Publicado

2023-08-11

Occupational hazards and economic indicators in the scheduling of a make-to-order system

Peligros ocupacionales e indicadores económicos en la programación de un sistema por pedido

DOI:

https://doi.org/10.15446/dyna.v90n227.107105

Palabras clave:

multiobjetivo; sostenibilidad; peligros ocupacionales; indicadores económicos; indicador C-Metric (es)
multi-objective; sustainability; occupational hazards; economic indicators; C-Metric indicator (en)

Autores/as

Este artículo examina algunos peligros ocupacionales específicos y ciertos indicadores económicos de sostenibilidad en un sistema de manufactura por pedido. Al respecto, se estructuran dos métodos multiobjetivo. El primero es un algoritmo genético rediseñado; el segundo está basado en sumas normalizadas. La comparación establece que el primero supera al segundo en 528%, en lo concerniente al indicador “C-Metric”.

This paper examines some specific occupational hazards and certain economic indicators of sustainability in a make-to-order manufacturing system. In this respect, two multi-objective protocols are structured. The first is a redesigned genetic algorithm; the second is based on normalized summations. The comparison establishes that the former outperforms the latter by 528%, concerning to the "C-Metric" indicator.

Referencias

Hale, J., Legun, J., Campbell, H., and Carolan, M., Social sustainability indicators as performance. Geoforum, 103, pp. 47-55, 2019. DOI: https://doi.org/10.1016/j.geoforum.2019.03.008

Hutchins, M., Richter, J., Henry, M. and Sutherland, J., Development of indicators for the social dimension of sustainability in a U.S. business context. Journal of Cleaner Production, 212, pp. 687-697, 2019. DOI: https://doi.org/10.1016/j.jclepro.2018.11.199

Khalid, A., Khan, Z., Idrees, M., Kirisci, P., Ghrairi, Z., Thoben, K. and Pannek, J., Understanding vulnerabilities in cyber physical production systems, International Journal of Computer Integrated Manufacturing, 35(6), pp. 569-582, 2021. DOI: https://doi.org/10.1080/0951192X.2021.1992656

Naciones Unidas, La Agenda 2030 y los objetivos de desarrollo sostenible: una oportunidad para América Latina y el Caribe LC/G. 2681-P/Rev, [online]. 2018. Available at: https://www.un.org/sustainabledevelopment/es/objetivos-de-desarrollo-sostenible/

Berti, N., Artigues, C., Battaïa, O., Guillaume, R. and Battini, D., Heuristic approaches for scheduling manufacturing tasks while talking into account accumulated human fatigue. IFAC-PapersOnLine, 52(13), pp. 963-968, 2019. DOI: https://doi.org/10.1016/j.ifacol.2019.11.319

Yung, M., Kolus, A., Wells, R., and Neumann, W.P., Examining the fatigue-quality relationship in manufacturing. Applied Ergonomics, 82, art. 102919, 2020. DOI: https://doi.org/10.1016/j.apergo.2019.102919

Kempen, E., Casas, M., Pershagen, G. and Foraster, M., Who environmental noise guidelines for the European Region: a systematic review on environmental noise and cardiovascular and metabolic effects. A summary. International Journal of Environmental Research and Public Health, 15(2), art. 5020379, 2018. DOI: https://doi.org/10.3390/ijerph15020379

Amiri, F., Shirazi, B. and Tajdin, A., Multi-objective simulation optimization for uncertain resource assignment and job sequence in automated flexible job shop. Applied Soft Computing Journal, 75, pp. 190-202, 2019. DOI: https://doi.org/10.1016/j.asoc.2018.11.015

Gu, X., Huang, M. and Liang, X., A discrete particle swarm optimization algorithm with adaptive inertia weight for solving multiobjective flexible job-shop scheduling problem. IEEE Access, 8, pp. 33125-33136, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2974014

Bissoli, D., Zufferey, N. and Amaral, A., Lexicographic optimization-based clusteringsearch metaheuristic for the multiobjective flexible job shop scheduling problem. International Transactions in Operational Research, 28(3), pp. 1-26, 2019. DOI: https://doi.org/10.1111/itor.12745

Rahmati, S., Zandieh, M., and Yazdani, M., Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 64(5-8), pp. 915-932, 2012. DOI: http://doi.org/10.1007/s00170-012-4051-1.

