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Gopal, KartikModeling and Optimization of Hospital Transportation System
Doctor of Philosophy, University of Akron, 2016, Mechanical Engineering
Health care cost has significantly risen in the United States in the past decade. It is important to keep the cost down as much as possible. Patient transportation, though menial is an important part of patient care. Various departments run on a schedule, and have a lineup of patients for whom a battery of tests need to be performed. The patients are transported from one department to another by the transportation department. The transportation department handles thousands of intra hospital patient transports. This makes it a challenging task for the transporters to transport the patient to various locations within the hospital in a timely manner. An efficient, safe and timely transport of patients within the hospital is critical to achieve certain standards, business and financial goals. Any delays in transporting patients can lead to a delay in performing certain procedure which in turn affects the cost, customer satisfaction and scorecards. Chapter 3 looks at the implementation of a computerized scheduling system to optimize staffing level for the transportation department. The tool utilizes historical data to look at demand level at half hour intervals. This data then combined with service level and patient waiting time is utilized to find the optimum number of transporters required using queuing theory. The optimum number of transporters generated is then optimized using mixed integer programming in generating an optimum schedule for the transportation department. This tool helps in optimizing staffing levels while keeping the required service levels and meeting patient transfer demands.

Committee:

Shengyong Wang (Committee Chair); Chen Ling (Committee Member); Kwek-Tze Tan (Committee Member); Ping Yi (Committee Member); Richard Einsporn (Committee Member)

Subjects:

Health Care; Operations Research; Transportation

Keywords:

operational research; Transportation; Health Care; Integer Programming; Queuing Theory; Simulation

Cha, Jin SeobObtaining information from stochastic Lanchester-type combat models /
Doctor of Philosophy, The Ohio State University, 1989, Graduate School

Committee:

Not Provided (Other)

Subjects:

Operations Research

Chae, Kyung ChulA reliability model with two measures of mission duration /
Doctor of Philosophy, The Ohio State University, 1984, Graduate School

Committee:

Not Provided (Other)

Subjects:

Operations Research

Keywords:

Reliability ;Variables ;Stochastic processes

Hottenstein, Kristi NA Qualitative Case Study on Human Subject Research Public Policy Implementation at One Council on Undergraduate Research Institution.
Doctor of Philosophy, University of Toledo, 2016, Higher Education
Regulations for research involving human subjects in higher education have long been a critical issue. Federal public policy for research involving human subjects impacts institutions of higher education by requiring all federally funded research to be passed by an IRB. Undergraduate research is no exception. Given the literature on the benefits of undergraduate research to students, faculty, and institutions, how human subject research public policy is being implemented at the undergraduate level was a significant gap in the literature. This qualitative single case study examined the human subject research policies and practices of a selective, Mid-western, Council on Undergraduate Research institution. The purpose of the study was to determine how this institution implemented human subject research public policy to benefit its students. This institution used a hybrid approach of public policy implementation that met federal requirements while capitalizing on the role local actors can play in the implementation process. This model resulted in a student friendly implementation emphasizing various learning outcomes and student mentoring. Although there is considerable research and public discussion on the negative aspects of IRBs, if approached in a manner that embraces student learning, the IRB experience can be an extremely beneficial aspect of the institution’s learning environment.

Committee:

David Meabon (Committee Chair)

Subjects:

Biomedical Research; Education; Education Policy; Educational Leadership; Educational Theory; Higher Education Administration; Operations Research; Organization Theory; Social Research

Keywords:

IRB; institutional review board; CUR; council on undergraduate research; undergraduate research; UR; public policy; implementation; human subject research; implementation theory; hybrid theories; student mentoring; benefits of undergraduate research

Xiao, ZhifuA Comparative Analysis of an Interior-point Method and a Sequential Quadratic Programming Method for the Markowitz Portfolio Management Problem
BA, Oberlin College, 2016, Mathematics
In this paper, I give a brief introduction of the general optimization problem as well as the convex optimization problem. The portfolio selection problem, as a typical type of convex optimization problem, can be easily solved in polynomial time. However, when the number of available stocks in the portfolio becomes large, there might be a significant difference in the running time of different polynomial-time solving methods. In this paper, I perform a comparative analysis of two different solving methods and discuss the characteristics and differences.

Committee:

Robert Bosch (Advisor)

Subjects:

Applied Mathematics; Industrial Engineering; Mathematics; Operations Research

Keywords:

optimization;interior-point method;portfolio optimization;convex optimization;sequential quadratic programming;

Naviroj, NatachaiA Warehouse Managed Inventory System for Multiple Retailers and Multiple Product
Doctor of Philosophy, Case Western Reserve University, 2016, EECS - System and Control Engineering
An inventory model for a large system consisting of multiple suppliers, one warehouse, multiple retailers, and multiple products was developed. The model and assumptions are based on the Mall Group’s supermarket division in Thailand and their warehouse project. Additional constraints on the system include vehicle capacity and vehicle fill rates. The objective is to determine the lowest cost shipping schedule that is easily implementable and serves as an operational guideline for warehouse managers. A twostep procedure which works in series was used to solve the warehouse-retailer and the supplier-warehouse level of the model. The result was tested on The Mall’s demand data and is shown to give improvement over the current system.

