Design and fabrication of electro-thermal microcantilevers for ultrafast molecular sorting and delivery

Improvements to the current state of the art in microfabricated cantilevers are investigated in order to realize enhanced functionality and increased versatility for use in ultrafast electrophoretic molecular sorting and delivery. Design rationale and fabrication process flow are described for six types of electro-thermal microcantilevers. Devices have been tailored for the process of separating mixtures of heterogeneous molecules into discrete detectable bands based on electrophoretic mobility, and delivering them to a conductive substrate using electric fields. Four device types include integrated heating elements capable of warming samples to catalyze reactions or cleaning the device for reuse. Similar devices have been shown to be capable of targeting temperatures between ambient conditions and the melting point of silicon, to within 0.1˚C precision or better. All microcantilevers types are equipped with a highly doped conductive silicon tip capable of interacting with a conductive substrate to deliver molecules under the presence of an electric field. Devices are equipped with additional electrodes to aid in sorting molecules on the surface of the probe end. Two designs contain two legs and one additional sorting electrode while four designs contain three legs and have two sorting electrodes. Devices having two sorting electrodes are designed to be capable of sorting three or more molecular species, a distinctive advancement in the state of the art. A detailed process flow of the fabrication process for all six electro-thermal cantilever designs are explained in detail.

Scheduling of track inspection and maintenance activities in railroad networks

The U.S. railroad companies spend billions of dollars every year on railroad track maintenance in order to ensure safety and operational efficiency of their railroad networks. Besides maintenance costs, other costs such as train accident costs, train and shipment delay costs and rolling stock maintenance costs are also closely related to track maintenance activities. Optimizing the track maintenance process on the extensive railroad networks is a very complex problem with major cost implications. Currently, the decision making process for track maintenance planning is largely manual and primarily relies on the knowledge and judgment of experts. There is considerable potential to improve the process by using operations research techniques to develop solutions to the optimization problems on track maintenance. In this dissertation study, we propose a range of mathematical models and solution algorithms for three network-level scheduling problems on track maintenance: track inspection scheduling problem (TISP), production team scheduling problem (PTSP) and job-to-project clustering problem (JTPCP). TISP involves a set of inspection teams which travel over the railroad network to identify track defects. It is a large-scale routing and scheduling problem where thousands of tasks are to be scheduled subject to many difficult side constraints such as periodicity constraints and discrete working time constraints. A vehicle routing problem formulation was proposed for TISP, and a customized heuristic algorithm was developed to solve the model. The algorithm iteratively applies a constructive heuristic and a local search algorithm in an incremental scheduling horizon framework. The proposed model and algorithm have been adopted by a Class I railroad in its decision making process. Real-world case studies show the proposed approach outperforms the manual approach in short-term scheduling and can be used to conduct long-term what-if analyses to yield managerial insights. PTSP schedules capital track maintenance projects, which are the largest track maintenance activities and account for the majority of railroad capital spending. A time-space network model was proposed to formulate PTSP. More than ten types of side constraints were considered in the model, including very complex constraints such as mutual exclusion constraints and consecution constraints. A multiple neighborhood search algorithm, including a decomposition and restriction search and a block-interchange search, was developed to solve the model. Various performance enhancement techniques, such as data reduction, augmented cost function and subproblem prioritization, were developed to improve the algorithm. The proposed approach has been adopted by a Class I railroad for two years. Our numerical results show the model solutions are able to satisfy all hard constraints and most soft constraints. Compared with the existing manual procedure, the proposed approach is able to bring significant cost savings and operational efficiency improvement. JTPCP is an intermediate problem between TISP and PTSP. It focuses on clustering thousands of capital track maintenance jobs (based on the defects identified in track inspection) into projects so that the projects can be scheduled in PTSP. A vehicle routing problem based model and a multiple-step heuristic algorithm were developed to solve this problem. Various side constraints such as mutual exclusion constraints and rounding constraints were considered. The proposed approach has been applied in practice and has shown good performance in both solution quality and efficiency.