Environmental regulations or the necessity for a green image due to growing environmental concerns as well as the potential economical benefits of product recovery have pushed manufacturers to integrate product recovery management with their manufacturing process. Consequently, production planning and inventory control of recoverable manufacturing systems has gained significant interest among researchers who aim to contribute to industrial practice. This dissertation considers inventory optimization of a single product recoverable manufacturing system where stochastic demand is met by either newly manufactured items or remanufactured items. Lead times and set up costs for manufacturing and remanufacturing are considered. The inventory optimization problem for this system is formulated as a Markov decision process (MDP) and through an empirical study, optimal or near-optimal policy characterizations under several cost configurations and several lead time cases for manufacturing and remanufacturing are determined. The effects of a change in cost parameters of the system on the optimal policy structure as well as policy parameter values are investigated. Results indicate that the existence of set up cost for either manufacturing or remanufacturing has a significant effect on policy structure. Consequently, an MDP-based search procedure is introduced to determine the inventory policy characterizations given that appropriate policy structures under certain cost configurations are known. Further, a neural network analysis is performed to determine the functional relationships between cost parameters of the system and the inventory policy parameter values. Results indicate that the policy characterizations found by either MDP-based search methodology or the formulae provided by neural network are optimal or near-optimal with small deviations (usually, less than 1%) from optimal cost. Finally, the optimal inventory policies are investigated through the entire product life cycle of a remanufacturable product. Benefiting from the MDP analysis, the optimal or near-optimal policy characterizations with only a few parameters are determined for every stage of the product life cycle. The effects of a change in the demand and return rates on the optimal inventory policies are investigated. Further, the performance of these long-run policy characterizations is evaluated in a finite-horizon setting, and the importance of frequently revising the inventory policies over the product life cycle is illustrated numerically.

The purpose of the research has been to develop mesh structures with tailored properties for hip stems. Stress shielding is one of the crucial problems with current hip implants due to the modulus mismatch between the bone and the hip stem. Solid titanium or cobalt-chromium stems are changing the natural stress patterns in the femur. Stresses are transferred through the hip stem and are concentrated more at the distal end of the stem, which weakens the top portion of the femur and overloads the distal portion of the femur. Stress shielding in the long term may result in implant failure due to bone loss, which is costly and painful for the patient. Naturally, biomaterials have tailored structures that display optimal behaviors under tensile, bending and other applied loads. Bones are also tailored to the loading conditions and show stress patterns accordingly. This thesis reports on the development of new hip implants where the mechanical properties of the stems are tailored to match the boneâs properties using open cell structures and mesh structures. Solid free form fabrication techniques are used in this thesis to manufacture parts in the Electron Beam Melting (EBM) by Arcam AB, Sweden. This processing method offers a unique way of making hip stems with mesh and solid sections together in one build. Different designs of hip stems have been manufactured and tested. Their affects on the bones have been analyzed and demonstrated using the Finite Element Analysis (ABAQUS). Hip implants were tested on cadaver bones to measure the difference between mesh stems and solid commercial hip stems. This thesis also includes studies about the mesh quality under different processing conditions, and the applications of structural optimization.

A novel annuloplasty ring with a shape memory alloy core has been developed to facilitate minimally invasive mitral valve repair. In its activated (Austenitic) phase, this prototype ring provides comparable mechanical properties as commercial semi-rigid rings. In its pre-activated (Martensitic) phase, this ring is flexible enough to be introduced through an 8 mm trocar and easily manipulated with robotic instruments within the confines of a left atrial model. The core is constructed of 0.508 mm diameter NiTi, which is maintained below its Ms temperature (24 °C) during deployment and suturing. After suturing, the stiffener is heated above its Af temperature (37 °C, slightly below normal human body temperature) enabling the NiTi to retain its optimal geometry and stiffness characteristics indefinitely. The NiTi core is shape set in a furnace to the appropriate size and optimal geometry during fabrication. The ring is cooled in a saline bath prior to surgery, making it compliant and easy to manipulate. Evaluation of the ring included mechanical testing, robotic evaluation, static pressure testing, dynamic flow testing and fatigue testing. Experimental results suggest that the NiTi core ring could be a viable alternative to flexible bands in robot-assisted mitral valve repair.

