Sequencing of operations by simulation in the company Puntadas, S.G.

Main Article Content

Erick Orozco-Crespo
Neyfe Sablón-Cossío
Yadamy Rodríguez-Sánchez
Jenifer Cristina González-Garzón
Fabiola Sánchez-Galván

Abstract

The sequencing of customer orders is a challenge for scheduling operations in a company. This becomes even more complex if it is a company in the textile sector, since it responds to increasingly personalized orders. The order given to these orders has a significant impact on the performance of operations. In this context, a simulation model was designed in FlexSim to measure the impact of the application of different priority rules for the sequencing of customer orders in the case of the operations of the company “Puntadas, S.G.”. The results were presented by sequencing the orders corresponding to four weeks of the Master Production Program (MPS) in the simulation model. With this, it was determined that the Shortest Processing Time (SPT) priority rule would be the one that would best impact the general performance of operations, especially through an increase in reliability of 4.49%, compared to the First-Come First-Served rule. (FCFS) which is currently implemented.

Article Details

How to Cite
Orozco-Crespo, E., Sablón-Cossío, N., Rodríguez-Sánchez, Y., González-Garzón, J. C., & Sánchez-Galván, F. (2021). Sequencing of operations by simulation in the company Puntadas, S.G. Tecnología En Marcha Journal, 34(1), Pág. 55–68. https://doi.org/10.18845/tm.v34i1.4823
Section
Artículo científico

References

F. Gu, J. Guo, P. Hall, and X. Gu, “An integrated architecture for implementing extended producer responsibility in the context of Industry 4.0,” (in English), International Journal of Production Research, vol. 57, no. 5, pp. 14 58-1477, 2019, doi: 10.1080/00207543.2018.1489161.

A. de la Calle Vicente, A. Barinaga Naves, and J. C. Gietz Jiménez, “La colaboración como estrategia en la cadena de suministro: una visión metodológica,” Dyna Management, vol. 4, no. 1, 2016, doi: 10.6036/MN7809

L. J. Krajewski, M. K. Malhotra, and L. P. Ritzman, Operations Management Processes and Supply Chains. Boston: Pearson Education, 2019.

S. Mittal, M. A. Khan, D. Romero, and T. Wuest, “A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs),” (in English), Journal of Manufacturing Systems, Review vol. 49, pp. 194-214, 2018, doi: 10.1016/j.jmsy.2018.10.005.

D. A. Burbano, J. D. López, and O. A. Rojas, “Definición de un método para la programación de la producción desde el paradigma de los sistemas holónicos de manufactura”, Ingeniería y Competitividad, vol. 17, no. 2, pp. 29-40, 2015.

J. A. A. Araúzo, J. J. de Benito Martín, R. del Olmo Martínez, and P. A. S. Angulo, “Situación actual y expectativas de los sistemas de fabricación basados en agentes”, in VIII Congreso de Ingeniería de Organización, Leganés, 2004, pp. 1043-1052.

J. D. Romero-Rojas, V. K. Ortiz-Triana, and Á. J. Caicedo-Rolón, “La teoría de restricciones y la optimización como herramientas gerenciales para la programación de la producción. Una Aplicación en la Industria de Muebles”, Revista de Métodos Cuantitativos para la Economía y la Empresa, vol. 27, pp. 74-90, 2019.

R. B. Chase and F. R. Jacobs, Administración de operaciones: producción y cadena de suministros. México: McGraw-Hill, 2014.

A. A. Correa Espinal, E. Rodríguez Velásquez, and M. I. Londoño Restrepo, “Secuenciación de operaciones para configuraciones de planta tipo flexible Job Shop. Estado del arte”, Avances en Sistemas e Informática, vol. 5, no. 3, pp. 151-161, 2008.

R. Najarro, R. López, R. E. Racines, and A. Puris, “An Hybrid Genetic Algorithm to Optimization of Flow Shop Scheduling Problems under Real Environments Constraints,” Enfoque UTE, vol. 8, no. 5, pp. 14 - 25, 12/18 2017, doi: 10.29019/enfoqueute.v8n5.176.

L. S. Dalenogare, G. B. Benitez, N. F. Ayala, and A. G. Frank, “The expected contribution of Industry 4.0 technologies for industrial performance,” (in English), International Journal of Production Economics, vol. 204, pp. 383-394, 2018, doi: 10.1016/j.ijpe.2018.08.019.

E. Orozco-Crespo, N. Sablón-Cossio, and R. V. Saraguro-Piarpuezán, “Discrete event simulation for resource programming: Case of Ecuadorian textile industry,” in 2nd European International Conference on Industrial Engineering and Operations Management.IEOM 2018, 2018, vol. 2018: IEOM Society, JUL ed., pp. 1430-1443. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066935485&partnerID=40&md5=03b0f0748f21d8fca61fb168732a596d

L. Ang, K. Y. Wong, and W. P. Wong, “Simulation of sequencing rules in a five-similar-machine job shop,” in 16th International Business Information Management Association Conference, IBIMA 2011, vol. 1, Kuala Lumpur, 2011, International Business Information Management Association, IBIMA, pp. 61-67. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905123281&partnerID=40&md5=75c647382c1068b21538abd651438514

J. Cano, E. Campo, and R. Gómez, “Simulación de eventos discretos en la planificación de producción para sistemas de confección modular”, Revista Técnica de la Facultad de Ingeniería Universidad del Zulia, vol. 41, no. 1, pp. 50-58, 01/01 2018.

