[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121010-en":3,"doc-seo-121010-105":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":20,"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},121010,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning in Combinatorial Optimization - An Application to Machine Scheduling","The dissertation investigates how machine learning can enhance combinatorial optimization for machine scheduling, focusing on serial-batch scheduling under practical constraints. It develops and analyzes learning-augmented methods that improve the anticipation of complex nesting solutions and strengthen heuristic performance for parallel serial-batch processing machines. Contributions also include theory-oriented steps toward integrating ML components into optimization pipelines, along with algorithm selection and genetic-algorithm frameworks tailored to serial-batch scheduling scenarios. Results are discussed via key findings and quantified evaluation metrics.","MACHINE LEARNING IN COMBINATORIAL OPTIMIZATION – AN APPLICATION TO MACHINE  \nSCHEDULING  \naykut uzunoglu  \nAykut Uzunoglu: Machine Learning in Combinatorial Optimization – An Application to Machine Scheduling, December 2023  \nMACHINE LEARNING IN COMBINATORIAL OPTIMIZATION – AN APPLICATION TO MACHINE SCHEDULING  \nCumulative Doctoral Dissertation  \nat the Faculty of Business Administration and Economics of the University of Augsburg  \nsubmitted in partial fulfillment of the requirements for the degree of Doctor of Economic Sciences (Dr. rer. pol.)  \nsubmitted by  \nAykut Uzunoglu, M.Sc.  \nDecember 2023  \nFirst reviewer: Prof. Dr. Axel Tuma  \nSecond reviewer: Prof. Dr. Robert Klein  \nChairman of the oral exam: Prof. Dr. Marco C. Meier  \nDate of oral exam: 22.02.2024  \nlist of scientific contributions vi  \nlist of appendices vii  \n1 introduction 1  \n2 preliminaries 4  \n2.1 Application Case: A Serial-Batch Scheduling Problem ..... 4  \n2.2 Solving the PSBIJF ......................... 6  \n2.3 Machine Learning Theory ..................... 9  \n2.4 Machine Learning Enhanced Serial-Batch Scheduling ..... 14  \n3 constituent elements of this dissertation 18  \n3.1 Contribution A ........................... 18  \n3.2 Contribution B ............................ 21  \n3.3 Contribution C ........................... 23  \n3.4 Contribution D ........................... 25  \n4 scientific contributions 27  \n4.1 Contribution A ........................... 27  \n4.2 Contribution B ............................ 29  \n4.3 Contribution C ........................... 30  \n4.4 Contribution D ........................... 31  \n5 conclusion 32  \n5.1 Key Findings and Added Value .................. 32  \n5.2 Outlook and Future Research ................... 34  \nAppendix 36  \na appendix 37  \na.1 Contribution D ........................... 37  \nbibliography 90  \nThe following published and submitted scientific contributions arepresented within this doctoral dissertation. The articles are sorted following their order of publication. The journal rankings correspond to VHB-JOURQUAL3, published by the German Academic Association for Business Research (VHB) .  \nContribution A  \n(Published in the European Journal of Operational Research, ranked A) Gahm, C., Uzunoglu, A., Wahl, S., Ganschinietz, C., & Tuma, A. (2022) . Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning. European Journal of Operational Research, 296(3), 819–836. doi: 10. 1016/j.ejor.2021.04.006.  \nContribution B  \n(Published in Computers & Operations Research, ranked B) Uzunoglu, A., Gahm, C., Wahl, S., & Tuma, A. (2023a) . Learningaugmented heuristics for scheduling parallel serial-batch processing machines. Computers & Operations Research, 151, 106122. doi: 10. 1016/j.cor.2022.106122 .  \nContribution C  \n(Published in Annals of Operations Research, ranked B)  \nUzunoglu, A., Gahm, C., & Tuma, A. (2023b) . A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem. Annals of Operations Research. doi: 10.1007/s 10479-023-05541-w.  \nContribution D  \n(Submitted to Computers & Operations Research, ranked B) Uzunoglu, A., Gahm, C., & Tuma, A. (2023c) . Machine learning based Algorithm Selection and Genetic Algorithms for serial-batch scheduling. Computers & Operations Research.  \nContribution D  \n- The submitted manuscript  \nFigure 1 A serial-batch scheduling problem and its solution 6  \nFigure 2 Procedure to solve the PSBIJF ............ 9  \nFigure 3 Machine Learning enhanced procedure for the PSBIJF .......................... 15  \nFigure 4 Hierarchical planning problem ........... 19  \nACRONYMS  \nASP Algorithm Selection Problem GA Genetic Algorithm  \nML Machine Learning  \nMILP Mixed-Integer Linear Program MLGS Machine Learning Grid Search  \nMLPP Machine Learning Parameter Prediction MLRP Machine Learning Ranking Prediction MRIW Mean Relative Improvement to the Worst NN Neural Network  \nPSBIJF Parallel Serial-Batch","cbCaiacMg4rEto6y","https://ap.wps.com/l/cbCaiacMg4rEto6y","pdf",1380466,1,102,"English","en",105,"# Introduction\n## Machine Learning fundamentals\n## Supervised learning and predictive models\n# Preliminaries\n## Application case: a serial-batch scheduling problem\n## Solving the PSBIJF\n## Machine learning theory\n## Machine learning enhanced serial-batch scheduling\n# Constituent elements of this dissertation\n## Contribution A\n## Contribution B\n## Contribution C\n## Contribution D\n# Scientific contributions\n## Contribution A\n## Contribution B\n## Contribution C\n## Contribution D\n# Conclusion\n## Key findings and added value\n## Outlook and future research","[{\"question\":\"What is the main application area studied in the dissertation?\",\"answer\":\"The dissertation focuses on machine scheduling framed as combinatorial optimization problems, especially serial-batch and parallel serial-batch scheduling with constraints across job families.\"},{\"question\":\"How does machine learning get integrated into the scheduling approach?\",\"answer\":\"Machine learning is used to augment optimization by improving prediction and decision components inside heuristic procedures, including anticipating complex nesting solutions and enhancing search steps.\"},{\"question\":\"What kinds of contributions are presented?\",\"answer\":\"The work presents multiple contributions covering learning-augmented heuristics, machine learning enhanced scheduling procedures, and methods such as algorithm selection combined with genetic algorithms for serial-batch scheduling.\"}]","Machine Learning in Combinatorial Optimization - An Application to Machine Scheduling | PDF",1785733298,257,{"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},"machine-learning-in-combinatorial-optimization-an-application-to-machine-scheduling","",{"@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/machine-learning-in-combinatorial-optimization-an-application-to-machine-scheduling/121010/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main application area studied in the dissertation?","Question",{"text":75,"@type":76},"The dissertation focuses on machine scheduling framed as combinatorial optimization problems, especially serial-batch and parallel serial-batch scheduling with constraints across job families.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning get integrated into the scheduling approach?",{"text":80,"@type":76},"Machine learning is used to augment optimization by improving prediction and decision components inside heuristic procedures, including anticipating complex nesting solutions and enhancing search steps.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of contributions are presented?",{"text":84,"@type":76},"The work presents multiple contributions covering learning-augmented heuristics, machine learning enhanced scheduling procedures, and methods such as algorithm selection combined with genetic algorithms for serial-batch scheduling.","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"]