Three Essays in Resort and Sea Cargo Revenue Management
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Authors
Nekrashevich, Aliaksandr
Date
2025-07-08
Type
thesis
Language
eng
Keyword
Revenue management , Dynamic pricing , Resort analytics , Sea cargo
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Abstract
This dissertation develops revenue management theory and applies it to practical settings in three industrially relevant problems. The first essay is a theoretical study of tractable approximations resulting in practical policy optimization methods for resort revenue management. This research contributes to resort revenue management by introducing a novel setting and an integrated model capturing different industry aspects, including ancillary spending, free upgrades, cancellations, and no-shows. This work derives several computationally viable bid price policies based on randomized linear programming (RLP) and establishes multiple bounds and inequalities. This essay also proves that three RLP models are asymptotically optimal and provide a numerical comparison.
The second essay contains a data-driven study for resort revenue management. This work describes a complete revenue management system implemented for the golf resort setting with free upgrades and ancillary spending. The system includes components for demand uncensoring, ancillary spending estimation, bid price optimization, and visualization. This essay provides data-driven insights into resort operations, evaluation results, and a relative profit lift. The second essay also examines and extends the theory behind parametric uncensoring in the hotel setting. Demand Uncensoring is a problem of statistical learning and reconstruction; the broad question it solves is how to reconstruct the hidden demand. For uncensoring, the essay suggests an Expectation-Maximization algorithm parameterized by a linear function of the stay request features.
The third essay studies freight revenue management. Developing adequate optimization models for the sea cargo industry has been a challenging problem for several decades, with plenty of models and approaches available. However, most published approaches are non-scalable for industrial settings or model an incomplete set of business features and opportunities. In particular, scalable models usually do not consider uncertainty over several business lines simultaneously. Non-scalability typically occurs due to the network problem structure, which is motivated by the limited vessel capacity and container balancing at ports. However, with recent developments in subgradient optimization, optimizing effectively after relaxing such constraints with Lagrangian decomposition became possible. The proposed model provides a scalable stochastic approach with dynamic pricing and container management, with a profitable ability to ship empty containers.
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Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-ShareAlike 4.0 International