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- Creators: Department of Supply Chain Management
- Status: Published
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
Technology continues to impact human's daily lives and behavior, from how we purchase our groceries to how we get access to news and the means we communicate with others. New technologies are constantly being introduced and are not only influencing the public but also how businesses operate. During this technological era companies are investing more in research and development to learn more about the potential benefits of these technologies. This research, in particular, will address the need for companies' investment and continuous improvement in transportation management systems among complex supply chains to increase adoption rates of TMS technology. Also I will show how Transportation management systems have increased cost savings, customer satisfaction, the optimization of data, and planning. Such research is further supported by personal interviews with Intel, Big lots, Leslie’s Pools, and At Home, whom all have experience with transportation management systems within their business operations.
Ancient Roman society throughout the ages was highly successful at expansion and trade: this can be attributed to a vast and elaborate supply chain. They fueled their growth by implementing successful supply chain practices. Through these practices the average Roman citizen was able to buy items previously reserved as luxury items. The history behind these practices comes to light through historical documents and archaeological remains. Translations can be misconstrued due to modern contexts and other attempts at translations which contain typos. This can lead to variances in translations and understanding of the texts. Taking all these factors into account, this paper will examine the supply chain practices that made the Romans highly successful, what explicitly they traded, how certain items were transported, and the sea routes that were present that were able to transport such huge quantities of goods. Although Roman trade methods might be seen as antiquated, modern society can take away important supply chain lessons that we can apply today.
In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.
In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.
A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
In the hubs, it was found that there was significant room for optimization to ensure that the aircraft are truly being used to their full potential versus long ramp wait times between flights. When looking at outstations, planes typically only spent the minimum required amount of time on the ground. The exception is if the plane was going to Remain Overnight (RON), however this also meant it was the last flight of the day, and it arrived in the evening or later. The thesis specifically looks at the flows for the week of September 14-20, 2019.
Transportation around campus on time is crucial for in-person college students looking to succeed in their studies. Unfortunately, inequities have arisen between the ability of able-bodied students to get to and from class and permanently or temporarily disabled students looking to do the same. ASU’s solution to this problem, the Disability Access and Resource Transportation (DART) service, does adequately address the needs of its targeted customers properly. Unfortunately, student surveys and anecdotal evidence from students’ lived experiences have demonstrated that DART often leaves students waiting for more than half an hour for a ride, causes students to miss class, and is altogether unreliable in today’s age where punctuality is key to success. Our goal in our thesis project was to create an equal on-campus transportation playing field for students with and without mobility issues so that a students’ ability to get around campus would never serve as a hindrance to his/her ability to, at a minimum, earn a degree; ideally empowering all students to thrive regardless of their personal circumstances.