Committee:

Vira Chankong (Committee Chair); Kamlesh Mathur (Committee Member); Hong Mingguo (Committee Member); Prica Marija (Committee Member)

Subjects:

Operations Research; Systems Design

Scott, Nehemiah D.Antecedents and Outcomes of Ambidexterity in the Supply Chain: Theoretical Development and Empirical Validation
Doctor of Philosophy, University of Toledo, 2015, Manufacturing and Technology Management
As the degree of uncertainty and intensity within the complex business environment continues to increase, firms are forced to make timely changes to their product and process technologies. Such environments are often characterized by unpredictable demand patterns, rapid industry clockspeed, and heightened competitive intensity. As a result, firms face many difficulties in realizing sustained and superior performance, and are at risk of realizing decreases in market and financial performance. Firms can alleviate this risk by engaging in innovation practices that enable them to secure current profitability (efficiently serving existing customer segments) alongside future viability (adapting to meet the demand of new customer segments). Simultaneously serving existing and breakthrough product markets means that organizations must be able to balance paradoxical tensions in their innovation management capabilities. Ambidexterity is one such capability, in which the successful balance of exploration and exploitation promises to offer supernormal business performance. While the performance benefits of ambidexterity have been noted, research has not yet adequately sought to understand whether a firm’s decision to pursue ambidexterity is contingent on certain business environment factors. Likewise, research has also not yet explored the intangible resources a firm must build and/or acquire and manage after deciding to pursue ambidexterity. Additionally, because no single firm can adapt and survive without the same being achieved by its supply chain partners, ambidexterity in supply chain capabilities is vital. However, the literature base examining the importance of ambidexterity in the supply chain is scarce. Lastly, ambidexterity and the process for implementing it within the firm and across the supply chain remains misunderstood amongst practitioners. To fill the aforementioned research gaps, this study builds a strategic innovation-based and interdisciplinary theoretical framework of ambidexterity that links environmental antecedents, ambidextrous resources, ambidextrous supply chain capabilities, innovation outcomes and firm performance. The central purpose of this research is to explore and advance the scholarly understanding of how organizations within complex business environments can leverage their supply chains to successfully implement ambidexterity, and to empirically validate assertions concerning the antecedents and outcomes of ambidexterity within the supply chain. This research seeks to explore how characteristics of a complex environment influence a firm’s decision to invest in internal (ambidextrous firm resources) and external (ambidextrous alliance portfolio) resources. This study also examines the relationship between ambidextrous supply chain capabilities (ambidextrous supply chain collaboration and supply chain adaptability), innovation outcomes (innovation ambidexterity) and firm performance (market and financial performance). In fulfilling this purpose, this research utilizes and integrates literature from the strategy, organizational management, operations and supply chain management, and innovation management research streams. The hypotheses are based on practitioner and academic rationale, and the rich theoretical premises of ambidexterity theory, paradox theory, environmental determinism, strategic choice theory, contingency theory, resource-based view, and resource dependency theory. This study makes the following assertions: (1) ambidexterity is a higher-order paradox that is comprised of micro-paradoxes; (2) a way to achieve adaptable and endurable performance is by managing innovation through the ambidexterity paradox; (3) as the business environment becomes more complex, firms will tend to invest more in both internal and external ambidextrous resources; (4) ambidextrous supply chain collaboration and supply chain adaptability are essential capabilities that mediate the association between ambidextrous resources and innovation ambidexterity; (5) realization of innovation ambidexterity enables a firm to achieve greater financial and market performance relative to its competitors. A primary data collection methodology was employed in this study. Specifically, an online survey was used to facilitate the large-scale data collection process. Data from 215 respondents representing manufacturers across five industries was collected and analyzed. To test the proposed hypotheses, covariance-based structural equation modeling (CB-SEM) was used. The findings indicate that firms do not wait for external environment factors to control their pursuit of ambidexterity. Instead, it is the strategic choice of firm leaders that results in their proactive development of internal and external ambidextrous resources. Also, a firm’s internal ambidextrous resources such as absorptive capacity, ambidextrous leadership and organizational mindfulness are critical for (1) developing an ambidextrous alliance portfolio based on function, structure and attributes, and (2) increasing supply chain adaptability. The study also finds that ambidextrous alliance portfolio is a strong and direct antecedent of ambidextrous supply chain collaboration, while ambidextrous firm resources has a positive and significant indirect impact on ambidextrous supply chain collaboration. Furthermore, ambidextrous supply chain collaboration and supply chain adaptability are found to enhance a firm’s achievement of innovation ambidexterity. Lastly, innovation ambidexterity has a positive and significant association with firm performance (market and financial performance). This study makes multiple contributions. First, this study has progressed the existing conception of ambidexterity by infusing it with tenets of paradox theory. Secondly, this study employs an interdisciplinary framework of ambidexterity that has been empirically tested. Third, this is the first empirical study to develop ambidextrous firm resources, ambidextrous alliance portfolio and ambidextrous supply chain collaboration constructs and model relationships between them. Fourth, a scale has been developed to measure ambidextrous alliance portfolio. Additionally, this study provides findings that are both expected and counterintuitive. This study ends with a detailed discussion of the aforementioned contributions, research and managerial implications, research limitations and opportunities for future research.

