Learning Curve Consideration in Makespan Computation Using Artificial Neural Network Approach

Document Type: Research Paper

Authors

Department of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Yanbu Al Sinaiyah, Kingdom of Saudi Arabia

Abstract

This paper presents an alternative method using artificial neural network (ANN) to develop a scheduling scheme which is used to determine the makespan or cycle time of a group of jobs going through a series of stages or workstations. The common conventional method uses mathematical programming techniques and presented in Gantt charts forms. The contribution of this paper is in three fold. Firstly, the learning curve which is characterized by a coefficient is considered in the computation work. Secondly, this work is limited to small number of jobs and is useful for project based pilot runs which involve learning. Lastly, the scheduling scheme is developed in ANN as an alternate method. Extensive and successful training using the input and output vector pairs were done to establish the proposed method. Comparison was done for the tested outputs and results produced seem reliable.

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[1] Argote L., Epple D. (1990), Learning curves in manufacturing; Science 247(4945); 920-924.
[2] Bohlen G.A., Barany J.W. (1976), A learning curve prediction model for operators performing industrial bench assembly operations; International Journal of Production Research 14(2); 295-303.
[3] Guo Z.X., Wong W.K, Leung S.Y.S, Fan F.T (2009), Intelligent production control decision support system for flexible assembly lines; Expert Systems with Applications: An International Journal 36(8); 4268- 4277.
[4] Kumar S., Omar M.K. (2005a), Stochastic re-entrant line modeling for an environmental stress testing in a semiconductor assembly industry; Applied Mathematics and Computation 173(1); 603-615.
[5] Kumar S., Omar M.K. (2005b), Performance measure in a probabilistic reflow screening line using mean value analysis; AIUB Journal of Science and Engineering 4(1); 53-58.
[6] Kumar S. (2007), Performance analysis of a probabilistic re-entrant line in an environmental stress testing operation; Doctoral Thesis; Multimedia University, Malaysia.
[7] Kumar S. (2008), Modeling of a probabilistic re-entrant line bounded by limited operation utilization time; Journal of Industrial and Systems Engineering 2(1); 28-40.
[8] McCollum P. (1997), An introduction to back-propagation neural networks; The Newsletter of the Seattle Robotics Society; November.
[9] Moghaddam R.T., Kanani Y.G., Cheraghalizadeh R. (2008), A genetic algorithm and memetic algorithm to sequencing and scheduling of cellular manufacturing systems; International Journal of Management Science and Engineering Management 3(2); 119-130.

[10] Shihab K. (2006), A back propagation neural network for computer network security; Journal of Computer Science 2(9); 1549-3636.
[11] Stafford E.F., Tseng F.T. (2002), Two models for a family of flowshop sequencing Problems; European Journal of Operational Research 142; 282-293.
[12] Stevenson W.J. (2006), Operations Management; 9th ed. McGraw-Hill; New York, 349-357.