{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:48:00Z","timestamp":1782809280296,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>Industry\u202f5.0 emphasizes the central role of human operators within industrial processes, highlighting the need for job\u2011allocation strategies that safeguard well\u2011being while maintaining operational performance. Traditional scheduling approaches typically assume stable human productivity and overlook the impact of cognitive and physiological conditions on task execution. This paper addresses this gap by proposing a human\u2011centric job allocation method grounded in process modelling and simulation.\n\nThe method integrates real\u2011time, sensor\u2011based measures and self-reported assessments to define a unified operational stress level, which dynamically reflects each operator\u2019s state. In parallel, tasks are characterized through a generalizable framework of product\u2011related factors that influence operator strain. A decision logic then aligns operator condition and task criticality, enabling adaptive allocation rules designed to balance productivity and human well\u2011being.\n\nThe proposed approach is validated through a discrete\u2011event simulation of an industrial manufacturing process. Results show that stress\u2011aware allocation preserves planned throughput while redistributing high\u2011demand tasks away from overloaded operators, thereby reducing operational risk. This work demonstrates that integrating human\u2011state indicators into business\u2011process simulation can support more resilient, sustainable, and human\u2011centred industrial operations.<\/jats:p>","DOI":"10.7148\/2026-0070","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:36:46Z","timestamp":1782808606000},"page":"70-76","source":"Crossref","is-referenced-by-count":0,"title":["Integrating operator stress into industrial task allocation"],"prefix":"10.7148","author":[{"given":"Alessandra","family":"Papetti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marianna","family":"Ciccarelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Germani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:36:49Z","timestamp":1782808609000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0070_bpmi_ecms2026_0114.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0070","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}