Stochastic Optimization in Freight Revenue Management

Student: Nekrashevich Aliaksandr

Educational Programme: Statistical Learning Theory (Master)

Final Grade: 8

Year of Graduation: 2019

In this work, authors study freight revenue management. Describing the industry have been a challenging problem for several decades, with plenty models and approaches provided. However, by authors knowledge, they are either non-scalable for industrial setting, or track incomplete set of industrial features and peculiarities. Particularly, scalable ones do not track uncertainty over several business lines together. Non-scalability appears due to network problem structure, which arrives due to limited vessel capacity, as well as port container balancing. However, due to recent developments in optimization, it became possible to relax these constraints. In authors’ new model, a scalable stochastic approach with dynamic pricing and container management is presented, with a peculiar ability to ship empty containers.

Full text (added May 29, 2019)

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