PhD / In recent years, mobile communication networks have experienced significant evolution. The 3G mobile communication system, UMTS, employs WCDMA as the air interface standard, which leads to quite different mobile network planning and dimensioning processes compared with 2G systems. The UMTS system capacity is limited by the received interference at NodeBs due to the unique features of WCDMA, which is denoted as `soft capacity'. Consequently, the key challenge in UMTS radio network planning has been shifted from channel allocation in the channelized 2G systems to blocking and outage probabilities computation under the `cell breathing' effects which are due to the relationship between network coverage and capacity. The interference characterization, especially for the other-cell interference, is one of the most important components in 3G mobile networks planning. This monograph firstly investigates the system behavior in the operation of UMTS uplink, and develops the analytic techniques to model interference and system load as fully-characterized random variables, which can be directly applicable to the performance modeling of such networks. When the analysis progresses from single-cell scenario to multi-cell scenario, as the target SIR oriented power control mechanism is employed for maximum capacity, more sophisticated system operation, `feedback behavior', has emerged, as the interference levels at different cells depend on each other. Such behaviors are also captured into the constructed interference model by iterative and approximation approaches. The models are then extended to cater for the features of the newly introduced HSUPA, which provides enhanced dedicated channels for the packet switched data services such that much higher bandwidth can be achieved for best-effort elastic traffic, which allows network operators to cope with the coexistence of both circuit-switched and packet-switched traffic and guarantee the QoS requirements. During the derivation, we consider various propagation models, traffic models, resource allocation schemes for many possible scenarios, each of which may lead to different analytical models. All the suggested models are validated with either Monte-Carlo simulations or discrete event simulations, where excellent matches between results are always achieved. Furthermore, this monograph studies the optimization-based resource allocation strategies in the UMTS uplink with integrated QoS/best-effort traffic. Optimization techniques, both linear-programming based and non-linear-programming based, are used to determine how much resource should be assigned to each enhanced uplink user in the multi-cell environment where each NodeB possesses full knowledge of the whole network. The system performance under such resource allocation schemes are analyzed and compared via Monte-Carlo simulations, which verifies that the proposed framework may serve as a good estimation and optimal reference to study how systems perform for network operators.

PhD / In recent years, mobile communication networks have experienced significant evolution. The 3G mobile communication system, UMTS, employs WCDMA as the air interface standard, which leads to quite different mobile network planning and dimensioning processes compared with 2G systems. The UMTS system capacity is limited by the received interference at NodeBs due to the unique features of WCDMA, which is denoted as `soft capacity'. Consequently, the key challenge in UMTS radio network planning has been shifted from channel allocation in the channelized 2G systems to blocking and outage probabilities computation under the `cell breathing' effects which are due to the relationship between network coverage and capacity. The interference characterization, especially for the other-cell interference, is one of the most important components in 3G mobile networks planning. This monograph firstly investigates the system behavior in the operation of UMTS uplink, and develops the analytic techniques to model interference and system load as fully-characterized random variables, which can be directly applicable to the performance modeling of such networks. When the analysis progresses from single-cell scenario to multi-cell scenario, as the target SIR oriented power control mechanism is employed for maximum capacity, more sophisticated system operation, `feedback behavior', has emerged, as the interference levels at different cells depend on each other. Such behaviors are also captured into the constructed interference model by iterative and approximation approaches. The models are then extended to cater for the features of the newly introduced HSUPA, which provides enhanced dedicated channels for the packet switched data services such that much higher bandwidth can be achieved for best-effort elastic traffic, which allows network operators to cope with the coexistence of both circuit-switched and packet-switched traffic and guarantee the QoS requirements. During the derivation, we consider various propagation models, traffic models, resource allocation schemes for many possible scenarios, each of which may lead to different analytical models. All the suggested models are validated with either Monte-Carlo simulations or discrete event simulations, where excellent matches between results are always achieved. Furthermore, this monograph studies the optimization-based resource allocation strategies in the UMTS uplink with integrated QoS/best-effort traffic. Optimization techniques, both linear-programming based and non-linear-programming based, are used to determine how much resource should be assigned to each enhanced uplink user in the multi-cell environment where each NodeB possesses full knowledge of the whole network. The system performance under such resource allocation schemes are analyzed and compared via Monte-Carlo simulations, which verifies that the proposed framework may serve as a good estimation and optimal reference to study how systems perform for network operators.