Zhang, F., Bail, J., Yang D., and Wang, O., Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision. Scientific Reports, 12, art. 1546, 2022. DOI: https://doi.org/10.1038/s41598-022-05304-w

Guo, Y., Huang, M., Wang, Q. and León, V., Single-machine rework rescheduling to minimize total waiting time with fixed sequence of jobs and release times, in: IEEE Access, 9, pp. 1205-1218, 2021. DOI: https://doi.org/10.1109/ACCESS.2019.2957132

Nicoara, E., Multi-objective flexible job sho scheduling optimization models. Economic Inshights - Trends and Challenges, 49(2), pp. 79-86, 2015. Available at: https://upg-bulletin-se.ro/old_site/archive/2012-2/7.%20Nicoara.pdf

Ozturk, G., Bahadir, O., and Teymourifar, A., Extracting priority rules for dynamic multiobjective flexible job shop scheduling problems using gene expression programming. International Journal of Production Research, 57(19), pp. 3121-3137, 2019. DOI: https://doi.org/10.1080/00207543.2018.1543964

Valenzuela, V., Cosío, M., and Romero, A., A cooperative coevolutionary algorithm approach to the no-wait job shop scheduling problem. Expert Systems With Applications, 194(15), art. 116498, 2022. DOI: https://doi.org/10.1016/j.eswa.2022.116498

Deb, K., and Jain, H., An evolutionary many-objective optimization algorithm using reference point-based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), pp. 577-601, 2014. DOI: https://doi.org/10.1109/TEVC.2013.2281535

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197, 2002. Available at: https://ieeexplore.ieee.org/document/996017 DOI: https://doi.org/10.1109/4235.996017

Zitzler, E., Laumanns, M., and Thiele, L., SPEA2: improving the strength pareto evolutionary algorithm. ETH Zentrum. Research Collection, 103, pp. 1-22, 2001. DOI: https://doi.org/10.3929/ethz-a-004284029

Huang, W., Zhao. Y., and Ma, X., An improved genetic algorithm for job-shop scheduling problem with process sequence flexibility. International. Journal of Simulation Modeling, 13(4), pp. 510-522, 2014. Available at: http://m.ijsimm.com/Full_Papers/Fulltext2014/text13-4_510-522.pdf DOI: https://doi.org/10.2507/IJSIMM13(4)CO20

Wang, Y., Stein, N., Bäck, T., and Emmerich, M., Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search. GECCO '20: proceedings of the 2020 genetic and evolutionary computation conference companion, [online]. 2020. pp. 181-182. Available at: https://scholarlypublications.universiteitleiden.nl/handle/1887/3618523 DOI: https://doi.org/10.1145/3377929.3389924

Xie, J., Li, X., Gao, L. and Gui, L., A hybrid algorithm with a new neighborhood structure for job shop scheduling problems. Computers and Industrial Engineering, 169, art. 108205, 2022. DOI: https://doi.org/10.1016/j.cie.2022.108205

Aschauer, A., Roetzer, F., Steinboeck, A., and Kugi, A., Efficient scheduling of a stochastic no-wait job shop with controllable processing times. Expert Systems with Applications, 162, art. 113879, 2020. DOI: https://doi.org/10.1016/j.eswa.2020.113879

Geng, Z., Yuan, J., and Yuan, J., Scheduling with or without precedence relations on serial-batch machine to minimize makespan and maximum cost. Applied Mathematics and Computation, 332, pp. 1-18, 2018. DOI: https://doi.org/10.1016/j.amc.2018.03.001

Safarzadeh, H., and Kianfar, F., Job shop scheduling with the option of jobs outsourcing. International Journal of Production Research, 57(10), pp. 3255-3272, 2019. DOI: https://doi.org/10.1080/00207543.2019.1579934

Zheng, F., and Sui, Y., Bi-objective optimization of multiple-route job shop scheduling with route cost. IFAC-PapersOnLine, 52(13), pp. 881-886, 2019. DOI: https://doi.org/10.1016/j.ifacol.2019.11.241

Golpîra, H., Rehman, K., and Zhang, Y., Robust Smart energy efficient production planning for a general job-shop manufacturing system under combined demand and supply uncertainty in the presence of grid-connected microgrid. Journal of Cleaner Production, 202, pp. 649-665, 2018. DOI: https://doi.org/10.1016/j.jclepro.2018.08.151

Huo, D., Xiao, X., and Pan, Y., Multi-objective energy-saving job-shop scheduling based on improved NSGA-II. International Journal of Simulation Modelling, [online]. 19(3), pp. 494-504, 2020. Available at: http://www.ijsimm.com/Full_Papers/Fulltext2020/text19-3_CO12.pdf DOI: https://doi.org/10.2507/IJSIMM19-3-CO12

Masmoudi, O., Delorme, X., and Gianessi, P., Job-shop scheduling problem with energy consideration. International Journal of Production Economics, 216, pp. 12-22, 2019. DOI: https://doi.org/10.1016/j.ijpe.2019.03.021

