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

Doctor of Philosophy, The Ohio State University, 2024, Industrial and Systems Engineering

Energy storage is widely used to respond to the uncertain balance of electricity supply and demand and prepare for the contingency. Among many purposes of energy storage, this dissertation will focus on arbitrage trade, peak load shift, and frequency regulation. For the first part, a two-stage stochastic programming model is introduced to schedule energy storage devices and maximize arbitrage profits for the storage operator. In addition, the model considers adjustments depending on the uncertain price of the real-time electricity market when the decision in the day-ahead market is made. Then, value of stochastic solution is computed to see effect of the stochastic programming. Furthermore, several interesting cases are observed and illustrated, such as simultaneous charging and discharging. These are considered as an sub-optimal solution in general, but this occurs in specific conditions. Second, when storage is used for peak load shift, it improves resource adequacy of the power systems by contribution of the power from energy storage. In this chapter, a non-performance penalty is imposed to ensure that energy storage operators reserve energy for such shortages. A stochastic dynamic programming model is used to obtain optimal decision policy for the storage device. Using this model, case studies are conducted for the two different systems. System load of these systems are peaked in the summer and winter, so these are analyzed and compared. In the third part, energy storage capacity value and expected profits are estimated when it provides energy, capacity, and frequency regulation services. To estimate capacity value, three steps approach is adopted. First, discretized stochastic dynamic programming is used to obtain decisions policies for the discretized states. These decision policies are used to get actual decisions by solving mixed-integer optimization in a rolling-horizon fashion. Then, capacity value of energy storage is estimated using simulation. A case (open full item for complete abstract)

Committee: Chen Chen (Advisor); Ramteen Sioshansi (Committee Member); Antonio Conejo (Committee Member); Matthew Pratola (Committee Member) Subjects: Energy; Industrial Engineering; Operations Research

Doctor of Philosophy, The Ohio State University, 2024, Electrical and Computer Engineering

This dissertation focuses on the coordinated operation and expansion planning of power and freshwater systems in regions experiencing freshwater shortages. Our work is motivated by the important challenges posed by the increasing installation of desalination plants, which rely almost exclusively on electricity to produce freshwater, and link power and freshwater systems. We propose models and solution techniques to address these challenges, namely, comprehending the coordinated operation of power and freshwater systems, investigating such coordination in the case of high renewable penetration, and studying the expansion planning and operation of fully renewable power and freshwater systems. Each of these challenges is comprehensively analyzed in separate chapters of this dissertation. The first challenge pertains to coordinating the operation of power and freshwater systems. We propose a model that integrates the dispatch of the freshwater system with the network-constrained scheduling of the thermal units of the power system, where the freshwater electric loads are incorporated into the supply-balance constraints of the power system. This model is mixed-integer nonlinear due to the nonlinear constraints describing the operation of the freshwater system. To achieve tractability, we employ a piecewise linearization technique to approximate nonlinear single-variable constraints and a triangular linearization to approximate nonlinear two-variable constraints. We conclude that a coordinated operation of the power and freshwater systems yields lower operation costs for both systems as compared to an uncoordinated operation. The second challenge pertains to the integration of weather-dependent renewable units and its impact on the coordinated operation of power and freshwater systems. We propose a model that integrates the dispatch of the freshwater system with the network-constrained scheduling of the thermal units of the power system, and includes a (open full item for complete abstract)

Committee: Antonio Conejo (Advisor); Mahesh Illindala (Committee Member); Stephanie Stockar (Committee Member); Rebecca Haidt (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Ohio University, 2021, Industrial and Systems Engineering (Engineering and Technology)