In this thesis we address capacity planning problems with different demand and service characteristics, motivated by healthcare applications. In the first application, we develop, implement, and assess the impact of analytical models, accompanied by a decision-support tool, for operating room (OR) staff planning decisions with different service lines. First, we propose a methodology to forecast the staff demand by service line. We use these results in a two-phase mathematical model that defines the staffing budget for each service line, and then decides how many staff to assign to each potential shift and day pair while considering staff overtime and pooling policies and other staff planning constraints. We also propose a heuristic to solve the model's second phase. We implement these models using historical data from a community hospital and analyze the effect of different model parameters and settings. Compared with the current practice, we reduce delays and staff pooling at no additional cost. We validate these conclusions through a simulation model. In the second application, we consider the problem of staff planning and scheduling when there is an accepted time window between each order's arrival and fulfillment, with the goal of obtaining a balanced schedule that focuses on on-time demand fulfillment but also considers staff characteristics and operational practices. Hence, solving this problem requires simultaneously scheduling the staff and the forecasted demand. We propose, implement, and analyze the results of a model for staff and demand scheduling under this setting, accompanied by a decision-support tool. We implement this model in a company that offers document processing and other back-office services to healthcare providers. We provide details on the model validation, implementation, and results, including a 25\% increase in the company's staff productivity. Finally, we provide insights on the effects of some of the model's parameters and settings, and assess the performance of a proposed heuristic to solve this problem. In the third application, we consider a non-consumable resource planning problem. Demand consists of a set of jobs, each job has a scheduled start time and duration, and belongs to a particular demand class that requires a subset of resources. Jobs can be `accepted' or `rejected,' and the service level is measured by the (weighted) percentage of accepted jobs. The goal is to find the capacity level that minimizes the total cost of the resources, subject to global and demand-class-based service level constraints. We first analyze the complexity of this problem and several of its special cases, and then we propose a model to find the optimal inventory for each type of resource. We show the convergence of the sample average approximation method to solve a stochastic extension of the model. This problem is motivated by the inventory planning decisions for surgical instruments for ORs. We study the effects of different model parameters and settings on the cost and service levels, based on surgical data from a community hospital.

One of the impacts of rising demand for community health services is on long term capacity planning. Demand for community services arises directly from the community-mainly seniors- as well as from those discharged from the hospital. This thesis is focused on developing a simulation model based on patient flow in a set of community care facilities in order to help reduce the back log of patients remaining in acute care due to a lack of capacity in these facilities. Our model will provide the user with policy recommendations that address capacity allocation in different post-acute care alternatives over a multi-year time-horizon. In the model, patients differentiated by age and gender flow through the system with stochastic lengths of stay at each node (representing a facility type). We used historical data to classify patients. Proposed factors that influence the arrival and LOS parameters such as age and gender are tested on available data. We used Excel, Minitab and ARENA Input Analyzer to estimate the distribution of LOS, the arrival pattern and the age and gender distribution of new patients. We used Arena software for the simulation. The objective is to minimize patients waiting in the system subject to a constraint on the rate of expansion of facilities. Scenarios are informed by a previous queuing network model that provides the ideal capacity plan. The proposed method seeks to provide a means of determining the potential impact of various rates of expansion and changes in demand in order to more adequately plan future development.

by Xiaoqing Wang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 43-45). / Abstracts in English and Chinese. / Acknowledgments --- p.ii / Abstract --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- The General Architecture of the VoD System and the Related Issues --- p.4 / Chapter 2.1 --- A Brief Description of VoD System --- p.4 / Chapter 2.2 --- Why Video Streams in VoD Service are VBR in Nature? --- p.6 / Chapter 2.3 --- The Video Storage Media in the VoD Systems --- p.8 / Chapter 2.4 --- The Data Placement Scheme in the VoD System --- p.9 / Chapter 2.5 --- An Overview of Disk Scheduling in VoD System --- p.10 / Chapter 2.6 --- The Admission Control in VoD System --- p.12 / Chapter 3 --- Our Admission Control Algorithm for VoD System --- p.14 / Chapter 3.1 --- QoS Requirements We Choose --- p.14 / Chapter 3.2 --- System Model --- p.15 / Chapter 3.3 --- The Admission Control for the Storage Sub-system --- p.19 / Chapter 3.4 --- The Admission Control for Network Sub-system --- p.21 / Chapter 3.4.1 --- Preliminaries --- p.22 / Chapter 3.4.2 --- The Admission Control Algorithm for Network Sub-system --- p.27 / Chapter 4 --- Experiment --- p.33 / Chapter 5 --- Conclusion and Future Work --- p.41 / Chapter 5.1 --- Conclusion --- p.41 / Chapter 5.2 --- Future Work --- p.42

