Thesis (M. Phil.)--University of Hong Kong, 1980.

abstract: In today’s rapidly changing world and competitive business environment, firms are challenged to build their production and distribution systems to provide the desired customer service at the lowest possible cost. Designing an optimal supply chain by optimizing supply chain operations and decisions is key to achieving these goals. In this research, a capacity planning and production scheduling mathematical model for a multi-facility and multiple product supply chain network with significant capital and labor costs is first proposed. This model considers the key levers of capacity configuration at production plants namely, shifts, run rate, down periods, finished goods inventory management and overtime. It suggests a minimum cost plan for meeting medium range demand forecasts that indicates production and inventory levels at plants by time period, the associated manpower plan and outbound shipments over the planning horizon. This dissertation then investigates two model extensions: production flexibility and pricing. In the first extension, the cost and benefits of investing in production flexibility is studied. In the second extension, product pricing decisions are added to the model for demand shaping taking into account price elasticity of demand. The research develops methodologies to optimize supply chain operations by determining the optimal capacity plan and optimal flows of products among facilities based on a nonlinear mixed integer programming formulation. For large size real life cases the problem is intractable. An alternate formulation and an iterative heuristic algorithm are proposed and tested. The performance and bounds for the heuristic are evaluated. A real life case study in the automotive industry is considered for the implementation of the proposed models. The implementation results illustrate that the proposed method provides valuable insights for assisting the decision making process in the supply chain and provides significant improvement over current practice. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2020

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<p> This dissertation studies two important problems in the field of biomass supply chain network. In the first part of the dissertation, we study the pre-disaster planning problem that seeks to strengthen the links between the multi-modal facilities of a biomass supply chain network. A mixed-integer nonlinear programming model is developed to determine the optimal locations for multi-modal facilities and bio-refineries, offer suggestions on reliability improvement at vulnerable links, production at bio-refineries, and make transportation decision under both normal and disrupted scenarios. The aim is to assist investors in determining which links&rsquo; reliability can be improved under specific budget limitations so that the bio-fuel supply chain network can prevent possible losses when transportation links are disrupted because of natural disasters. We used states Mississippi and Alabama as a testing ground for our model. As part of numerical experimentation, some realistic hurricane scenarios are presented to determine the potential impact that pre-investing may have on improving the bio-mass supply chain network&rsquo;s reliability on vulnerable transportation links considering limited budget availability. </p><p> In the second part of the dissertation, we study the impact of feedstock supply uncertainty on the design and management of an inbound biomass co-firing supply chain network. A two-stage stochastic mixed integer linear programming model is developed to determine the optimal use of multi-modal facilities, biomass storage and processing plants, and shipment routes for delivering biomass to coal plants under feedstock supply uncertainty while considering congestion into account. To represent a more realistic case, we generated a scenario tree based on the prediction errors obtained from historical and forecasted feedstock supply availability. We linearized the nonlinear problem and solved with high quality and in a time efficient manner by using a hybrid decomposition algorithm that connects a Constraint generation algorithm with Sample average approximation algorithm and enhanced Progressive hedging algorithm. We used states Mississippi and Alabama as a testing ground for our study and conducted thorough computational experiments to test our model and to draw managerial insights.</p>

<p> Most of the hospitals in the USA carry out their purchasing of supplies, including pharmaceuticals, through Group Purchasing Organizations (GPO). GPO is an organization, which aggregates procuring volumes of their member hospitals and negotiates low prices from manufacturers or vendors. According to 2013 statistics, 98% of hospitals in U.S. are purchasing their bulk health care products through GPOs, and it saves U.S. health care industry approximately $36 billion annually. Through these hospitals enjoy advantages by purchasing through their GPOs, there are some disadvantages such as paying membership fees to their GPOs, restricting the purchasing power of the hospitals outside their GPOs, making it more complicated to buy better or advanced products from new vendors. As various political and economic factors are forcing hospitals merge into large hospital associations, the concept of self-contracting or managing supplies directly, comes into the picture.</p><p> In this research, the concepts of healthcare supply chains with GPOs are described in detail. Purchasing systems under self- contracting are then discussed. Three possible options for the hospitals are then examined, namely, continuing current purchasing through their GPOs, direct purchasing from manufacturers (self &ndash;contracting), and finally, forming an association with other hospitals and purchasing through this association. The preferable options are discussed under the concepts of Game Theory. This research also examines the changes needed in the supply chain if any of the above new options is selected.</p><p> A regular supply-chain consists of Hospital, GPO, and vendor or manufacturer. As healthcare delivery systems are merging into one group or forming hospital associations, they have an additional option of carrying out their purchasing through these associations. In this work, it is assumed that the individual hospitals take their decisions based on total costs of supplies, and they chose the supplier by comparing the various options available. In this research, these questions are answered by following a game-theoretic model, by making some assumptions. Concepts of game theory such as Nash equilibrium, Mixed Strategy Nash Equilibrium (MSNE), etc. are discussed.</p>

