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


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.


[1] B. Zhang, and M. Hue, "Forecasting with artificial neural networks", The state of the art," International Journal of Forecasting, vol. 14, 1998.
[2] D.E. Goldberg," Genetic Algorithms in Search Optimization & Machine Learning", Addison-Wesley. Reading, 1989.
[3] G.E. Vieira, F. Favaretto, and P.C. Ribas," Comparing genetic algorithms and simulated annealing in master production scheduling problems", Proceeding of 17th International Conference on Production Research. Blacksburg, Virginia, USA, 2004.
[4] G.E. Vieira, and C.P. Ribas," A new multi-objective optimization method for master production scheduling problems using simulated annealing" International Journal of Production Research, Vol. 42, No. 21, Pp.4609-4622 , 2003 .
[5] G.E. Vieira," A practical view of the complexity in developing master production schedules: fundamentals, examples, and implementation", In J. W. Herrmann. Handbook of Production Scheduling, Springer, Maryland, USA pp. 149-176, 2004.
[6] J.A. Sultan,"Proposed Hybrid Techniques for Solving Fuzzy Multi-Objective Linear Programming with Application. Master thesis", university of Mosul, Mosul, Iraq ,2013.
[7] J. Cox, J. Blackstone," APICS Dictionary", 10th edn. APICS, Alexandria ,2001.
[8] J. F. Proud," Master Scheduling", 2nd Edition, John-Wile-Sons Inc., 1999.
[9] J.H. Holland," Adoption in Natural and Artificial Systems", University of Michigan, Ann Arbor ,1975.
[10] MathWorks. Matlab Documentation. MathWorks Inc., 2004.
[11] M. Garey, and D. Johnson," Computer, complexity and intractability. A guide to theory of NP-Completeness", Freeman, San Francisco, USA ,1979.
[12] M.M. Soares, and G.E. Vieira," A New multi-objective optimization method for master production scheduling problems based on genetic algorithm", International Journal of Advanced Manufacturing Technology, DOI 10.1007/s00170-008-1481-x. ,2008.
[13] N. S. Slack, " Operation Management. Prentice Hall", 3rd Edition, New Jersey, USA ,2001.
[14] P. Higgins, and J. Browne," Master production scheduling a concurrent planning approach. Production Planning & Control", Vol. 3, no.1.pp 2-18 ,1992 .
[15] P. Ribas," Análise do uso de têmpera simulada na otimização do planejamento mestre da produção", Pontifícia Universidade Católica do Paraná, Curitiba, 2003.
[16] S. Supriyanto," Fuzzy Multi-Objective Linear Programming and Simulation Approach to the Development of Valid and Realistic Master Production Schedule", Doctor thesis, university of Duisburg, Essen, Germany, 2011.
[17] T. Bäck ," Evolutionary algorithms in theory and practice", New York, Oxford Univ Press ,1996.
[18] T.E. Vollmann, W.L. Berry, and D.C. Whybark," Manufacturing planning and control system", New York. 4rd Edition, McGraw-Hill, 1997.
[19] Y. Wu, M. Liu, and C. Wu," A genetic algorithm for optimizing the MPS of a processing-assembly production line with identical machines", Proceedings of the First International Conference on Machine Learning and Cybernetics. Beijing, 2002.
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: <>. Date accessed: 16 june 2021. doi: