[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122425-en":3,"doc-seo-122425-105":30,"detail-sidebar-cat-0-en-105":90},{"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},122425,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",6,"Technology","Intelligent Scheduling based on Discrete-Time Simulation using Machine Learning","Dynamic planning becomes increasingly critical as external and internal conditions change, demanding fast reactions. Because planning problems are too complex for closed-loop optimization, organizations rely on heuristics and discrete-time simulation, which in turn require selecting and orchestrating many interdependent simulation parameters. Machine learning enables adaptive parameter configuration from data rather than manual tuning. This paper proposes an ML-driven approach for optimized planning parameter setups and evaluates it using real industrial data sets.","[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia CIRP 126 (2024) 48–51  \n17th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME‘23)  \nIntelligent Scheduling based on Discrete-Time Simulation using  \nMachine Learning  \nGünter Bitsch*, Pascal Senjic  \nReutlingen University, Alteburgstraße 150. 72762 Reutlingen, Germany  \n* Corresponding author. Tel.: +49-7121-271-3079 ; [E-mail address:](E-mail address: guenter.bitsch@reutlingen-university.de)[ guenter.bitsch@reutlingen-university.de](E-mail address: guenter.bitsch@reutlingen-university.de)  \nAbstract  \nDynamic planning is becoming more and more critical. The changing external and internal conditions for planning require a fast response. Due to the complexity, planning tasks cannot be solved in a closed loop. Instead, heuristics are used to solve the task. A frequently used method is the discrete-time simulation. However, this requires different simulation parameters to carry out the planning. The orchestration of the parameters is intricate because of many parameters, respectively, parameters combinations and their dependencies within the simulation. Machine Learning (ML) is an essential technology for adapting parameters under changing conditions. Using ML, this paper presents an approach that allows an optimized configuration of the planning parameters depending on the data. The method is evaluated based on real-world data sets from the industry.  \n© 2024 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRPICME‘23)  \nKeywords: Reinforcement Lerarning; Production Scheduling; Discret Event-Simulation  \n1. Introduction  \nModern production planning systems face significant challenges due to the inherent complex problem-solving nature [1] and dynamic changes, both externally and internally driven. Frequently used heuristics [2], partly based on experiential knowledge, are increasingly less effective in addressing these issues, especially given the multitude of influencing factors. A suitable strategy for managing the changes and the variety offactors is to integrate artificial intelligence (AI) methods [3, 4] .  \nIn principle, AI-based methods can be used for (a) optimizing and verifying data, (b) solution algorithms, or (c) parameter optimization. The presented approach focuses on parameter optimization, as integrating AI methods for solving real production systems is still critical regarding runtime [5] . A particular emphasis of the approach is on practical applicability, as many previous works only deal with simulation or test data [6] .  \nThe paper is structured as follows. First, Section 2 discusses the fundamentals of dynamic planning using AI support. Building on this, Section 3 presents the conceptual and technical dimensions of the approach. Subsequently, Section 4 evaluates the method using a use case. Finally, Section 5 summarizes the results and provides a brief outlook.  \n2. Foundation and Related Work  \nAccording to Ding et al. [3], AI-supported techniques for dynamic planning can be divided into Directed Heuristics and Autonomous Learning.  \nUnder the category of Directed Heuristics, a distinction can be made between 'Evolutionary Computation' and 'Swarm Intelligence Optimization approaches.  \nEvolutionary computation approaches include Genetic Algorithms (GA) and Evolutionary Programming.  \n2212-8271 © 2024 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific co","cbCaidc5N7ngF7nI","https://ap.wps.com/l/cbCaidc5N7ngF7nI","pdf",413239,1,4,"English","en",105,"# Abstract\n# Introduction\n# Foundation and Related Work\n## Directed Heuristics\n## Autonomous Learning","[{\"question\":\"Why are dynamic planning and fast responses important in production scheduling?\",\"answer\":\"Dynamic planning is crucial because both external and internal conditions change during operations. These changes require quick planning adjustments to remain effective.\"},{\"question\":\"What challenge arises when using discrete-time simulation for planning?\",\"answer\":\"Discrete-time simulation needs multiple parameters, including many parameter combinations with dependencies. Orchestrating these parameters is intricate and can hinder efficient planning.\"},{\"question\":\"How does machine learning improve the configuration of planning parameters?\",\"answer\":\"Machine learning adapts simulation and planning parameters based on data. This allows optimized parameter configurations under changing conditions rather than relying solely on manual or static heuristics.\"}]","Intelligent Scheduling based on Discrete-Time Simulation using Machine Learning | PDF",1785810557,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"intelligent-scheduling-based-on-discrete-time-simulation-using-machine-learning","",{"@graph":36,"@context":84},[37,53,67],{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/intelligent-scheduling-based-on-discrete-time-simulation-using-machine-learning/122425/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why are dynamic planning and fast responses important in production scheduling?","Question",{"text":74,"@type":75},"Dynamic planning is crucial because both external and internal conditions change during operations. These changes require quick planning adjustments to remain effective.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What challenge arises when using discrete-time simulation for planning?",{"text":79,"@type":75},"Discrete-time simulation needs multiple parameters, including many parameter combinations with dependencies. Orchestrating these parameters is intricate and can hinder efficient planning.",{"name":81,"@type":72,"acceptedAnswer":82},"How does machine learning improve the configuration of planning parameters?",{"text":83,"@type":75},"Machine learning adapts simulation and planning parameters based on data. This allows optimized parameter configurations under changing conditions rather than relying solely on manual or static heuristics.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,112,117,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":110,"slug":111},50,"technology",{"id":113,"doc_module":4,"doc_module_name":46,"category_name":114,"show_sort_weight":115,"slug":116},7,"Healthcare",40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]