Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry
This research proposes a mathematical model of the problem of job rotation considering ergonomic aspects in repetitive works, lifting tasks and awkward postures in manufacturing environments with high variability. The mathematical model is formulated as a multi-objective optimization problem integra...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9144
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9144
- Palabra clave:
- Ergonomic constraints
Genetic algorithm
Job rotation
Manufacturing
Combinatorial optimization
Ergonomics
Genetic algorithms
Industrial research
Manufacture
Multiobjective optimization
Occupational risks
Scheduling algorithms
Combinatorial optimization problems
Computational time
Job rotation
Manufacturing environments
Manufacturing industries
Multi-objective optimization problem
Non- dominated sorting genetic algorithms
Similar solution
Computational efficiency
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry |
| title |
Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry |
| spellingShingle |
Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry Ergonomic constraints Genetic algorithm Job rotation Manufacturing Combinatorial optimization Ergonomics Genetic algorithms Industrial research Manufacture Multiobjective optimization Occupational risks Scheduling algorithms Combinatorial optimization problems Computational time Job rotation Manufacturing environments Manufacturing industries Multi-objective optimization problem Non- dominated sorting genetic algorithms Similar solution Computational efficiency |
| title_short |
Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry |
| title_full |
Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry |
| title_fullStr |
Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry |
| title_full_unstemmed |
Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry |
| title_sort |
Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry |
| dc.subject.keywords.none.fl_str_mv |
Ergonomic constraints Genetic algorithm Job rotation Manufacturing Combinatorial optimization Ergonomics Genetic algorithms Industrial research Manufacture Multiobjective optimization Occupational risks Scheduling algorithms Combinatorial optimization problems Computational time Job rotation Manufacturing environments Manufacturing industries Multi-objective optimization problem Non- dominated sorting genetic algorithms Similar solution Computational efficiency |
| topic |
Ergonomic constraints Genetic algorithm Job rotation Manufacturing Combinatorial optimization Ergonomics Genetic algorithms Industrial research Manufacture Multiobjective optimization Occupational risks Scheduling algorithms Combinatorial optimization problems Computational time Job rotation Manufacturing environments Manufacturing industries Multi-objective optimization problem Non- dominated sorting genetic algorithms Similar solution Computational efficiency |
| description |
This research proposes a mathematical model of the problem of job rotation considering ergonomic aspects in repetitive works, lifting tasks and awkward postures in manufacturing environments with high variability. The mathematical model is formulated as a multi-objective optimization problem integrating the ergonomic constraints and is solved using improved non-dominated sorting genetic algorithm. The proposed algorithm allows the generation of diversified results and a greater search convergence on the Pareto front. The algorithm avoids the loss of convergence in each border by means of change and replacement of similar solutions. In this strategy, a single similar result is preserved and the best solution of the previous generation is included. If the outcomes are similar, new randomly generated individuals are proposed to encourage diversity. The obtained results improve the conditions of 69% of the workers. The results show that if the worker rotates starting from a high risk, his variation in risk always decreases in his next assignment. Within the job rotation scheme, no worker is exposed simultaneously to high ergonomic risk thresholds. The model and the algorithm provide good results while considering ergonomic risks. The proposed algorithm shows the potentiality to generate a set of quality of response (Pareto Frontier) in a combinatorial optimization problem in an efficient computational time. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature. |
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2019 |
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2019 |
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2020-03-26T16:33:03Z |
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2020-03-26T16:33:03Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Artículo |
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publishedVersion |
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Journal of Ambient Intelligence and Humanized Computing; Vol. 10, Núm. 5; pp. 2063-2090 |
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18685137 |
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https://hdl.handle.net/20.500.12585/9144 |
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10.1007/s12652-018-0814-3 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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15078194000 57194034904 57202852177 57193533853 |
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Journal of Ambient Intelligence and Humanized Computing; Vol. 10, Núm. 5; pp. 2063-2090 18685137 10.1007/s12652-018-0814-3 Universidad Tecnológica de Bolívar Repositorio UTB 15078194000 57194034904 57202852177 57193533853 |
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https://hdl.handle.net/20.500.12585/9144 |
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eng |
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eng |
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Springer Verlag |
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Springer Verlag |
