{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T16:24:39Z","timestamp":1774283079225,"version":"3.50.1"},"reference-count":93,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Funds","award":["LA\/P\/0063\/2020"],"award-info":[{"award-number":["LA\/P\/0063\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>The advent of Industry 4.0, driven by automation and real-time data analysis, offers significant opportunities to revolutionize manufacturing, with mobile robots playing a central role in boosting productivity. In smart job shops, scheduling tasks involves not only assigning work to machines but also managing robot allocation and travel times, thus extending traditional problems like the Job Shop Scheduling Problem (JSSP) and Traveling Salesman Problem (TSP). Common solution methods include heuristics, metaheuristics, and hybrid methods. However, due to the complexity of these problems, existing models often struggle to provide efficient optimal solutions. Machine learning, particularly reinforcement learning (RL), presents a promising approach by learning from environmental interactions, offering effective solutions for task scheduling. This systematic literature review analyzes 71 papers published between 2014 and 2024, critically evaluating the current state of the art of task scheduling with mobile robots. The review identifies the increasing use of machine learning techniques and hybrid approaches to address more complex scenarios, thanks to their adaptability. Despite these advancements, challenges remain, including the integration of path planning and obstacle avoidance in the task scheduling problem, which is crucial for making these solutions stable and reliable for real-world applications and scaling for larger fleets of robots.<\/jats:p>","DOI":"10.3390\/robotics14060075","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T08:07:58Z","timestamp":1748592478000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Task Scheduling with Mobile Robots\u2014A Systematic Literature Review"],"prefix":"10.3390","volume":"14","author":[{"given":"Catarina","family":"Rema","sequence":"first","affiliation":[{"name":"INESC TEC\u2013Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0435-8419","authenticated-orcid":false,"given":"Pedro","family":"Costa","sequence":"additional","affiliation":[{"name":"INESC TEC\u2013Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0593-2865","authenticated-orcid":false,"given":"Manuel","family":"Silva","sequence":"additional","affiliation":[{"name":"INESC TEC\u2013Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"ISEP, Polytechnic of Porto, rua Dr. Ant\u00f3nio Bernardino de Almeida, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3224-4926","authenticated-orcid":false,"given":"Eduardo J. Solteiro","family":"Pires","sequence":"additional","affiliation":[{"name":"INESC TEC\u2013Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes and Alto Douro (UTAD), 5000-811 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sahar, N., Basile, A., and Yan, B. (September, January 28). A Tightened Formulation for Job Shop Scheduling with Mobile Robots. Proceedings of the 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), Bari, Italy.","DOI":"10.1109\/CASE59546.2024.10711558"},{"key":"ref_2","first-page":"100387","article-title":"Energy-efficient open-shop scheduling with multiple automated guided vehicles and deteriorating jobs","volume":"30","author":"He","year":"2022","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102506","DOI":"10.1016\/j.rcim.2022.102506","article-title":"A novel MILP model for job shop scheduling problem with mobile robots","volume":"81","author":"Yao","year":"2023","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107300","DOI":"10.1016\/j.engappai.2023.107300","article-title":"Robust and efficient task scheduling for robotics applications with reinforcement learning","volume":"127","author":"Tejer","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., and Group, T.P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med., 6.","DOI":"10.1371\/journal.pmed.1000097"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, T.S., Ip, A., Tavana, M., and Jain, V. (2020). Comparative Study and Hybrid Modeling of Vehicle Routing Problem and Job Shop Scheduling Problem. Recent Trends in Decision Science and Management, Springer. Advances in Intelligent Systems and Computing.","DOI":"10.1007\/978-981-15-3588-8"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liaqait, R., Hamid, S., Warsi, S., and Khalid, A. (2021). A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0. Sustainability, 13.","DOI":"10.3390\/su13147684"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105731","DOI":"10.1016\/j.cor.2022.105731","article-title":"A Survey of Job Shop Scheduling Problem: The Types and Models","volume":"142","author":"Xiong","year":"2022","journal-title":"Comput. Oper. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s10846-022-01803-0","article-title":"Market Approaches to the Multi-Robot Task Allocation Problem: A Survey","volume":"107","author":"Quinton","year":"2023","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_10","first-page":"68","article-title":"A Systematic Literature Review on Multi-Robot Task Allocation","volume":"57","author":"Subramaniam","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_11","unstructured":"Freitas, V. (2025, March 24). Parsifal. Available online: https:\/\/parsif.al\/."},{"key":"ref_12","unstructured":"(2025, March 04). NotebookLM. Available online: https:\/\/notebooklm.google\/."},{"key":"ref_13","unstructured":"(2024, October 24). ACM Digital Library, n.d. Available online: https:\/\/dl.acm.org\/."},{"key":"ref_14","unstructured":"Scopus (2024, October 24). Scopus, n.d. Available online: https:\/\/www.scopus.com\/search\/form.uri."},{"key":"ref_15","unstructured":"IEEE Xplore (2024, October 24). IEEE Xplore Digital Library, n.d. Available online: https:\/\/ieeexplore.ieee.org\/Xplore\/home.jsp."},{"key":"ref_16","unstructured":"Web of Science (2024, October 24). Web of Science, n.d. Available online: https:\/\/www.webofscience.com\/wos\/woscc\/basic-search."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: VOSviewer, a computer program for bibliometric mapping","volume":"84","author":"Waltman","year":"2010","journal-title":"Scientometrics"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ding, Y., Rousseau, R., and Wolfram, D. (2014). Visualizing bibliometric networks. Measuring Scholarly Impact, Springer.","DOI":"10.1007\/978-3-319-10377-8"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, X., and Liu, S. (2023, January 3\u20136). Distributed Cooperative Task Planning for Autonomous Mobile Robots in Intralogistics. Proceedings of the 9th 2023 International Conference on Control, Decision and Information Technologies, CoDIT 2023, Rome, Italy.","DOI":"10.1109\/CoDIT58514.2023.10284078"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, G.B., Li, H.B., Liu, X.T., and Peng, Y.J. (2024). Simulation Budget Allocation for Improving Scheduling and Routing of Automated Guided Vehicles in Warehouse Management. J. Oper. Res. Soc. China.","DOI":"10.1007\/s40305-024-00553-0"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100437","DOI":"10.1016\/j.eij.2023.100437","article-title":"Optimizing flexible job shop scheduling with automated guided vehicles using a multi-strategy-driven genetic algorithm","volume":"25","author":"Li","year":"2024","journal-title":"Egypt. Inform. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"124223","DOI":"10.1016\/j.eswa.2024.124223","article-title":"An effective multi-restart iterated greedy algorithm for multi-AGVs dispatching problem in the matrix manufacturing workshop","volume":"252","author":"Liu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110420","DOI":"10.1016\/j.cie.2024.110420","article-title":"Automated mobile robots routing and job assignment in automated factory","volume":"195","author":"Pang","year":"2024","journal-title":"Comput. Ind. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1002\/net.22245","article-title":"A dynamic programming algorithm for order picking in robotic mobile fulfillment systems","volume":"84","author":"Justkowiak","year":"2024","journal-title":"Networks"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103752","DOI":"10.1016\/j.tre.2024.103752","article-title":"Making better order fulfillment in multi-tote storage and retrieval autonomous mobile robot systems","volume":"192","author":"Qin","year":"2024","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1016\/j.procs.2024.01.093","article-title":"Implementing Swarm Production System with Multi-Robot Simulation","volume":"232","author":"Avhad","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_27","first-page":"225","article-title":"Solving an Integrated Job-Shop\u2013Mobile Robot Scheduling Problem in Flexible Manufacturing System using Enhanced Genetic Algorithm Structure with Local Search Method","volume":"8","author":"Samsuria","year":"2024","journal-title":"Appl. Model. Simul."