An improved Genetic Algorithm For Fuzzy Production PlanningProblems with Application

  • Jalal Abdulkareem Sultan Department of Mathematics, College of Al-Hamdaniah Education, University of Mosul, Al-Hamdaniah, Iraq
  • Omar Ramzi Jasim Department of Accounting, College of Management and Economic, University of Al-Hayat, Erbil, Iraq
  • Sarmad Abdulkhaleq Salih Department of Statistics & Information, College of Mathematics and Computer Sciences, University of Mosul, Mosul

Abstract

Production Planning or Master Production Schedule (MPS) is a key interface between marketing and manufacturing, since it links customer service directly to efficient use of production resources. Mismanagement of the MPS is considered as one of fundamental problem in operation and it can potentially lead to poor customer satisfaction.  In this paper, an improved Genetic Algorithm (IGA) is used to solving fuzzy multi-objective master production schedule (FMOMPS). The main idea is to integrate GA with local search operator. The FMOMPS was applied in the Cotton and medical gauzes plant in Mosul city. The application involves determine the gross requirements by demand forecasting using artificial neural networks. The IGA proved its efficiency in solving MPS problems compared with the genetic algorithm for fuzzy and non-fuzzy model, as the results clearly showed the ability of IGA to determine intelligently how much, when, and where the additional capacities (overtimes) are required such that the inventory can be reduced without affecting customer service level.

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Published
2015-08-31
How to Cite
SULTAN, Jalal Abdulkareem; JASIM, Omar Ramzi; SALIH, Sarmad Abdulkhaleq. An improved Genetic Algorithm For Fuzzy Production PlanningProblems with Application. Journal of University of Human Development, [S.l.], v. 1, n. 3, p. 390-396, aug. 2015. ISSN 2411-7765. Available at: <https://journals.uhd.edu.iq/index.php/juhd/article/view/651>. Date accessed: 16 june 2021. doi: https://doi.org/10.21928/juhd.v1n3y2015.pp390-396.
Section
Articles