Ren, J., Ye, C., and Li, Y., A two-stage optimization algorithm for multi-objective job-shop scheduling problem considering job transport. Journal Europeen des Systemes Automatises, 53(6), pp. 915-924, 2020. DOI: https://doi.org/10.1007/s10845-009-0294-6

Renna, P. and Materi, S., Switch off policies in job-shop manufacturing systems including workload evaluation. International Journal of Management Science and Engineering Management, 16(4), pp. 254-263, 2021. DOI: https://doi.org/10.1080/17509653.2021.1941369

Salido, M., Escamilla, J., Barber, F., and Giret, A. rescheduling in job-shop problems for sustainable manufacturing systems. Journal of Cleaner Production, 162(20), pp. 121-132, 2017. DOI: https://doi.org/10.1016/j.jclepro.2016.11.002.

Wen, X., Wang, K., Li, H., Sun, H., Wang, H., and Jin, L., Two-stage solution method based on NSGA-II for Green Multi-Objective integrated process planning and scheduling in a battery packaging machinery workshop. Swarm and Evolutionary Computation, 61, art. 100820, 2021. DOI: https://doi.org/10.1016/j.swevo.2020.100820

Zhao, J., Peng, S., and Li, T., Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi -machine level. Front. Mech. Eng. 14, pp. 474-488, 2019. DOI: https://doi-org.ezproxy.unal.edu.co/10.1007/s11465-019-0560-z

Duan, J., and Wang, J., Robust scheduling for flexible machining job shop subject to machine breakdowns and new job arrivals considering system reusability and task recurrence. Systems with Applications, 203, art. 117489, 2022. DOI: https://doi.org/10.1016/j.eswa.2022.117489

Para, J., Del Ser, J., and Nebro, A., Energy-aware multi-objective job shop scheduling optimization with metaheuristics in manufacturing industries: a critical survey, results, and perspectives. Applied Sciences. 12(3), art. 1491, 2022. DOI: https://doi.org/10.3390/app12031491

Daneshamooz, F., Fattahi, P., and Hosseini, S., Mathematical modeling and two efficient branch and bound algorithms for job shop scheduling problem followed by an assembly stage. Kybernetes. ahead-of-print. 2021. DOI: https://doi.org/10.1108/K-08-2020-0521

Shen, X., Zhang, M., and Fu, J., Multi-objective dynamic job shop scheduling a survey and prospects. International Journal of Innovative, [online]. 10(6), pp. 2113-2126, 2014, Available at: http://www.ijicic.org/ijicic-14-01022.pdf

Mu, H., Disruption management of flexible job shop scheduling considering behavior perception and machine fault based on improved NSGA-II algorithm. Journal Européen des Systèmes Automatisés, 52(2), pp. 149-156, 2019, DOI: https://doi.org/10.18280/jesa.520206

Santos, V., Carvalho, F., Assis, L., Weiss-Cohen, M., and Guimarães, F., Multi-objective iterated local search based on decomposition for job scheduling problems with machine deterioration effect. Engineering Applications of Artificial Intelligence, 112, art. 104826, 2022. DOI: https://doi.org/10.1016/j.engappai.2022.104826

Shokouhi, E., Integrated multi-objective process planning and flexible job shop scheduling considering precedence constraints. Production & Manufacturing Research, 6(1), pp. 61-89, 2018. DOI: https://doi.org/10.1080/21693277.2017.1415173

Zhang, S., Li, X., Zhang, B., and Wang, S., Multi-objective optimization in flexible assembly job shop scheduling using a distributed ant colony system. European Journal of Operational Research, 283(2), pp. 441-460, 2020. DOI: https://doi.org/10.1016/j.ejor.2019.11.016

Zhu, H., He, B., and Li, H., Modified bat algorithm for the multi-objective flexible job shop scheduling problem. International Lournal of Performability Endineering, [online]. 13(7), pp. 999-1012, 2017. Available at: http://www.ijpe-online.com/EN/10.23940/ijpe.17.07.p1.9991012

Gayathri, D., Mishra, R., and Madan, A., A dynamic adaptive firefly algorithm for flexible job shop scheduling. Intelligent Automation & Soft Computing. 31(1), pp. 429-448, 2022. DOI: https://doi.org/10.32604/iasc.2022.019330

Renke, L., Piplani, R., and Toro, C., A review of dynamic scheduling: context, techniques and prospects. In: Toro, C., Wang, W., and Akhtar, H., Eds. Implementing Industry 4.0. Intelligent Systems Reference Library. 202. Springer, Cham, [online]. 2021. DOI: https://doi-org.ezproxy.unal.edu.co/10.1007/978-3-030-67270-6_9

