Matching Items (5)

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Optimization of surgery delivery systems

Description

Optimization of surgical operations is a challenging managerial problem for surgical suite directors. This dissertation presents modeling and solution techniques for operating room (OR) planning and scheduling problems. First, several

Optimization of surgical operations is a challenging managerial problem for surgical suite directors. This dissertation presents modeling and solution techniques for operating room (OR) planning and scheduling problems. First, several sequencing and patient appointment time setting heuristics are proposed for scheduling an Outpatient Procedure Center. A discrete event simulation model is used to evaluate how scheduling heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared to current practice. Next, a bi-criteria Genetic Algorithm is used to determine if better solutions can be obtained for this single day scheduling problem. The efficacy of the bi-criteria Genetic Algorithm, when surgeries are allowed to be moved to other days, is investigated. Numerical experiments based on real data from a large health care provider are presented. The analysis provides insight into the best scheduling heuristics, and the tradeoff between patient and health care provider based criteria. Second, a multi-stage stochastic mixed integer programming formulation for the allocation of surgeries to ORs over a finite planning horizon is studied. The demand for surgery and surgical duration are random variables. The objective is to minimize two competing criteria: expected surgery cancellations and OR overtime. A decomposition method, Progressive Hedging, is implemented to find near optimal surgery plans. Finally, properties of the model are discussed and methods are proposed to improve the performance of the algorithm based on the special structure of the model. It is found simple rules can improve schedules used in practice. Sequencing surgeries from the longest to shortest mean duration causes high expected overtime, and should be avoided, while sequencing from the shortest to longest mean duration performed quite well in our experiments. Expending greater computational effort with more sophisticated optimization methods does not lead to substantial improvements. However, controlling daily procedure mix may achieve substantial improvements in performance. A novel stochastic programming model for a dynamic surgery planning problem is proposed in the dissertation. The efficacy of the progressive hedging algorithm is investigated. It is found there is a significant correlation between the performance of the algorithm and type and number of scenario bundles in a problem instance. The computational time spent to solve scenario subproblems is among the most significant factors that impact the performance of the algorithm. The quality of the solutions can be improved by detecting and preventing cyclical behaviors.

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Date Created
  • 2010

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Managing solar uncertainty in neighboring systems with stochastic unit commitment

Description

As renewable energy becomes more prevalent in transmission and distribution systems, it is vital to understand the uncertainty and variability that accompany these resources. Microgrids have the potential to mitigate

As renewable energy becomes more prevalent in transmission and distribution systems, it is vital to understand the uncertainty and variability that accompany these resources. Microgrids have the potential to mitigate the effects of resource uncertainty. With the ability to exist in either an islanded mode or maintain connections with the main-grid, a microgrid can increase reliability, defer T&D; infrastructure and effectively utilize demand response. This study presents a co-optimization framework for a microgrid with solar photovoltaic generation, emergency generation, and transmission switching. Today unit commitment models ensure reliability with deterministic criteria, which are either insufficient to ensure reliability or can degrade economic efficiency for a microgrid that uses a large penetration of variable renewable resources. A stochastic mixed integer linear program for day-ahead unit commitment is proposed to account for uncertainty inherent in PV generation. The model incorporates the ability to trade energy and ancillary services with the main-grid, including the designation of firm and non-firm imports, which captures the ability to allow for reserve sharing between the two systems. In order to manage the computational complexities, a Benders' decomposition approach is utilized. The commitment schedule was validated with solar scenario analysis, i.e., Monte-Carlo simulations are conducted to test the proposed dispatch solution. For this test case, there were few deviations to power imports, 0.007% of solar was curtailed, no load shedding occurred in the main-grid, and 1.70% load shedding occurred in the microgrid.

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Created

Date Created
  • 2013

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Performance Enhancement of Power System Operation and Planning through Advanced Advisory Mechanisms

Description

This research develops decision support mechanisms for power system operation and planning practices. Contemporary industry practices rely on deterministic approaches to approximate system conditions and handle growing uncertainties from renewable

This research develops decision support mechanisms for power system operation and planning practices. Contemporary industry practices rely on deterministic approaches to approximate system conditions and handle growing uncertainties from renewable resources. The primary purpose of this research is to identify soft spots of the contemporary industry practices and propose innovative algorithms, methodologies, and tools to improve economics and reliability in power systems.

First, this dissertation focuses on transmission thermal constraint relaxation practices. Most system operators employ constraint relaxation practices, which allow certain constraints to be relaxed for penalty prices, in their market models. A proper selection of penalty prices is imperative due to the influence that penalty prices have on generation scheduling and market settlements. However, penalty prices are primarily decided today based on stakeholder negotiations or system operator’s judgments. There is little to no methodology or engineered approach around the determination of these penalty prices. This work proposes new methods that determine the penalty prices for thermal constraint relaxations based on the impact overloading can have on the residual life of the line. This study evaluates the effectiveness of the proposed methods in the short-term operational planning and long-term transmission expansion planning studies.

