Vol. 29 No. 1 (2024): January-April


Computational Intelligence

Transgenic Algorithm Applied to the Job Shop Rescheduling Problem

Algoritmo transgénico aplicado al Job Shop Rescheduling Problem



disruptions, efficiency, stability, job shop, rescheduling, transgenic algorithm (en).


interrupciones, eficiencia, estabilidad, job shop, rescheduling, algoritmo transgénico (es).

Abstract (en)

Context: Job sequencing has been approached from a static perspective, without considering the occurrence of unexpected events that might require modifying the schedule, thereby affecting its performance measures.

Method: This paper presents the development and application of a genetic algorithm to the Job Shop Rescheduling Problem (JSRP), a reprogramming of the traditional Job Shop Scheduling Problem. This novel approach seeks to repair the schedule in such a way that theoretical models accurately represent real manufacturing environments.

Results: The experiments designed to validate the algorithm aim to apply five classes of disruptions that could impact the schedule, evaluating two performance measures. This experiment was concurrently conducted with a genetic algorithm from the literature in order to facilitate the comparison of results. It was observed that the proposed approach outperforms the genetic algorithm 65% of the time, and it provides better stability measures 98% of the time.

Conclusions: The proposed algorithm showed favorable outcomes when tested with well-known benchmark instances of the Job Shop Scheduling Problem, and the possibility of enhancing the tool's performance through simulation studies remains open.

Abstract (es)

Contexto: La secuenciación de trabajos ha sido abordada desde un enfoque estático, sin considerar la aparición de eventos inesperados que requieran modificar el cronograma, lo que incide en sus medidas de desempeño.

Método: Este artículo expone el desarrollo y aplicación de un algoritmo transgénico al Job Shop Rescheduling Problem (JSRP), una reprogramación del tradicional Job Shop Scheduling Problem. Este enfoque novedoso busca reparar el cronograma de modo que los modelos teóricos representen los entornos de manufactura reales.

Resultados: Los experimentos diseñados para validar el algoritmo pretenden aplicar cinco clases de interrupciones que pueden afectar el cronograma, evaluando dos medidas de desempeño. Este experimento se realizó simultáneamente en un algoritmo genético de la literatura para facilitar la comparación de los resultados. Se observó que el enfoque propuesto tiene un desempeño superior al del algoritmo genético el 65 % de las veces y lo supera en la medida de estabilidad el 98 % de las veces.

Conclusiones: El algoritmo propuesto mostró buenos resultados al ser probado con instancias de comparación reconocidas del Job Shop Scheduling Problem (JSSP), y queda abierta la posibilidad de mejorar el desempeño de la herramienta por medio de estudios de simulación.

Author Biographies

Néstor Andrés Beltrán-Bernal, University of La Salle

Industrial engineer, Universidad Distrital Francisco José de Caldas, MSc in Industrial Engineering, Universidad Distrital Francisco José de Caldas. Lecturer, Industrial Engineering Program, Department of Engineering, Universidad de la Salle.

José Ignacio Rodríguez-Molano, District University of Bogotá

Industrial engineer, Universidad Distrital Francisco José de Caldas. PhD in Informatics Engineering, Universidad de Oviedo. Full profesor, Industrial Engineering Curricular Project, Department of Engineering, Universidad Distrital Francisco José de Caldas.

Diego Ernesto Mendoza-Patiño, Universidad Antonio Nariño

Industrial engineer, Universidad Distrital Francisco José de Caldas. PhD in Administration, Universidad Autónoma de Querétaro, Mexico. Associate researcher, Department of Engineering, Universidad Antonio Nariño.


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How to Cite


Beltrán-Bernal, N. A., Rodríguez-Molano, J. I., and Mendoza-Patiño, D. E. (2024). Transgenic Algorithm Applied to the Job Shop Rescheduling Problem . Ingeniería, 29(1), e21162.


Beltrán-Bernal, N.A. et al. 2024. Transgenic Algorithm Applied to the Job Shop Rescheduling Problem . Ingeniería. 29, 1 (Jan. 2024), e21162. DOI:


Beltrán-Bernal, N. A.; Rodríguez-Molano, J. I.; Mendoza-Patiño, D. E. Transgenic Algorithm Applied to the Job Shop Rescheduling Problem . Ing. 2024, 29, e21162.


BELTRÁN-BERNAL, Néstor Andrés; RODRÍGUEZ-MOLANO, José Ignacio; MENDOZA-PATIÑO, Diego Ernesto. Transgenic Algorithm Applied to the Job Shop Rescheduling Problem . Ingeniería, [S. l.], v. 29, n. 1, p. e21162, 2024. DOI: 10.14483/23448393.21162. Disponível em: Acesso em: 23 feb. 2024.


Beltrán-Bernal, Néstor Andrés, José Ignacio Rodríguez-Molano, and Diego Ernesto Mendoza-Patiño. 2024. “Transgenic Algorithm Applied to the Job Shop Rescheduling Problem ”. Ingeniería 29 (1):e21162.


Beltrán-Bernal, N. A., Rodríguez-Molano, J. I. and Mendoza-Patiño, D. E. (2024) “Transgenic Algorithm Applied to the Job Shop Rescheduling Problem ”, Ingeniería, 29(1), p. e21162. doi: 10.14483/23448393.21162.


N. A. Beltrán-Bernal, J. I. Rodríguez-Molano, and D. E. Mendoza-Patiño, “Transgenic Algorithm Applied to the Job Shop Rescheduling Problem ”, Ing., vol. 29, no. 1, p. e21162, Jan. 2024.


Beltrán-Bernal, Néstor Andrés, et al. “Transgenic Algorithm Applied to the Job Shop Rescheduling Problem ”. Ingeniería, vol. 29, no. 1, Jan. 2024, p. e21162, doi:10.14483/23448393.21162.


Beltrán-Bernal, Néstor Andrés, José Ignacio Rodríguez-Molano, and Diego Ernesto Mendoza-Patiño. “Transgenic Algorithm Applied to the Job Shop Rescheduling Problem ”. Ingeniería 29, no. 1 (January 13, 2024): e21162. Accessed February 23, 2024.


Beltrán-Bernal NA, Rodríguez-Molano JI, Mendoza-Patiño DE. Transgenic Algorithm Applied to the Job Shop Rescheduling Problem . Ing. [Internet]. 2024 Jan. 13 [cited 2024 Feb. 23];29(1):e21162. Available from:

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