This paper introduces the parallel machine scheduling problems with work related musculoskeletal disorder (WMSD) risk considerations. By the WMSD consideration, we mean that job processing times are an increasing function of occupational repetitive technical actions (OCRA) risk factor. The OCRA index was recruited for risk assessment of occupational repetitive technical actions. WMSD risks were modeled with cumulative mean value of OCRA index. Due to NP-Hard structure of parallel machine scheduling problems with WMSD considerations and learning rate, the solution cannot be found always. However, problem can be solved by transforming assignment problem. In spite of the fact that the computational effort remains O (n4), problem is solved within more efficient time. In this study, a model that includes learning effect and WMSD risk was proposed. WMSD risk was considered as cumulative mean of risk value throughout shift. In order to the balance between productivity and WMSD risk, jobs’ foreseeable cycle time (FCT) value was changed. It is aimed to decrease mean of risk along schedule without increasing total basic process time. The value of sacrifice from the FCT was compensating from job which have optimal, acceptable, borderline or slight risk level. Process time and risk values were recalculated by using new FCT value. Thus, balanced process times and risk values obtained. Proposed model was solved with Lingo software and sequence of jobs was obtained. Total flow time and mean of risk were compared for balanced and none balanced schedules. Total flow time and mean of risk belong to balanced schedule is smaller than none balanced schedule. It was shown that handled problem is solvable at the polynomial time and total flow time can be improved by bringing balance between WMSD risks and productivity.
Erciyes University Scientific Research Projects Unit
Proje Numarası
FDK-2017-7265
Teşekkür
This study is supported by Erciyes University Scientific Research Projects Unit in the scope of “Scheduling under Ergonomic Risk Factors” coded FDK-2017-7265 project.
Kaynakça
Akyol, Ş. D., & Baykasoğlu, A. (2016). ErgoALWABP: a multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem under ergonomic risk factors. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-016-1246-6
Antwi-Afari, M. F., Li, H., Edwards, D. J., Pärn, E. A., Seo, J., & Wong, A. Y. L. (2017). Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers. Automation in Construction, 83, 41-47. doi: https://doi.org/10.1016/j.autcon.2017.07.007
Arık, O. A., & Toksarı, M. D. (2017). Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects. International Journal of Production Research, 1-18. doi: 10.1080/00207543.2017.1388932
Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1-13. doi: https://doi.org/10.1016/j.ejor.2015.12.023
Battini, D., Glock, C. H., Grosse, E. H., Persona, A., & Sgarbossa, F. (2015). Ergo-Lot-Sizing: Considering Ergonomics in Lot-Sizing Decisions. IFAC-PapersOnLine, 48(3), 326-331. doi: https://doi.org/10.1016/j.ifacol.2015.06.102
Biskup, D. (1999). Single-machine scheduling with learning considerations. European Journal of Operational Research, 115(1), 173-178. doi: https://doi.org/10.1016/S0377-2217(98)00246-X
Boenzi, F., Digiesi, S., Mossa, G., Mummolo, G., & Romano, V. A. (2013). Optimal Break and Job Rotation Schedules of High Repetitive – Low Load Manual Tasks in Assembly Lines: an OCRA – Based Approach. IFAC Proceedings Volumes, 46(9), 1896-1901. doi: https://doi.org/10.3182/20130619-3-RU-3018.00625
Fulmer, S., Buchholz, B., Scribani, M., & Jenkins, P. (2017). Musculoskeletal Disorders in Northeast Lobstermen. Safety and Health at Work, 8(3), 282-289. doi: https://doi.org/10.1016/j.shaw.2016.12.004
Hignett, S., & McAtamney, L. (2000). Rapid Entire Body Assessment (REBA). Applied Ergonomics, 31(2), 201-205. doi: https://doi.org/10.1016/S0003-6870(99)00039-3
