TY - GEN
T1 - A Genetic Algorithm to Design Job Rotation Schedules with Low Risk Exposure
AU - Rodrigues, João
AU - Gamboa, Hugo
AU - Mollaei, Nafiseh
AU - Osório, Daniel
AU - Assunção, Ana
AU - Fujão, Carlos
AU - Carnide, Filomena
N1 - info:eu-repo/grantAgreement/FCT/OE/PD%2FBDE%2F142816%2F2018/PT#
info:eu-repo/grantAgreement/FCT/OE/PD%2FBDE%2F142973%2F2018/PT#
info:eu-repo/grantAgreement/FCT/OE/SFRH%2FBDE%2F102750%2F2014/PT#
Grant UID/DTP/UI447/2019 to CIPER–Centro Interdisciplinar para o Estudo da Performance Humana (unit 447
PY - 2020
Y1 - 2020
N2 - In automotive industries, the manufacturing processes are characterized by repetitive tasks and physically demanding work, with possible long-term implications on the musculoskeletal health of the workers. One key organizational strategy that provides an improvement in the prevention of musculoskeletal disorders are job rotation schedules. These are usually designed manually, being (1) time demanding and (2) a subjective evaluation of the schedule’s risk. In this work, a genetic algorithm is presented, to generate automatically a daily job rotation schedule. The quality of the schedule is based on objective scores as- signed to workstations by the European Assembly Worksheet (EAWS) risk screening tool, guiding the algorithm in reaching a final solution that promotes schedules with lower exposure to the sequence of workstations assigned to each worker of the team. The schedules generated by the algorithm were compared to schedules designed by the team leaders and presented a better overall result.
AB - In automotive industries, the manufacturing processes are characterized by repetitive tasks and physically demanding work, with possible long-term implications on the musculoskeletal health of the workers. One key organizational strategy that provides an improvement in the prevention of musculoskeletal disorders are job rotation schedules. These are usually designed manually, being (1) time demanding and (2) a subjective evaluation of the schedule’s risk. In this work, a genetic algorithm is presented, to generate automatically a daily job rotation schedule. The quality of the schedule is based on objective scores as- signed to workstations by the European Assembly Worksheet (EAWS) risk screening tool, guiding the algorithm in reaching a final solution that promotes schedules with lower exposure to the sequence of workstations assigned to each worker of the team. The schedules generated by the algorithm were compared to schedules designed by the team leaders and presented a better overall result.
KW - Genetic algorithm
KW - Job rotation
KW - Occupational exposure
UR - http://www.scopus.com/inward/record.url?scp=85084855544&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-45124-0_38
DO - 10.1007/978-3-030-45124-0_38
M3 - Conference contribution
AN - SCOPUS:85084855544
SN - 978-3-030-45123-3
T3 - IFIP Advances in Information and Communication Technology
SP - 395
EP - 402
BT - Technological Innovation for Life Improvement - 11th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Farhadi, Nastaran
A2 - Lopes, Fábio
A2 - Pereira, Helena
PB - Springer
CY - Cham
T2 - 11th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020
Y2 - 1 July 2020 through 3 July 2020
ER -