[{"id":19355,"title":"Randomized Robust Price Optimization","permalink":"https:\/\/bschool.nus.edu.sg\/biz-events\/event\/randomized-robust-price-optimization\/","category":"Seminars and talks","event_dept":{"value":"analytics-operations","label":"Analytics & Operations"},"event_sec_dept":false,"event_details":{"event_start_date":"6 December 2023","event_end_date":"6 December 2023","event_start_time":"10:00 am","event_end_time":"11:30 am","event_dress_code":"NA"},"event_loc":{"eve_address_selection":"1","eve_location_1":{"eve_org":"NUS Business School","eve_build":"Mochtar Riady Building","eve_room":"BIZ1 0302","eve_add":"15 Kent Ridge Drive","eve_count":"Singapore","eve_copos":"119245","eve_map_url":"https:\/\/goo.gl\/maps\/Q1kyjwxHNE22"},"eve_location_2":{"eve_org":"Shaw Foundation Alumni House","eve_build":"","eve_room":"Clove and Lemongrass Room Level 2","eve_add":"11 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119244,"eve_map_url":"https:\/\/goo.gl\/maps\/docgThkDWFxKdb9c7"},"eve_location_3":{"eve_org":"Hon Sui Sen Memorial Library Auditorium","eve_build":"","eve_room":"","eve_add":"1 Hon Sui Sen Drive","eve_count":"Singapore","eve_copos":117588,"eve_map_url":"https:\/\/goo.gl\/maps\/NJjWK4RMpC92"},"eve_location_4":{"eve_org":"NUSS Kent Ridge Guild House","eve_build":"","eve_room":"Dalvey Room","eve_add":"9 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119241,"eve_map_url":"https:\/\/goo.gl\/maps\/nXn2Luh96pH2"},"eve_location_5":{"eve_org":"Institute of Data Science","eve_build":"Innovation 4.0","eve_room":"1-3","eve_add":"3 Research Link","eve_count":"Singapore","eve_copos":117602,"eve_map_url":"https:\/\/goo.gl\/maps\/i1xocvvDh27QUXem7"},"eve_location_6":{"eve_org":"","eve_build":"","eve_room":"","eve_add":"","eve_count":"","eve_copos":"","eve_map_url":""},"eve_location_7":""},"event_introduction":"","event_short_intro":"","event_topic":null,"event_banner":false,"event_external_url":"","event_registration_details":{"event_registration_form":false,"event_registration_message":"","event_registration_deadline":null,"eve_registration_url":"","event_form":"","event_registration_ack":""},"event_speaker":[{"event_speaker_name":"Xinyi Guan","event_speaker_designation":"","event_speaker_affiliation":"UCLA Anderson School of Management","event_speaker_picture":false,"event_speaker_url":"","event_speaker_introduction":"Xinyi Guan is a fifth-year PhD student in Decisions, Operations, and Technology Management at the UCLA Anderson School of Management, advised by Professor Velibor Misic. Her research centers around proposing data-driven methodologies for problems involving decision making under uncertainty with the applications in finance, healthcare and revenue management.<\/p>\n"}],"event_agenda":false,"event_photo_gallery":false,"event_presentations":false,"event_custom_heading":[{"event_custom_title":"Abstract","event_custom_details":"
The robust multi-product pricing problem is to determine the prices of a collection of products so as to maximize the worst-case revenue, where the worst case is taken over an uncertainty set of demand models that the firm expects could be realized in practice. A tacit assumption in this approach is that the pricing decision is a deterministic decision: the prices of the products are fixed and do not vary. In this paper, we consider a randomized approach to robust pricing, where a decision maker specifies a distribution over potential price vectors so as to maximize its worstcase revenue over an uncertainty set of demand models. We formally define this problem \u2013 the randomized robust price optimization problem — and analyze when a randomized price scheme performs as well as a deterministic price vector, and identify cases in which it can yield a benefit.<\/p>\n
We also propose two solution methods for obtaining an optimal randomization scheme over a discrete set of candidate price vectors based on constraint generation and double column generation, respectively, and show how these methods are applicable for common demand models, such as the linear, semi-log and log-log demand models. We numerically compare the randomized approach against the deterministic approach on a variety of synthetic and real problem instances. On real data instances derived from a grocery retail scanner dataset, the improvement can be as high as 92%.<\/p>\n"}],"event_enquiry_details":{"event_enq_full_name":"","event_enq_department":"","event_enq_email":"","event_enq_telephone":"","event_enq_website":""}}]