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An efficient simulation–neural network–genetic algorithm for flexible flow shops with sequence-dependent setup times, job deterioration and learning effects

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Abstract

This study presents an integrated approach based on artificial neural network (ANN), genetic algorithm (GA) and computer simulation to explore all the solution space in stochastic flexible flow shop with sequence-dependent setup times, job deterioration and learning effects. The objective of this study is minimizing total tardiness of jobs in the sequences. In this study, the outputs of ANN are inputted to GA and outputs of simulation model are inputted to ANN. We consider learning effects in this problem which means that workers become more experienced with the passage of time, and thus, the processing duration decreases. Deterioration of job means that processing time is a decreasing function of its execution start time. It is not possible to propose a mathematical optimization model for the stated problem; therefore, a simulation optimization approach based on ANN–GA is introduced for a relatively large problem. Finally, actual experiments are conducted to show the applicability of the proposed novel algorithm in finding near-optimal solutions with normal, uniform and exponential processing and setup times. This is the first study that presents an integrated intelligent approach for optimal solution of stochastic flexible flow shop problem with sequence-dependent setup times, job deterioration and learning effects in a real case study.

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Acknowledgements

This study was supported by a grant from University of Tehran (Grant No. 8106013/1/11). The authors are grateful for the support provided by the College of Engineering, University of Tehran, Iran.

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Authors and Affiliations

  1. School of Industrial and Systems Engineering, Center of Excellence for Intelligent Based Experimental Mechanic, University of Tehran, Tehran, Iran

    A. Azadeh, M. Hasannia Kolaee & S. Jebreili

  2. Department of Engineering Research, College of Engineering, University of Tehran, Tehran, Iran

    A. Azadeh, M. Hasannia Kolaee & S. Jebreili

  3. Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

    A. Hasani Goodarzi

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  1. A. Azadeh
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  3. M. Hasannia Kolaee
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Correspondence to A. Hasani Goodarzi.

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Appendix

Appendix

Data generations in normal and uniform distribution are indicated in following tables (Tables 6, 7, 8, 9, 10). For exponential distribution mean of times is reverse of mean in normal distribution. Some figures of proposed ANN–GA are depicted in this section. Predicted and actual values are shown in Figs. 9, 10, 11 and 12.

Table 6 Mean of processing times of each job with normal distribution
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Table 7 Limit of ranges for processing times with uniform distribution
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Table 8 Normally distributed sequence-dependent setup times for stage 1 with standard deviation 1.5
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Table 9 Normally distributed sequence-dependent setup times for stage 2 with standard deviation 1.5
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Table 10 Normally distributed sequence-dependent setup times for stage 3 with standard deviation 1.5
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Fig. 9
figure 9

Proposed ANN–GA with normal processing and setup times (maximum of processing times of each job at all stages)

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Fig. 10
figure 10

Test plot (maximum processing times of each job at all stages)

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Fig. 11
figure 11

Proposed ANN–GA with normal processing and setup times (mean of processing times of each job at all stages)

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Fig. 12
figure 12

Test plot (mean of processing times of each job at all stages)

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Azadeh, A., Goodarzi, A.H., Kolaee, M.H. et al. An efficient simulation–neural network–genetic algorithm for flexible flow shops with sequence-dependent setup times, job deterioration and learning effects. Neural Comput & Applic 31, 5327–5341 (2019). https://doi.org/10.1007/s00521-018-3368-6

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  • DOI: https://doi.org/10.1007/s00521-018-3368-6

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