Doctor of Philosophy, The Ohio State University, 2010, Business Administration

This research attempts to extend the inventory theoretic approach in three stand-alone papers. The first chapter introduces the inventory theoretic approach and gives a brief overview of the remaining chapters. Chapter two develops an optimization model based on the inventory theoretic approach in an effort to aide managers in selecting the best carrier/mode for their product. Findings suggest that total logistics costs are minimized by selecting a faster mode of transportation as the value of the product and the coefficient of variation in demand increase. The model extends the existing state of the art in the inventory theoretic transportation selection literature by precluding the need for conducting multiple experiments among all available transportation options. Chapter three uses the inventory theoretic approach in a discrete event simulation in an effort to investigate the accuracy of the numerical approach in estimating total logistics costs and rank-ordering the best to worst carriers. Empirical data for this study are gathered from a company that uses a portfolio of carriers to ship product world-wide. Findings suggest that the numerical approach used in the inventory theoretic approach is robust for selecting the best carriers. In addition, carrier schedules were found to have an impact on which carrier provides the lowest total logistics cost. Finally, delays such as equipment shortages, ordering errors, and carrier overbooking were quantified. The results suggest that delays should be tracked by shippers, because an excessive number of delays by a carrier can impact the rank-ordering of carriers. Chapter four extends the inventory theoretic approach to the postponement literature stream. A review of the postponement literature found that transportation uncertainty is largely ignored, lacks examples of an (s, Q) inventory model, and generally ignores the cost of in-transit stock, which is considered here. The fourth chapter also explores the concept of p (open full item for complete abstract)

Committee: Martha Cooper PhD (Committee Chair); Keely Croxton PhD (Committee Member); Alan Johnson PhD (Committee Member); John Saldanha PhD (Committee Member); Walter Zinn PhD (Committee Member) Subjects: Business Community; Business Education; Industrial Engineering; Operations Research; Transportation

Master of Science, The Ohio State University, 1960, Graduate School

Committee: Not Provided (Other) Subjects:

Doctor of Management, Case Western Reserve University, 2022, Weatherhead School of Management

Direct-to-consumer (D2C) entrepreneurial activity is disrupting both traditional retailers and manufacturers by directly linking consumers with products and services, disintermediating elements of the traditional value chain. We conducted a mixed methods, three-phase study to better understand how supply chain strategy choices influence D2C firms' competitive advantage. In phase one we conducted in-depth interviews with leaders of small to medium sized D2C businesses. One key finding: only a subset of D2C companies are willing to invest in differentiated supply chain capabilities. Many viewed supply chain as a utility function. This informed phase two, a quantitative analysis (scenario-based experiment) of the degree to which logistics quality drives customer satisfaction, finding a strong positive effect. This motivated phase three, a quantitative modeling exercise determining the relationship between distribution network size (number of DCs) and proximity to customers, an indicator of lead time performance. This analysis suggests that most D2C firms, with just one or two DCs are significantly disadvantaged, with deleterious consequences for customer service.

Committee: Kalle Lyytinen Ph.D. (Advisor); Manoj Malhotra Ph.D. (Advisor); Yunmei Wang Ph.D. (Advisor) Subjects: Entrepreneurship; Operations Research

Doctor of Philosophy, The Ohio State University, 2022, Business Administration

Supply chains have grown increasingly global and interconnected over the past decades. Technological advancements have enabled organizations to pursue improved performance while simultaneously reducing costs; all in pursuit of increased profits. However, this same supply chain globalization has amplified companies' risk exposures. These risks lead to increased supply chain disruptions- interruptions in the flow of materials and products between entities in supply chains. At the same time, high-impact disasters- from various causes- are increasing in frequency. For example, evidence from the COVID-19 pandemic has revealed how discrete events cause ripples felt across industries and geographies. Therefore, it is essential to improve our understanding of resilience- an organization's ability to withstand, recover and grow from disruption- not only to further scientific knowledge, but also to provide actionable guidance to our practitioner community. This dissertation examines how organizations respond to disruptions and communicate those disruptions- to partner organizations and public stakeholders. In Chapter 2, we utilize semi-structured interviews to explore how an organization's capabilities of agility, adaptability, and alignment (AAA capabilities) connect to and enable its resilience. We find empirical evidence suggesting that alignment between and within organizations drives the ability to respond to short-term disruptions (agility) and make long-term adjustments (adaptability). In Chapter 3, we utilize an online scenario-based experiment to assess whether the timing and accuracy of shared disruption-related information influence the relationship between a buyer and supplier. Much of the literature on information sharing during supply chain disruptions assumes that the information transmitted is accurate, an assumption unlikely to be true given the uncertainty surrounding supply chain disruptions. We show that when a supplier shares information quickly, regard (open full item for complete abstract)