Jiang, Z., Chen, F., & Kang, H. (2013). Single-machine scheduling problems with actual time-dependent and job-dependent learning effect. European Journal of Operational Research, 227(1), 76-80. doi: https://doi.org/10.1016/j.ejor.2012.12.007
Koulamas, C. (2017). Common due date assignment with generalized earliness/tardiness penalties. Computers & Industrial Engineering, 109, 79-83. doi: https://doi.org/10.1016/j.cie.2017.04.040
Koulamas, C., & Kyparisis, G. J. (2010). Single-machine scheduling problems with past-sequence-dependent delivery times. International Journal of Production Economics, 126(2), 264-266. doi: https://doi.org/10.1016/j.ijpe.2010.03.016
Lu, Y.-Y., Wang, J.-J., & Huang, X. (2015). Scheduling jobs with position and sum-of-processing-time based processing times. Applied Mathematical Modelling, 39(14), 4013-4021. doi: 10.1016/j.apm.2014.12.021
MacCarthy, B. L., Wilson, J. R., & Crawford, S. (2001). Human performance in industrial scheduling: A framework for understanding. Human Factors and Ergonomics in Manufacturing & Service Industries, 11(4), 299-320. doi: doi:10.1002/hfm.1016
McAtamney, L., & Nigel Corlett, E. (1993). RULA: a survey method for the investigation of work-related upper limb disorders. Applied Ergonomics, 24(2), 91-99. doi: https://doi.org/10.1016/0003-6870(93)90080-S
Micheli, G. J. L., & Marzorati, L. M. (2018). Beyond OCRA: Predictive UL-WMSD risk assessment for safe assembly design. International Journal of Industrial Ergonomics, 65, 74-83. doi: https://doi.org/10.1016/j.ergon.2017.07.005
Mosheiov, G., & Sidney, J. B. (2003). Scheduling with general job-dependent learning curves. European Journal of Operational Research, 147(3), 665-670. doi: https://doi.org/10.1016/S0377-2217(02)00358-2
Occhipinti, E. (1998). OCRA: a concise index for the assessment of exposure to repetitive movements of the upper limbs. Ergonomics, 41(9), 1290-1311. doi: 10.1080/001401398186315
Occhipinti, E., & Colombini, D. (2016). A toolkit for the analysis of biomechanical overload and prevention of WMSDs: Criteria, procedures and tool selection in a step-by-step approach. International Journal of Industrial Ergonomics, 52, 18-28. doi: https://doi.org/10.1016/j.ergon.2015.08.001
Oron, D. (2016). Scheduling controllable processing time jobs with position-dependent workloads. International Journal of Production Economics, 173, 153-160. doi: https://doi.org/10.1016/j.ijpe.2015.12.014
Otto, A., & Scholl, A. (2011). Incorporating ergonomic risks into assembly line balancing. European Journal of Operational Research, 212(2), 277-286. doi: 10.1016/j.ejor.2011.01.056
Schaub, K., Caragnano, G., Britzke, B., & Bruder, R. (2013). The European Assembly Worksheet. Theoretical Issues in Ergonomics Science, 14(6), 616-639. doi: 10.1080/1463922X.2012.678283
Şenyiğit, E., & Atici, U. (2017a). Computer-aided work related musculoskeletal disorder risk assessment tool: WMSD-RA. Paper presented at the International Symposium on Industry 4.0 and Applications, Karabuk,Turkey.
Şenyiğit, E., & Atici, U. (2017b). Ergo-Scheduling. New Trends and Issues Proceedings on Humanities and Social Sciences, 4(10), 208-217.
Şenyiğit, E., & Atici, U. (2018). Scheduling with Job Dependent Learning Effect and Ergonomic Risk Deterioration. Proceedings of the 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 63-66.
Toksari, M. D., & Arık, O. A. (2017). Single machine scheduling problems under position-dependent fuzzy learning effect with fuzzy processing times. Journal of Manufacturing Systems, 45, 159-179. doi: https://doi.org/10.1016/j.jmsy.2017.08.006
Toksarı, M. D., Oron, D., & Güner, E. (2009). Single machine scheduling problems under the effects of nonlinear deterioration and time-dependent learning. Mathematical and Computer Modelling, 50(3-4), 401-406. doi: 10.1016/j.mcm.2009.05.026
TSE. (2011). Safety of machinery - Human physical performance Part-5: Risk assessment for repetitive handling at high frequency (Vol. TS EN 1005-5). Ankara.
