Doctor of Philosophy (PhD), Ohio University, 2017, Mechanical and Systems Engineering (Engineering and Technology)

Demand uncertainty has been an important obstacle in production systems and supply chains in recent decades. This uncertainty in demand has stimulated many researchers and practitioners to find new ways--such as combining Make to Stock (MTS) and Make to Order (MTO) manufacturing systems--to handle the uncertainty problem. In this research, applying the hybrid MTS/MTO system is developed in three different manufacturing frameworks. The first framework considers a production line, which produces semi-finished products based on a Make To Stock (MTS) strategy until a specific process is finished, and operates based on a Make To Order (MTO) strategy after this process. Two scenarios are studied in the first model: 1- In the first Scenario, the production line applies the MTO strategy after the OPP, which leads to an idleness cost when there is no order in the system. 2- In the second Scenario, the production line either applies the MTO strategy or the MTS strategy after the OPP, based on the presence of an order for semi-finished products in the line. This second scenario comprises the holding cost of completed products on MTS strategy, but does not have an idle cost. The second framework considers the demand uncertainties in two consecutive echelons of a supply chain--unlike most of the field research--which has only focused on the final customer's demand uncertainty. In order to decrease the operating costs of a manufacturer, a model is proposed to use hybrid manufacturing in two different levels of a supply chain with two echelons of manufacturers. The output of the presented model is the quantity of semi-finished products ordered by the decoupling point upstream manufacturer. The third problem studies a multi-product, two-echelon supply chain within a newsvendor framework, in which semi-finished products are produced by a supplier and customized according to specific customer orders. The focus of this paper is to investigate a situation where the manufacture (open full item for complete abstract)

Committee: Gary Weckman (Advisor); William Young (Committee Member); Dale Masel (Committee Member); Benjamin Sperry (Committee Member); Yong Wang (Committee Member) Subjects: Business Costs; Industrial Engineering; Management; Marketing

Doctor of Philosophy, The Ohio State University, 2006, Statistics

Relapsing remitting multiple sclerosis (RRMS) is a chronic and autoimmune disease where the disease states alternate between the relapse and remission. Magnetic resonance imaging (MRI) is widely used to monitor the pathological progression of this disease. The longitudinal T1-weighted Gadolinium-enhancing MRI lesion count sequences provide information on the onset and sojourn time of the lesion enhancement. We construct biologically interpretable queueing models for the longitudinal data of these lesion counts that describe the natural evolution of the lesions. The infinite-server queue with Poisson arrival process and exponential service (M/M/∞) is proposed for this purpose. The rate of the Poisson arrival process can also be allowed to be governed by a two-state hidden Markov chain. We describe the likelihood function for each model based on appropriate assumptions and fit these models to data from 9 RRMS patients. We obtain the maximum likelihood estimators of the parameters of interest arising from these models and study their asymptotic properties through simulation. We discuss the validation of the assumptions for the proposed models and examine the robustness of these estimators. We suggest the application of the models for characterizing the disease progression and testing treatment effect and discuss implication for planning of RRMS clinical trials.

Committee: Haikady Nagaraja (Advisor); Catherine Calder (Other); Kottil Rammohan (Other); Thomas Santner (Other) Subjects: Statistics