The first section of this research develops a mathematical model to determine vehicle routing in the context of humanitarian logistics denominated Social Cost Vehicle Routing Problem. The objective function of the model minimizes social cost which incorporates private and deprivation cost. Private costs include logistics, procurement, and transportation cost. Deprivation cost account for survivors' suffering due to the lack of access to critical supplies. Due to the NP-hard nature of the problem, a hybrid metaheuristic algorithm with a novel local search is developed. The algorithm uses Tabu Search (TS), Simulated Annealing (SA), and Variable Neighborhood Search (VNS) in a combined manner that SA is embedded in TS and VNS implements randomized number of local searches. The model is applied to the case study of water distribution in Puerto Rico; similar challenge faced after Hurricanes Irma and Maria in 2017. This research develops a strategy for water distribution in post disasters. Numerical experiments indicate the efficiency of the algorithm to provide optimal or near-optimal solutions in reasonable execution times that make the methodology/solution procedure viable for operational implementation. Sensitivity analysis scenarios evaluate the robustness of the model as water rationing changes. The second section of this research develops a two-stage stochastic programming model applicable for Post-Disaster Humanitarian Logistics (PD-HL). The model solves the Social Cost Vehicle Routing Problem incorporating uncertainty in travel times. The first stage concerns with determining vehicle routings prior to the realization of travel time. The main decision in the second stage is travel time which affects the arrival time of the vehicle to the affected population. The objective of the model minimizes social cost which includes logistics cost and deprivation cost. Considering the NP hard nature of the problem, TS-SA-VNS is applied. The case study of water distribution (open full item for complete abstract)

Committee: Felipe Aros-Vera (Advisor) Subjects: Industrial Engineering

Doctor of Philosophy (PhD), Ohio University, 2020, Mechanical and Systems Engineering (Engineering and Technology)

In the world of manufacturing, different strategies could be followed to handle the rapidly changing consumer needs and desires in order to remain competitive, and enable their manufacturing systems to respond quickly to new demand and handle the fluctuation in demand. Since the cellular manufacturing system is an important part of the manufacturing system, a new design method, multi-stage cellular manufacturing system design, is proposed in this dissertation. Three performance measures, total number of machines, total machine cost, and %actual risk level, are utilized to evaluate the performance of the proposed design. Considering the uncertainty in the product demand and processing times, two types of the multi-stage cellular manufacturing system are studied. The first type is a deterministic multi-stage cellular manufacturing system. This type of system is propounded to improve the flexibility of the system where the possibility of adding new machines, mini-cells, and stages is existent. Based on the similarity coefficient type used to group the operations into a stage, two design methods are introduced. The first design method is the multi-stage cellular manufacturing system based on the similarity among machines. A new mathematical model is developed to group the machines into stages by maximizing the similarity coefficient among machines. The second design method is the multi-stage cellular manufacturing system based on the similarity among products. A novel heuristic algorithm and mathematical model are proposed to assign machines to stages based on the newly similarity coefficient “cumulative similarity coefficient among products”. In the two design methods, two mini-cell types, regular and flexible flowshop mini-cells, are used in a stage considering the type of products and the possibility to duplicate the machine type. Additionally, the number of stages and product families is un-predetermined and predetermined to minimize the total number of machines. Th (open full item for complete abstract)

Committee: Gürsel A. Süer Dr. (Advisor); Tao Yuan Dr. (Committee Member); Dusan Sormaz Dr. (Committee Member); M. Khurrum S. Bhutta Dr. (Committee Member); Ana L. Rosado Feger Dr. (Committee Member) Subjects: Design; Engineering; Industrial Engineering

Doctor of Philosophy, The Ohio State University, 2019, Electrical and Computer Engineering

In this dissertation, we study some problems concerning the integration of renewable energy. First, we design and analyze a two-stage mechanism for selling a stochastic resource where penalties are paid at the second stage to compensate for shortfalls in the amounts contracted in the first stage. We consider a setting in which a renewable generator, whose generation is a random variable with some probability distribution, is selling its generation to a number of flexible buyers through a two-stage electricity market. The system operator runs an auction in which the seller bids its probability distribution and the buyers submit their valuation functions. The system operator inputs these bids to a stochastic program to decide on an efficient allocation that maximizes social welfare, and then uses Myerson's payment to price electricity. The closed-form solution we obtain for the allocation and payment rules of our mechanism allows us to analyze the mechanism and prove its incentive compatibility in dominant strategies, individual rationality, and budget balancedness. We also investigate the sensitivity of the mechanism to the seller's manipulation of its distribution. We show through running some simulations that increasing the number of buyers in the system reduces the gains the seller obtains from manipulation. In this work, we also analyze the effects of limited ramping capacity on the price of electricity. We study the problem of dynamic economic dispatch with ramping constraints and stochastic demand, which can ramp up or ramp down significantly due to the presence renewable generation. In conclusion, we show the value of investing in ramping capacity and highlight the possibility of exercising market powers by generators monopolizing it. Our study of the evolution of prices in this setting leads to the understanding that limited ramping of some generators enhances the market power of the flexible ones.

