insights are provided.

Traditional active learning tries to identify instances for which the acquisition of the label increases model performance under budget constraints. Less research has been devoted to the task of actively acquiring feature values, whereupon both the instance and the feature must be selected intelligently and even less to a scenario where the instances arrive in a stream with feature drift. We propose an active feature acquisition strategy for data streams with feature drift, as well as an active feature acquisition evaluation framework. We also implement a baseline that chooses features randomly and compare the random approach against eight different methods in a scenario where we can acquire at most one feature at the time per instance and where all features are considered to cost the same. Our initial experiments on 9 different data sets, with 7 different degrees of missing features and 8 different budgets show that our developed methods outperform the random acquisition on 7 data sets and have a comparable performance on the remaining two. © 2020, The Author(s).

Traditional active learning tries to identify instances for which the acquisition of the label increases model performance under budget constraints. Less research has been devoted to the task of actively acquiring feature values, whereupon both the instance and the feature must be selected intelligently and even less to a scenario where the instances arrive in a stream with feature drift. We propose an active feature acquisition strategy for data streams with feature drift, as well as an active feature acquisition evaluation framework. We also implement a baseline that chooses features randomly and compare the random approach against eight different methods in a scenario where we can acquire at most one feature at the time per instance and where all features are considered to cost the same. Our initial experiments on 9 different data sets, with 7 different degrees of missing features and 8 different budgets show that our developed methods outperform the random acquisition on 7 data sets and have a comparable performance on the remaining two. © 2020, The Author(s).

When benefits of proposed infrastructure investments are forecasted, residential location is usually treated as fixed, since very few operational transport models are able to forecast residential relocation. It has been argued that this may constitute a source of serious error or bias when evaluating and comparing the benefits of proposed infrastructure investments. We use a stylized simulation model of a metropolitan region to compare calculated benefits for a large number of infrastructure investments with and without taking changes in residential location into account. In particular, we explore the changes in project selection when assembling an optimal project portfolio under a budget constraint. The simulation model includes endogenous land prices and demand for residential land, heterogeneous preferences and wage offers across residents, and spillover mechanisms which affect wage rates in zones. The model is calibrated to generate realistic travel patterns and demand elasticities. Our results indicate that ignoring residential relocation has a small but appreciable effect on the selected project portfolio, but only a very small effect on achieved total benefits., QC 20201221

We reexamine the well-known assignment market model in a more general and more practical environment where agents may be financially constrained. These constraints will be shown to have an important impact on the set of Walrasian equilibria. We prove that a price adjustment process will either find a unique minimal Walrasian equilibrium price vector, or exclusively validate the nonexistence of equilibrium

This paper presents a novel multi-period stochastic optimization model for studying long-term power generation capacity planning in developing countries. A stylized model is developed to achieve three objectives: (1) to serve as a tool for determining optimal mix, size and timing of power generation types in the face of budget constraint, (2) to help decision makers appreciate the consequences of capacity expansion decisions on level of unserved electricity demand and its attendant impact on the national economy, and (3) to encourage the habit of periodic savings towards new generation capacity financing. The problem is modeled using a stochastic mixed-integer linear programming (MILP) technique under demand uncertainty. The effectiveness of the model, together with valuable insights derived from considering different levels of budget constraints are demonstrated using Ghana as a case study. The results indicate that at an annual savings equivalent to 0.75% of GDP, Ghana could finance the needed generation capacity to meet approximately 95% of its annual electricity demand between 2016 and 2035. Additionally, it is observed that as financial constraint becomes tighter, decisions on the mix of new generation capacities tend to be more costly compared to when sufficient funds are available. © 2016 Elsevier Ltd

[EN] The economic production quantity method is one of the simplest models to control production and inventory. However, the assumptions on which it is based make its application difficult in practical situations. In most companies, set-up and inventory costs are unknown, or may not even exist as such, although other data are known, such as available capacity in machining hours and limits set by the financing subsystem. The present work offers a multi-item inventory model that sets reference values using known values such as inventory investment limits and production capacity. In order to confer the proposed model's larger practical content, a spreadsheet available to be downloaded has been implemented. Finally, the model dynamics is illustrated as a numerical example obtained from a real company.