Committee: Keely Croxton (Committee Co-Chair); A. Michael Knemeyer (Committee Co-Chair); Xiang Wan (Committee Member); Mikaella Polyviou (Committee Member) Subjects: Business Administration

Doctor of Philosophy (PhD), Ohio University, 2022, Industrial and Systems Engineering (Engineering and Technology)

In this study, I aim to solve the problem of locating mobile parcel lockers considering traffic and customer preferred modes of transportation on urban road networks. The considered road network is a real road network of Rockford, Illinois. I used the publicly available real traffic data for a subset of the considered network to estimate the traffic for the entire road network, and then used the estimated traffic data to estimate travel time for the entire road network. Travel time data, customer preferred modes of transportation, and parcel weights were incorporated in the total cost in the objective function. To solve the problem of locating mobile parcel lockers, I used a heuristic clustering algorithm and multi-threaded Dijkstra's algorithm. In addition, I compared the results of the heuristic algorithm to the exact solution of a mathematical model. Next, I compared the performance of the mobile parcel lockers to stationary parcel lockers based on a set of customer-convenience metrics in four scenarios. The results show a promising improvement in customer convenience when mobile parcel lockers are used for last-mile delivery. In addition, the considered scenarios were also compared in different controlled settings, namely, traffic, and density of demand points. Finally, the effect of the number of stops on mobile parcel lockers convenience was studied.

Committee: Dale Masel (Advisor); Saeed Ghanbartehrani (Committee Member); William Young (Committee Member); Vardges Melkonian (Committee Member); Felipe Aros-Vera (Committee Member) Subjects: Engineering; Industrial Engineering

Doctor of Philosophy, The Ohio State University, 2022, Food, Agricultural and Biological Engineering

Corn (Zea mays L.) grain and stover are the primary feedstock for first- and second-generation biofuel production in the U.S. due to their abundant availability. While corn grain-based biofuel has already reached the mandated target, cellulosic biofuel production from corn stover has been a struggle. Harvest and post-harvest logistics of corn stover is one of the major challenges faced by the cellulosic biofuel producers. Existing corn stover harvest and post-harvest logistics system uses a multi-pass approach to bale the biomass in the field, collects biomass with high soil contamination, and produce bales with low bulk density that doesn't fulfill the payload capacity of the trucks used for transportation. The novel whole-plant (WP) corn harvest and post-harvest logistics system addresses all of these challenges by cutting the corn plant at the ear level and baling the corn plant with its ear intact corn ear in a single-pass, which also reduces the harvest operations and soil contamination of the biomass. In addition, with the inclusion of corn ear in the bale, the bulk density of the bales produced is increased, which improves the productivity of the post-harvest logistical operations including handling, storage and transportation. Thus, the main objectives of this dissertation were to evaluate the harvest timing and physico-chemical properties of WP corn in season, evaluate the storage characteristics of WP corn when densified into small and large rectangular bales, and assess the techno-economic feasibility and life-cycle energy use and greenhouse gas (GHG) emissions associated with the WP corn logistics system. Corn grain and stover harvest timing is mainly dictated by their moisture, where corn grain harvest is followed by stover harvest. Since they are harvested at the same time in this system, it was important to determine the suitable harvest timing for WP corn that would minimize storage losses. Moisture and dry matter of the corn plant were tracked we (open full item for complete abstract)

Committee: Ajay Shah (Advisor) Subjects: Agricultural Engineering

Doctor of Philosophy, University of Toledo, 2021, Manufacturing and Technology Management