Committee: Abhishek Gupta (Advisor); Kevin Passino (Committee Member); Andrea Serrani (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2018, Electrical and Computer Engineering

A well-planned electric transmission network is essential for attaining an effective electricity market and the reliable operation of the associated power system. In this dissertation, we address the transmission expansion planning (TEP) problem. The goal of the thesis work is to develop models and algorithms to help system planners to identify optimal investments in the transmission network. First, we propose a candidate-line selection algorithm based on a set of systematic rules to generate an appropriate candidate-line set for TEP studies. The expertise of system planners and the characteristics of a network are both considered for candidate-line selection. Second, we develop an adaptive robust stochastic optimization model for TEP problems that specifically differentiates long- and short-term uncertainties. The long-term uncertainty pertains to year-to-year changes including the peak demand and available generating capacity of the system during the planning target year. Then, within the target year, the short-term uncertainty pertains to the production of weather-dependent renewable capacity and the load. Next, we expand the adaptive robust stochastic optimization model to consider the coordinated investment in transmission and storage facilities. Such model provides an effective tool to identify the best trade-off between these two types of facilities. Finally, we conclude by providing conclusions, contributions and suggestions for future work.

Committee: Antonio Conejo (Advisor); Ramteen Sioshansi (Committee Member); Mahesh Illindala (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2018, Industrial and Systems Engineering

Many decision-making problems arising in science, engineering, and business involve uncertainties. One way to address these problems is to use stochastic optimization. A crucial task when building stochastic optimization models is quantifying a probability distribution to represent the uncertainty. Most often, partial information about the uncertainty is available through a series of historical data. In such circumstances, classical stochastic optimization models rely on approximating the underlying probability distribution. However, in many real-world applications, the underlying probability distribution cannot be accurately determined, even when historical data are available. This distributional ambiguity might lead to highly suboptimal decisions. An alternative approach to handle such an issue is to use distributionally robust stochastic optimization (DRSO for short), which assumes the underlying probability distribution is unknown but lies in an ambiguity set of distributions. Many existing studies on DRSO focus on how to construct the ambiguity set and how to transform the resulting DRSO into equivalent (well-studied) models such as mixed-integer programming and semidefinite programming. This dissertation, however, addresses more fundamental questions, in a different manner than the literature. An overarching question that motivates most of this dissertation is which scenarios/uncertainties are critical to a stochastic optimization problem? A major contribution of this dissertation is a precise mathematical definition of what is meant by a critical scenario and investigation on how to identify them for DRSO. As has never been done before for DRSO (to the best of our knowledge), we introduce the notion of effective and ineffective scenarios for DRSO. This dissertation considers DRSOs for which the ambiguity set contains all probability distributions that are not far---in the sense of the so-called total variation distance---from a (open full item for complete abstract)

Committee: Guzin Bayraksan PhD (Advisor); Antonio Conejo PhD (Committee Member); David Sivakoff PhD (Committee Member) Subjects: Industrial Engineering; Operations Research

Doctor of Philosophy, The Ohio State University, 2017, Electrical and Computer Engineering

The power system is provisioned to evolve into a smart grid that is greener, safer, and more efficient. DC microgrid, a new form of distribution system and an emerging electrical network on ships/airplanes/electronic devices, has risen to prominence as an important building block of the future grid and many other applications. With proper operation and coordination, DC microgrids can exploit the flexibility in generation as well as consumption units, which have been standing unresponsive for decades, to approach a more robust and efficient grid such that every component in a power grid can reach its full potential to vibrantly participate in grid services. This dissertation presents systematic approaches to solve DC microgrid control and optimization problems that are usually marked by challenges like uncertainty, nonlinearity, tractability, and structural constraint issues. First, it is well known that when a DC microgrid is operated in island mode, the stability critical power balance is shadowed by uncertain and volatile generation and consumption. We propose a robust stability framework containing a set of sufficient conditions to provide provable stability guarantee for such systems. We then further investigate into robust control design to improve the performance of the system. In view of the physical communication structures that commonly exist in a microgrid, decentralized/distributed controllers are recognized to be more applicable in practice for their limited reliance on information transmissions. Nevertheless, with the communication structural constraints, the decentralized/distributed control design problem is NP-hard in general, and an ill-designed controller may as well render an originally operative system unstable. We propose an algorithm to design a structurally constrained controller in such a way that it can guarantee a design direction with provable improving performance. Second, for DC microgrids that are in grid-connected mode, the (open full item for complete abstract)