Committee:

Dr. Paul Hong (Committee Co-Chair); Dr. Monideepa Tarafdar (Committee Co-Chair); Dr. Jenell Wittmer (Committee Member); Dr. Mark Gleim (Committee Member); Dr. Lakeesha Ransom (Committee Member)

Subjects:

Business Administration; Management; Operations Research; Organization Theory; Technology

Keywords:

organizational ambidexterity; exploration; exploitation; supply chain; innovation management; complex business environment; theoretical development; empirical validation

Goel, SaumyaDynamic Probabilistic Lot-Sizing with Service Level Constraints
Master of Science, The Ohio State University, 2011, Industrial and Systems Engineering
We consider inventory control problems with stochastic demand in which a specific service level must be met. We assume that demand and cost distributions over the planning horizon are finite, discrete and non-stationary. We formulate this problem as a chance-constrained program, whose deterministic equivalent is a large-scale mixed-integer program (MIP). We study the structure of the formulations and develop methods for solving them efficiently. We add mixing cuts to tighten these formulations and propose new valid inequalities. We also decompose these large-scale mixed integer programs using Benders decomposition technique and branch-and-price-and-cut method, both of which could incorporate mixing cuts to improve their performance.

Committee:

Simge Küçükyavuz (Advisor); Marc Posner (Committee Member); Suvrajeet Sen (Committee Member)

Subjects:

Industrial Engineering; Operations Research

Keywords:

Stochastic programming; mixed integer programming; large scale programming; production planning

Vijayakumar, BharathwajSCHEDULING SURGICAL CASES IN A CONSTRAINED ENVIRONMENT
Master of Science in Engineering (MSEgr), Wright State University, 2011, Industrial and Human Factors Engineering
This research examines a complex surgical case scheduling problem for a publicly-funded hospital in the Midwest United States. Publicly-funded hospitals are typically under tight budget constraints and these hospitals strive to maximize the utilization of their resources such as beds, staff, equipment, operating rooms, etc. These resources are relatively fixed for a publically funded hospital. A manual scheduling approach followed by this hospital does not guarantee optimal solutions and consequently has led to large variation in the utilization of resources. This real-world problem is described in this research as a multi-day, multi-resource, and patient-priority-based surgical case scheduling problem with the objective of maximizing the weighted sum of surgical case priorities. The surgical case scheduling problem herein is conceptualized as an unequal-sized multi-dimensional multi-bin dual bin-packing problem. A mixed integer programming model is proposed to generate implementable schedules. For each surgical case, the solution obtained will provide detail information about the start time and day of the surgery, the operating room to perform the surgery, and the surgeon's name. Resource availability, patient priorities, and surgical time of the surgeons are key features included in the model. However, the combinatorial nature of this problem limited the MIP model to solving only small problem instances. Consequently, an efficient First Fit Decreasing based heuristic is proposed and its performance is benchmarked against the MIP model. The benefit of pooling surgical cases over the commonly used First Come First Serve scheduling policy is also demonstrated. Results quantify the extent to which pooling can increase the number of high priority surgeries performed.

Committee:

Pratik Parikh, PhD (Advisor); Rosalyn Scott, MD (Committee Member); Jennie Gallimore, PhD (Committee Member)

Subjects:

Industrial Engineering; Mathematics; Operations Research; Surgery

Keywords:

surgical case scheduling; operations research; heuristics; mathematical model; mixed integer programming; first fit decrease

Newsom, Mi Kyong KimContinuous Improvement and Dynamic Capabilities
Doctor of Philosophy, The Ohio State University, 2009, Business Administration

The main objective of this dissertation is to study the role of continuous improvement as a mechanism to build dynamic capabilities. Through three related essays we address how continuous improvement projects are related to performance. The first essay illustrates a configuration research method, qualitative comparative analysis (QCA). Based on its descriptions in the literature, QCA appears to be an appropriate method for examining multiple paths to performance using set theory. The main benefit of QCA in contrast to traditional statistical methods is the assumption of complex causality and nonlinear relationships.

In the second essay we employ the lens of the problem solving to derive a list of learning activities related to continuous improvement. Further, we analyze how organizations that have deployed continuous improvement conduct projects leading to success. We use content analysis to code 111 projects from five organizations that have deployed continuous improvement programs. We investigate universal causes and complementary causes that lead to project success to examine the equifinality. The QCA analysis identified multiple configurations that achieved project success inferring that multiple paths lead to project success. The commonality of dynamic capability functionsin the configurations establishes that continuous improvement is a mechanism for building dynamic capabilities.