It is well known that cycle times in capacitated production systems increase nonlinearly with resource utilization, which creates considerable difficulty for the traditional linear programming (LP) models used for production planning. Clearing Functions capture this nonlinear relationship and embed it in the optimization model. In this thesis, we evaluate the fitting methodology for clearing functions and show the importance of the fitting methodology on the production planning. We then perform a systematic comparison of the production planning models incorporating the clearing functions with the conventional linear programming models for production planning under different scenarios. The computational experiments applied to a scaled-down semiconductor manufacturing line illustrate the benefits of clearing function approach compared to fixed lead time approaches.

This thesis presents a minimal surface perturbation method for the surface registration of unstructured point cloud data. The registration problem is applicable to many disciplines. Computer vision (stereo vision), computer graphics, image processing, and reverse engineering and quality inspection are examples of areas currently implementing surface registration techniques. The surface registration problem is defined as: Given the unstructured point cloud data sets, find the rotation and translation of one data set such that the two sets are properly aligned in a single coordinate frame. Current techniques fail to find global alignment in instances where the initial starting points are not favorable. In this paper, the proposed method generates a ?geometric handler? to approximate the unstructured data sets using a small number of points capturing the global shape of the data while reducing local variations or features. An iterative searching method is proposed to minimize both the surface perturbation of point cloud data and the orientation variation by using a self-organizing neural network. The transformation of the geometric handlers is found using orientation invariant methods and is applied to the original point cloud. Once the global registration is accomplished, a local registration method is invoked requiring a search in local neighborhoods only. The proposed method can be used for the global surface registration of overlapped unstructured point cloud data sets. The presented techniques can be used in reverse engineering and CAD/CAM systems for product development and surface generation from scanned data.

In this thesis we extend the Markowitz Mean-Variance model to a rebalancing portfolio optimization problem incorporating realistic considerations such as transaction costs and a risk-free asset with short-selling allowed, and we apply the Tabu Search (TS) heuristic to solve practical portfolio problems. First of all, we propose a biobjective portfolio optimization model which we expect to yield a portfolio equilibrium by combining the two objectives: maximize the portfolioâs expected return and minimize its risk. For realistic portfolio problems we consider the multi-objective portfolio optimization models incorporating the risk-free asset and its short-selling and nonlinear transaction costs based on a single-period and a rebalancing portfolio optimization problem. Especially, to solve the rebalancing portfolio problem, we develop an adaptive, advanced TS algorithm having an evolutionary neighborhood structure, and we solve the problem with an iterative folding back procedure in the decision tree structure. Computational studies are performed with a risk-free asset and the number of risky assets to be 5, 10, 12, and 15 for both the single-period and rebalancing portfolio problems.

This dissertation studies neural networks for pattern classification and universal approximation. The objective is to develope a new neural network model for pattern classification, and relax the conditions for Radial-Basis Function networks to be universal approximators. First, the problem of pattern classification is introduced, which is followed by a brief introduction of three popular nonlinear classification techniques, that is, Multi-Layer Perceptrons (MLP), Radial-Basis Function (RBF) networks, and Support Vector Machines (SVM). Then, based on the basic concepts of MLP, RBF and SVM, a new neural network model with bounded weights is proposed, and some experimental results are reported. Later, the problem of universal approximation by neural networks is introduced, and the researches on ridge activation functions and radial-basis activation functions are reviewed. Then, the relaxed conditions for RBF networks to be universal approximators are presented. We show that RVF networks can uniformly approximate any continuous function on a compact set provided that the radial basis activation function is continuous almost every where, locally essentially bounded, and not a polynomial. Some experimental results are reported to illustrate our findings. The dissertation ends with the conclusion and future research.

