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.

Genomic and biochemical investigation of soybean antioxidant metabolism in response to growth at elevated carbon dioxide and elevated ozone

Metabolism in an environment containing of 21% oxygen has a high risk of oxidative damage due to the formation of reactive oxygen species. Therefore, plants have evolved an antioxidant system consisting of metabolites and enzymes that either directly scavenge ROS or recycle the antioxidant metabolites. Ozone is a temporally dynamic molecule that is both naturally occurring as well as an environmental pollutant that is predicted to increase in concentration in the future as anthropogenic precursor emissions rise. It has been hypothesized that any elevation in ozone concentration will cause increased oxidative stress in plants and therefore enhanced subsequent antioxidant metabolism, but evidence for this response is variable. Along with increasing atmospheric ozone concentrations, atmospheric carbon dioxide concentration is also rising and is predicted to continue rising in the future. The effect of elevated carbon dioxide concentrations on antioxidant metabolism varies among different studies in the literature. Therefore, the question of how antioxidant metabolism will be affected in the most realistic future atmosphere, with increased carbon dioxide concentration and increased ozone concentration, has yet to be answered, and is the subject of my thesis research. First, in order to capture as much of the variability in the antioxidant system as possible, I developed a suite of high-throughput quantitative assays for a variety of antioxidant metabolites and enzymes. I optimized these assays for Glycine max (soybean), one of the most important food crops in the world. These assays provide accurate, rapid and high-throughput measures of both the general and specific antioxidant action of plant tissue extracts. Second, I investigated how growth at either elevated carbon dioxide concentration or chronic elevated ozone concentration altered antioxidant metabolism, and the ability of soybean to respond to an acute oxidative stress in a controlled environment study. I found that growth at chronic elevated ozone concentration increased the antioxidant capacity of leaves, but was unchanged or only slightly increased following an acute oxidative stress, suggesting that growth at chronic elevated ozone concentration primed the antioxidant system. Growth at high carbon dioxide concentration decreased the antioxidant capacity of leaves, increased the response of the existing antioxidant enzymes to an acute oxidative stress, but dampened and delayed the transcriptional response, suggesting an entirely different regulation of the antioxidant system. Third, I tested the findings from the controlled environment study in a field setting by investigating the response of the soybean antioxidant system to growth at elevated carbon dioxide concentration, chronic elevated ozone concentration and the combination of elevated carbon dioxide concentration and elevated ozone concentration. In this study, I confirmed that growth at elevated carbon dioxide concentration decreased specific components of antioxidant metabolism in the field. I also verified that increasing ozone concentration is highly correlated with increases in the metabolic and genomic components of antioxidant metabolism, regardless of carbon dioxide concentration environment, but that the response to increasing ozone concentration was dampened at elevated carbon dioxide concentration. In addition, I found evidence suggesting an up regulation of respiratory metabolism at higher ozone concentration, which would supply energy and carbon for detoxification and repair of cellular damage. These results consistently support the conclusion that growth at elevated carbon dioxide concentration decreases antioxidant metabolism while growth at elevated ozone concentration increases antioxidant metabolism.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.