The use of Public-Private Partnerships (PPPs) to realize and operate public infrastructures is often associated with fiscal-circumventing motivations. Using data at the municipal level, this paper investigates whether budget-constrained public authorities adopt PPPs in order to hide public debt. The results show that financial difficulties often lead to a preference for PPPs instead of traditional forms of public procurement, but this behavior is not explained by the possibility of debt-hiding. Explanations for these findings are discussed.

[EN] The economic production quantity method is one of the simplest models to control production and inventory. However, the assumptions on which it is based make its application difficult in practical situations. In most companies, set-up and inventory costs are unknown, or may not even exist as such, although other data are known, such as available capacity in machining hours and limits set by the financing subsystem. The present work offers a multi-item inventory model that sets reference values using known values such as inventory investment limits and production capacity. In order to confer the proposed model's larger practical content, a spreadsheet available to be downloaded has been implemented. Finally, the model dynamics is illustrated as a numerical example obtained from a real company.

This paper presents a novel multi-period stochastic optimization model for studying long-term power generation capacity planning in developing countries. A stylized model is developed to achieve three objectives: (1) to serve as a tool for determining optimal mix, size and timing of power generation types in the face of budget constraint, (2) to help decision makers appreciate the consequences of capacity expansion decisions on level of unserved electricity demand and its attendant impact on the national economy, and (3) to encourage the habit of periodic savings towards new generation capacity financing. The problem is modeled using a stochastic mixed-integer linear programming (MILP) technique under demand uncertainty. The effectiveness of the model, together with valuable insights derived from considering different levels of budget constraints are demonstrated using Ghana as a case study. The results indicate that at an annual savings equivalent to 0.75% of GDP, Ghana could finance the needed generation capacity to meet approximately 95% of its annual electricity demand between 2016 and 2035. Additionally, it is observed that as financial constraint becomes tighter, decisions on the mix of new generation capacities tend to be more costly compared to when sufficient funds are available. © 2016 Elsevier Ltd

We analyzed the planning problem for HIV screening, testing, and care. This problem consists of determining the optimal fraction of patients to be screened in every period as well as the optimum staffing level at each part of the healthcare system to maximize the total health benefits to the patients measured by quality-adjusted life-years (QALYs) gained. We modeled this problem as a nonlinear mixed integer programming program comprising disease progression (the transition of the patients across health states), system dynamics (the flow of patients in different health states across various parts of the healthcare delivery system), and budgetary and capacity constraints. We applied the model to the Greater Los Angeles (GLA) station in the Veterans Health Administration system. We found that a Centers for Disease Control and Prevention recommended routine screening policy in which all patients visiting the system are screened for HIV irrespective of risk factors may not be feasible because of budgetary constraints. Consequently, we used the model to develop and evaluate managerially relevant policies within existent capacity and budgetary constraints to improve upon the current risk based screening policy of screening only high risk patients. Our computational analysis showed that the GLA station can achieve substantial increase (20% to 300%) in the QALYs gained by using these policies over risk based screening. The GLA station has already adapted two of these policies that could yield better patient health outcomes over the next few years. In addition, our model insights have influenced the decision making process at this station.

This paper discusses a novel approach to identify soft budget constraint expectations, exploiting the idea that financial investors rationally incorporate in sub-national bond prices also expectations of future bailouts by the central government. We consider as a testing ground the municipal bond market in Italy. We first build an original dataset of all the 1 388 bond issuances from 1996 (the year when the national legislation provided an organic discipline for municipal bonds) to 2011, involving 529 cities out of about 8 000. We then regress bond yields at issuance on bond characteristics, central government bond yields, and three proxies for bailout expectations on soft budget constraints (an index of vertical fiscal imbalance and of central government budget tightness, plus a dummy for the city being subject to the Domestic Stability Pact). Our results suggest that soft budget constraint problems arise frequently, especially in small cities and those located in the poorer South.

