[{"id":15622,"title":"Constant Regret Re-solving Heuristics for Revenue Management Problems","permalink":"https:\/\/bschool.nus.edu.sg\/biz-events\/event\/constant-regret-re-solving-heuristics-for-revenue-management-problems\/","category":"Seminars and talks","event_dept":{"value":"analytics-operations","label":"Analytics & Operations"},"event_sec_dept":[{"event_tag_to":"analytics-operations"}],"event_details":{"event_start_date":"25  November  2022","event_end_date":"25  November  2022","event_start_time":"10:00 am","event_end_time":"11:30 am","event_dress_code":"NA"},"event_loc":{"eve_address_selection":"5","eve_location_1":{"eve_org":"NUS Business School","eve_build":"Mochtar Riady Building","eve_room":"3-2","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":"TBA","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":"He Wang","event_speaker_designation":"","event_speaker_affiliation":"","event_speaker_picture":false,"event_speaker_url":"https:\/\/www.isye.gatech.edu\/users\/he-wang","event_speaker_introduction":"<p>He Wang is an Assistant Professor and Colonel John B. Day Early Career Professor in the School of Industrial and Systems Engineering at Georgia Tech. His research interests include pricing and revenue management, supply chain, transportation, and machine learning. His works have received 1<sup>st<\/sup>\u00a0place in INFORMS Junior Faculty Interest Group paper competition, Best Paper in Operation Research Award by the MSOM Society, NSF CAREER Award, and faculty research awards from Amazon and Didi.<\/p>\n"}],"event_agenda":false,"event_photo_gallery":false,"event_presentations":false,"event_custom_heading":[{"event_custom_title":"Abstract","event_custom_details":"<p>We will discuss a classic network revenue management model of Gallego and van Ryzin (1997), which considers a retailer who sells a product (or multiple products) subject to initial inventory constraints over T consecutive periods. Because the optimal policy via dynamic programming is computationally intractable, researchers have proposed various approximate policies for this problem. We are interested in the so-called \u201cre-solving heuristic,\u201d which periodically solves the fluid approximation model. In the quantity-based revenue management setting with discrete types (joint work with P. Bumpensanti), we find that the re-solving heuristic has a worst-case regret of O(T^{1\/2}) compared to the optimal policy, whereas a modified re-solving heuristic can achieve uniformly bounded O(1) regret. In the price-based revenue management setting with continuous price sets (joint work with Yining Wang), we show that the re-solving heuristic attains O(1) regret compared to the value of the optimal policy. This improves the O(lnT) regret upper bound established by Jasin (2014). In addition, we prove that there is an \u03a9(lnT) gap between the value of the optimal policy and that of the fluid model, implying that the fluid model is not an adequate benchmark for constant regret.<\/p>\n"}],"event_enquiry_details":{"event_enq_full_name":"WONG Cecilia\/TAN Dorothy ","event_enq_department":"","event_enq_email":"","event_enq_telephone":"6516 6225\/6516 3067","event_enq_website":""}}]