[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124570-en":3,"doc-seo-124570-105":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},124570,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Choosing Solution Strategies for Scheduling Automated Guided Vehicles in Production Using Machine Learning","Artificial intelligence supports the evolution of smart manufacturing, where automated guided vehicles (AGVs) are critical for improving internal logistics through greater production flexibility. System productivity depends on schedule quality, enabling cost savings by minimizing delays and overall makespan. Traditional scheduling algorithms struggle under changing conditions and their effectiveness varies by problem type. This work uses design science to create an algorithm selection approach using machine learning, validated through benchmark studies and a field experiment in a learning factory.","applied sciences  \nArticle  \nChoosing Solution Strategies for Scheduling Automated Guided Vehicles in Production Using Machine Learning  \nFelicia Schweitzer 1,2, *, Günter Bitsch 1 and Louis Louw 2  \nCitation: Schweitzer, F.; Bitsch, G.;  \nLouw, L. Choosing Solution Strategies for Scheduling Automated Guided Vehicles in Production Using Machine Learning. Appl. Sci. 2023, 13, 806. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)app13020806  \nAcademic Editor: Dimitris Mourtzis  \nReceived: 20 December 2022  \nRevised: 30 December 2022  \nAccepted: 4 January 2023  \nPublished: 6 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 ESB Business School, Reutlingen University, 72762 Reutlingen, Germany  \n2 Department of Industrial Engineering, Stellenbosch University, Stellenbosch 7600, South Africa  \n* Correspondence: [felicia.schweitzer@student.reutlingen-university.de or 26426625@sun.ac.za](felicia.schweitzer@student.reutlingen-university.de or 26426625@sun.ac.za)  \nFeatured Application: The artifact developed in this article is applicable to AGV and production planning optimization problems that align with the principles of the job-shop scheduling problem (JSSP) and the flexible job-shop scheduling problem (FJSSP).  \nAbstract: Artificial intelligence is considered to be a significant technology for driving the future evolution of smart manufacturing environments. At the same time, automated guided vehicles (AGVs) play an essential role in manufacturing systems due to their potential to improve internal logistics by increasing production flexibility. Thereby, the productivity of the entire system relies on the quality of the schedule, which can achieve production cost savings by minimizing delays and the total makespan. However, traditional scheduling algorithms often have difficulties in adapting to changing environment conditions, and the performance of a selected algorithm depends on the individual scheduling problem. Therefore, this paper aimed to analyze the scheduling problem classes of AGVs by applying design science research to develop an algorithm selection approach. The designed artifact addressed a catalogue of characteristics that used several machine learning algorithms to find the optimal solution strategy for the intended scheduling problem. The contribution of this paper is the creation of an algorithm selection method that automatically selects a scheduling algorithm, depending on the problem class and the algorithm space. In this way, production efficiency can be increased by dynamically adapting the AGV schedules. A computational study with benchmark literature instances unveiled the successful implementation of constraint programming solvers for solving JSSP and FJSSP scheduling problems and machine learning algorithms for predicting the most promising solver. The performance of the solvers strongly depended on the given problem class and the problem instance. Consequently, the overall production performance increased by selecting the algorithms per instance. A field experiment in the learning factory at Reutlingen University enabled the validation of the approach within a running production scenario.  \nKeywords: AGV scheduling; optimization; constraint programming; machine learning; algorithm selection  \n1. Introduction  \nWith the introduction of Industry 4.0, production systems have become increasingly complex within the last few years [1] . This is, on one hand, due to the growing size, but it is also due to the extended automation influences in manufacturing environments. As a result, the demands on intralogistics in terms of flexibility and internal control are increas","cbCaiqVajeusGKmH","https://ap.wps.com/l/cbCaiqVajeusGKmH","pdf",2797441,1,20,"English","en",105,"# Introduction\n## Problem background and motivation\n## Scheduling fundamentals and challenges","[{\"question\":\"Why does AGV scheduling quality significantly affect manufacturing performance?\",\"answer\":\"Schedule quality determines productivity by minimizing delays and reducing the overall makespan, which directly impacts production cost savings.\"},{\"question\":\"What limitation do traditional scheduling algorithms face in AGV settings?\",\"answer\":\"They often have difficulty adapting to changing shop-floor conditions, and performance depends strongly on the specific scheduling problem type.\"},{\"question\":\"How does the proposed approach select a scheduling algorithm?\",\"answer\":\"It develops an artifact that catalogs problem characteristics and applies multiple machine learning algorithms to identify the optimal solution strategy for the given scheduling problem class and instance.\"}]","Choosing Solution Strategies for Scheduling Automated Guided Vehicles in Production Using Machine Learning | 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does AGV scheduling quality significantly affect manufacturing performance?","Question",{"text":75,"@type":76},"Schedule quality determines productivity by minimizing delays and reducing the overall makespan, which directly impacts production cost savings.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitation do traditional scheduling algorithms face in AGV settings?",{"text":80,"@type":76},"They often have difficulty adapting to changing shop-floor conditions, and performance depends strongly on the specific scheduling problem type.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach select a scheduling algorithm?",{"text":84,"@type":76},"It develops an artifact that catalogs problem characteristics and applies multiple machine learning algorithms to identify the optimal solution strategy for the given scheduling problem class and 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