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dc.contributor.authorBezoui, Madani
dc.contributor.authorTurki Almaktoom, Abdulaziz
dc.contributor.authorBounceur, Ahcène
dc.contributor.authorMian Qaisar, Saeed
dc.contributor.authorChouman, Mervat
dc.date.accessioned2024-04-09T09:45:22Z
dc.date.available2024-04-09T09:45:22Z
dc.date.issued2024-03-21
dc.identifier.doihttps://doi.org/10.1109/LT60077.2024.10469011en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1539
dc.descriptionThis work is financially supported by the Effat University under the grant number (UC#9/12June2023/7.1-21(4)11)en_US
dc.description.abstractThis paper explores the transition to Industry 5.0, highlighting its focus on sustainable, human-centred and resilient industrial progress. In this new era, the integration of advanced technology with human expertise is crucial, emphasising the importance of balancing efficiency, cost, quality, and sustainability. At the heart of this research is Multi-Objective Optimisation (MOO), which is used to address the complex challenges of modern manufacturing systems. We propose an innovative approach that combines mathematical modelling with swarm intelligence to tackle complex optimisation problems. A detailed Multi-Objective Mixed Integer Linear Programming (MILP) model is developed and its effectiveness is demonstrated through the application of Multi-Objective Particle Swarm Optimisation (MOPSO). The study compares the performance of MOPSO with traditional optimisation methods using synthetic data analysis. The results not only demonstrate the potential of MOPSO in modern manufacturing, but also set the stage for future research to integrate human ergonomics into the optimization framework, thereby contributing to the holistic advancement of Industry 5.0.en_US
dc.description.sponsorshipEffat Universityen_US
dc.publisherIEEEen_US
dc.subjectIndustry 5.0en_US
dc.subjectMulti-Objective Optimizationen_US
dc.subjectMetahurestic Algorithmsen_US
dc.subjectParticle Swarm Optimizationen_US
dc.titleHybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimizationen_US
dc.contributor.researcherUniversity Collaborationen_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSABUS , MGT & ACCTen_US
dc.contributor.ugstudent0en_US
dc.contributor.alumnae0en_US
dc.title.projectMulti-Objective Optimization based Manufacturing Production Performance Augmentation in the Industry 5.0 using the Artificial Intelligence and Meta-heuristicsen_US
dc.source.indexScopusen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.contributor.pgstudent0en_US
dc.contributor.firstauthorBezoui, Madani
dc.conference.locationJeddah, Saudi Arabiaen_US
dc.conference.name2024 21st Learning and Technology Conference (L&T)en_US
dc.conference.date2024-01-16


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