SLAK, Aleš ;TAVČAR, Jože ;DUHOVNIK, Jože . Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 57, n.2, p. 110-124, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/application-of-genetic-algorithm-into-multicriteria-batch-manufacturing-scheduling/>. Date accessed: 22 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2010.122.
Slak, A., Tavčar, J., & Duhovnik, J. (2011). Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling. Strojniški vestnik - Journal of Mechanical Engineering, 57(2), 110-124. doi:http://dx.doi.org/10.5545/sv-jme.2010.122
@article{sv-jmesv-jme.2010.122,
author = {Aleš Slak and Jože Tavčar and Jože Duhovnik},
title = {Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {57},
number = {2},
year = {2011},
keywords = {genetic algorithm; multicriteria scheduling; batch production; target function},
abstract = {Technical innovations in the area of manufacturing logistics are introduced partially and thus fail to realize their full potential. In order to optimise the efficiency of turning manufacturing processes, the production planning and scheduling, cutting tools and material flow process, manufacturing capacities have been analysed. All data from production operations, quantities and the, duration of operations are now kept in the ERP system. It provided the necessary condition for the establishment of a robust planning model, which includes stock control of material and cutting tools. An update was required for the whole lifecycle of products and means of work. The article presents information and an algorithm for a dynamic scheduling model, based on a genetic algorithm. The orders on the machines are scheduled on the basis of a genetic algorithm, according to the target function criteria. The algorithm provides a satisfactory, almost ideal solution, which is good enough for implementation in practice. With the GA the machine utilization increased, throughput time was reduced and costs and delivery delays improved. The presented model of GA also allows further optimisation of manufacturing plans and the machines layout.},
issn = {0039-2480}, pages = {110-124}, doi = {10.5545/sv-jme.2010.122},
url = {https://www.sv-jme.eu/article/application-of-genetic-algorithm-into-multicriteria-batch-manufacturing-scheduling/}
}
Slak, A.,Tavčar, J.,Duhovnik, J. 2011 June 57. Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 57:2
%A Slak, Aleš %A Tavčar, Jože %A Duhovnik, Jože %D 2011 %T Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling %B 2011 %9 genetic algorithm; multicriteria scheduling; batch production; target function %! Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling %K genetic algorithm; multicriteria scheduling; batch production; target function %X Technical innovations in the area of manufacturing logistics are introduced partially and thus fail to realize their full potential. In order to optimise the efficiency of turning manufacturing processes, the production planning and scheduling, cutting tools and material flow process, manufacturing capacities have been analysed. All data from production operations, quantities and the, duration of operations are now kept in the ERP system. It provided the necessary condition for the establishment of a robust planning model, which includes stock control of material and cutting tools. An update was required for the whole lifecycle of products and means of work. The article presents information and an algorithm for a dynamic scheduling model, based on a genetic algorithm. The orders on the machines are scheduled on the basis of a genetic algorithm, according to the target function criteria. The algorithm provides a satisfactory, almost ideal solution, which is good enough for implementation in practice. With the GA the machine utilization increased, throughput time was reduced and costs and delivery delays improved. The presented model of GA also allows further optimisation of manufacturing plans and the machines layout. %U https://www.sv-jme.eu/article/application-of-genetic-algorithm-into-multicriteria-batch-manufacturing-scheduling/ %0 Journal Article %R 10.5545/sv-jme.2010.122 %& 110 %P 15 %J Strojniški vestnik - Journal of Mechanical Engineering %V 57 %N 2 %@ 0039-2480 %8 2018-06-28 %7 2018-06-28
Slak, Aleš, Jože Tavčar, & Jože Duhovnik. "Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling." Strojniški vestnik - Journal of Mechanical Engineering [Online], 57.2 (2011): 110-124. Web. 22 Dec. 2024
TY - JOUR AU - Slak, Aleš AU - Tavčar, Jože AU - Duhovnik, Jože PY - 2011 TI - Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2010.122 KW - genetic algorithm; multicriteria scheduling; batch production; target function N2 - Technical innovations in the area of manufacturing logistics are introduced partially and thus fail to realize their full potential. In order to optimise the efficiency of turning manufacturing processes, the production planning and scheduling, cutting tools and material flow process, manufacturing capacities have been analysed. All data from production operations, quantities and the, duration of operations are now kept in the ERP system. It provided the necessary condition for the establishment of a robust planning model, which includes stock control of material and cutting tools. An update was required for the whole lifecycle of products and means of work. The article presents information and an algorithm for a dynamic scheduling model, based on a genetic algorithm. The orders on the machines are scheduled on the basis of a genetic algorithm, according to the target function criteria. The algorithm provides a satisfactory, almost ideal solution, which is good enough for implementation in practice. With the GA the machine utilization increased, throughput time was reduced and costs and delivery delays improved. The presented model of GA also allows further optimisation of manufacturing plans and the machines layout. UR - https://www.sv-jme.eu/article/application-of-genetic-algorithm-into-multicriteria-batch-manufacturing-scheduling/
@article{{sv-jme}{sv-jme.2010.122},
author = {Slak, A., Tavčar, J., Duhovnik, J.},
title = {Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {57},
number = {2},
year = {2011},
doi = {10.5545/sv-jme.2010.122},
url = {https://www.sv-jme.eu/article/application-of-genetic-algorithm-into-multicriteria-batch-manufacturing-scheduling/}
}
TY - JOUR AU - Slak, Aleš AU - Tavčar, Jože AU - Duhovnik, Jože PY - 2018/06/28 TI - Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 57, No 2 (2011): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2010.122 KW - genetic algorithm, multicriteria scheduling, batch production, target function N2 - Technical innovations in the area of manufacturing logistics are introduced partially and thus fail to realize their full potential. In order to optimise the efficiency of turning manufacturing processes, the production planning and scheduling, cutting tools and material flow process, manufacturing capacities have been analysed. All data from production operations, quantities and the, duration of operations are now kept in the ERP system. It provided the necessary condition for the establishment of a robust planning model, which includes stock control of material and cutting tools. An update was required for the whole lifecycle of products and means of work. The article presents information and an algorithm for a dynamic scheduling model, based on a genetic algorithm. The orders on the machines are scheduled on the basis of a genetic algorithm, according to the target function criteria. The algorithm provides a satisfactory, almost ideal solution, which is good enough for implementation in practice. With the GA the machine utilization increased, throughput time was reduced and costs and delivery delays improved. The presented model of GA also allows further optimisation of manufacturing plans and the machines layout. UR - https://www.sv-jme.eu/article/application-of-genetic-algorithm-into-multicriteria-batch-manufacturing-scheduling/
Slak, Aleš, Tavčar, Jože, AND Duhovnik, Jože. "Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 57 Number 2 (28 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 57(2011)2, 110-124
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Technical innovations in the area of manufacturing logistics are introduced partially and thus fail to realize their full potential. In order to optimise the efficiency of turning manufacturing processes, the production planning and scheduling, cutting tools and material flow process, manufacturing capacities have been analysed. All data from production operations, quantities and the, duration of operations are now kept in the ERP system. It provided the necessary condition for the establishment of a robust planning model, which includes stock control of material and cutting tools. An update was required for the whole lifecycle of products and means of work. The article presents information and an algorithm for a dynamic scheduling model, based on a genetic algorithm. The orders on the machines are scheduled on the basis of a genetic algorithm, according to the target function criteria. The algorithm provides a satisfactory, almost ideal solution, which is good enough for implementation in practice. With the GA the machine utilization increased, throughput time was reduced and costs and delivery delays improved. The presented model of GA also allows further optimisation of manufacturing plans and the machines layout.