The third essay empirically addresses the question of how continuous improvement contributes to growth performance. Adapting existing scales for growth performance constructs, data on 78 improvement projects is collected and analyzed using qualitative comparative analysis. Dynamic capability functions and project success enables growth performance. These causes are always present when growth performance occurs but does not guarantee growth performance. In addition, we examine how improvement projects combine implementation and identification or formulation to achieve growth performance. Thus, the three essays provide insights of how continuous improvement builds dynamic capabilities and how improvement projects contribute to project success and growth performance.

Committee:

Peter Ward, D.B.A. (Committee Chair); Jay Anand, Ph.D. (Committee Member); Ken Boyer, Ph.D. (Committee Member); Randy Hodson, Ph.D. (Committee Member); Gopesh Anand, Ph.D. (Committee Member)

Subjects:

Management; Operations Research

Keywords:

continuous improvement; content analysis; qualitative comparative analysis; configuration research; dynamic capabilities

White, Denise L.Operational Planning and Scheduling in the Outpatient Clinic Environment
PhD, University of Cincinnati, 2010, Business Administration : Business Administration

Many researchers have explored the outpatient clinic environment. However, few have integrated demand management, resource policies, and process design. Through a series of three related research efforts, this dissertation investigates the operational influence of demand and capacity management while considering patient flow. The first research effort develops an integrated view of demand and capacity management decisions as variations in patient flow are modeled. The study examines the interactions between patient appointment policies and capacity allocation policies (i.e., the number of available exam rooms). The second effort evaluates the clinic response to queue disciplines (FCFS, SPT, and appointment time) and appointment scheduling policies as patient arrival times deviate from the schedule and patients fail to arrive for their appointment (no-shows). By evaluating various levels of arrival variability and no-shows, the robustness of policy decisions in the outpatient environment is assessed. The third study examines the appropriate deployment of labor resources that are able to substitute for the physician in an outpatient clinic. Two deployment methods are evaluated using measures of operations performance, patient waiting, and clinic profits.

The results of this research accentuate the value of integrated analysis of demand and capacity management decisions with a focus on patient flow. The development of appointment scheduling policies should consider variability of the physician service times and the patient flow through the system as both elements influence policy selection. When considering the effect of operations management decisions on both clinic and patient performance measures, this research demonstrates that the results of managerial decisions can and often do move in different directions. Some decisions require a trade-off between operations and the patient, while others benefit (or harm) both. This research demonstrates that excluding patient flow analysis from research efforts reduces the scope of activities that can be analyzed and creates opportunities for erroneous decisions.

Committee:

Craig Froehle, PhD (Committee Chair); Michael Magazine, PhD (Committee Member); David Kelton, PhD (Committee Member); Kenneth Klassen, PhD (Committee Member)

Subjects:

Operations Research

Keywords:

Scheduling;Capacity Management;Demand Management;Patient Flow;Healthcare

Chakravarthy, Arvindkumar RaviMODEL AND SOLUTION APPROACHES FOR THE EQUIPMENT SCHEDULING UNDER DISRUPTION PROBLEMS IN USPS MAIL PROCESSING AND DISTRIBUTION CENTERS
Doctor of Philosophy (PhD), Wright State University, 2008, Engineering PhD

This research addresses the equipment scheduling problem under disruptions in United States Postal Service mail processing and distribution centers. These facilities contain a large variety of equipment and employ a non-homogeneous workforce that work on shifts of various lengths and start times. The scheduling of equipment (the determination of the configuration and usage of equipment to match mail arrivals) and the scheduling of workforce (the determination of the optimal size and composition of the workforce, their days off / lunch assignments, and overtime usage) to meet processing service commitment with a constantly changing demand are some of the most challenging problems.

Over the years, there have been many research studies that focused on solution of the postal equipment and staff scheduling problems. A comprehensive review of these studies is conducted. In the most general sense, each of the equipment and staff schedule problems can be decomposed temporally so and hierarchical analytic approaches have been adopted. Along the time axis, these studies can be classified into strategic, tactical and operational levels.

This thesis focuses on the operational equipment scheduling problem or equipment scheduling under disruptions and addresses the adjustment of production plans and workforce schedules through the use of overtime and flexible employees in the face of disruptions such as demand fluctuation and absenteeism that happen on a daily basis and may significantly change demand and the size of workforce. This problem is modeled as a large-scale integer program, which contains equipment scheduling, shift scheduling and overtime management, and break assignment modules. Comprehensive experiments have been designed to investigate the effects of the use of overtime, the control of absenteeism, and the importance of integrating equipment and workforce scheduling simultaneously. The model integrates seamlessly with other research studies and provides the necessary tools to manage the resources in a facility on a routine basis.