Commercial drones are among the technologies that are capable of changing business drastically. This dissertation aims to study the adoption and operations of commercial drones to provide methodologies and insights for scholars and practitioners into different stages of the commercial drones' lifecycle. The first essay reviews the current body of knowledge towards a broader scope. It provides a comprehensive literature review targeting published articles in top business journals. By using the Technology-Organization-Environment (TOE) framework and Diffusion Innovation theory and synthesizing the reviewed literature, the first essay identifies the influential factors on the rate of commercial drones' adoption. The second essay focuses on the post-adoption operational issues of commercial drones in a deterministic environment in which the customers' locations are known and fixed. It analytically studies the logistics planning of hybrid commercial drones and aims to maximize the sustainability of the logistics by evaluating social, environmental, economic costs. Two mathematical models are proposed to maximize the sustainability of logistics planning in a deterministic environment. The third essay incorporates the uncertainty in the customers' locations and proposes a two-step methodology to address such an uncertainty. The proposed methodology consists of scenario-based mathematical modeling to help with strategic decision-making, and a simulation-optimization approach to aid in tactical and operational decision-making. Overall, this dissertation studies the rapid adoption and sustainable operations of commercial drones in deterministic and stochastic environments. It identifies influential factors in the rate of adoption of commercial drones, develops two mathematical models to aid in sustainable logistics planning of commercial drones considering the deterministic customer locations, and proposes a two-step analytical method to assist in strategic, tactical, (open full item for complete abstract)

Committee: Yue Zhang PhD (Committee Co-Chair); Marcelo Alvarado-Vargas PhD (Committee Co-Chair); Xinghao Yan PhD (Committee Member); Matthew Franchetti PhD (Committee Member) Subjects: Management; Operations Research; Sustainability; Technology

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

Transportation and logistics of valuable items (e.g., banknotes and coins, credit cards, securities, gold, jewelry, safes, and special pharmaceutical items like vaccines) are generally exposed to the risk of robbery and armored car heist. An inherent problem in designing supply chain networks for the transportation of valuables is to mitigate the risk while decrease the total cost by appropriate configuration of facility location and routing. In this research, the vehicle routing problem for the transportation of valuables is developed in several directions. From the modeling side, the risk mitigation approach that encompasses both the amount/number of valuables carried by a vehicle and the traveltime of a route is considered to minimize the risk of robbery and generate safe routes. The utilized risk function relaxes a pre-defined parameter of the risk threshold. Furthermore, the risk-minimizing problem is developed by incorporating various real-world characteristics and constraints into the model. At the expense of adding solution complexity, such formulation offers a more realistic model applicable to real situations.The model is extended from different angles, such as developing risk modeling by considering the vulnerability component and diversified arrival time using the multigraph network, and demand forecasting using an extensive evaluation of statistical and machine learning model. From the methodology side, multiple exact and metaheuristic methodologies are utilized and evaluated on several small to medium-sized instances and a case study. The augmented 휖-constraint 2 is used to solve the small instances of the problem. While solvable on small-sized instances, it poses computational challenges when applied to a large-scale rich problem. Therefore, five metaheuristics, namely, non-dominated sorting genetic algorithms (NSGAII and NSGAIII), strength of Pareto evolutionary algorithm 2 (SPEA2), indicator-based evolutionary algorithm (IBEA), and archived multi (open full item for complete abstract)

Committee: Gary Weckman (Advisor); Tao Yuan (Committee Member); Saeed Ghanbartehrani (Committee Member); Ashly Metcalf (Committee Member); Ehsan Ardjmand (Committee Member) Subjects: Banking; Industrial Engineering; Management; Statistics; Transportation; Transportation Planning

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, University of Toledo, 2021, Manufacturing and Technology Management