The second part of this dissertation investigates an advanced methodology to handle uncertainties associated with high penetration of renewable resources, which poses new challenges to power system reliability and calls attention to include stochastic modeling within resource scheduling applications. However, the inclusion of stochastic modeling within mathematical programs has been a challenge due to computational complexities. Moreover, market design issues due to the stochastic market environment make it more challenging. Given the importance of reliable and affordable electric power, such a challenge to advance existing deterministic resource scheduling applications is critical. This ongoing and joint research attempts to overcome these hurdles by developing a stochastic look-ahead commitment tool, which is a stand-alone advisory tool. This dissertation contributes to the derivation of a mathematical formulation for the extensive form two-stage stochastic programming model, the utilization of Progressive Hedging decomposition algorithm, and the initial implementation of the Progressive Hedging subproblem along with various heuristic strategies to enhance the computational performance.

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  • 2017

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Development of Complementary Fresh-Food Systems Through the Exploration and Identification of Profit-Maximizing, Supply Chains

Description

One of the greatest 21st century challenges is meeting the needs of a growing world population expected to increase 35% by 2050 given projected trends in diets, consumption and income.

One of the greatest 21st century challenges is meeting the needs of a growing world population expected to increase 35% by 2050 given projected trends in diets, consumption and income. This in turn requires a 70-100% improvement on current production capability, even as the world is undergoing systemic climate pattern changes. This growth not only translates to higher demand for staple products, such as rice, wheat, and beans, but also creates demand for high-value products such as fresh fruits and vegetables (FVs), fueled by better economic conditions and a more health conscious consumer. In this case, it would seem that these trends would present opportunities for the economic development of environmentally well-suited regions to produce high-value products. Interestingly, many regions with production potential still exhibit a considerable gap between their current and ‘true’ maximum capability, especially in places where poverty is more common. Paradoxically, often high-value, horticultural products could be produced in these regions, if relatively small capital investments are made and proper marketing and distribution channels are created. The hypothesis is that small farmers within local agricultural systems are well positioned to take advantage of existing sustainable and profitable opportunities, specifically in high-value agricultural production. Unearthing these opportunities can entice investments in small farming development and help them enter the horticultural industry, thus expand the volume, variety and/or quality of products available for global consumption. In this dissertation, the objective is three-fold: (1) to demonstrate the hidden production potential that exist within local agricultural communities, (2) highlight the importance of supply chain modeling tools in the strategic design of local agricultural systems, and (3) demonstrate the application of optimization and machine learning techniques to strategize the implementation of protective agricultural technologies.

As part of this dissertation, a yield approximation method is developed and integrated with a mixed-integer program to estimate a region’s potential to produce non-perennial, vegetable items. This integration offers practical approximations that help decision-makers identify technologies needed to protect agricultural production, alter harvesting patterns to better match market behavior, and provide an analytical framework through which external investment entities can assess different production options.

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Date Created
  • 2017

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Production scheduling and system configuration for capacitated flow lines with application in the semiconductor backend process

Description

A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product

A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process is still very difficult due to the wide product mix, large number of parallel machines, product family related setups, machine-product qualification, and weekly demand consisting of thousands of lots. In this research, a novel mixed-integer-linear-programming (MILP) model is proposed for the batch production scheduling of a semiconductor back-end facility. In the MILP formulation, the manufacturing process is modeled as a flexible flow line with bottleneck stages, unrelated parallel machines, product family related sequence-independent setups, and product-machine qualification considerations. However, this MILP formulation is difficult to solve for real size problem instances. In a semiconductor back-end facility, production scheduling usually needs to be done every day while considering updated demand forecast for a medium term planning horizon. Due to the limitation on the solvable size of the MILP model, a deterministic scheduling system (DSS), consisting of an optimizer and a scheduler, is proposed to provide sub-optimal solutions in a short time for real size problem instances. The optimizer generates a tentative production plan. Then the scheduler sequences each lot on each individual machine according to the tentative production plan and scheduling rules. Customized factory rules and additional resource constraints are included in the DSS, such as preventive maintenance schedule, setup crew availability, and carrier limitations. Small problem instances are randomly generated to compare the performances of the MILP model and the deterministic scheduling system. Then experimental design is applied to understand the behavior of the DSS and identify the best configuration of the DSS under different demand scenarios. Product-machine qualification decisions have long-term and significant impact on production scheduling. A robust product-machine qualification matrix is critical for meeting demand when demand quantity or mix varies. In the second part of this research, a stochastic mixed integer programming model is proposed to balance the tradeoff between current machine qualification costs and future backorder costs with uncertain demand. The L-shaped method and acceleration techniques are proposed to solve the stochastic model. Computational results are provided to compare the performance of different solution methods.

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Date Created
  • 2011