Committee: Wei Zhang (Advisor); Giorgio Rizzoni (Advisor); Antonio Conejo (Committee Member); Mahesh Illindala (Committee Member); Andrej Rotter (Other) Subjects: Electrical Engineering; Energy; Engineering; Operations Research

Master of Science (MS), Ohio University, 2017, Industrial and Systems Engineering (Engineering and Technology)

The main purpose of this thesis is to design a global supply chain network for a pharmaceutical company located in Puerto Rico, which manufactures blood sugar strips products. As a design aspect of supply chain, layered cellular manufacturing systems consist of dedicated, shared and remainder cells are considered. One of the main differences between classical cellular manufacturing systems and layered cellular manufacturing systems is, in layered cellular design, some cells may needed to be utilized by various parts of product families. Depending on the required demand and similarity in essential processes or manufacturing characteristics for each product family, products are grouped together and form a product family. If the product family assigned to one cell and just one product family utilizes that cell, the cell is a dedicated cell. Shared cells and remainder cells are employed by, two families and three or more families, respectively. In the first part of this study, a new heuristic layered-cellular manufacturing design approach is proposed and later in the second part, two mathematical models are proposed. The first one is with the objective of minimizing number of cells and cost of opening cells, and the second one is maximizing Net Present Value considering budget limitations for the whole manufacturing system. In the first step, the required number of cells are determined for a product family to meet an acceptable demand coverage (MADC) percentage. It is assumed that customer demand follows normal distribution with the established parameters mean (µ) and standard deviation (s). An attempt is made to increase the utilization of each cell by combining multiple families (thus creating shared and remainder cells) to increase the utilization of each cell as long as it is economically acceptable. As demand coverage increases, revenue also increases. However, this also increases operational costs. The expected profit is calculated based on the expected cell ut (open full item for complete abstract)

Committee: Gursel Suer (Advisor); Tao Yuan (Committee Member); Diana Schwerha (Committee Member); Ashley Metcalf (Committee Member) Subjects: Industrial Engineering

Doctor of Philosophy, The Ohio State University, 2016, Industrial and Systems Engineering

Electric vehicles hold great promise in improving transportation energy consumption efficiency. However, battery-related "range-anxiety" hampers large-scale EV adoption. To relieve range anxiety and accelerate wider EV adoption, two issues should be addressed. The first is how to develop and optimize the layout of a network of public EV charging stations to keep pace with increasing EV charging demand. Second, the strains of added EV charging loads on the electricity distribution infrastructure should be managed. This dissertation will address these two issues through new optimization models and simulations. Specifically, we formulate a stochastic flow-capturing charging station location model to solve the first issue and obtain an optimal charging station network layout. For the second issue, stochastic control models are created to schedule EV charging loads. This model assumes some flexibility to defer EV charging demands, to minimize distribution infrastructure investment and degradation costs. We expand this model to allow the charging station to participate ancillary service markets, either by modulating EV charging or by using distributed resources. The outcome of this dissertation will help EV infrastructure planners develop an adequate charging station network and facilitate wider EV adoption. It will also help system operators and energy planners better understand the challenges of vehicle-grid integration and prepare appropriate strategies to address these issues.

Committee: Ramteen Sioshansi (Advisor); Guzin Bayraksan (Committee Member); Lixin Ye (Committee Member) Subjects: Energy; Operations Research

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

Master of Sciences, Case Western Reserve University, 2016, EECS - Electrical Engineering

The networked microgrids, or microgrid community (MGC) have been recognized as a promising future electrical infrastructure, for its operational and economic benefits along with unique control and challenges. Built as a practical example of IEEE Standard 1547.4, the MGC in this study contains three microgrids interconnected through the medium voltage (MV) feeder system. To study the static and dynamic performance of the system, a detailed electromagnetic model was constructed in PSCAD software. Typical scenarios are simulated in both grid connected and islanded modes to demonstrate the operational feasibility of MGC in responding to secondary control signals, and the effectiveness of primary controls in facilitating energy transaction and mitigating fluctuations in voltage and frequency during network disturbances. Also to study the energy management of the MGC, a two-stage stochastic mixed integer programming model was built in the AIMMS modeling environment. Optimal operational decisions are solved with uncertainties in load, renewable generation and bulk grid electricity price.