Drones are a hot topic. While once exclusively a military tool, in recent years, drones have found a number of civilian applications. They are in the news, shown in movies, given as toys for children, used by hobbyists for shooting videos, and the government for surveillance. They are becoming part of our culture. This has created a great deal of anticipation and speculation as to how far this new technology could be pushed. The most obvious possibility would be for package delivery. The race to commercialize this new customer service channel is advancing quickly outside of the U.S. Pizza delivery by drone is being tested in the U.K. Textbooks are being delivered in Sydney, Australia. Medical supplies are rushed to emergency sites across Europe through drones. Yet, in the United States, concerns over the risks of accidents have lead regulators to all but prohibit autonomous drones. However, firms such as Amazon, DHL, and UPS are eagerly waiting for these regulations to be lifted, and it is only a matter of time. Meanwhile, many firms wait on the sidelines and consider how using drones might benefit them. Some will wait until the technology has matured, but others want to grasp a potential first-mover advantage. However, there are many questions that have to be answered. The use of drones is surrounded by a good deal of hype and speculation. A firm considering drone delivery would want to know what drones can do. They would also want to determine how drones might be integrated into their existing operations - or what new operational models they might adopt. Potential cost savings would have to be explored. Finally, market research would be needed to determine if a potential customer would even want the service. This doctoral dissertation seeks to address these questions in separate chapters. The first chapter explores how drones are currently being used, the legal hurdles to overcome, development efforts by large domestic firms, and an over (open full item for complete abstract)

Committee: Yue Zhang Dr. (Committee Chair); Xinghao Yan Dr. (Committee Member); Christine Fox Dr. (Committee Member); Wittmer Jenell Dr. (Committee Member) Subjects: Business Administration; Business Costs; Management; Marketing

Master of Science (MS), Bowling Green State University, 2020, Applied Statistics (ASOR)

When studying relevant research in the emerging field of green supply chain optimization, it is currently difficult to find modeling that provides a realistic, multi-nodal distribution process concerned with large-scale freight manufacturing and transport. Seen even more rarely is a combined objective that is focused on minimizing both cost and environmental impact. This thesis aims to fill that gap, as it follows products sold by a major US retailer from its manufacturing in Southern China to arrival, processing, and distribution in the United States. A multi-objective, linear optimization model is used. Decision variables include manufacturer, transportation method, port selection, distribution center selection, and timing. The paper utilizes published methodology for carbon emissions calculation and carbon market prices, and provides sensitivity analyses to extrapolate findings into real-world solutions. Many global organizations and nations have taken steps aimed at becoming carbon neutral. As the transportation sector makes up nearly one-third of all greenhouse gas emissions, it is important to explore options related to freight that reduce these emissions prior to any government regulations being enforced. The formulated linear program provides a breadth of decisions that contribute to moving products and satisfying demand. These decisions not only include location sourcing and time period selection, but also carbon-focused aspects of freight movement, including vehicle choice. The model formulated in this thesis allows interested corporations to customize inputs and evaluate the model using organization-specific parameters, enabling a holistic view of current operations from both an economic and environmentally conscious angle.

Committee: Ibrahim Capar Dr. (Advisor); Zubair Mohamed Dr. (Committee Member); Christopher Rump Dr. (Committee Member); William Sawaya Dr. (Committee Member) Subjects: Industrial Engineering; Operations Research; Statistics

Master of Science, The Ohio State University, 2020, Mechanical Engineering

With the on-going electrification and data-intelligence trends in logistics industries, enabled by the advances in powertrain electrification, and connected and autonomous vehicle technologies, the traditional ways vehicles are designed by engineering experience and sales data are to be updated with a design for operation notion that relies intensively on operational data collection and large scale simulations. In this work, this design for operation notion is revisited with a specific combination of optimization and control techniques that promises accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pick-up and delivery truck that is limited to a given driving mission. A Gaussian Process (GP) based statistical learning approach is used to refine the search for the most accurate, optimal designs. Five hybrid powertrain architectures are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. A cross-architecture performance and cost comparison is performed and the selected architecture is developed further in the form of a forward simulator with a dedicated ECMS controller. In the end, a traffic-in-the-loop simulation is performed by integrating the selected powertrain architecture with a SUMO traffic simulator to evaluate the performance of the developed controller against varying driving conditions.

