{"663174":{"#nid":"663174","#data":{"type":"event","title":"ISyE Seminar - Huseyin Topaloglu","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EJoint Inventory Allocation and Assortment Personalization with Performance Guarantees\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn this talk, we give approximation algorithms for a joint inventory allocation and assortment personalization problem motivated by\u0026nbsp;an online retail setting. In our problem, we have a limited amount of storage capacity that needs to be allocated among multiple products to\u0026nbsp;serve customers that arrive over a selling horizon. At the beginning of the selling horizon, we decide how many units of each product to\u0026nbsp;stock. Over the selling horizon, customers arrive at the platform one by one to make a purchase. Based on the remaining inventories of the\u0026nbsp;products and the information available on the arriving customer, we offer a personalized assortment of products to each customer. The\u0026nbsp;customer either makes a choice within the offered assortment or leaves without a purchase. Our goal is to decide how many units of each\u0026nbsp;product to stock at the beginning of the selling horizon and to find a policy to figure out which personalized assortment to offer to each\u0026nbsp;arriving customer to maximize the total expected revenue over the selling horizon. Our problem is motivated by same-day-delivery\u0026nbsp;applications in online retail, where the retailer needs to allocate the limited storage capacity in an urban warehouse among different\u0026nbsp;variants in a product category, while having the capability of offering personalized assortments to customers to make better use of\u0026nbsp;remaining inventories. Allocating the storage capacity among the products requires tackling a combinatorial problem, whereas finding an\u0026nbsp;assortment personalization policy requires approximating a dynamic program with a high-dimensional state variable. When the choices of\u0026nbsp;the customers are governed by the multinomial logit model, we give a constant-factor approximation algorithm for this joint inventory\u0026nbsp;allocation and assortment personalization problem. Under a general choice model, we give an algorithm that is asymptotically optimal as\u0026nbsp;the storage capacity gets large. In the latter result, the demand can be scaled in an arbitrary fashion along with the storage capacity. This\u0026nbsp;is joint work with Yicheng Bai, Omar El Housni and Paat Rusmevichientong.\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EHuseyin Topaloglu is the Howard and Eleanor Morgan Professor in the School of Operations Research and Information Engineering at\u0026nbsp;Cornell Tech. He holds a Ph.D. in Operations Research and Financial Engineering from Princeton. His recent research focuses on\u0026nbsp;constructing tractable solution methods for large-scale network revenue management problems and building approximation strategies for\u0026nbsp;retail assortment planning. Huseyin Topaloglu is currently serving as an area editor for Analytics in Operations area at Manufacturing and\u0026nbsp;Service Operations Management.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EIn this talk, we give approximation algorithms for a joint inventory allocation and assortment personalization problem motivated by\u0026nbsp;an online retail setting. In our problem, we have a limited amount of storage capacity that needs to be allocated among multiple products to\u0026nbsp;serve customers that arrive over a selling horizon. At the beginning of the selling horizon, we decide how many units of each product to\u0026nbsp;stock. Over the selling horizon, customers arrive at the platform one by one to make a purchase. Based on the remaining inventories of the\u0026nbsp;products and the information available on the arriving customer, we offer a personalized assortment of products to each customer. The\u0026nbsp;customer either makes a choice within the offered assortment or leaves without a purchase. Our goal is to decide how many units of each\u0026nbsp;product to stock at the beginning of the selling horizon and to find a policy to figure out which personalized assortment to offer to each\u0026nbsp;arriving customer to maximize the total expected revenue over the selling horizon. Our problem is motivated by same-day-delivery\u0026nbsp;applications in online retail, where the retailer needs to allocate the limited storage capacity in an urban warehouse among different\u0026nbsp;variants in a product category, while having the capability of offering personalized assortments to customers to make better use of\u0026nbsp;remaining inventories. Allocating the storage capacity among the products requires tackling a combinatorial problem, whereas finding an\u0026nbsp;assortment personalization policy requires approximating a dynamic program with a high-dimensional state variable. When the choices of\u0026nbsp;the customers are governed by the multinomial logit model, we give a constant-factor approximation algorithm for this joint inventory\u0026nbsp;allocation and assortment personalization problem. Under a general choice model, we give an algorithm that is asymptotically optimal as\u0026nbsp;the storage capacity gets large. In the latter result, the demand can be scaled in an arbitrary fashion along with the storage capacity. This\u0026nbsp;is joint work with Yicheng Bai, Omar El Housni and Paat Rusmevichientong.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Joint Inventory Allocation and Assortment Personalization with Performance Guarantees"}],"uid":"34977","created_gmt":"2022-11-14 19:39:28","changed_gmt":"2022-12-02 16:11:18","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2022-12-01T11:00:00-05:00","event_time_end":"2022-12-01T12:00:00-05:00","event_time_end_last":"2022-12-01T12:00:00-05:00","gmt_time_start":"2022-12-01 16:00:00","gmt_time_end":"2022-12-01 17:00:00","gmt_time_end_last":"2022-12-01 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"166896","name":"seminar"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}