<p>In recent years, the accelerated deployment of renewable electricity generation resources, especially wind and photovoltaic (PV) solar, has added challenges to the operation and planning of the power grid. One of the challenges is that the variability of solar and wind power output may increase the variation of the load that must be followed by dispatchable resources and increase the ramping capacity needs. Moreover, the decision about the configuration of a PV solar generation systems has operational and economic implications because peak solar energy production does not always precisely occur when the wholesale electricity prices of the system are highest. Therefore, as the renewable capacity levels grow, it becomes increasingly important to examine the potential impacts on the system cost and portfolio of conventional generating units to respond to the intermittent nature of some renewable generation technologies. Three related analyses explored in this dissertation address some of the challenges of integrating utility-scale PV solar and wind projects into a power system using a case study for Indiana.</p> <p>The first analysis identifies the optimal azimuth and tilt angles of solar PV installations that alternatively maximize the annual electricity generation or the economic value of the resource. The economic implications of the configuration of solar PV installations within Indiana are estimated based on wholesale prices of electricity and simulated solar output for different combinations of angles and types of array installations. The results show that solar projects across the state would need to have azimuth angles within the 177 and 182 degrees range to obtain maximum annual energy and 180 to 190.5 degrees to maximize annual value, independently of their array types. Furthermore, southern and northwestern zones showed the highest impacts from using an optimal angle configuration of the solar installations. Nevertheless, on average, the benefits in annual electricity generated or economic value from their reconfiguration across the state are minor, amounting to less than one percent. </p> <p>The second analysis explores the effects of additional solar and wind power investments on the 2035 requirements for baseload and peaking generation capacity, the amount of energy supplied by various types of generation technologies and the costs of Indiana’s electric supply system. From a capacity planning and unit commitment/dispatch perspective, the results of this analysis indicated that with a portfolio that includes more solar and/or wind power generation, there would be need to add new peaking generation units. However, the total need for additional peaking resources declines as more renewables are added to the generation mix. Because Indiana still heavily relies on coal and other baseload resources to generate electricity, no new baseload capacity is required in the future. Generally, additions of PV solar and wind capacity amplify the variation in load net of renewable generation and create greater needs for ramping services from conventional units. However, results of the analysis show that the existing portfolio of conventional generation resources in Indiana would have sufficient operational flexibility to be able to accommodate ramping requirements even with PV solar and wind capacity penetration levels as high as 30% of total electricity generation. However, at those levels of renewables capacity there are a times during the year when the optimal operational strategy is to curtail solar and wind generation. From a technical perspective, the results indicated that larger thermal generating units are used more for load following and turned on and off (cycled) more frequently with the additional renewables than without them but mainly during days with low levels of demand and high levels of generation from renewable technologies. From the cost perspective, the results of the model support the idea that it would be cheaper in the long-term to invest in a combination of solar and wind generation resources than in solar generation resources alone. Moreover, the reductions in variable costs, driven by the zero variable cost added to the system by the additional solar and wind capacity, were not sufficient to outweigh the increases in capital costs regardless of the levels of capacity additions. </p> <p>For the third analysis, the proposed capacity expansion model was used to estimate the value of capacity of PV solar and PV solar in combination with wind capacity in terms of baseload/peaking resources from a deterministic system peak load reliability perspective and for various penetration levels of these resources. The capacity values of solar, which refer to the contribution of PV solar plants to reliably meeting the system peak demand, for all the wind capacity levels analyzed, fall as the amount of solar capacity increases. This is because as solar generation increases and closely coincides with the occurrence of the system peak load, there is a shift of the peak load net of renewable generation time to later afternoon hours, when solar installations begin to reduce their production, therefore decreasing their contribution to reliably meeting system peak demand. The calculated solar capacity values are between 2.7% and 67.3% of the corresponding solar nameplate capacity considering all zones and types of PV solar arrays in Indiana, and vary with the level of solar penetration. The range of values obtained are in line with the ones found in other studies using stochastic reliability-based methods.</p> <p>This dissertation contributes to the literature on the interaction between PV solar with other generation resources and to their economic, operational and policy implications. Furthermore, it provides another decision-making tool from a planning perspective for policymakers, utility companies and project developers.</p>