Committee: Mingguo Hong (Advisor); Kenneth Loparo (Committee Member); Marija Prica (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy (PhD), Ohio University, 2016, Mechanical and Systems Engineering (Engineering and Technology)

Global supply chain decisions, such as facility location, manufacturing system design, resource allocation, and distribution center location are long-term strategic decisions in nature and involve many uncertainties. Traditionally, a hierarchical approach is used design supply chain networks and manufacturing systems. First, the location of the facilities are determined, and then the manufacturing systems are designed at the selected locations. In this dissertation, a multi-stage supply chain network model is developed where locations of the plants and inner manufacturing system design are determined simultaneously for labor-intensive manufacturing companies. This dissertation aims to develop a decision making framework to integrate manufacturing systems and supply chain network design decisions considering optimal operator assignment and layered cellular manufacturing in mind. The industry studied is fashion jewelry manufacturing where labor cost is one of the major cost factors. Hence, optimizing the number of workers required for each operation, cell, and plant is critical for the cost efficiency of the entire supply chain. The optimal number of operators are determined for each manufacturing process, and then the optimal cell sizes are found for each manpower level using a heuristic procedure. The optimal number of manufacturing cells required to cover the uncertain demand is determined with mathematical modeling, and the designed layered cellular manufacturing systems for manufacturing stages are evaluated using Arena simulation models. The results of these models and methods are used as inputs while finding the optimal locations of the plants and allocating the optimal number of cells, workers, and machines for each selected plant. Different supply chain design alternatives considering various factors such as the shortest lead times, minimum capacity allocations, and multiple shifts are also studied.

Committee: Gursel A. Suer Ph.D. (Advisor) Subjects: Industrial Engineering; Operations Research

Master of Science in Engineering (MSEgr), Wright State University, 2008, Industrial and Human Factors Engineering

Production planning plays a vital role in the management of manufacturingfacilities. The problem is to determine the production loading plan consisting of the quantity of production and the workforce level - to fulfill a future demand. Although the deterministic version of the problem has been widely studied in the literature, the stochastic production planning problem has not. The application of production planning models could be limited if the stochastic nature of the problem, for example, uncertainty in future demand, is not addressed. This study addresses such a stochastic production planning problem under uncertain demand and its application in an enclosure manufacturing facility. The thesis first addresses the forecast of the demand where seasonal fluctuation is present. A decomposition model is utilized in the forecast and compared with other forecasting methods. Although forecast models could be used to improve the accuracy of forecast, error and uncertainty still exists. To deal with this uncertainty, a two stage stochastic scenario based production planning model is developed to minimize the total cost consisting of production cost, labor cost, inventory cost and overtime cost under uncertain demand. The model is solved with data from a local manufacturing facility and the results are compared with various deterministic production models to show the effectiveness of the developed stochastic model. Parametric analysis are performed to derive managerial insights related to issues such as overtime usage and inventory holding cost and the proper selection of scenarios under pessimist, neutral and optimist forecasts. An extension of the stochastic model, i.e., a robust model is also solved in an effort to minimize changes in the solutions under various scenarios. The stochastic production planning model has been implemented in the manufacturing facility, provided guidance for material acquisition and production plans and has dramatically increased the company' (open full item for complete abstract)

Committee: Xinhui Zhang PhD (Advisor); Frank Ciarallo PhD (Committee Member); Yan Liu PhD (Committee Member) Subjects: Industrial Engineering

PhD, University of Cincinnati, 2005, Business Administration : Quantitative Analysis