The influence of sintering temperature and hold time on the microstructure of of a microwave sintered LSM-YSZ (lanthanum strontium manganate - yttria stabilized zirconia) cathode has been studied. For experimental purposes, a microwave furnace was designed and fabricated with a closed loop temperature feedback control system. A type R thermocouple was used to provide accurate temperature readings inside the microwave cavity. An Allen Bradley Micrologix PLC was used to control the system. This furnace was used as a test bed for experiments involving the rapid sintering of solid oxide fuel cell (SOFC) materials. The SOFC specimens were made by depositing LSM-YSZ cathode material onto YSZ-8 electrolyte buttons. The specimens were sintered for a variety of temperatures and hold times. The microstructure obtained through microwave sintering showed equal or better pore size distribution as compared to those obtained through conventional sintering. The sintered structure was found to be less dense and to contain smaller pores as the sintering temperature was reduced to 1100ËC or lower. Rapid sintering of SOFC materials has potential advantages in terms of SOFC performance and offers potential energy savings when compared with conventional sintering. This research has demonstrated the feasibility of rapid sintering of porous SOFC materials. The next step is to optimize the microwave sintering schedule with respect to the electrical performance and the long term stability of the SOFC.

Supply Chain Management has become the primary competitive weapon in many industries. This thesis describes a model of intelligent supply chain networks that will improve information visibility and flow within the supply chain. In the proposed model, products will have the intelligence to direct themselves throughout the distribution network and will have the capability to be purchased and sold while in transit. The report gives an overview of the supporting technologies that make such a supply chain a reality. It is intended to provide a preliminary outlook at the various issues related to implementation and could be used for future research as a basis for building the infrastructure required for the new model. An effort has been made to provide a design structure for XML identification tags, which will be one of the most critical components of the system. In addition to that, this report will describe the results of a simulation analysis using a model of a hypothetical public logistics network covering the southeastern United States. The network consists of 36 nodes representing the public distribution centers and 59 arcs, which represents various interstate highways connecting them. The simulations help identify the critical parameters associated with the material flow through the network and provide an insight into the capacity requirement of the public distribution centers. The results of the simulations will be used as a benchmark for future research and development associated with building an actual negotiating agent model.

A machine capable of making ?anything? has always existed in the realm of science fiction. The advent of the Rapid Prototyping machines partially fulfilled the realization of a personal fabricator by breaking the boundaries on the geometric form that could be realized through a machine in a single set up. The proliferation of the rapid prototyping machines into the industry and finally for domestic use, has been hampered by their costs, size and process limitations. The current trends in the Rapid Prototyping industry has been to develop machines capable of manufacturing parts in functionally graded materials. In order to achieve this, there is a need to develop means to precisely deposit a controlled combination of materials within the volume of a part. Electrophotography has been used for decades for monochrome and multicolor dry toner printing. The application of electrophotography for the generation of 3D parts through layered manufacturing has been left mostly unexplored. This thesis suggests guidelines for the development of an electrophotography based rapid prototyping process that would be cost effective in comparison with current commercial rapid prototyping technologies, as well as have the capability of depositing multiple materials. The initial research involved attempts to adapt a commercial electrophotographic printer to print in 3D. Later, experiments were conducted to indigenously build an electrophotography based layered manufacturing system. The research involved the development of transmission systems, development of power supplies to facilitate electrostatic charging, testing of polygon mirror based laser-scanning system, development of fusing and pressing station and experiments with multiple materials. Though a electrophotography based rapid prototyping machine was not realized at the end of this research, substantial evidence was generated to validate future research towards the development of such a system. Future work would involve the development of a completely automated system. Upon the completion of this system, further research could be carried out in the fields of personal fabrication, micro Rapid Prototyping, materials with directional properties, bio and materials, direct write technologies for printing circuits and functionally graded materials.