by Ma Yiguang. / "April 2000." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (p. 117-[124]). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Decisions on capacity are often treated separately from those of production and inventory. In most situations, capacity issues are longer-term, so capacity-related decisions are considered strategic and thus not part of supply planning. This research focuses on optimal supply planning with emphasis on variable capacity to meet uncertain demand. It also defines three levels of capacity change: operating hours, labor availability and production hardware availability. The work presented here deals with the fundamental decisions to determine capacity, production, and inventory to meet customer demand while optimizing revenue and costs over a planning horizon (typically the life of the product). With the Lagrangian technique for constrained optimization, it can be shown that the optimal supply capacity has upper and lower bounds. The optimal feedback policy prescribes increasing the supply capacity when at the beginning of the planning interval it is below the lower bound. Similarly, the supply capacity should be decreased to the upper bound when it is above the upper bound. This paper will present arguments for characterizing forecast evolution and information sharing in the supply chain to obtain a predictor-corrector approach to supply chain control. / Singapore-MIT Alliance (SMA)

Pricing and capacity allocation are two important decisions that a service provider needs to make to maximize service quality and profit. This thesis attempts to address the pricing and capacity planning problems in operations management from the following three aspects. We first study a capacity planning and short-term demand management problem faced by firms with industrial customers that are insensitive to price incentives when placing orders. Industrial customers usually have downstream commitments that make it too costly to instantaneously adjust their schedule in response to price changes. Rather, they can only react to prices set at some earlier time. We propose a hierarchical planning model where price decisions and capacity allocation decisions must be made at different points of times. Customers first sign a service contract specifying how capacity at different times will be priced. Then, when placing an order, they choose the service time that best meets their needs. We study how to price the capacity so that the customers behave in a way that is consistent with a targeted demand profile at the order period. We further study how to optimally allocate capacity. Our numerical computations show that the model improves the operational revenue substantially. Second, we explore how a profit maximizing firm is to locate a single facility on a general network, to set its capacity and to decide the price to charge for service. Stochastic demand is generated from nodes of the network. Customers demand is sensitive to both the price and the time they expect to spend on traveling and waiting. Considering the combined effect of location and price on the firm's profit while taking into account the demand elasticity, our model provides managerial insights about how the interactions of these decision variables impact the firm's profit. Third, we extend this single facility problem to a multiple facility problem. Customers have multiple choices for service. The firm maximizes its profit subject to customers' choice criteria. We propose a system optimization model where customers cooperate with the firm to choose the facility for service and a user equilibrium model where customers choose the facilities that provide the best utility to them. We investigate the properties of the optimal solutions. Heuristic algorithms are developed for the user equilibrium model. Our results show that capacity planning and location decisions are closely related to each other. When customers are highly sensitive to waiting time, separating capacity planning and location decisions could result in a highly suboptimal solution.

Pricing and capacity allocation are two important decisions that a service provider needs to make to maximize service quality and profit. This thesis attempts to address the pricing and capacity planning problems in operations management from the following three aspects. We first study a capacity planning and short-term demand management problem faced by firms with industrial customers that are insensitive to price incentives when placing orders. Industrial customers usually have downstream commitments that make it too costly to instantaneously adjust their schedule in response to price changes. Rather, they can only react to prices set at some earlier time. We propose a hierarchical planning model where price decisions and capacity allocation decisions must be made at different points of times. Customers first sign a service contract specifying how capacity at different times will be priced. Then, when placing an order, they choose the service time that best meets their needs. We study how to price the capacity so that the customers behave in a way that is consistent with a targeted demand profile at the order period. We further study how to optimally allocate capacity. Our numerical computations show that the model improves the operational revenue substantially. Second, we explore how a profit maximizing firm is to locate a single facility on a general network, to set its capacity and to decide the price to charge for service. Stochastic demand is generated from nodes of the network. Customers demand is sensitive to both the price and the time they expect to spend on traveling and waiting. Considering the combined effect of location and price on the firm's profit while taking into account the demand elasticity, our model provides managerial insights about how the interactions of these decision variables impact the firm's profit. Third, we extend this single facility problem to a multiple facility problem. Customers have multiple choices for service. The firm maximizes its profit subject to customers' choice criteria. We propose a system optimization model where customers cooperate with the firm to choose the facility for service and a user equilibrium model where customers choose the facilities that provide the best utility to them. We investigate the properties of the optimal solutions. Heuristic algorithms are developed for the user equilibrium model. Our results show that capacity planning and location decisions are closely related to each other. When customers are highly sensitive to waiting time, separating capacity planning and location decisions could result in a highly suboptimal solution.