The recent emergence of social media and online retailing become increasingly important and continue to grow. More and more people use social media to share their real life to the digital world, at the same time, browse the virtual Internet to buy the real products. In the process, a huge amount of data is generated and we investigate the data and crowdsourcing for areas of the public transportation and last-mile delivery for online orders in the perspective of data analytics and operations optimization.
We first focus on the transit flow prediction by crowdsourced social media data. Subway flow prediction under event occurrences is a very challenging task in transit system management. To tackle this challenge, we leverage the power of social media data to extract features from crowdsourced content to gather the public travel willingness. We propose a parametric and convex optimization-based approach to combine the least squares of linear regression and the prediction results of the seasonal autoregressive integrated moving average model to accurately predict the NYC subway flow under sporting events.
The second part of the thesis focuses on the last-mile same-day delivery with store fulfillment problem (SDD-SFP) using real-world data from a national retailer. We propose that retailers can take advantage of their physical local stores to ful?ll nearby online orders in a direct-to-consumer fashion during the same day that order placed. Optimization models and solution algorithms are developed to determine store selections, fleet-sizing for transportation, and inventory in terms of supply chain seasonal planning. In order to solve large-scale SDD-SFP with real-world datasets, we create an accelerated Benders decomposition approach that integrates the outer search tree and local branching based on mixed-integer programming and develops optimization-based algorithms for initial lifting constraints.
In the last part of the dissertation, we drill down SDD-SFP from supply chain planning to supply chain operation level. The aim is to create an optimal exact order ful?llment plan to specify how to deliver each received customer order. We adopt crowdsourced shipping, which utilizes the extra capacity of the vehicles from private drivers to execute delivery jobs on trips, as delivery options, and define the problem as same-day delivery with crowdshipping and store fulfillment (SDD-CSF). we develop a set of exact solution approaches for order fulfillment in form of rolling horizon framework. It repeatedly solves a series of order assignment and delivery plan problem following the timeline in order to construct an optimal fulfillment plan from local stores. Results from numerical experiments derived from real sale data of a retailer along with algorithmic computational results are presented.
|Commitee:||Gao, Jing, Karwan, Mark H., Walteros, Jose L.|
|School:||State University of New York at Buffalo|
|School Location:||United States -- New York|
|Source:||DAI-B 79/08(E), Dissertation Abstracts International|
|Subjects:||Industrial engineering, Transportation, Operations research|
|Keywords:||Crowdsourcing, Data analytics, Last-mile delivery, Optimization, Same-day delivery, Social media|
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