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106864","DOI":"10.1016\/j.engappai.2023.106864","article-title":"Multi-objective green scheduling of integrated flexible job shop and automated guided vehicles","volume":"126","author":"Xu","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101538","DOI":"10.1016\/j.swevo.2024.101538","article-title":"A dual population collaborative genetic algorithm for solving flexible job shop scheduling problem with AGV","volume":"86","author":"Han","year":"2024","journal-title":"Swarm Evol. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1080\/17477778.2023.2214685","article-title":"A simulation and scheduling method for analyzing the peak time capacity of the dual-robot in-line stocker","volume":"18","author":"Chung","year":"2024","journal-title":"J. Simul."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yan, Y., and Hu, Y. (2023, January 9\u201311). Integrated Scheduling of Flexible Job Shop and Energy-Efficient Automated Guided Vehicles. Proceedings of the 2023 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023, Sanya, China.","DOI":"10.1109\/ICARM58088.2023.10218812"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100254","DOI":"10.1016\/j.dajour.2023.100254","article-title":"An integrated strategy-based game-theoretic model and decentralized queueing system for mobile multi-robot task coordination","volume":"7","author":"Pradhan","year":"2023","journal-title":"Decis. Anal. J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, Q., and Lin, L. (2023, January 2\u20134). A Cooperative Hybrid Evolutionary Algorithm for Flexible Scheduling with AGVs. Proceedings of the ICSMD 2023\u2014International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, Proceedings, Xi\u2019an, China.","DOI":"10.1109\/ICSMD60522.2023.10490770"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1007\/s00170-022-10619-z","article-title":"An integrated scheduling approach considering dispatching strategy and conflict-free route of AMRs in flexible job shop","volume":"127","author":"Liu","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Durst, P., Jia, X., and Li, L. (2023, January 24\u201326). Multi-Objective Optimization of AGV Real-Time Scheduling Based on Deep Reinforcement Learning. Proceedings of the Chinese Control Conference (CCC), Tianjin, China.","DOI":"10.23919\/CCC58697.2023.10240797"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hu, E., He, J., and Shen, S. (2023). A dynamic integrated scheduling method based on hierarchical planning for heterogeneous AGV fleets in warehouses. Front. Neurorobot., 16.","DOI":"10.3389\/fnbot.2022.1053067"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"108932","DOI":"10.1016\/j.cie.2022.108932","article-title":"An automated guided vehicle conflict-free scheduling approach considering assignment rules in a robotic mobile fulfillment system","volume":"176","author":"Lu","year":"2023","journal-title":"Comput. Ind. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1109\/TRO.2022.3216068","article-title":"Robust Task Scheduling for Heterogeneous Robot Teams under Capability Uncertainty","volume":"39","author":"Fu","year":"2023","journal-title":"IEEE Trans. Robot."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3875","DOI":"10.1007\/s10845-023-02208-y","article-title":"Deep reinforcement learning for dynamic scheduling of energy-efficient automated guided vehicles","volume":"35","author":"Zhang","year":"2024","journal-title":"J. Intell. Manuf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e6950","DOI":"10.1002\/cpe.6950","article-title":"Optimum scheduling of machines, automated guided vehicles and tools without tool delay in a multi-machine flexible manufacturing system using symbiotic organisms search algorithm","volume":"34","author":"Mareddy","year":"2022","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kim, M.S., Oh, S.C., Chang, E., Lee, S., Wells, J., Arinez, J., and Jang, Y. (2022, January 20\u201324). A dynamic programming-based heuristic algorithm for a flexible job shop scheduling problem of a matrix system in automotive industry. Proceedings of the IEEE International Conference on Automation Science and Engineering, Mexico City, Mexico.","DOI":"10.1109\/CASE49997.2022.9926440"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1049\/cim2.12016","article-title":"Scheduling multi\u2013mode resource\u2013constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm","volume":"3","author":"Xiao","year":"2021","journal-title":"IET