Dabbagh, R., and Yousefi, S., Hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis. Journal of Safety Research, 71, pp. 111-123, 2019. DOI: https://doi.org/10.1016/j.jsr.2019.09.021

Villicaña, E., and Ponce, J., Sustainable strategic planning for a national natural gas energy system accounting for unconventional sources. Energy Conversion and Management, [online]. 181, pp. 382-397, 2019. Available at: https://www.scopus.com/inward/record.uri?eid=2-s2.0- DOI: https://doi.org/10.1016/j.enconman.2018.12.023

García, F., Medina, S., Gonzales, R., Huertas, I., Ferrari, A., and Settembre, D., Industry 4.0-based dynamic Social Organizational Life Cycle Assessment to target the social circular economy in manufacturing. Journal of Cleaner Production. 327, art. 129439, 2021. DOI: https://doi.org/10.1016/j.jclepro.2021.129439

Autuori, J., Hnaien, F., and Yalaoui, F., Three metaheuristics improved by a mapping method. IFAC - International Federation of Automatic Control, 49(12), pp. 1472-1477, 2016. DOI: https://doi.org/10.1016/j.ifacol.2016.07.779

Zhang, R., and Chiong, R., Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. Journal of Cleaner Production, 112(4), pp. 3361-3375, 2016. DOI: https://doi.org/10.1016/j.jclepro.2015.09.097

Rashno, A., Shafipour, M., and Fadaei S., Particle ranking: an efficient method for multi-objective particle swarm optimization feature selection. Knowledge-Based Systems, 245, art. 108640, 2022. DOI: https://doi.org/10.1016/j.knosys.2022.108640

Zhang, F., Bail, J., Yang, D., and Wang, O., Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision. Scientific Reports, [online]. 12, art. 1546, 2022, Available at: https://www.nature.com/articles/s41598-022-05304-w DOI: https://doi.org/10.1038/s41598-022-05304-w

Cómo citar

IEEE

[1]
G. Coca-Ortegón, «Occupational hazards and economic indicators in the scheduling of a make-to-order system», DYNA, vol. 90, n.º 227, pp. 117–125, jul. 2023.

ACM

[1]
Coca-Ortegón, G. 2023. Occupational hazards and economic indicators in the scheduling of a make-to-order system. DYNA. 90, 227 (jul. 2023), 117–125. DOI:https://doi.org/10.15446/dyna.v90n227.107105.

ACS

(1)
Coca-Ortegón, G. Occupational hazards and economic indicators in the scheduling of a make-to-order system. DYNA 2023, 90, 117-125.

APA

Coca-Ortegón, G. (2023). Occupational hazards and economic indicators in the scheduling of a make-to-order system. DYNA, 90(227), 117–125. https://doi.org/10.15446/dyna.v90n227.107105

ABNT

COCA-ORTEGÓN, G. Occupational hazards and economic indicators in the scheduling of a make-to-order system. DYNA, [S. l.], v. 90, n. 227, p. 117–125, 2023. DOI: 10.15446/dyna.v90n227.107105. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/107105. Acesso em: 16 mar. 2026.

Chicago

Coca-Ortegón, Germán. 2023. «Occupational hazards and economic indicators in the scheduling of a make-to-order system». DYNA 90 (227):117-25. https://doi.org/10.15446/dyna.v90n227.107105.

Harvard

Coca-Ortegón, G. (2023) «Occupational hazards and economic indicators in the scheduling of a make-to-order system», DYNA, 90(227), pp. 117–125. doi: 10.15446/dyna.v90n227.107105.

MLA

Coca-Ortegón, G. «Occupational hazards and economic indicators in the scheduling of a make-to-order system». DYNA, vol. 90, n.º 227, julio de 2023, pp. 117-25, doi:10.15446/dyna.v90n227.107105.

Turabian

Coca-Ortegón, Germán. «Occupational hazards and economic indicators in the scheduling of a make-to-order system». DYNA 90, no. 227 (julio 11, 2023): 117–125. Accedido marzo 16, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/107105.

Vancouver

1.
Coca-Ortegón G. Occupational hazards and economic indicators in the scheduling of a make-to-order system. DYNA [Internet]. 11 de julio de 2023 [citado 16 de marzo de 2026];90(227):117-25. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/107105

Descargar cita

CrossRef Cited-by

CrossRef citations0

Dimensions

PlumX

Visitas a la página del resumen del artículo

418

Descargas

Los datos de descargas todavía no están disponibles.