[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123739-en":3,"doc-seo-123739-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123739,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Optimization Based Machine Learning Methods for Business Analytics - Ph.D. Dissertation","The dissertation addresses modern business analytics by leveraging the combination of optimization methods and machine learning. It introduces two mixed-integer optimization frameworks for (1) product design aimed at market share maximization under a logit choice model and (2) learning optimal decision trees while incorporating side constraints from real operations. For product design, the work reformulates an intractable discrete problem into a mixed-integer convex program and provides an exact solution approach with integer, convex, and conic optimization. For decision trees, it proposes a theoretically strong mixed-integer formulation and a constraint-generation solution method that improves tractability and runtime on real-world data sets.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nOptimization Based Machine Learning Methods for Business Analytics  \nPermalink  \n[https://escholarship.org/uc/item/0554z4bf](https://escholarship.org/uc/item/0554z4bf)  \nAuthor  \nAkcakus, Emine Irem  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nOptimization Based Machine Learning Methods for Business Analytics  \nA dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Management  \nby  \nEmine Irem Akcakus  \n2023  \n􀀍c Copyright by  \nEmine Irem Akcakus  \n2023  \nABSTRACT OF THE DISSERTATION  \nOptimization Based Machine Learning Methods  \nfor Business Analytics  \nby  \nEmine Irem Akcakus  \nDoctor of Philosophy in Management  \nUniversity of California, Los Angeles, 2023 Professor Velibor Mi􀀔si􀀓c, Chair  \nThe growing availability of data and recent developments in optimization methods and machine learning have led to a revolution in modern business analytics. In this Ph.D. dissertation, we propose two frameworks based on mixed-integer optimization that advance business analytics in the context of two important problems: product design with market share maximization and learning optimal decision trees.  \nIn the 􀀌rst problem, we aim to 􀀌nd a product, as de􀀌ned by its attributes, that maximizes market share, which is a weighted sum of logistic probabilities when we assume each customer segment follows a logit choice model to make a purchase. At 􀀌rst glance, this problem appears hopeless: one must optimize an objective function that is neither convex nor concave over an exponentially-sized discrete set of attribute combinations. Surprisingly, we show that this problem can be reformulated as a mixed-integer convex program by exploiting an economic model. We further propose an exact methodology for solving this problem based on modern integer, convex, and conic optimization techniques. Using synthetic problem instances and instances derived from real conjoint data sets, we show that our methodology can solve large  \nproblem instances to provable optimality or near-optimality within operationally feasible time frames.  \nIn the second problem, we propose a mixed-integer program that learns optimal decision trees from data. While decision trees are among the most widely-used machine learning methods, their learning algorithms are usually based on top-down heuristics and cannot incorporate side constraints arising from real-world business operations. We show that our proposed mixed-integer formulation is theoretically stronger than other formulations in the literature by exploring its relaxation properties. We also develop a large-scale solution method based on constraint generation. Based on computational studies on real-world data sets, we show that our proposed model is signi􀀌cantly more tractable than alternative mixed-integer optimization models and our large-scale method based on constraint generation can further improve the solution time in several data sets.  \nOverall, we contribute to business analytics by proposing exact solution methods based on optimization to two signi􀀌cant but computationally challenging problems and developing e􀀎cient algorithms that make them more practical to use.  \nThe dissertation of Emine Irem Akcakus is approved.  \nElisa Long  \nCharles Corbett  \nFrancisco Castro  \nFelipe Caro Velibor Mi􀀔si􀀓c, Committee Chair  \nUniversity of California, Los Angeles 2023  \nTo my parents Sevim and Mustafa Akcakus  \nv  \nTABLE OF CONTENTS  \n1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1  \n1.1 Exact Logit-Based Product Design ....................... 2  \n1.2 An Integer Programming Approach to Binary Decision Trees ......... 3  \n2 Exact Logit-Based Product Design : : : : : : : : : : : : : : : : : : : : : : : 6  \n2.1 L","cbCaiiYrSTa8buXL","https://ap.wps.com/l/cbCaiiYrSTa8buXL","pdf",775611,1,149,"English","en",105,"# Introduction\n## Exact Logit-Based Product Design\n## An Integer Programming Approach to Binary Decision Trees\n# Exact Logit-Based Product Design\n## Literature Review\n## Model\n## Solution Approaches\n## Extensions\n## Numerical Experiments\n# An Integer Programming Approach to Binary Decision Trees\n## Literature Review\n## Model\n## Constraint Generation-Based Solution Method\n## Computational Experiments\n# Conclusion\n## Appendix to Chapter 2","[{\"question\":\"What are the two main business analytics problems studied in the dissertation?\",\"answer\":\"The dissertation focuses on product design for market share maximization and learning optimal decision trees from data.\"},{\"question\":\"How is the product design problem modeled and solved?\",\"answer\":\"It assumes customer segments follow a logit choice model and reformulates the discrete attribute optimization as a mixed-integer convex program, solved with modern integer, convex, and conic optimization techniques.\"},{\"question\":\"What makes the decision tree learning approach different from standard methods?\",\"answer\":\"The proposed method uses a mixed-integer formulation that can incorporate side constraints from real business operations, and it is paired with a constraint-generation solution approach for improved scalability.\"}]","Optimization Based Machine Learning Methods for Business Analytics - Ph.D. Dissertation | PDF",1785818265,375,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimization-based-machine-learning-methods-for-business-analytics-phd-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/optimization-based-machine-learning-methods-for-business-analytics-phd-dissertation/123739/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What are the two main business analytics problems studied in the dissertation?","Question",{"text":75,"@type":76},"The dissertation focuses on product design for market share maximization and learning optimal decision trees from data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the product design problem modeled and solved?",{"text":80,"@type":76},"It assumes customer segments follow a logit choice model and reformulates the discrete attribute optimization as a mixed-integer convex program, solved with modern integer, convex, and conic optimization techniques.",{"name":82,"@type":73,"acceptedAnswer":83},"What makes the decision tree learning approach different from standard methods?",{"text":84,"@type":76},"The proposed method uses a mixed-integer formulation that can incorporate side constraints from real business operations, and it is paired with a constraint-generation solution approach for improved scalability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]