We analyzed the planning problem for HIV screening, testing, and care. This problem consists of determining the optimal fraction of patients to be screened in every period as well as the optimum staffing level at each part of the healthcare system to maximize the total health benefits to the patients measured by quality-adjusted life-years (QALYs) gained. We modeled this problem as a nonlinear mixed integer programming program comprising disease progression (the transition of the patients across health states), system dynamics (the flow of patients in different health states across various parts of the healthcare delivery system), and budgetary and capacity constraints. We applied the model to the Greater Los Angeles (GLA) station in the Veterans Health Administration system. We found that a Centers for Disease Control and Prevention recommended routine screening policy in which all patients visiting the system are screened for HIV irrespective of risk factors may not be feasible because of budgetary constraints. Consequently, we used the model to develop and evaluate managerially relevant policies within existent capacity and budgetary constraints to improve upon the current risk based screening policy of screening only high risk patients. Our computational analysis showed that the GLA station can achieve substantial increase (20% to 300%) in the QALYs gained by using these policies over risk based screening. The GLA station has already adapted two of these policies that could yield better patient health outcomes over the next few years. In addition, our model insights have influenced the decision making process at this station.

The system reliability (Rd,c) calculation is based on the generation of all the lower boundary points for the given demand, d, such that the total transmission cost is less than or equal to c (budget). This generation of the lower boundary points is based on finding all feasible solutions to the flow vector, F. The number of feasible solutions cannot be anticipated. Thus, a genetic algorithm is useful for solving this problem. Therefore, this paper presents a genetic algorithm to generate all feasible solutions to the flow vector, F, and then calculate Rd,c. The algorithm is applied to two problem models. In the first, each arc of a flow network has several capacities and may fail. In the second, both the arc and node have several capacities and may fail. The results show that the algorithm is efficient at solving a flow problem and is useful for calculating Rd,c.

[EN] The methodology that has been developed suggests a sequence of interventions in the water distribution network, identifying the hierarchy of groups of pipes that must be renewed, taking into consideration all the factors associated, both endogenous and exogenous, and the technical limitations in the functioning of the water network considering the annual budget available for the rehabilitation of pipelines. The aim of this work is to develop a model to facilitate the decision-making to the managers of water distribution systems, considering a budget constraint, the amount of linear meters of each group of pipes that provides a maximum benefit in relation to the valuation of influence factors, the benefits and the cost of the rehabilitation. To accomplish this, we use the genetic algorithms tool through Evolver and fuzzy logic, using EPANET to determine the cost of ¿saving¿ electricity and the recovered leakage flow. This model is applied to a case study in the city of Celaya, Guanajuato, Mexico., [ES] La metodología que se ha desarrollado propone una secuencia de rehabilitaciones en la red de distribución de agua, identificando jerárquicamente los grupos de tuberías que deben ser objeto de renovación, tomando en consideración todos los factores asociados tanto endógenos como exógenos y las limitaciones técnicas en el funcionamiento de la red de agua, considerando los presupuestos anuales disponibles para la rehabilitación de tuberías. El objetivo de este trabajo es desarrollar un modelo para facilitar la toma de decisiones de los gestores en las redes de abastecimiento, considerando una restricción presupuestal, qué cantidad de metros lineales de cada grupo de tuberías proporciona un máximo beneficio con relación a la valoración de los factores de influencia, de los beneficios obtenidos y el costo de la rehabilitación. Para lograr esto utilizaremos la herramienta de algoritmos genéticos a través de Evolver y lógica difusa, la utilización de EPANET para determinar el costo del ¿ahorro¿ de la energía eléctrica y del caudal recuperado de las fugas. Dicho modelo se aplicará a un caso de estudio en la ciudad de Celaya, Guanajuato, México.