To improve computational time, an efficient LP based decomposition algorithm has been developed. The algorithm uses linear programming solutions as target solutions to construct a local search process to examine neighboring integer solutions. The heuristic was first proposed for the equipment scheduling under disruptions and then extended to the staff scheduling problem where multiple diverse initial solutions were generated to cover the solution landscape. These heuristics were computational efficient and were able to quickly obtains high-quality feasible solutions and delivers final solutions on par with the state of the art branch and bound algorithm in the solution of integer programs.

Committee:

Xinhui Zhang, PhD (Advisor); George Polak, PhD (Committee Member); Yan Liu, PhD (Committee Member); James Moore, PhD (Committee Member); S. Narayanan, PhD, PE (Committee Member)

Subjects:

Management; Operations Research

Keywords:

Postal Operations; Equipment Scheduling; Workforce Scheduling; Overtime Management; Integer Programming

Ghosh, SuvankarEssays on Emerging Practitioner-Relevant Theories and Methods for the Valuation of Technology
PHD, Kent State University, 2009, College of Business Administration / Department of Management and Information Systems

This dissertation comprises of a set of three essays on emerging practitioner relevant theories and methods such as Real Options (RO) and Economic Value Added (EVA) for the valuation of investments in technology. The first essay develops an innovative approach for assessing practitioner relevance of academic research that is based on determining Granger causality between academic and practitioner interests in a given topic, as proxied by publication activity on that topic. The academic and practitioner interests are modeled as a two-component vector autoregressive (VAR) process and in addition to gauging Granger causality, which is done on stabilized components of the VAR model, I also utilize cointegration to evaluate the equilibrium relationship between the components of the VAR regardless of their stationarity. This model is tested on the two topics of EVA and RO.

The second essay develops an alternative to the Technology Acceptance Model (TAM) called the Methodology Adoption Decision Model (MADM) for the adoption of new methodology by a firm. Analogous to the TAM, the MADM is a parsimonious model which views the theoretical soundness and the practical applicability of a methodology as the key drivers of firm-level adoption of methodology. The theoretical soundness and practical applicability are proxied by the sentiments expressed in the academic and practitioner literatures on the methodology in question. The MADM is used to assess the comparative likelihood of adoption of EVA and RO based on a sentiment extraction experiment for determining the inclinations of the academic and practitioner communities towards EVA and RO.

The third essay applies RO to the context of investments by firms in XML-based enterprise integration (EI) technology. An interpretive hermeneutic approach is employed to develop a set of decision-making heuristics for the exercise of real options that optimize the RO value construct of Strategic Net Present Value (SNPV). This decision-making framework is characterized by decision-context uncertainty and firm-level capability with XML-based EI technology. The heuristics provide managerial prescription on preferred strategies for exercising real options such as whether the firm should deploy an Enterprise Services Bus (ESB), versus an EAI Suite, under given conditions of uncertainty and firm capability for building the enterprise integration infrastructure of the firm.

Committee:

Marvin Troutt, PhD (Committee Chair); Alan Brandyberry, PhD (Committee Member); Felix Offodile, PhD (Committee Member); John Thornton, PhD (Committee Member)

Subjects:

Finance; Information Systems; Operations Research

Keywords:

Real Options; Economic Value Added; EVA; Enterprise Integration; XML; Time Series Analysis; Granger Causality; Cointegration; Multivariate Analysis of Variance

Imaev, Aleksey A.Hierarchical Modeling of Manufacturing Systems Using Max-Plus Algebra
Doctor of Philosophy (PhD), Ohio University, 2009, Electrical Engineering (Engineering and Technology)

The dissertation presents a novel hierarchical block-diagram modeling framework for manufacturing systems. A block can be a single manufacturing operation, a single machine, a single part or a factory. Each block has three inputs and three outputs and is represented by a set of linear max-plus algebraic equations. A complex manufacturing system can be modeled as a network of basic manufacturing blocks. Routing of parts and resources through the block diagram graphically corresponds to machine-flow and resource-flow interconnection of blocks and is mathematically modeled by part-flow and machine-flow interconnection matrices, respectively. A formula for composing a network of manufacturing blocks into a single manufacturing block is derived. The model can be used for: (a) performance evaluation, (b) deadlock detection, (c) structural analysis, (d) scheduling, (e) design, and (f) control of manufacturing systems.

The dissertation develops an elegant analysis tool called a matrix signal flow graph (MSFG) over max-plus algebra (also called a synchronous MSFG) for these models. New topological methods for evaluating gains of synchronous MSFGs are presented. Synchronous MSFG provide a straightforward way to covert the graphical block-diagram representation of the system to the max-plus algebraic view.

The dissertation also shows that in the case of a permutation flow shop, an inverse Monge matrix represents the resulting algebraic equations for the system. The dissertation proves that the class of inverse Monge matrices is closed under max-plus algebraic multiplication, and provides an efficient algorithm for computing an eigenvector of an inverse Monge matrix. These properties allow for efficient computation of performance characteristics of permutation flow shops.