Collab. Intell. Manuf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1017\/pds.2021.17","article-title":"A multi-agent reinforcement learning framework for intelligent manufacturing with autonomous mobile robots","volume":"Volume 1","author":"Agrawal","year":"2021","journal-title":"Proceedings of the Design Society"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.procir.2021.11.169","article-title":"Coordinative scheduling of the mobile robots and machines based on hybrid GA in flexible manufacturing systems","volume":"104","author":"Qu","year":"2021","journal-title":"Procedia CIRP"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gu, W., Li, Y., Zheng, K., and Yuan, M. (2020). A bio-inspired scheduling approach for machines and automated guided vehicles in flexible manufacturing system using hormone secretion principle. Adv. Mech. Eng., 12.","DOI":"10.1177\/1687814020907787"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"27346","DOI":"10.1109\/ACCESS.2021.3058190","article-title":"Adaptive Task Planning for Multi-Robot Smart Warehouse","volume":"9","author":"Bolu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TASE.2020.3012879","article-title":"Using CP\/SMT Solvers for Scheduling and Routing of AGVs","volume":"18","author":"Riazi","year":"2021","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Mayer, S., Hohme, N., Gankin, D., and Endisch, C. (2019, January 22\u201325). Adaptive production control in a modular assembly system\u2014Towards an agent-based approach. Proceedings of the IEEE International Conference on Industrial Informatics (INDIN), Helsinki, Finland.","DOI":"10.1109\/INDIN41052.2019.8972152"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"255","DOI":"10.14311\/NNW.2018.28.016","article-title":"Fuzzy multi-objective optimization algorithms for solving multi-mode automated guided vehicles by considering machine break time and artificial neural network","volume":"28","author":"Nabovati","year":"2018","journal-title":"Neural Netw. World"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Khosiawan, Y., Khalfay, A., and Nielsen, I. (2018). Scheduling unmanned aerial vehicle and automated guided vehicle operations in an indoor manufacturing environment using differential evolution-fused particle swarm optimization. Int. J. Adv. Robot. Syst., 15.","DOI":"10.1177\/1729881417754145"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, F., Zhang, Y., and Su, Z. (2019). A novel scheduling method for automated guided vehicles in workshop environments. Int. J. Adv. Robot. Syst., 16.","DOI":"10.1177\/1729881419844152"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.cie.2015.01.003","article-title":"An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs","volume":"86","author":"Evazabadian","year":"2015","journal-title":"Comput. Ind. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mousavi, M., Yap, H., Musa, S., Tahriri, F., and Md Dawal, S. (2017). Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169817"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4773","DOI":"10.1080\/00207543.2015.1087656","article-title":"A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles","volume":"54","author":"Baruwa","year":"2016","journal-title":"Int. J. Prod. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s00170-015-7343-4","article-title":"Integrated tasks assignment and routing for the estimation of the optimal number of AGVS","volume":"82","author":"Vivaldini","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Mudrova, L., and Hawes, N. (2015, January 26\u201330). Task scheduling for mobile robots using interval algebra. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139027"},{"key":"ref_57","unstructured":"LopezIbanez, M. (2019, January 13\u201317). Adaptive Large Neighborhood Search for Scheduling of Mobile Robots. Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO\u201919), Prague Czech Republic."