This dissertation consists of three essays that address issues in production and inventory management. The first essay focuses on inventory management. We study a fixed-reorder-interval, order-up-to (R, nT) inventory replenishment policy in a two-stage serial system with stochastic demand at the lower stage. We develop a simulation based optimization procedure to estimate the long-run average cost and optimal parameter values. The numerical results show that the (R, nT) policy is, on average, 4.4% (5.8%) more expensive than the continuous review (r, nQ) policy (lower bounds). The cost difference is much smaller when the setup cost at the upstream stage and the demand rate are larger. The (R, nT) costs are relatively insensitive to the choice of reorder intervals, T, provided the best corresponding order-up-to level, R, is selected. The second essay deals with production scheduling. We consider the computationally-hard, re-entrant flow, cyclic scheduling problem considered by Graves et al. (1983) and Roundy (1992). We present two problem formulations to minimize job flow time (work-in-process), given a target cycle length (throughput). We describe an efficient optimization method and a new ImproveAlignment (IA) heuristic. Numerical experiments indicate that proposed optimization method was significantly faster than CPLEX-8.0 and solved 40% more test instances to optimality within the specified run time and memory limits. The proposed IA heuristic quickly produced solutions which were, on average, (i) 22% better than those from the Graves' et al. heuristic and (ii) within 14% of the optimal. The third essay focuses on resource planning. We examine a single end-product, discrete-time inventory replenishment problem in a material requirements planning (MRP) environment with demand uncertainty and supply capacity limits on replenishment orders. We develop a simulation-based optimization approach and two novel heuristics. We also evaluate the traditional MRP and safety st (open full item for complete abstract)

Committee: Uday Rao (Advisor) Subjects: Business Administration, Management

PHD, Kent State University, 2013, College of Arts and Sciences / Department of Mathematical Sciences

We study the problem of pricing swing options, a class of multiple early exercise options that are traded in energy market, particularly in the electricity and natural gas markets. These contracts permit the option holder to periodically exercise the right to trade a variable amount of energy with a counterparty, subject to local volumetric constraints. In addition, the total amount of energy traded from settlement to expiration with the counterparty is restricted by a global volumetric constraint. Violation of this global volumetric constraint is allowed but would lead to penalty settled at expiration. The pricing problem is formulated as a stochastic optimal control problem in discrete time and state space. We present a stochastic dynamic programming algorithm which is based on piecewise linear concave approximation of value functions. This algorithm yields the value of the swing option under the assumption that the optimal exercise policy is applied by the option holder. We present a proof of an almost sure convergence that the algorithm generates the optimal exercise strategy as the number of iterations approaches to infinity. Finally, we provide a numerical example for pricing a natural gas swing call option.

Committee: Oana Mocioalca (Advisor) Subjects: Applied Mathematics

Doctor of Philosophy (PhD), Ohio University, 2012, Mechanical and Systems Engineering (Engineering and Technology)

Cellular manufacturing has been an important phenomenon in manufacturing in recent decades. Tremendous amount of work has been done regarding issues such as cell formation, cell loading and job scheduling. However, majority of literature lacks consideration of uncertainty in the problem definition phase, thus methodology. In this dissertation, the impact of uncertainty of demand, processing times and capacity requirements on a cellular manufacturing system (CMS) performance are addressed and stochastic optimization approaches are developed and applied to ten case problems from industrial companies and cellular manufacturing literature. This dissertation consists of mainly three phases, namely: stochastic CMS design, stochastic CMS control and the integrated modeling and analysis of CMS design and CMS control. Capacitated cell formation under the impact of uncertain demand and processing times is defined as the stochastic CMS design problem. On the other hand, cell loading, job sequencing and manpower allocation considering probabilistic demand and processing times are the main issues addressed in the stochastic CMS control phase. Finally, the relationship between stochastic CMS design and stochastic CMS control comprises the "integration" phase. Nonlinear stochastic programming models are developed to optimize each phase and simulation models are also built to validate the results of mathematical optimization and assess manufacturing system performance. To deal with larger problems, as one of the widely used metaheuristic optimization techniques, Genetic Algorithms (GA) is utilized; a GA model is developed and compared with stochastic programming model by using simulation modeling and statistical analysis. Results indicated that stochastic programming can assist with a better decision making on CMS design and control due to its capability of capturing probabilistic nature of problems. In all cases, the proposed stochastic optimization approaches outperformed the con (open full item for complete abstract)

Committee: Gursel Suer (Advisor); Dale Masel (Committee Member); Diana Schwerha (Committee Member); Ana L. Rosado Feger (Committee Member); M. Khurrum S. Bhutta (Committee Member) Subjects: Industrial Engineering; Management; Operations Research; Statistics; Systems Design