Effects of OCRA parameters and learning rate on machine scheduling. (English) Zbl 07557114
Summary: In this paper, the effects of Occupational Repetitive Actions (OCRA) parameters, learning rate on process times, and machine scheduling were investigated. We propose that Work-Related Musculoskeletal Disorder (WMSD) risks should be taken into account in machine scheduling. To the best of our knowledge, none of the earlier methods simultaneously considered effects of WMSD risks and the learning rate on processing times. The OCRA index method was employed for WMSD risk assessments. In this context, OCRA parameters such as duration, recovery, force, posture, and repetitiveness were analyzed. Observed process times of each factor were obtained from video records. Statistical analysis (ANOVA) revealed a positive \((r=0.616)\) relationship on processing times with OCRA indexes in independent t-tests at significance level 0.05. To investigate the effects of WMSD risk, our Scheduling with Learning Effect under Risk Deterioration (SLE&RD) model was compared with six existing machine scheduling models in the literature. Detailed machine scheduling instances of 9 jobs with WMSD risks revealed that job sequences and makespan varied under different scenarios. This means that WMSD risks and OCRA factors affect machine scheduling with a deterioration effect. The results confirmed that when WMSD risks are included, actual process time and makespan move closer to observed process times. To obtain more accurate machine scheduling, which is close to real-life applications, WMSD risks, and learning rates should be considered simultaneously. The SLE&RD model is promising in machine scheduling for real-life problems and presents a holistic view of machine scheduling and WMSD risks.
MSC:
| 90Bxx | Operations research and management science |
Keywords:
machine scheduling; learning rate; risk assessment; risk based deterioration; WMSD; OHSAS; ergonomics; OCRA; ANOVAReferences:
| [1] | Abedi M, Chiong R, Noman N, Zhang R (2020) A multi-population, multi-objective memetic algorithm for energy-efficient job-shop scheduling with deteriorating machines, p. 113348 |
| [2] | Akyol, SD; Baykasoğlu, A., Ergonomic assembly line balancing, J Fac Eng Archit Gazi Univ, 29, 785-792 (2017) |
| [3] | Akyol, SD; Baykasoğlu, A., ErgoALWABP: a multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem under ergonomic risk factors, J Intell Manufac, 30, 291-302 (2019) · doi:10.1007/s10845-016-1246-6 |
| [4] | Aven, T., Risk assessment and risk management: Review of recent advances on their foundatione, Eur J Operat Res, 253, 1-13 (2016) · Zbl 1346.91102 · doi:10.1016/j.ejor.2015.12.023 |
| [5] | Baykasoglu, A.; Tasan, SO; Tasan, AS; Akyol, SD, Modeling and solving assembly line design problems by considering human factors with a real-life application, Human Fact Ergon Manuf Service Ind, 27, 96-115 (2017) · doi:10.1002/hfm.20695 |
| [6] | Bidiawati, JRA; Eva Suryan, E., Improving the work position of worker’s based on quick exposure check method to reduce the risk of work related musculoskeletal disorders, Procedia Manuf, 4, 496-503 (2015) · doi:10.1016/j.promfg.2015.11.068 |
| [7] | Biskup, D., Single-machine scheduling with learning considerations, Eur J Oper Res, 115, 173-178 (1999) · Zbl 0946.90025 · doi:10.1016/S0377-2217(98)00246-X |
| [8] | Boenzi, F.; Digiesi, S.; Mossa, G.; Mummolo, G.; Romano, VA, Optimal break and job rotation schedules of high repetitive – low load manual tasks in assembly lines: an OCRA - based approach, IFAC Proc, 46, 1896-1901 (2013) |
| [9] | Chen, Q.; Lin, L.; Tan, Z.; Yan, Y., Coordination mechanisms for scheduling games with proportional deterioration, Eur J Oper Res, 263, 2, 380-389 (2017) · Zbl 1380.90109 · doi:10.1016/j.ejor.2017.05.021 |
| [10] | Chen, R.; Yuan, J.; Geng, Z., ND-agent scheduling of linear-deteriorating tasks with positional due indices to minimize total completion time and maximum cost, Appl Math Comput, 365, 124697 (2020) · Zbl 1426.91314 · doi:10.1016/j.cam.2019.112395 |