Wang, J.-B. (2007). Single-machine scheduling problems with the effects of learning and deterioration. Omega, 35(4), 397-402. doi: https://doi.org/10.1016/j.omega.2005.07.008
Wang, J.-B. (2008). Single-machine scheduling with general learning functions. Computers & Mathematics with Applications, 56(8), 1941-1947. doi: https://doi.org/10.1016/j.camwa.2008.04.019
Wang, J.-B., Huang, X., Wang, X.-Y., Yin, N., & Wang, L.-Y. (2009). Learning effect and deteriorating jobs in the single machine scheduling problems. Applied Mathematical Modelling, 33(10), 3848-3853. doi: https://doi.org/10.1016/j.apm.2009.01.004
Wang, X.-R., Huang, X., & Wang, J.-B. (2011). Single-machine scheduling with linear decreasing deterioration to minimize earliness penalties. Applied Mathematical Modelling, 35(7), 3509-3515. doi: 10.1016/j.apm.2011.01.005
This paper introduces the parallel machine scheduling problems with work related musculoskeletal disorder (WMSD) risk considerations. By the WMSD consideration, we mean that job processing times are an increasing function of occupational repetitive technical actions (OCRA) risk factor. The OCRA index was recruited for risk assessment of occupational repetitive technical actions. WMSD risks were modeled with cumulative mean value of OCRA index. Due to NP-Hard structure of parallel machine scheduling problems with WMSD considerations and learning rate, the solution cannot be found always. However, problem can be solved by transforming assignment problem. In spite of the fact that the computational effort remains O (n4), problem is solved within more efficient time. In this study, a model that includes learning effect and WMSD risk was proposed. WMSD risk was considered as cumulative mean of risk value throughout shift. In order to the balance between productivity and WMSD risk, jobs’ foreseeable cycle time (FCT) value was changed. It is aimed to decrease mean of risk along schedule without increasing total basic process time. The value of sacrifice from the FCT was compensating from job which have optimal, acceptable, borderline or slight risk level. Process time and risk values were recalculated by using new FCT value. Thus, balanced process times and risk values obtained. Proposed model was solved with Lingo software and sequence of jobs was obtained. Total flow time and mean of risk were compared for balanced and none balanced schedules. Total flow time and mean of risk belong to balanced schedule is smaller than none balanced schedule. It was shown that handled problem is solvable at the polynomial time and total flow time can be improved by bringing balance between WMSD risks and productivity.
Akyol, Ş. D., & Baykasoğlu, A. (2016). ErgoALWABP: a multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem under ergonomic risk factors. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-016-1246-6
Antwi-Afari, M. F., Li, H., Edwards, D. J., Pärn, E. A., Seo, J., & Wong, A. Y. L. (2017). Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers. Automation in Construction, 83, 41-47. doi: https://doi.org/10.1016/j.autcon.2017.07.007
Arık, O. A., & Toksarı, M. D. (2017). Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects. International Journal of Production Research, 1-18. doi: 10.1080/00207543.2017.1388932
Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1-13. doi: https://doi.org/10.1016/j.ejor.2015.12.023
Battini, D., Glock, C. H., Grosse, E. H., Persona, A., & Sgarbossa, F. (2015). Ergo-Lot-Sizing: Considering Ergonomics in Lot-Sizing Decisions. IFAC-PapersOnLine, 48(3), 326-331. doi: https://doi.org/10.1016/j.ifacol.2015.06.102
Biskup, D. (1999). Single-machine scheduling with learning considerations. European Journal of Operational Research, 115(1), 173-178. doi: https://doi.org/10.1016/S0377-2217(98)00246-X
Boenzi, F., Digiesi, S., Mossa, G., Mummolo, G., & Romano, V. A. (2013). Optimal Break and Job Rotation Schedules of High Repetitive – Low Load Manual Tasks in Assembly Lines: an OCRA – Based Approach. IFAC Proceedings Volumes, 46(9), 1896-1901. doi: https://doi.org/10.3182/20130619-3-RU-3018.00625
Fulmer, S., Buchholz, B., Scribani, M., & Jenkins, P. (2017). Musculoskeletal Disorders in Northeast Lobstermen. Safety and Health at Work, 8(3), 282-289. doi: https://doi.org/10.1016/j.shaw.2016.12.004
Hignett, S., & McAtamney, L. (2000). Rapid Entire Body Assessment (REBA). Applied Ergonomics, 31(2), 201-205. doi: https://doi.org/10.1016/S0003-6870(99)00039-3
Jiang, Z., Chen, F., & Kang, H. (2013). Single-machine scheduling problems with actual time-dependent and job-dependent learning effect. European Journal of Operational Research, 227(1), 76-80. doi: https://doi.org/10.1016/j.ejor.2012.12.007