<p>This research was conducted to develop and qualify a vacuum GaAs semiconductor monolithic microwave integrated circuit die attach process. Research was done to understand the causes and effects of voiding levels on device performance and reliability. Simultaneous investigation was done to qualify vacuum-attach as a successful methodology by which minimal voiding levels were achieved. After an initial vacuum-attach trial was completed to verify the methodology, internal accept/reject criteria were developed to qualify die attach interfaces. A dual phase attachment methodology was created to minimize tolerance stacking resulting in more consistent component placement. MATLAB image processing code was developed to quantify the voiding levels against the accept/reject criteria. Statistical methodologies were employed to troubleshoot root causes for special cause variation of initial attachment failures. A design of experiment was conducted testing three factors each at two levels (process gas [Gas A, Gas B], leaking chamber [yes, no], and carrier supplier [Supplier A, Supplier B]). The DOE identified process gas and its interaction with the carrier supplier to be significant. Further investigation of the carriers identified plating contamination, resulting in the process gas the primary factor of interest. A secondary experiment focusing on process gas identified no statistical difference between Gas A? and Gas B (Gas A? indicating a high purity form of Gas A). With this information, Gas A? was selected as the process gas. A total of 56 attachment interfaces were then produced yielding 0.7485% voiding, on average, following a Weibull distribution (?= 1.04171, ? = 0.75967) with zero rejections. The process?s consistency of minimal voiding levels were deemed a success and the process was released to production.

Statistical Process Control (SPC) is a statistical method for monitoring variability of processes. Process variation can be categorized as common cause and special cause. Common causes are the natural or expected variation of some change in the process. The presence of a special cause indicates that the process is not in a state of statistical control. The SPC methodology dictates that a search should be initiated when a special cause is detected. This thesis is about the set-up of magnitude robust control chart and CUSUM charts for detecting changes in Weibull processes. The research includes the comparison of the ARL performance of the control charts. / A Thesis submitted to the Department of Industrial and Manufacturing Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Degree Awarded: Spring Semester, 2009. / Date of Defense: October 31, 2008. / Statistical Process Control, Weibull Distribution, Magnitude Robust Control Chart, CUSUM Chart, ARL, Maximum Likelihood Estimates, Maximum Likelihood Ratio Test / Includes bibliographical references. / Joseph J. Pignatiello, Jr., Professor Directing Thesis; Samuel A. Awoniyi, Committee Member; Arda Vanli, Committee Member; Okenwa Okoli, Committee Member.

This dissertation presents a systematic study on the dimension variation prediction and control for polymer matrix fiber reinforced composites. A dimension variation model was developed for process simulation based on thermal stress analysis and finite element analysis (FEA). This model was validated against the experimental data, the analytical solutions and the data from literature. Using the FEA-based dimension variation model, the deformations of typical composite structures were studied and the regression-based dimension variation model was developed. The regression-based dimension variation model can significantly reduce computation time and provide a quick design guide for composite products with reduced dimension variations. By introducing the material modification coefficient, this comprehensive model can handle various fiber/resin types and stacking sequences. It eliminates the complicated, time-consuming finite element meshing and material parameter defining process. The deformation compensation through tooling design was investigated using the FEA-based and the regression-based dimension variation models. The structural tree method (STM) was developed to compute the assembly deformation from the deformations of individual components, as well as the deformation of general shape composite components. The STM enables rapid dimension variation analysis/synthesis for complex composite assemblies with the regression-based dimension variation model. Using the STM and the regression-based dimension variation model, design optimization and tolerance analysis/synthesis were conducted. The exploring work presented in this research provides a foundation to develop practical and proactive dimension control techniques for composite products. / A Dissertation submitted to the Department of Industrial Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Degree Awarded: Summer Semester, 2003. / Date of Defense: July 7, 2003. / Composites / Includes bibliographical references. / Chuck Zhang, Professor Directing Dissertation; George Buzyna, Outside Committee Member; Zhiyong Liang, Committee Member; Okenwa Okoli, Committee Member; Ben Wang, Committee Member.