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Al-Momani, H., and Al-Aubidv, K.M. (2020, January 20\u201323). Fuzzy-Based Task Scheduling of Mobile Robots in Flexible Manufacturing Systems. Proceedings of the 2020 17th International Multi-Conference on Systems, Signals & Devices (SSD), Sfax, Tunisia.","DOI":"10.1109\/SSD49366.2020.9364247"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s10732-018-9391-z","article-title":"Scheduling of mobile robots for transportation and manufacturing tasks","volume":"25","author":"Dang","year":"2019","journal-title":"J. Heuristics"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Bakshi, S., Feng, T., Yan, Z., and Chen, D. (2019, January 10\u201312). A Regularized Quadratic Programming Approach to Real-Time Scheduling of Autonomous Mobile Robots in a Prioritized Task Space. Proceedings of the 2019 American Control Conference (ACC), Philadelphia, PA, USA.","DOI":"10.23919\/ACC.2019.8814986"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"124589","DOI":"10.1016\/j.eswa.2024.124589","article-title":"Deep reinforcement learning driven cost minimization for batch order scheduling in robotic mobile fulfillment systems","volume":"255","author":"Cheng","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"102647","DOI":"10.1016\/j.aei.2024.102647","article-title":"Knowledge-based multi-objective evolutionary algorithm for energy-efficient flexible job shop scheduling with mobile robot transportation","volume":"62","author":"Yao","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"7394","DOI":"10.1109\/TMC.2023.3334589","article-title":"A Maintenance-Aware Approach for Sustainable Autonomous Mobile Robot Fleet Management","volume":"23","author":"Atik","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"104683","DOI":"10.1016\/j.robot.2024.104683","article-title":"Adaptive fuzzy-genetic algorithm operators for solving mobile robot scheduling problem in job-shop FMS environment","volume":"176","author":"Samsuria","year":"2024","journal-title":"Robot. Auton. Syst."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/s10845-018-1459-y","article-title":"A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand","volume":"31","author":"Li","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"597956","DOI":"10.1155\/2015\/597956","article-title":"Genetic Scheduling and Reinforcement Learning in Multirobot Systems for Intelligent Warehouses","volume":"2015","author":"Dou","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"7556","DOI":"10.1109\/TASE.2024.3464857","article-title":"Heuristic Scheduling for Robotic Job Shops Using Petri Nets and Artificial Potential Fields","volume":"22","author":"Yi","year":"2025","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Leet, C., Oh, C., Lora, M., Koenig, S., and Nuzzo, P. (2023, January 1\u20135). Task Assignment, Scheduling, and Motion Planning for Automated Warehouses for Million Product Workloads. Proceedings of the 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA.","DOI":"10.1109\/IROS55552.2023.10341755"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.ejor.2023.03.037","article-title":"Robotized sorting systems: Large-scale scheduling under real-time conditions with limited lookahead","volume":"310","author":"Boysen","year":"2023","journal-title":"Eur. J. Oper. Res."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Cechinel, A., and De Pieri, E. (2024). Centralized multi-robot logistic system: An approach using the island model genetic algorithm as task scheduler. Int. J. Adv. Robot. Syst., 21.","DOI":"10.1177\/17298806241279595"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1109\/JIOT.2018.2871346","article-title":"Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop","volume":"6","author":"Wang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhu, K., Song, X., and Zhang, J. (2023, January 12\u201314). An AGV Task Scheduling Method Based on Multi-Agent Reinforcement Learning. Proceedings of the 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS), Xiangtan, China.","DOI":"10.1109\/DDCLS58216.2023.10166593"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Sun, C., Wang, Y., Zhang, F., Li, D., Tan, Y., and Zhang, J. (2023, January 12\u201314). Chaotic Sparrow Search Algorithm Based Scheduling for Flexible Job Shop with Automatic Guided Vehicle. Proceedings of the 2023 4th International Conference on Information Science, Parallel and Distributed Systems (ISPDS), Guangzhou, China.","DOI":"10.1109\/ISPDS58840.2023.10235475"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Geng, S., Guo, Y., Huang, S., and Sitahong, A. (2024, January 29). Multi-agent Deep Reinforcement Learning Based Integrated Scheduling of Machines and AGVs in Discrete Manufacturing Workshop. Proceedings of the 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), Shah Alam, Malaysian.","DOI":"10.1109\/I2CACIS61270.2024.10649828"},{"key":"ref_75","unstructured":"Dong, X., Wan, G., and Zeng, P. (2024, January 16\u201318). Flexible job shop machines and AGVs cooperative scheduling on the basis of DQN algorithm. Proceedings of the 2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Dalian, China."