Committee: Giorgio Rizzoni (Advisor); Qadeer Ahmed (Committee Member) Subjects: Automotive Engineering; Engineering; Mechanical Engineering; Sustainability; Systems Design; Transportation

Master of Sciences (Engineering), Case Western Reserve University, 2020, EMC - Aerospace Engineering

To fuel transportation systems, there exists an opportunity to reduce launch costs by an order of magnitude by launching the necessary propellant on existing commercial launch vehicles (CLVs). This research analyzed various architectures that deliver propellant to near-rectilinear halo orbit (NRHO). An automated tool was developed and utilized to rapidly trade architectures. First-order results indicate many feasible architecture options exist for commercially launched propellant. Active cryogenic fluid management (CFM) tankers were shown to have negligible improvements over passive tankers that rendezvous with a reusable (active CFM) bus. CLV long-duration upper stages deliver more propellant than ZBO tankers if, on average, tanker inert mass is greater than 51% of the CLV usable payload. “Topping-off” long-duration upper stages with propellant in LEO permits a mean of 13 metric tons per launch delivered to NRHO. Reusable tugs were shown to increase delivered propellant per launch by 180% on average.

Committee: Paul Barnhart PhD (Committee Chair); Sunniva Collins PhD (Committee Member); Yasuhiro Kamotani PhD (Committee Member) Subjects: Aerospace Engineering

Doctor of Philosophy, The Ohio State University, 2019, Food, Agricultural and Biological Engineering

Lactic acid is a platform chemical that can be utilized for a variety of applications in food, medical, packaging, and cosmetics industries. The global market for lactic acid is expected to grow substantially from $2.1 billion in 2016 to $9.8 billion in 2025. Lactic acid can be produced from both petroleum and biobased sources; however, the demand for lactic acid produced from biobased feedstock sources is expected to increase due to growing consumer awareness of the need for sustainability, biodegradability, recyclability and green packaging in different industries. Biobased lactic acid can be produced from starch-rich feedstocks, such as corn grain, and lignocellulosic feedstocks, such as corn stover and miscanthus. Lactic acid produced from biobased feedstocks can yield lactic acid isomers suitable for applications in the food and medical industries. Despite huge growth potential, the techno-economics and environmental impacts of different pathways for biobased lactic acid production are not well studied. Thus, the objective of this dissertation was to analyze the techno-economics and life cycle environmental impacts of producing lactic acid from biobased feedstocks, including corn grain, corn stover and miscanthus. These feedstocks were selected based on their current availability and future potential for biorefinery use in the U.S. The study evaluated different logistics and conversion pathways by developing comprehensive techno-economic and life cycle assessment models. The models incorporated data collected from the literature related to biomass production, harvest, post-harvest logistics and conversion to lactic acid. Three grain logistics scenarios were defined based on intermediate grain storage at 1) farm storage structure, 2) country elevator, and 3) both, before the grain delivery to the biorefinery. The 90% central range (CR) for the grain logistics cost, including costs for grain harvest, hauling, and storage, was $36-50 per metric ton (t). The grai (open full item for complete abstract)

Committee: Ajay Shah (Advisor); Erdal Ozkan (Committee Member); Harold M. Keener (Committee Member); Katrina Cornish (Committee Member) Subjects: Agricultural Engineering; Engineering

Doctor of Philosophy, The Ohio State University, 2018, Comparative Studies

The National Mall in Washington, D.C. has become an essential site of national meaning making through complex processes of design, regulation, and human use. Through a combination of ethnographic, ethnohistoric, and theoretical approaches, my dissertation explores the production of public gatherings on the National Mall. The first part of this project is an ethnographic study of a year on the Mall in 2016. It traces the increasingly routinized current uses of the Mall for purposes of leisure, learning, memorialization, and protest. The second part of this project investigates the use of the Mall as a site of popular protest across the twentieth century. With case histories of the 1932 Bonus March, the 1963 March on Washington, and the 1968 Poor People's Campaign, I offer new frameworks and terminology to describe how vulnerable populations have sought out this space to call attention to their causes. In both sections, I focus on the interaction among infrastructures, logistics, and repertoires: the amenities and facilities available for public use, the organizational strategies and customary knowledge of actors appropriating Mall space, and the performance genres through which publics and institutions create and interpret actions on the Mall. For the scholar of social movements and civic participation, attention to these “backstage” worlds can reveal crucial information about the priorities, capabilities, and constraints of all parties involved, from the vulnerable to the powerful. For citizens at large, understanding the material requirements of gathering in this symbolically potent space offers a key to the broader challenge of creating effective infrastructures to support civic participation and wellbeing.

