{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T07:39:26Z","timestamp":1782718766548,"version":"3.54.5"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Plan for Science, Technology, and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia","award":["13-SPA1130-02"],"award-info":[{"award-number":["13-SPA1130-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>This paper presents an optimization framework for Multi-Robot Task Allocation (MRTA) for a heterogeneous robot fleet operating in dynamic, failure-prone environments. In contrast to traditional MRTA approaches that handle only the initial allocation, our system extends functionality by integrating real-time crisis response and intelligent task recovery from failure points. The framework combines island model genetic algorithm-based initial optimization with an event-driven architecture for handling robot failures during mission execution. Our key contribution is the integration of crisis-aware capabilities with the island model paradigm, enabling task resumption from failure points and dynamic reoptimization, while preserving the diversity benefits of multi-population evolution. When a robot fails, the system intelligently substitutes replacement robots and resumes interrupted tasks from their exact failure point, rather than restarting from the beginning. This significantly improves mission efficiency and resilience. We introduce a temporal scheduling mechanism that tracks actual task execution states and calculates remaining work upon failure, enabling true task continuation. Experimental validation across 57 diverse scenarios with 2340 independent runs demonstrates that the island model achieves higher fitness scores, maintains greater population diversity, exhibits more consistent performance, and recovers faster from crisis events compared to the standard single-population genetic algorithm.<\/jats:p>","DOI":"10.3390\/robotics15020032","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T10:08:57Z","timestamp":1769681337000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimizing Multi-Robot Task Allocation with Dynamic Crisis Response: A Genetic Algorithm Approach with Task Resumption and Island Model Enhancement"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5145-6484","authenticated-orcid":false,"given":"Ameur","family":"Touir","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4499-577X","authenticated-orcid":false,"given":"Mohsen","family":"Denguir","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3646-6959","authenticated-orcid":false,"given":"Achraf","family":"Gazdar","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0727-7494","authenticated-orcid":false,"given":"Safwan","family":"Qasem","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Engineering, Al Yamamah University, Riyadh 11512, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"ref_1","first-page":"68","article-title":"A Systematic Literature Review on Multi-Robot Task Allocation","volume":"57","author":"Athira","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_2","unstructured":"Duchetto, F.D., Kucukyilmaz, A., and Hanheide, M. (June, January 29). In-the-Wild Failures in a Long-Term HRI Deployment. Proceedings of the International Conference on Robotics and Automation (ICRA), London, UK. Available online: https:\/\/nottingham-repository.worktribe.com\/output\/22183314."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.26599\/TST.2023.9010117","article-title":"Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots","volume":"29","author":"Zhou","year":"2024","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_4","unstructured":"Hu, Z., Wu, R., Enock, N., Li, J., Kadakia, R., Erickson, Z., and Kumar, A. (2025). RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7922","DOI":"10.1109\/TSMC.2025.3598298","article-title":"Learning-Based Approach to Integrated Operational Optimization Problems in Robot-Assisted Multistation Warehouse Systems","volume":"55","author":"Zhao","year":"2025","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16314","DOI":"10.1109\/JIOT.2024.3352658","article-title":"Order Picking Optimization in Smart Warehouses With Human\u2013Robot Collaboration","volume":"11","author":"Zhao","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s10514-021-10022-9","article-title":"Dynamic multi-robot task allocation under uncertainty and temporal constraints","volume":"46","author":"Choudhury","year":"2022","journal-title":"Auton. Robot."},{"key":"ref_9","unstructured":"Emam, Y., Mayya, S., Notomista, G., Bohannon, A., and Egerstedt, M. (August, January 31). Adaptive Task Allocation for Heterogeneous Multi-Robot Teams with Evolving and Unknown Robot Capabilities. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Paris, France."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/TRO.2021.3102379","article-title":"A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multirobot Systems","volume":"38","author":"Notomista","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"ref_11","unstructured":"Park, S., Zhong, Y.D., and Leonard, N.E. (June, January 30). Multi-Robot Task Allocation Games in Dynamically Changing Environments. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kalempa, V.C., Piardi, L., Limeira, M., and de Oliveira, A.S. (2021). Multi-Robot Preemptive Task Scheduling with Fault Recovery: A Novel Approach to Automatic Logistics of Smart Factories. Sensors, 21.","DOI":"10.3390\/s21196536"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kalempa, V.C., Piardi, L., Limeira, M., and de Oliveira, A.S. (2023). Multi-Robot Task Scheduling for Consensus-Based Fault-Resilient Intelligent Behavior in Smart Factories. Machines, 11.","DOI":"10.3390\/machines11040431"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"219730","DOI":"10.1109\/ACCESS.2020.3020265","article-title":"Scheduling of a Robot\u2019s Tasks with the TaskER Framework","volume":"8","author":"Dudek","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s43154-022-00079-4","article-title":"Resilient Robot Teams: A Review Integrating Decentralised Control, Change-Detection, and Learning","volume":"3","author":"Bossens","year":"2022","journal-title":"Curr. Robot. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gong, J., Kim, H., and Lee, S. (2024). Resilient Multi-Robot Coverage Path Redistribution Using Boustrophedon Decomposition for Environmental Monitoring. Sensors, 24.","DOI":"10.3390\/s24237482"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103905","DOI":"10.1016\/j.robot.2021.103905","article-title":"Multi-robot task allocation in disaster response: Addressing dynamic tasks with deadlines and robots with range and payload constraints","volume":"147","author":"Ghassemi","year":"2022","journal-title":"Robot. Auton. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103560","DOI":"10.1016\/j.robot.2020.103560","article-title":"Coupled task scheduling for heterogeneous multi-robot system of two robot types performing complex-schedule order fulfillment tasks","volume":"131","author":"Wang","year":"2020","journal-title":"Robot. Auton. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110628","DOI":"10.1016\/j.asoc.2023.110628","article-title":"Solving the Multi-robot task allocation with functional tasks based on a hyper-heuristic algorithm","volume":"146","author":"Yan","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"74327","DOI":"10.1109\/ACCESS.2024.3404823","article-title":"Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams","volume":"12","author":"Ferreira","year":"2024","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/TRO.2021.3128313","article-title":"Resilient Monitoring in Heterogeneous Multi-Robot Systems Through Network Reconfiguration","volume":"38","author":"Ramachandran","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7032","DOI":"10.1016\/j.ifacol.2023.10.551","article-title":"SA-reCBS: Multi-robot task assignment with integrated reactive path generation","volume":"56","author":"Bai","year":"2023","journal-title":"IFAC-PapersOnLine"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sung, Y., Shome, R., and Stone, P. (2024, January 13\u201317). Synchronous Task Plan Refinement for Multi-Robot Task and Motion Planning. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10610503"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100754","DOI":"10.1016\/j.simpa.2025.100754","article-title":"MRTASim: An agent-based multi-robot task allocation simulation","volume":"24","year":"2025","journal-title":"Softw. Impacts"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, C., Luo, W., and Sycara, K. (June, January 30). Online Connectivity-aware Dynamic Deployment for Heterogeneous Multi-Robot Systems. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561748"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kunz, C.A., and Pieri, E.R.D. (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_27","unstructured":"Bichler, J. (2025). MRTA-Benchmark Dataset: 250K Optimal Multi-Robot Task Allocation Instances with Heterogeneous Robots, Precedence Constraints & Dynamic Coalitions, 4TU.ResearchData, TU Delft Library."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.asoc.2014.08.025","article-title":"A comparative review of approaches to prevent premature convergence in GA","volume":"24","author":"Pandey","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1109\/TEVC.2002.800880","article-title":"Parallelism and evolutionary algorithms","volume":"6","author":"Alba","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_30","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1093\/genetics\/16.2.97","article-title":"Evolution in Mendelian Populations","volume":"16","author":"Wright","year":"1931","journal-title":"Genetics"}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/15\/2\/32\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T08:37:36Z","timestamp":1769762256000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/15\/2\/32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":31,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["robotics15020032"],"URL":"https:\/\/doi.org\/10.3390\/robotics15020032","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]}}}