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"96088","DOI":"10.1109\/ACCESS.2020.2997663","article-title":"Optimal Scheduling of Flexible Manufacturing System Using Improved Lion-Based Hybrid Machine Learning Approach","volume":"8","author":"Abidi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"9389","DOI":"10.1109\/ACCESS.2023.3236843","article-title":"Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study","volume":"11","author":"Krishnamoorthy","year":"2023","journal-title":"IEEE Access"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Song, X., Zhu, K., Zhao, Y., and Zhang, J. (2023, January 12\u201314). Heterogeneous AGVs Scheduling in Hospital Using ALNS-based Metaheuristic Algorithm. Proceedings of the 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS), Xiangtan, China.","DOI":"10.1109\/DDCLS58216.2023.10167266"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"101701","DOI":"10.1109\/ACCESS.2024.3432594","article-title":"An Effective Discrete Jaya Algorithm for Multi-AGVs Scheduling Problem With Dynamic Unloading Time","volume":"12","author":"Cui","year":"2024","journal-title":"IEEE Access"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1109\/TEVC.2022.3219238","article-title":"A Learning-Based Multipopulation Evolutionary Optimization for Flexible Job Shop Scheduling Problem with Finite Transportation Resources","volume":"27","author":"Pan","year":"2023","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wang, M., and Xin, B. (2019, January 16\u201319). A Genetic Algorithm for Solving Flexible Flow Shop Scheduling Problem with Autonomous Guided Vehicles. Proceedings of the 2019 IEEE 15th International Conference on Control and Automation (ICCA), Edinburgh, Scotland, UK.","DOI":"10.1109\/ICCA.2019.8899914"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"67327","DOI":"10.1109\/ACCESS.2021.3076919","article-title":"Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams","volume":"9","author":"Gambardella","year":"2021","journal-title":"IEEE Access"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Yu, N., Li, T., and Wang, B. (2021, January 11\u201312). Multi-load AGVs scheduling algorithm in automated sorting warehouse. Proceedings of the 2021 14th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID52796.2021.00037"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Lin, Z., Ding, P., and Li, J. (2021, January 3\u20135). Task Scheduling and Path Planning of Multiple AGVs via Cloud and Edge Computing. Proceedings of the 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), Xiamen, China.","DOI":"10.1109\/ICNSC52481.2021.9702191"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TASE.2022.3221352","article-title":"Federated Deep Reinforcement Learning for Task Scheduling in Heterogeneous Autonomous Robotic System","volume":"21","author":"Ho","year":"2024","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_86","unstructured":"Li, M.P., Sankaran, P., Kuhl, M.E., Ptucha, R., Ganguly, A., and Kwasinski, A. (2020, January 8\u201311). Task selection by autonomous mobile robots in a warehouse using deep reinforcement learning. Proceedings of the WSC \u201919, National Harbor, MD, USA."},{"key":"ref_87","unstructured":"Dang, Q.V., Martagan, T., Adan, I., and Kleinlugtenbeld, J. (2023, January 11\u201314). Order Release Strategies for a Collaborative Order Picking System. Proceedings of the WSC \u201922, Singapore."},{"key":"ref_88","unstructured":"Holland, J.H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1287\/trsc.1050.0135","article-title":"An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows","volume":"40","author":"Ropke","year":"2006","journal-title":"Transp. Sci."},{"key":"ref_90","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1287\/opre.43.6.1058","article-title":"A time window approach to simultaneous scheduling of machines and material handling system in an FMS","volume":"43","author":"Bilge","year":"1995","journal-title":"Oper. Res."},{"key":"ref_92","unstructured":"Lawrence, S. (1984). Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques, Graduate School of Industrial Administration, Carnegie-Mellon University. Technical Report."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/BF02023073","article-title":"Routing and scheduling in a flexible job shop by tabu search","volume":"41","author":"Brandimarte","year":"1993","journal-title":"Ann. Oper. 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