Committee: Dorothy Noyes Ph.D. (Advisor); Jason Baird Jackson Ph.D. (Committee Member); Katherine Borland Ph.D. (Committee Member); Isaac Weiner Ph.D. (Committee Member); Shilarna Stokes Ph.D. (Committee Member) Subjects: Comparative; Folklore

Doctor of Philosophy, The Ohio State University, 2018, Business Administration

Buy-online-ship-from-store (BOSS) is a relatively new and increasingly popular omnichannel fulfillment strategy for retailers. Shipping from stores allows retailers the capability to offer in-store inventory to online customers. The combined inventory available in stores and their locations closer to the customer make them attractive for online direct-to-consumer order fulfillment. In three essays, we identify statistical-economies-of-scale benefits from this pooling technique across asymmetrical retail channels. Working with a retailer, we detect ship-from-store contributed store stockouts. We also examine consumer behavior in response to stockouts according to the channel of intended purchase. In the first study we use chance constrained analytical models and Monte Carlo simulation to demonstrate the distinctly different inventory pooling behavior caused network-wide when implementing buy-online-ship-from-store. The asymmetrical availability of inventory to online customers versus in-store customers allows for unique to omnichannel captive inventories within stores. When online demand is strong enough, all safety stocks can be redirected to online customers. Otherwise, safety stocks can remain stagnant in the stores. In our second study, we partner with an apparel retailer and distinguish the counter-phenomenon observed in essay one. We observe stores unevenly stocking out when contributing to online demand fulfillment. Using panel data from the 2016 holiday season, we utilize store-based fixed-effects models to show that assigning stores to fulfill local online demand where both channels have strong sales leads to uneven store stockouts across the network. By utilizing consumer experiments, our third study demonstrates that in-store customers are more likely to remain with the retailer and substitute for out-of-stock products. Online customers are more likely to leave the retailer to seek their exact desired product elsewhere. Mitigating strategies for the ret (open full item for complete abstract)

Committee: August Knemeyer (Committee Co-Chair); Keely Croxton (Committee Co-Chair); Elliot Bendoly (Committee Member); Xiang Wan (Committee Member) Subjects: Business Administration; Business Costs; Management; Operations Research

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

Traditional facility location models used in industry are not adequate to decide facility locations for points of distribution in the context of post-disaster humanitarian logistics (PD-HL). They focus on facility placement and logistics costs, but deprivation costs, or cost of human suffering resulting for the deprivation of critical supplies are often omitted in objective function formulation. This thesis develops a mathematical model to determine facility location for points of distribution (PODs) and optimal delivery strategy for serving demand points. This model considers a combined set covering facility location and point-to-point inventory allocation model with the objective of minimizing social cost; including facility placement, logistics, and deprivation costs. The model is used to analyze the best facility location and delivery strategies in the context of natural disasters. This research highlights the importance of considering the temporal component of the system and resulting deprivation costs when making facility location decisions. This model contributes to the current research by integrating deprivation cost functions into the objective function of facility location decisions in PD-HL. It also allows for demand nodes to be served from multiple PODs. This is significant in conditions when one POD may not have the inventory to serve all demand points that it has been assigned. In practice, this model can help decision makers determine optimal facility locations for PODs as well as approximate the level of service that must be supplied at each demand point to reduce the suffering or survivors at each location. The model is solved using both a mixed integer nonlinear programming model (MINLP) and a heuristic approach developed to address some challenges that nonlinear solution presents.

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

Doctor of Philosophy, The Ohio State University, 1986, Graduate School

Committee: Not Provided (Other) Subjects: Business Administration

Doctor of Philosophy, The Ohio State University, 1986, Graduate School

Committee: Not Provided (Other) Subjects: Business Administration

Doctor of Philosophy, The Ohio State University, 1985, Graduate School

Committee: Not Provided (Other) Subjects: Business Administration