Committee:

Robert P. Judd, PhD (Advisor); Kenneth Cutright, PhD (Committee Member); William Kaufman, PhD (Committee Member); Douglas Lawrence, PhD (Committee Member); Dušan N. Šormaz, PhD (Committee Member); Constantinos Vassiliadis, PhD (Committee Member)

Subjects:

Computer Science; Electrical Engineering; Engineering; Industrial Engineering; Mathematics; Operations Research

Keywords:

Hierarchichal model; block diagram; max-plus algebra; synchronous matrix signal flow graph; discrete event system; inverse Monge matrix; manufacturing; performance evaluation; job shop scheduling; permutation flow shop; graph gain

Aka, MianJoint inventory/replacement policies
Doctor of Philosophy, Case Western Reserve University, 1993, Operations Research
Most replacement models assume that whenever a replacement is needed a spare is immediately available. This assumption implies that lead times are zero or that an inventory is maintained. Typically, the associated costs of managing inventory are ignored. This dissertation explores joint inventory replacement policies for a single unit and for multiple units operating in parallel. Such policies are particularly important when reorder lead times are significant and downtime costs large. Models within this field vary according to cost structures, stochastic processes generating failures, types of failures, permissible preventive actions and objective functions. In addition the status of the system may or may not be continually observable, and information on the failure parameters may or may not be known with certainty. In this work all our models have a common thread. First, the status of all units is continually updated and exact times to failure distributions are known. Second, information regarding the parameters of the failure distributions are known. Third, all units operate independently of each other. Fourth, the objective function in all cases is to minimize the long run average cost per unit of time. Three types of joint inventory/replacement policies are considered. The first two policies are concerned with managing a single operating unit. I n the first policy, an order is placed at a predetermined time s or when the unit fails whichever comes first. A preventive replacement is performed at time T. Failed units are of course replaced as soon as possible. The second policy permits more than one unit of inventory to be held. In particular when the inventory level falls to k, an order of size q units is placed. The third policy is concerned with managing n units working in parallel. To obtain tractable results, however, failure rates are assumed to be constant.

Committee:

Peter Ritchken (Advisor)

Subjects:

Operations Research

Keywords:

replacement models; inventory policies

Arunapuram, SundararajanVehicle routing and scheduling with full loads
Doctor of Philosophy, Case Western Reserve University, 1993, Operations Research
Truckload carriers are constantly faced with the problem of shipping truckloads of goods at minimal cost between pairs of cities or customers using a fleet of trucks located at one or more depots. While designing routes for the drivers, various real-world conditions must be considered. For instance, every truckload must be picked up between certain time windows only. Due to the presence of these time windows, a driver is forced to wait at a location if he arrives early for a pickup. In some situations the drivers are compensated at a certain hourly rate for waiting. The routes must also be designed in such a way that the Department of Transportation's (DOT) driving regulations are not violated. This problem appears in the literature as Vehicle Routing Problem with Full Loads, or in short, VRPFL. This dissertation presents an exact algorithm based on the column generation approach to solve the VRPFL. This algorithm takes into consideration the time windows and waiting costs. The layovers, which is one of DOT's requirements is not considered in the exact algorithm. However, a procedure to incorporate them using this algorithm has been developed and tested. The algorithm was coded and validated for accuracy. Various computational tests were performed and problems up to 100 loads were solved efficiently. A real-world p roblem was also tested successfully using this model. Finally several ideas for incorporating some real-world constraints are provided.

Committee:

Kamlesh Mathur (Advisor)

Subjects:

Operations Research

Keywords:

Vehicle routing scheduling full loads

Liang, HongyanThree Essays on Performance Evaluation in Operations and Supply Chain Management
PHD, Kent State University, 2017, College of Business Administration / Department of Management and Information Systems
In today’s globally competitive marketplace, organizations are challenged to increase their levels of customer service while under pressure to simultaneously reduce operating costs and the time to market of products and services. In meeting these challenges, organizations have adopted performance measurement systems to gauge current performance and to set benchmarks for improving future performance. As discussed in Neely et al. (1995), managerial success in improving performance is perquisite on having a formal performance measurement system that provides management with meaningful short term (day-to-day) as well as long term performance goals. Within the operations and supply chain management literatures the importance of integrating performance measurement systems into decision making has been addressed by many researchers (see for example, Ramaa et al., 2013; Martin & Patterson, 2009; Shepherd & Gunter, 2006; Gunasekaran et al. 2004). For effective performance measurement, formal quantitative models for performance measurement are needed (Suwignjo et al., 2000; Bititci et al., 2001). In this dissertation, we examine three different classes of performance evaluation models, which are used by decision makers in operations and supply chain management. The general forms of these three classes of models are: i) learning-based models for continuous improvement, ii) stochastic inventory models for shortages, and iii) cost-volume profit models for decision analysis. Despite a vast supporting literature for each class of model, there are adaptations of these models that can lead to further contributions that will be of interest to both the academic and practitioner communities. In the research of improving supply chain delivery performance, Guiffrida & Nagi (2006) developed a cost-based delivery performance model whereby improvement in delivery performance is achieved by reducing the variance of the delivery time distribution using a learning-based function. A limitation of Guiffrida & Nagi (2006) is the failure to include forgetting into the learning process. Given the discrete nature of the delivery process, learning can be lost during the time periods that accrue between deliveries. The first essay extends the research of Guiffrida & Nagi (2006) to include forgetting into the learning based approached for improving supply chain delivery performance. The literature on green and sustainable inventory management is quite limited and has mainly focused on the carbon footprint resulting with inventory management decisions (Bouchery et al. 2012). An examination of review papers on sustainable inventory models exposes the lack of integration of green and sustainable measures into stochastic inventory models. Of particular interest are stochastic inventory models with stockouts which examine the tradeoffs between backorders and lost sales. A limitation of these models is the failure to address environmental concerns in the stochastic inventory models with stockout decisions. The second essay integrates green and sustainable measures into stochastic inventory models which examine tradeoffs between backorders and lost sales. A review of stochastic cost-volume-profit (CVP) analysis models indicates that the inputs of the models as well as the resulting profit function are modeling using either the normal or the lognormal distribution. Using normal and lognormal distributions in stochastic CVP modeling represents a limitation to the general applicability of the models. In the third essay, we employ Mellin Transforms to expand and generalize the stochastic CVP model.

Committee:

Alfred Guiffrida (Committee Co-Chair); Butje Eddy Patuwo (Committee Co-Chair); Michael Y. Hu (Committee Member)

Subjects:

Business Administration; Operations Research

Keywords:

Supply chain delivery performance; learning forgetting curve model; stochastic inventory model; Cost-volume-profit analysis

Jiang, TianyuData-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes
Master of Science, The Ohio State University, 2017, Industrial and Systems Engineering
Cyber vulnerability can be exploited by cyber-attackers to achieve valuable information, alter or destroy a cyber-target. Finding a way to generate appropriate cyber vulnerability maintenance policies (a combination of maintenance actions) is crucial for cyber security administrators. The purpose of this thesis is to apply a data-driven Markov decision processes model to generate cyber vulnerability policies that minimize administrative costs, including maintenance action cost and incident risk cost, in the long term. Optimal policies aim if not to eliminate then at least to reduce the incident risk to an acceptable level. By exploiting the real-world data of Nessus scan reports and incident reports from the OSU, a host-based dataset is built to analyze the characteristics of hosts and develop host-based policies. After solving the MDP model, the optimal policies and related costs are presented in comparison with existing policy. The results show that, for hosts in management groups, the incident risk and action costs are significantly lower than for hosts with administrative privilege, and more advanced actions can be taken to protect the hosts from cyber-attacks as the result of the discounted action costs. The consequences of a successful intrusion into a critical server are more serious than for a normal host, therefore, more powerful actions are required for critical servers. For the remainder of hosts, applying only auto patching is recommended for most situations, especially for non-general-purpose hosts such as printers and routers.

Committee:

Theodore Allen (Advisor); Cathy Xia (Committee Member)

Subjects:

Operations Research

Keywords:

Cyber attackers, Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities; Markov Decision Processes

SUI, ZHENHUANHierarchical Text Topic Modeling with Applications in Social Media-Enabled Cyber Maintenance Decision Analysis and Quality Hypothesis Generation
Doctor of Philosophy, The Ohio State University, 2017, Industrial and Systems Engineering
Many decision problems are set in changing environments. For example, determining the optimal investment in cyber maintenance depends on whether there is evidence of an unusual vulnerability such as “Heartbleed” that is causing an especially high rate of incidents. This gives rise to the need for timely information to update decision models so that the optimal policies can be generated for each decision period. Social media provides a streaming source of relevant information, but that information needs to be efficiently transformed into numbers to enable the needed updates. This dissertation first explores the use of social media as an observation source for timely decision-making. To efficiently generate the observations for Bayesian updates, the dissertation proposes a novel computational method to fit an existing clustering model, called K-means Latent Dirichlet Allocation (KLDA). The method is illustrated using a cyber security problem related to changing maintenance policies during periods of elevated risk. Also, the dissertation studies four text corpora with 100 replications and show that KLDA is associated with significantly reduced computational times and more consistent model accuracy compared with collapsed Gibbs sampling. Because social media is becoming more popular, researchers have begun applying text analytics models and tools to extract information from these social media platforms. Many of the text analytics models are based on Latent Dirichlet Allocation (LDA). But these models are often poor estimators of topic proportions for emerging topics. Therefore, the second part of dissertation proposes a visual summarizing technique based on topic models, a point system, and Twitter feeds to support passive summarizing and sensemaking. The associated “importance score” point system is intended to mitigate the weakness of topic models. The proposed method is called TWitter Importance Score Topic (TWIST) summarizing method. TWIST employs the topic proportion outputs of tweets and assigns importance points to present trending topics. TWIST generates a chart showing the important and trending topics that are discussed over a given time period. The dissertation illustrates the methodology using two cyber-security field case study examples. Finally, the dissertation proposes a general framework to teach the engineers and practitioners how to work with text data. As an extension of Exploratory Data Analysis (EDA) in quality improvement problems, Exploratory Text Data Analysis (ETDA) implements text as the input data and the goal is to extract useful information from the text inputs for exploration of potential problems and causal effects. This part of the dissertation presents a practical framework for ETDA in the quality improvement projects with four major steps of ETDA: pre-processing text data, text data processing and display, salient feature identification, and salient feature interpretation. For this purpose, various case studies are presented alongside the major steps and tried to discuss these steps with various visualization techniques available in ETDA.