[EN] The methodology that has been developed suggests a sequence of interventions in the water distribution network, identifying the hierarchy of groups of pipes that must be renewed, taking into consideration all the factors associated, both endogenous and exogenous, and the technical limitations in the functioning of the water network considering the annual budget available for the rehabilitation of pipelines. The aim of this work is to develop a model to facilitate the decision-making to the managers of water distribution systems, considering a budget constraint, the amount of linear meters of each group of pipes that provides a maximum benefit in relation to the valuation of influence factors, the benefits and the cost of the rehabilitation. To accomplish this, we use the genetic algorithms tool through Evolver and fuzzy logic, using EPANET to determine the cost of ¿saving¿ electricity and the recovered leakage flow. This model is applied to a case study in the city of Celaya, Guanajuato, Mexico., [ES] La metodología que se ha desarrollado propone una secuencia de rehabilitaciones en la red de distribución de agua, identificando jerárquicamente los grupos de tuberías que deben ser objeto de renovación, tomando en consideración todos los factores asociados tanto endógenos como exógenos y las limitaciones técnicas en el funcionamiento de la red de agua, considerando los presupuestos anuales disponibles para la rehabilitación de tuberías. El objetivo de este trabajo es desarrollar un modelo para facilitar la toma de decisiones de los gestores en las redes de abastecimiento, considerando una restricción presupuestal, qué cantidad de metros lineales de cada grupo de tuberías proporciona un máximo beneficio con relación a la valoración de los factores de influencia, de los beneficios obtenidos y el costo de la rehabilitación. Para lograr esto utilizaremos la herramienta de algoritmos genéticos a través de Evolver y lógica difusa, la utilización de EPANET para determinar el costo del ¿ahorro¿ de la energía eléctrica y del caudal recuperado de las fugas. Dicho modelo se aplicará a un caso de estudio en la ciudad de Celaya, Guanajuato, México.

When a monopolist has discretion over the timing of infrastructure investments, regulation of post-investment prices interferes with incentivizing socially optimal investment timing. In a model of regulated lumpy investment under uncertainty, we study regulation when the regulator can condition price caps on investment timing. We analyse optimal regulation when there is asymmetric information on investment costs and regulation has to respect a budget constraint. We show that optimal regulation involves a price cap that decreases as a function of the monopolist's chosen investment time.

The article analyzes the historical evolution of public investment and the budget constraint in transport infrastructure in Colombia during the period 1960-2011 by identifying periods of fiscal adjustment and its relationship to the evolution of investment in this sector. The purpose is to understand the causes of the progressive deterioration of infrastructure investment in the transport sector from the analysis of macroeconomic variables and economic policy for the sector during the period of investigation. The methodology is part of the complementarity of quantitative and qualitative analysis of macroeconomic variables with the historical analysis of economic policy making for the sector, resulting in a comprehensive diagnosis of the current situation in Colombia transport., El artículo analiza la evolución histórica de la inversión pública y la restricción presupuestaria en la infraestructura de transporte en Colombia durante el periodo 1960-2011 mediante la identificación de los periodos de ajustes fiscales y su relación con la evolución de la inversión en dicho sector. La finalidad es conocer las causas del deterioro progresivo en la inversión de infraestructura del sector transporte a partir del análisis de variables macroeconómicas y política económica para el sector durante el periodo de investigación. La metodología aplicada se inscribe en la complementariedad del análisis cuantitativo y cualitativo de variables macroeconómicas que junto con el análisis histórico de adopción de política económica para el sector, arroja un diagnóstico integral sobre la situación actual del transporte en Colombia.

