Optimization of Production Scheduling using Multi-Tasking by Multi-Population Genetic Algorithm

Forfattere

  • Isha Sanitha. S. Dept. of ME, ASET, Palakkad, Kerala, India
  • Tom Zacharia Assistant Professor/Dept.of.ME St.Joseph’s College of Engineering and Technology, Kottayam, Kerala- INDIA
  • Pradeepmon T. G. Dept. of ME, Mar Baselios Institute of Technology and Science, Ernakulam, Kerala, India

Nøgleord:

Parallel scheduling, Multi-population genetic algorithm

Resumé

Production scheduling is an important activity for manufacturing and engineering, where it can have a major impact on the productivity of a production system. Traditional in-house manufacturing involves production machines with dedicated operators. Here, multitasking technique is used to optimize the production scheduling problem. In parallel machine scheduling, there are n jobs and m machines and each job needs to be executed on one of the machines during a fixed processing time, without pre-emption. This type of scheduling can be applied to identical or non-identical machines. The objective of this paper is to develop a multi-population genetic algorithm for optimizing non-identical parallel machine scheduling problem of a discreet manufacturing unit, by employing multitasking techniques. Company have well developed supply chain management system in addition to manufacturing facilities of critical high valued items for their product range. The algorithm will be developed for specific scheduling requirements. The benefit in enhancing the throughput will be demonstrated by Primavera P6 Professional.Keywords: Parallel scheduling, Multi-population genetic algorithm Cite this Article Isha Sanitha S, Tom Zacharia, Pradeepmon TG. Optimization of Production Scheduling using Multi-Tasking by Multi-Population Genetic Algorithm. Journal of Production Research & Management. 2015; 5(1): 18–26p.

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Publiceret

2015-04-23

Nummer

Årg. 5 Nr. 1 (2015)

Sektion

Research Articles

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