Committee:

Theodore Allen (Advisor); Steven MacEachern (Committee Member); Cathy Xia (Committee Member); Nena Couch (Other)

Subjects:

Finance; Industrial Engineering; Operations Research; Statistics; Systems Science

Keywords:

Natural Language Processing, NLP, Machine Learning, Bayesian Statistics, Hierarchical Text Topic Modeling, Text Analytics, Cyber Maintenance, Decision Analysis, Quality Hypothesis Generation, Latent Dirichlet Allocation, Financial Engineering

Zimmo, Ahmed T.A Methodology to Locate Transfer Hubs Considering a Maximum Driving Time
Master of Science (MS), Ohio University, 2017, Industrial and Systems Engineering (Engineering and Technology)
Transporting goods from origin to destination is a complex task, since there are multiple routes and methods that can be used. Transportation companies face the challenge of driver turnover, which leads to a shortage of drivers. One way to improve the quality of the drivers’ job is to allow them to be home by the end of the day. This can be achieved by using hubs and locating them with consideration of a maximum traveling time. In this research, a mathematical model has been developed to minimize the transportation cost when determining the optimal route to use and the optimal locations of hubs. Different values of the maximum traveling time were tested, and the results show decreasing the maximum traveling time leads to a decrease in the number of nights that drivers must spend away from home. Using hubs also reduces the trips made by empty trucks returning to their starting location, which decreases the total cost compared with a non-hub system.

Committee:

Dale Masel (Advisor)

Subjects:

Engineering; Industrial Engineering; Operations Research

Keywords:

hub locations; facility locations; logistic; supply chain

Gonsalvez, David J. A.On orbital allotments for geostationary satellites /
Doctor of Philosophy, The Ohio State University, 1986, Graduate School

Committee:

Not Provided (Other)

Subjects:

Operations Research

Keywords:

Geostationary satellites;Artificial satellites in telecommunication

Lu, LuApproximation procedures for some multi-item inventory systems /
Doctor of Philosophy, The Ohio State University, 1991, Graduate School

Committee:

Not Provided (Other)

Subjects:

Operations Research

Thatcher, Richard KManagement guidance for research climate /
Doctor of Philosophy, The Ohio State University, 1980, Graduate School

Committee:

Not Provided (Other)

Subjects:

Operations Research

Keywords:

Research--Management

Scheimberg, Haskell ReedA cybernetic model of certain aspects of Soviet behavior /
Doctor of Philosophy, The Ohio State University, 1980, Graduate School

Committee:

Not Provided (Other)

Subjects:

Operations Research

Keywords:

Soviet Union

Liu, YixianElectricity Capacity Investments and Cost Recovery with Renewables
Doctor of Philosophy, The Ohio State University, 2016, Industrial and Systems Engineering
Electricity demand growth, power plant retirements, and new technology advances make it necessary to expand current electricity generation and transmission capacity to balance electricity supply and demand. Nowadays investments are made by electric utilities and subject to regulatory approval. Therefore, it is important for policy-makers to understand the trade-offs among technology, cost, system reliability and environment protection and approve investments prudently. This dissertation proposes several interactive models to investigate electricity capacity investments and the associated policy and pricing issues. Starting with a weather forecasting model, important weather variables that drive the demand and supply of the electricity system are forecasted to provide inputs to other models. Then generation and transmission investment decisions are analyzed by a multi-stage stochastic optimization model. The model considers multiple electricity-generating technologies and future uncertainties, seeking optimal investment decisions for the present and the future. With investment decisions modeled, the last part of the dissertation analyzes electricity pricing and cost recovery for power plants under different environmental regulations.

Committee:

Ramteen Sioshansi (Committee Member); Antonio J. Conejo (Committee Member); Matthew Roberts (Committee Member)

Subjects:

Energy; Operations Research

Keywords:

Electricity Capacity Investment, Cost Recovery, Representative Days, Clustering, Stochastic Programming, Weather Forecasting, Energy Policy and Pricing

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