The article analyzes the historical evolution of public investment and the budget constraint in transport infrastructure in Colombia during the period 1960-2011 by identifying periods of fiscal adjustment and its relationship to the evolution of investment in this sector. The purpose is to understand the causes of the progressive deterioration of infrastructure investment in the transport sector from the analysis of macroeconomic variables and economic policy for the sector during the period of investigation. The methodology is part of the complementarity of quantitative and qualitative analysis of macroeconomic variables with the historical analysis of economic policy making for the sector, resulting in a comprehensive diagnosis of the current situation in Colombia transport., El artículo analiza la evolución histórica de la inversión pública y la restricción presupuestaria en la infraestructura de transporte en Colombia durante el periodo 1960-2011 mediante la identificación de los periodos de ajustes fiscales y su relación con la evolución de la inversión en dicho sector. La finalidad es conocer las causas del deterioro progresivo en la inversión de infraestructura del sector transporte a partir del análisis de variables macroeconómicas y política económica para el sector durante el periodo de investigación. La metodología aplicada se inscribe en la complementariedad del análisis cuantitativo y cualitativo de variables macroeconómicas que junto con el análisis histórico de adopción de política económica para el sector, arroja un diagnóstico integral sobre la situación actual del transporte en Colombia.

Many of the investment and hedging problems we will encounter can be formulated as a minimization of a function over a set determined by the investor’s risk and budget constraints and other restrictions on the type of positions that the investor can take. Such problems become particularly tractable if both the function to be minimized and the set over which the minimization is done are convex. The minimization problem is in this case called a convex optimization problem. This chapter presents basic results for solving convex optimization problems that will be applied in subsequent chapters., QC 20211104

In this paper, we consider the transportation planning decision (TPD) problem with fuzzy goals, available supply and forecast demand. An interactive method is designed for solving the multi-objective TPD problem where the fuzzy data are represented by modified S-curve membership functions. The proposed method attempts to simultaneously minimize the total production and transportation costs and the total delivery time with reference to budget constraints and available supply, machine capacities at each source, as well as forecast demand and warehouse space constraints at each destination. An interactive fuzzy approach is applied to solve the multi-objective TPD problem and to find a preferred compromise solution. Finally, the performance of S-curve membership functions that represent uncertainty goals and constraints in TPD problems with linear membership functions in an industrial case is compared. © 2010 Elsevier B.V. All rights reserved.

In this paper, we consider the transportation planning decision (TPD) problem with fuzzy goals, available supply and forecast demand. An interactive method is designed for solving the multi-objective TPD problem where the fuzzy data are represented by modified S-curve membership functions. The proposed method attempts to simultaneously minimize the total production and transportation costs and the total delivery time with reference to budget constraints and available supply, machine capacities at each source, as well as forecast demand and warehouse space constraints at each destination. An interactive fuzzy approach is applied to solve the multi-objective TPD problem and to find a preferred compromise solution. Finally, the performance of S-curve membership functions that represent uncertainty goals and constraints in TPD problems with linear membership functions in an industrial case is compared. © 2010 Elsevier B.V. All rights reserved.

An auctioneer wishes to sell several heterogeneous indivisible items to a group of potential bidders. Each bidder has valuations over the items but faces a budget constraint and may therefore not be able to pay up to his valuations. In such markets, a competitive equilibrium typically fails to exist. We develop a dynamic auction and prove that the auction always finds a core allocation in finitely many rounds. The core allocation consists of an assignment of the items and its associated supporting price vector.

We consider the problem of a bidder with limited budget competing in a series of second-price auctions. A motivating example is that of sponsored search auctions, where advertisers bid in a sequence of repeated generalized second price auctions. To characterize the optimal bidding strategy, we formulate the problem as a discounted Markov Decision Process, and provide explicit solutions when the bidder is involved in a large number of auctions., QC 20140901

In this paper we consider the problem of financing infrastructure when the regulator faces a budget constraint. The optimal budget-constrained mechanism satisfies four properties. The first property is bunching at the top, that is the more efficient firms produce the same quantity irrespective of their costs. The second property is separability of less efficient firms. The third property is that the mechanism is a third best one, that is, the optimal budget constrained output is strictly lower than the second best output for any given type. Finally, if the budget constraint is too strong, then we have full bunching.

