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dc.contributor.advisorAmin, Saurabh
dc.contributor.authorLi, Alexandra
dc.date.accessioned2024-03-21T19:13:13Z
dc.date.available2024-03-21T19:13:13Z
dc.date.issued2024-02
dc.date.submitted2024-03-04T16:38:17.336Z
dc.identifier.urihttps://hdl.handle.net/1721.1/153880
dc.description.abstractWith the expansion of digital commerce and growth of the economy, the freight transportation scene has adapted to reflect such changes. Digital freight platforms, acting an intermediary between shippers and carriers, have gained traction to modernize the process and leverage technology to improve efficiency and increase the ease-of-use for all parties involved. Through their role in setting prices and presenting loads, these platforms can reduce the negative environmental impact of freight while simultaneously increasing the efficiency of carriers and satisfying the needs of shippers. The key challenge that these digital freight platforms face is understanding how carriers strategically select an action on the platform, which is difficult to capture despite having large amounts of data because naive estimation methods on historical data produce unrealistic results for different pricing methods. This thesis addresses this challenge by developing a simulation to evaluate the practicality of these estimates and iteratively revise the parameters based on constraints until they produce desirable results. In our research, we model the behavior through which carriers select a load to accept or reject with a 2-way latent class multinomial logit model. We tune the parameters of this model through a feedback loop where we perform a maximum likelihood estimate on the data to obtain model parameters, evaluate these parameters in the simulation, and use the results to perform a re-estimation to eventually obtain parameters that are both representative of the data and produce the expected results. We use this system to evaluate optimized pricing and load presentation methods. We experiment with bundling, or grouping a sequence of loads together to reduce the overhead time carriers spend finding suitable loads and to produce routes with less CO2 emissions. We solve for a mixed-integer linear program that maximizes the total utility of bundles proposed by the platform to generate few and non-overlapping bundles. We develop a dynamic programming based pricing method to generate carrier and time specific prices for bundles. We evaluate these methods in our model and analyze the effects of such methods on carrier interactions and behavior. Although these methods do not yet show a substantial decrease in freight carbon emissions, we have laid the groundwork for modeling this complex system and hope that future work can be done to reduce the negative environmental that the freight transportation sector leaves on this planet.
dc.publisherMassachusetts Institute of Technology
dc.rightsIn Copyright - Educational Use Permitted
dc.rightsCopyright retained by author(s)
dc.rights.urihttps://rightsstatements.org/page/InC-EDU/1.0/
dc.titleLearning Carrier Choice Models for Load Pricing in Digital Freight Platforms
dc.typeThesis
dc.description.degreeM.Eng.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
mit.thesis.degreeMaster
thesis.degree.nameMaster of Engineering in Electrical Engineering and Computer Science


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