A. Moeuf, R. Pellerin, S. Lamouri, S. Tamayo-Giraldo, and R. Barbaray, “The industrial management of SMEs in the era of Industry 4.0,” (in English), International Journal of Production Research, vol. 56, no. 3, pp. 1118-1136, 2018, doi: 10.1080/00207543.2017.1372647.

A. G. Frank, L. S. Dalenogare, and N. F. Ayala, “Industry 4.0 technologies: Implementation patterns in manufacturing companies,” (in English), International Journal of Production Economics, vol. 210, pp. 15-26, 2019, doi: 10.1016/j.ijpe.2019.01.004.

N. Wang and O. Ghrayeb, “Dispatching Rules Application for a Parallel Machine Scheduling Problem Using Simulation,” in IIE Annual Conference. Proceedings, 2009, Institute of Industrial and Systems Engineers (IISE), p. 2085.

N. Suresh Kumar and R. Sridharan, “Simulation-based metamodels for the analysis of scheduling decisions in a flexible manufacturing system operating in a tool-sharing environment,” The International Journal of Advanced Manufacturing Technology, vol. 51, no. 1, pp. 341-355, 2010/11/01 2010, doi: 10.1007/s00170-010-2603-9.

M. Padilla, J. Guo, and R. Moraga, “An industrial application of simulation-based multi-objective scheduling,” in IIE Annual Conference and Expo 2013, San Juan, Institute of Industrial Engineers, pp. 2457-2464. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900320707&partnerID=40&md5=f83bafa7d872e94ce10e9b4a987f9c94

J. A. Giraldo, C. A. Toro, and F. A. Jaramillo, “Learning about sequencing of jobs in a job shop simulation using,” (in Spanish), Formacion Universitaria, vol. 6, no. 4, pp. 27-38, 2013, doi: 10.4067/S0718-50062013000400004.

V. Sels, N. Gheysen, and M. Vanhoucke, “A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions,” (in English), International Journal of Production Research, vol. 50, no. 15, pp. 4255-4270, 2012, doi: 10.1080/00207543.2011.611539.

R. Tavakkoli-Moghaddam and M. Daneshmand-Mehr, “A computer simulation model for job shop scheduling problems minimizing makespan,” (in English), Computers and Industrial Engineering [Conference Paper] vol. 48, no. 4, pp. 811-823, 2005, doi: 10.1016/j.cie.2004.12.010.

C. R. Harrell, Simulation Using ProModel. McGraw-Hill Education, 2011.

M. S. Casas-Ramírez, J. F. Camacho-Vallejo, and I. A. Martínez-Salazar, “Approximating solutions to a bilevel capacitated facility location problem with customer’s patronization toward a list of preferences,” (in English), Applied Mathematics and Computation, vol. 319, pp. 369-386, 2018, doi: 10.1016/j.amc.2017.03.051.

E. García Dunna, H. García Reyes, and L. E. Cárdenas Barrón, Simulación y análisis de sistemas con ProModel. Pearson Educación, 2013.

E. Orozco-Crespo, N. Sablón-Cossio, R. V. Saraguro-Piarpuezan, D. D. Hermoso-Ayala, and Y. Rodríguez-Sánchez, “Optimización de recursos mediante la simulación de eventos discretos”, Revista Tecnología en Marcha, vol. 32, no. 2, pp. 146-164, 2019, doi: 10.18845/tm.v32i2.4356.

M. S. Casas-Ramírez, J. F. Camacho-Vallejo, R. G. González-Ramírez, J. A. Marmolejo-Saucedo, and J. M. Velarde-Cantú, “Optimizing a Biobjective Production-Distribution Planning Problem Using a GRASP,” Complexity, vol. 2018, p. 3418580, 2018/02/13 2018, doi: 10.1155/2018/3418580.

E. Shokouhi, “Integrated multi-objective process planning and flexible job shop scheduling considering precedence constraints,” (in English), Production and Manufacturing Research, vol. 6, no. 1, pp. 61-89, 2018, doi: 10.1080/21693277.2017.1415173.

D. D. Hermoso-Ayala, “Optimización del proceso de producción de medias corta logo en la fábrica Gardenia”, Bachelor Thesis, Departamento de Ingeniería Industrial, Universidad Técnica del Norte, Ibarra, 2017. [Online]. Available: http://repositorio.utn.edu.ec/handle/123456789/5985

C. J. Barniak-Velalcazar, “Optimización del proceso de abastecimiento de materia prima a las líneas de producción de la empresa Ecuajugos S.A.”, Bachelor Degree, Departamento de Ingeniería Industrial, Universidad Técnica del Norte, Ibarra, 2017. [Online]. Available: http://repositorio.utn.edu.ec/handle/123456789/6905

M. Golari, N. Fan, and T. Jin, “Multistage Stochastic Optimization for Production-Inventory Planning with Intermittent Renewable Energy,” (in English), Production and Operations Management, vol. 26, no. 3, pp. 409-425, 2017, doi: 10.1111/poms.12657.

F. Sánchez-Galván, C. L. Garay-Rondero, C. Mora-Castellanos, D. E. Gibaja-Romero, and H. Bautista-Santos, “Optimización de costos de transporte bajo el enfoque de teoría de juegos. Estudio de caso”, Nova scientia, vol. 9, no. 19, pp. 185-210, 2017, doi: 10.21640/ns.v9i19.1051

Most read articles by the same author(s)