Since 1953 there has been a fierce military competition between the Republic of Korea (ROK) and the Democratic People's Republic of Korea (DPRK). The DPRK has committed over 10% of its GNP since 1963 to defense expenditures, whereas, the ROK has spent a relatively small portion (3-6%) of GNP over the same period. Because the U.S. plans to withdraw from the ROK in the near future, a heavy burden will be imposed upon the South Korean government to maintain its national defense. The U.S. withdrawal will result in the delay of achieving a military balance in the Korean Peninsula. Under these conditions, it becomes essential to find the most efficient allocation of the defense budget so as to get the most high-tech weapons, and achieve military balance with the DPRK at the soonest possible time. The purpose of this thesis is to compute the service levels for critical items with the present budget and to determine whether the budget is used effectively in the ROK Army. Cost analysis methods are used to solve the problem. An application of this methodology is shown with an illustration. This analysis shows that different items call for different service levels., http://archive.org/details/acostnalysisford1094534840, Major, Republic of Korea Army, Approved for public release; distribution is unlimited.

Diplomová práce se zabývá analýzou poptávky s využitím standardních prostředků inženýrské matematiky. Jsou zkoumány matematické modely poptávky a to jak jednofaktorové, tak i vícefaktorové. Elasticita poptávky je aplikována při rozhodování o cenové politice. Jsou řešeny problémy optimalizace poptávky s ohledem na užitečnost a rozpočtové omezení. Je prezentována konstrukce křivky poptávky a kompenzované křivky poptávky. Výklad je doprovázen ilustrativními příklady s cílem naplnění metodických hledisek práce., The diploma thesis deals with the analyses of demand using standard tools of engineering mathematics. Mathematical models of demand, both single and multi- factor are investigated. Elasticity of demand is applied for decision making in price policy. Problems of optimization of demand reflecting utility and budget constraints are under consideration. Constructions of demand curve and compensated demand curve are presented. The text is accompanied by illustrative examples aiming at methodical aspects of the work

Diplomová práce se zabývá analýzou poptávky s využitím standardních prostředků inženýrské matematiky. Jsou zkoumány matematické modely poptávky a to jak jednofaktorové, tak i vícefaktorové. Elasticita poptávky je aplikována při rozhodování o cenové politice. Jsou řešeny problémy optimalizace poptávky s ohledem na užitečnost a rozpočtové omezení. Je prezentována konstrukce křivky poptávky a kompenzované křivky poptávky. Výklad je doprovázen ilustrativními příklady s cílem naplnění metodických hledisek práce., The diploma thesis deals with the analyses of demand using standard tools of engineering mathematics. Mathematical models of demand, both single and multi- factor are investigated. Elasticity of demand is applied for decision making in price policy. Problems of optimization of demand reflecting utility and budget constraints are under consideration. Constructions of demand curve and compensated demand curve are presented. The text is accompanied by illustrative examples aiming at methodical aspects of the work

Diplomová práce se zabývá analýzou poptávky s využitím standardních prostředků inženýrské matematiky. Jsou zkoumány matematické modely poptávky a to jak jednofaktorové, tak i vícefaktorové. Elasticita poptávky je aplikována při rozhodování o cenové politice. Jsou řešeny problémy optimalizace poptávky s ohledem na užitečnost a rozpočtové omezení. Je prezentována konstrukce křivky poptávky a kompenzované křivky poptávky. Výklad je doprovázen ilustrativními příklady s cílem naplnění metodických hledisek práce., The diploma thesis deals with the analyses of demand using standard tools of engineering mathematics. Mathematical models of demand, both single and multi- factor are investigated. Elasticity of demand is applied for decision making in price policy. Problems of optimization of demand reflecting utility and budget constraints are under consideration. Constructions of demand curve and compensated demand curve are presented. The text is accompanied by illustrative examples aiming at methodical aspects of the work