Koulamas, C. (2017). Common due date assignment with generalized earliness/tardiness penalties. Computers & Industrial Engineering, 109, 79-83. doi: https://doi.org/10.1016/j.cie.2017.04.040
Koulamas, C., & Kyparisis, G. J. (2010). Single-machine scheduling problems with past-sequence-dependent delivery times. International Journal of Production Economics, 126(2), 264-266. doi: https://doi.org/10.1016/j.ijpe.2010.03.016
Lu, Y.-Y., Wang, J.-J., & Huang, X. (2015). Scheduling jobs with position and sum-of-processing-time based processing times. Applied Mathematical Modelling, 39(14), 4013-4021. doi: 10.1016/j.apm.2014.12.021
MacCarthy, B. L., Wilson, J. R., & Crawford, S. (2001). Human performance in industrial scheduling: A framework for understanding. Human Factors and Ergonomics in Manufacturing & Service Industries, 11(4), 299-320. doi: doi:10.1002/hfm.1016
McAtamney, L., & Nigel Corlett, E. (1993). RULA: a survey method for the investigation of work-related upper limb disorders. Applied Ergonomics, 24(2), 91-99. doi: https://doi.org/10.1016/0003-6870(93)90080-S
Micheli, G. J. L., & Marzorati, L. M. (2018). Beyond OCRA: Predictive UL-WMSD risk assessment for safe assembly design. International Journal of Industrial Ergonomics, 65, 74-83. doi: https://doi.org/10.1016/j.ergon.2017.07.005
Mosheiov, G., & Sidney, J. B. (2003). Scheduling with general job-dependent learning curves. European Journal of Operational Research, 147(3), 665-670. doi: https://doi.org/10.1016/S0377-2217(02)00358-2
Occhipinti, E. (1998). OCRA: a concise index for the assessment of exposure to repetitive movements of the upper limbs. Ergonomics, 41(9), 1290-1311. doi: 10.1080/001401398186315
Occhipinti, E., & Colombini, D. (2016). A toolkit for the analysis of biomechanical overload and prevention of WMSDs: Criteria, procedures and tool selection in a step-by-step approach. International Journal of Industrial Ergonomics, 52, 18-28. doi: https://doi.org/10.1016/j.ergon.2015.08.001
Oron, D. (2016). Scheduling controllable processing time jobs with position-dependent workloads. International Journal of Production Economics, 173, 153-160. doi: https://doi.org/10.1016/j.ijpe.2015.12.014
Otto, A., & Scholl, A. (2011). Incorporating ergonomic risks into assembly line balancing. European Journal of Operational Research, 212(2), 277-286. doi: 10.1016/j.ejor.2011.01.056
Schaub, K., Caragnano, G., Britzke, B., & Bruder, R. (2013). The European Assembly Worksheet. Theoretical Issues in Ergonomics Science, 14(6), 616-639. doi: 10.1080/1463922X.2012.678283
Şenyiğit, E., & Atici, U. (2017a). Computer-aided work related musculoskeletal disorder risk assessment tool: WMSD-RA. Paper presented at the International Symposium on Industry 4.0 and Applications, Karabuk,Turkey.
Şenyiğit, E., & Atici, U. (2017b). Ergo-Scheduling. New Trends and Issues Proceedings on Humanities and Social Sciences, 4(10), 208-217.
Şenyiğit, E., & Atici, U. (2018). Scheduling with Job Dependent Learning Effect and Ergonomic Risk Deterioration. Proceedings of the 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 63-66.
Toksari, M. D., & Arık, O. A. (2017). Single machine scheduling problems under position-dependent fuzzy learning effect with fuzzy processing times. Journal of Manufacturing Systems, 45, 159-179. doi: https://doi.org/10.1016/j.jmsy.2017.08.006
Toksarı, M. D., Oron, D., & Güner, E. (2009). Single machine scheduling problems under the effects of nonlinear deterioration and time-dependent learning. Mathematical and Computer Modelling, 50(3-4), 401-406. doi: 10.1016/j.mcm.2009.05.026
TSE. (2011). Safety of machinery - Human physical performance Part-5: Risk assessment for repetitive handling at high frequency (Vol. TS EN 1005-5). Ankara.
Wang, J.-B. (2007). Single-machine scheduling problems with the effects of learning and deterioration. Omega, 35(4), 397-402. doi: https://doi.org/10.1016/j.omega.2005.07.008
Wang, J.-B. (2008). Single-machine scheduling with general learning functions. Computers & Mathematics with Applications, 56(8), 1941-1947. doi: https://doi.org/10.1016/j.camwa.2008.04.019
Wang, J.-B., Huang, X., Wang, X.-Y., Yin, N., & Wang, L.-Y. (2009). Learning effect and deteriorating jobs in the single machine scheduling problems. Applied Mathematical Modelling, 33(10), 3848-3853. doi: https://doi.org/10.1016/j.apm.2009.01.004
Wang, X.-R., Huang, X., & Wang, J.-B. (2011). Single-machine scheduling with linear decreasing deterioration to minimize earliness penalties. Applied Mathematical Modelling, 35(7), 3509-3515. doi: 10.1016/j.apm.2011.01.005
Şenyiğit, E., & Atıcı, U. (2019). Parallel Machine Scheduling with WMSD Risk Considerations. Avrupa Bilim Ve Teknoloji Dergisi336-342. https://doi.org/10.31590/ejosat.638286