| [11] | Cheng, TCE; Chin-Chia, W.; Wen-Chiung, L., Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects, Inf Sci, 178, 2476-2487 (2008) · Zbl 1172.90397 · doi:10.1016/j.ins.2008.02.002 |
| [12] | Chin-Chia, W.; Wen-Chiung, L., Single-machine scheduling problems with a learning effect, Appl Math Modell, 32, 1191-1197 (2008) · Zbl 1172.90415 · doi:10.1016/j.apm.2007.03.001 |
| [13] | Chung, BD; Kim, BS, A hybrid genetic algorithm with two-stage dispatching heuristic for a machine scheduling problem with step-deteriorating jobs and rate-modifying activities, Comput Ind Eng, 98, 113-124 (2016) · doi:10.1016/j.cie.2016.05.028 |
| [14] | Colombini, D.; Occhipinti, E., Risk analysis and management of repetitive actions a guide for applying the OCRA system (2017), Boca Raton: CRC Press, Boca Raton |
| [15] | Ding, J.; Shen, L.; Lü, Z.; Peng, B., Parallel machine scheduling with completion-time-based criteria and sequence-dependent deterioration, Comput Oper Res, 103, 35-45 (2019) · Zbl 1458.90285 · doi:10.1016/j.cor.2018.10.016 |
| [16] | Expósito-Izquierdo, C.; Angel-Bello, F.; Melián-Batista, B.; Alvarez, A.; Báez, S., A metaheuristic algorithm and simulation to study the effect of learning or tiredness on sequence-dependent setup times in a parallel machine scheduling problem, Expert Syst Appl, 117, 62-74 (2019) · doi:10.1016/j.eswa.2018.09.041 |
| [17] | Goode, N.; Newnam, S.; Van Mulken, MRH; Dekker, S.; Salmon, PM, investigating work-related musculoskeletal disorders: strengths and weaknesses of current practices in large australian organisations, Safety Sci, 112, 105-115 (2019) · doi:10.1016/j.ssci.2018.10.022 |
| [18] | Hanson, R.; Medbo, L.; Berlin, C.; Hansson, J., Manual picking from flat and tilted pallet containers, Int J Ind Ergon, 64, 199-212 (2018) · doi:10.1016/j.ergon.2017.07.001 |
| [19] | Hignett, S.; McAtamney, L., Rapid entire body assessment (REBA), Appl Ergon, 31, 201-205 (2000) · doi:10.1016/S0003-6870(99)00039-3 |
| [20] | Ji, M.; Cheng, TCE, Scheduling with job-dependent learning effects and multiple rate-modifying activities, Inf Process Lett, 110, 11, 460-463 (2010) · Zbl 1229.90061 · doi:10.1016/j.ipl.2010.04.015 |
| [21] | Ji, M.; Tang, X.; Zhang, X.; Cheng, TCE, Machine scheduling with deteriorating jobs and DeJong’s learning effect, Comput Ind Eng, 91, 42-47 (2016) · doi:10.1016/j.cie.2015.10.015 |
| [22] | Kuo, W-H; Yang, D-L, Minimizing the total completion time in a single-machine scheduling problem with a time-dependent learning effect, Eur J Oper Res, 174, 1184-1190 (2006) · Zbl 1103.90341 · doi:10.1016/j.ejor.2005.03.020 |
| [23] | Liu, S.; Pei, J.; Cheng, H.; Liu, X.; Pardalos, PM, Two-stage hybrid flow shop scheduling on parallel batching machines considering a job-dependent deteriorating effect and non-identical job sizes, Appl Soft Comput, 84, 105701 (2019) · doi:10.1016/j.asoc.2019.105701 |
| [24] | MacCarthy, BL; Wilson, JR; Crawford, S., Human performance in industrial scheduling: A framework for understanding, Hum Fact Ergon Manuf Serv Ind, 11, 299-320 (2001) · doi:10.1002/hfm.1016 |
| [25] | Micheli, GJL; Marzorati, LM, Beyond OCRA: predictive UL-WMSD risk assessment for safe assembly design, Int J Ind Ergon, 65, 74-83 (2018) · doi:10.1016/j.ergon.2017.07.005 |
| [26] | Mossa, G.; Boenzi, F.; Digiesi, S.; Mummolo, G.; Romano, VA, Productivity and ergonomic risk in human based production systems: a job-rotation scheduling model, Int J Prod Econ, 171, 471-477 (2016) · doi:10.1016/j.ijpe.2015.06.017 |
| [27] | Occhipinti, E., OCRA: a concise index for the assessment of exposure to repetitive movements of the upper limbs, Ergonomics, 41, 1290-1311 (1998) · doi:10.1080/001401398186315 |
| [28] | Padula, RS; Comper, MLC; Sparer, EH; Dennerlein, JT, Job rotation designed to prevent musculoskeletal disorders and control risk in manufacturing industries: a systematic review, Appl Ergon, 58, 386-397 (2017) · doi:10.1016/j.apergo.2016.07.018 |