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2020-03-26T16:33:03Z2020-03-26T16:33:03Z2019Journal of Ambient Intelligence and Humanized Computing; Vol. 10, Núm. 5; pp. 2063-209018685137https://hdl.handle.net/20.500.12585/914410.1007/s12652-018-0814-3Universidad Tecnológica de BolívarRepositorio UTB15078194000571940349045720285217757193533853This research proposes a mathematical model of the problem of job rotation considering ergonomic aspects in repetitive works, lifting tasks and awkward postures in manufacturing environments with high variability. The mathematical model is formulated as a multi-objective optimization problem integrating the ergonomic constraints and is solved using improved non-dominated sorting genetic algorithm. The proposed algorithm allows the generation of diversified results and a greater search convergence on the Pareto front. The algorithm avoids the loss of convergence in each border by means of change and replacement of similar solutions. In this strategy, a single similar result is preserved and the best solution of the previous generation is included. If the outcomes are similar, new randomly generated individuals are proposed to encourage diversity. The obtained results improve the conditions of 69% of the workers. The results show that if the worker rotates starting from a high risk, his variation in risk always decreases in his next assignment. Within the job rotation scheme, no worker is exposed simultaneously to high ergonomic risk thresholds. The model and the algorithm provide good results while considering ergonomic risks. The proposed algorithm shows the potentiality to generate a set of quality of response (Pareto Frontier) in a combinatorial optimization problem in an efficient computational time. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.Recurso electrónicoapplication/pdfengSpringer Verlaghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85049552472&doi=10.1007%2fs12652-018-0814-3&partnerID=40&md5=83295ee67c4cbae60651a73871d79b2dApplication of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industryinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Ergonomic constraintsGenetic algorithmJob rotationManufacturingCombinatorial optimizationErgonomicsGenetic algorithmsIndustrial researchManufactureMultiobjective optimizationOccupational risksScheduling algorithmsCombinatorial optimization problemsComputational timeJob rotationManufacturing environmentsManufacturing industriesMulti-objective optimization problemNon- dominated sorting genetic algorithmsSimilar solutionComputational efficiencySana S.S.Ospina-Mateus H.Arrieta F.G.Chedid J.A.Aptel, M., Cail, F., Gerling, A., Louis, O., Proposal of parameters to implement a workstation rotation system to protect against MSDs (2008) Int J Ind Ergon, 38 (11-12), pp. 900-909Arya, A., Using job rotation to extract employee information (2004) J Law Econ Organ, 20 (2), pp. 400-414Asensio-Cuesta, S., Diego-Mas, J.A., Canós-Darós, L., Andrés-Romano, C., A genetic algorithm for the design of job rotation schedules considering ergonomic and competence criteria (2012) Int J Adv Manuf Technol, 60 (9-12), pp. 1161-1174Asensio-Cuesta, S., Diego-Mas, J.A., Cremades-Oliver, L.V., González-Cruz, M.C., A method to design job rotation schedules to prevent work-related musculoskeletal disorders in repetitive work (2012) Int J Prod Res, 50 (24), pp. 7467-7478Ayough, A., Zandieh, M., Farsijani, H., GA and ICA approaches to job rotation scheduling problem: considering employee’s boredom (2012) Int J Adv Manuf Technol, 60 (5-8), pp. 651-666Azizi, N., Zolfaghari, S., Liang, M., Modeling job rotation in manufacturing systems: the study of employee’s boredom and skill variations (2010) Int J Prod Econ, 123 (1), pp. 69-85Bhadury, J., Radovilsky, Z., Job rotation using the multi-period assignment model (2006) Int J Prod Res, 44 (20), pp. 4431-4444Brunold, J., Durst, S., Intellectual capital risks and job rotation (2012) J Intellect Cap, 13 (2), pp. 178-195(2017) Nonfatal occupational injuries and illnesses resulting in days away from work in 2016, , Bureau of Labor Statistics, Washington, DCCardenas-Barron, L.E., Adaptive genetic algorithm for lot-sizing problem with self-adjustment operation rate: a discussion (2010) Int J Prod Econ, 123, pp. 243-245Cardenas-Barron, L.E., Taleizadeh, A.A., Hybrid metaheuristics algorithms for inventory management problems (2012) Meta-Heuristics Optim Algorithms Eng Bus Econ Finance, 11, pp. 312-356Carnahan, B.J., Redfern, M.S., Norman, B.A., A genetic algorithm for designing job rotation schedules considering ergonomic constraints (1999) Paper Presented at the Evolutionary Computation, 1999. 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optimization (2013) Eng Appl Artif Intell, 26 (1), pp. 327-333Yıldız, B.S., A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems (2017) Int J Veh Des, 73 (1-3), pp. 208-218Yıldız, B.S., Lekesiz, H., Fatigue-based structural optimisation of vehicle components (2017) Int J Veh Des, 73 (1-3), pp. 54-62Yildiz, A.R., Saitou, K., Topology synthesis of multi component structural assemblies in continuum domains (2011) J Mech Des, 133 (1), p. 011008Yildiz, A.R., Solanki, K.N., Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach (2012) Int J Adv Manuf Technol, 59 (1-4), pp. 367-376Yıldız, B.S., Yıldız, A.R., Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes (2017) Mater Test, 59 (5), pp. 425-429Yildiz, B.S., Lekesiz, H., Yildiz, A.R., Structural design of vehicle components using gravitational search and charged system search algorithms (2016) Mater Test, 58 (1), pp. 79-81Yıldız, A.R., Kurtuluş, E., Demirci, E., Yıldız, B.S., Karagöz, S., Optimization of thin-wall structures using hybrid gravitational search and Nelder-Mead algorithm (2016) Mater Test, 58 (1), pp. 75-78http://purl.org/coar/resource_type/c_6501THUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9144/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9144oai:repositorio.utb.edu.co:20.500.12585/91442021-02-02 14:35:44.689Repositorio Institucional UTBrepositorioutb@utb.edu.co |