Robust Optimization Models for Network Revenue Management by \u0130rem Bahtiyar<\/p>\n
Thesis Advisor: Prof. Dr. Mustafa \u00c7elebi P\u0131nar<\/p>\n
Date: July 9 Tuesday 2024
\nTime: 16.00
\nPlace: Zoom<\/p>\n
This is an online seminar. To obtain event details please send a message to department.<\/p>\n
Abstract:
\nEffective capacity allocation methods play a crucial role in Network Revenue Management. Yet, current methods for determining optimal capacity controls under uncertainty, such as stochastic optimization, often assume a known probability distribution for unknown parameters.<\/p>\n
This assumption may degrade a model\u2019s performance when faced with unexpected data patterns. This thesis explores a novel approach through robust optimization to address stochastic resource allocation problems.<\/p>\n
We introduce a heuristic based on these robust formulations to derive actionable results. Through extensive simulations focused on seat allocation problems within the revenue management domain, our proposed formulations demonstrate improved worst-case performances. Notably, even under favorable scenarios, our solutions remain comparable to existing methods in the revenue management literature.<\/p>\n","protected":false},"excerpt":{"rendered":"
Robust Optimization Models for Network Revenue Management by \u0130rem Bahtiyar Thesis Advisor: Prof. Dr. Mustafa \u00c7elebi P\u0131nar Date: July 9 Tuesday 2024 Time: 16.00 Place: Zoom This is an online seminar. To obtain event details please send a message to department. Abstract: Effective capacity allocation methods play a crucial role [\u2026] <\/p>\n