| [29] | Panwalkar, SS; Rajagopalan, R., Single-machine sequencing with controllable processing times, Eur J Oper Res, 59, 298-302 (1992) · Zbl 0760.90058 · doi:10.1016/0377-2217(92)90144-X |
| [30] | Rosecrance, J.; Paulsen, R.; Murgia, L., Risk assessment of cheese processing tasks using the strain Index and OCRA checklist, Int J Ind Ergon, 61, 142-148 (2017) · doi:10.1016/j.ergon.2017.05.009 |
| [31] | Schaub, K.; Caragnano, G.; Britzke, B.; Bruder, R., The european assembly worksheet, Theoretical Issues Ergon Sci, 14, 616-639 (2013) · doi:10.1080/1463922X.2012.678283 |
| [32] | Şenyiğit, E.; Atici, U., Ergo-scheduling, New Trends Issues Proc Hum Social Sci, 4, 208-217 (2018) |
| [33] | Soleimani, H.; Ghaderi, H.; Tsai, PW; Zarbakhshnia, N.; Maleki, M., Scheduling of unrelated parallel machines considering sequence-related setup time, start time-dependent deterioration, position-dependent learning and power consumption minimization, J Cleaner Prod, 249, 119428 (2020) · doi:10.1016/j.jclepro.2019.119428 |
| [34] | Sonne, M.; Villalta, DL; Andrews, DM, Development and evaluation of an office ergonomic risk checklist: ROSA - Rapid office strain assessment, Appl Ergon, 43, 98-108 (2012) · doi:10.1016/j.apergo.2011.03.008 |
| [35] | Sun, L.; Yu, AJ; Wu, B., Single machine common flow allowance group scheduling with learning effect and resource allocation, Comput Ind Eng, 139, 106126 (2020) · doi:10.1016/j.cie.2019.106126 |
| [36] | Thomas, RW; Putz-Anderson, V.; Garg, A., Revised NIOSH equation for the design and evaluation of manual lifting tasks, Ergonomics, 36, 749-76 (1993) · doi:10.1080/00140139308967892 |
| [37] | Tiacci, L.; Mimmi, M., Integrating ergonomic risks evaluation through OCRA index and balancing/sequencing decisions for mixed model stochastic asynchronous assembly lines, Omega, 78, 112-138 (2018) · doi:10.1016/j.omega.2017.08.011 |
| [38] | Toksari, MD; Arık, OA, Single machine scheduling problems under position-dependent fuzzy learning effect with fuzzy processing times, J Manuf Syst, 45, 159-179 (2017) · doi:10.1016/j.jmsy.2017.08.006 |
| [39] | Toksarı, MD; Oron, D.; Güner, E., Single machine scheduling problems under the effects of nonlinear deterioration and time-dependent learning, Math Comput Modell, 50, 3, 401-406 (2009) · Zbl 1185.90097 · doi:10.1016/j.mcm.2009.05.026 |
| [40] | Valentim DP, Sato TDO, Comper MLC, Silva AMS, Boas CV, Padula RS (2017) Reliability, construct validity and interpretability of the Brazilian version of the rapid upper limb assessment (RULA) and strain index (SI). JBrazilian J Phys Therapy 22: |
| [41] | Voutsinas, TG; Pappis, CP, Scheduling jobs with values exponentially deteriorating over time, Int J Prod Econ, 79, 163-169 (2002) · doi:10.1016/S0925-5273(02)00110-X |
| [42] | Wang, JB, Single-machine scheduling problems with the effects of learning and deterioration, Omega, 35, 397-402 (2007) · doi:10.1016/j.omega.2005.07.008 |
| [43] | Wang, JB, Single-machine scheduling with general learning functions, Comput Math Appl, 56, 8, 1941-1947 (2008) · Zbl 1165.90470 · doi:10.1016/j.camwa.2008.04.019 |
| [44] | Wen-Chiung, L., Note on deteriorating jobs and learning in single-machine scheduling problem, Int J Business Econ, 3, 83-89 (2004) |
| [45] | Woo, Y-B; Kim, BS, Matheuristic approaches for parallel machine scheduling problem with time-dependent deterioration and multiple rate-modifying activities, Comput Oper Res, 95, 97-112 (2018) · Zbl 1458.90373 · doi:10.1016/j.cor.2018.02.017 |
| [46] | Yoon, S-Y; Ko, J.; Jung, MC, A model for developing job rotation schedules that eliminate sequential high workloads and minimize between-worker variability in cumulative daily workloads: application to automotive assembly lines, Appl Ergon, 55, 8-15 (2016) · doi:10.1016/j.apergo.2016.01.011 |
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