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A bicriterian flow shop scheduling using artificial neural network

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Abstract

This paper considers the sequencing of jobs that arrive in a flow shop in different combinations over time. Artificial neural network (ANN) uses its acquired sequencing knowledge in making the future sequencing decisions. The paper focuses on scheduling for a flow shop with ‘m’ machines and ‘n’ jobs. The authors have used the heuristics proposed by Campbell et al.(1970, A heuristic algorithm for n-jobs m-machines sequencing problem) to find a sequence and makespan (MS). Then a pair wise interchange of jobs is made to find the optimal MS and total flow time (TFT). The obtained sequence is used for giving training to the neural network and a matrix called neural network master matrix (NNMM) is constructed, which is the basic knowledge of the neurons obtained after training. From the matrix, interpretations are made to determine the optimum sequence for the jobs that arrive in the future over a period of time. The results obtained by the ANN are compared with a constructive heuristics and an improvement heuristics. The results show that the quality of the measure of performance is better when ANN approach is used than obtained by constructive or improvement heuristics. It is found that the system’s efficiency (i.e., obtaining the optimal MS and TFT) increases with increasing numbers of training exemplars.

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References

  1. Campbell HR, Smith DM (1970) A heuristic algorithm for n-jobs m-machines sequencing problem. Manage Sci 16B:630−637

    Google Scholar 

  2. Elsayed EA, Boucher TO (1985) Analysis and control of production systems. Prentice-Hall, Upper Saddle River, NJ

  3. Rajendran C (1995) Theory and methodology heuristics for scheduling in flow shop with multiple objectives. Eur J Oper Res 82:540−555

    Article  MATH  Google Scholar 

  4. Sabuncuoglu I, Gurgun B (1996) A neural network model for scheduling problems. Eur J Oper Res 93:288–299

    Article  MATH  Google Scholar 

  5. Jain AS, Meeran S (1999) Job shop scheduling using neural networks. Int J Prod Res 37:1250−1268

    Google Scholar 

  6. Guh RS, Tannock JDT (1999) Recognition of Control Chart patterns using a neural network approach. Int J Prod Res 37(8):1743−1765

    Article  MATH  Google Scholar 

  7. Gaafar LK, Choueiki MH (2000) A neural network model for solving the lot sizing problem. Omega Int J Manage Sci 28:175−184

    Article  Google Scholar 

  8. Lee I, Shaw MJ (2000) A neural-net approach to real time flow-shop sequencing. Comput Ind Eng 38:125−147

    Article  Google Scholar 

  9. Chen R-M, Huang Y-M (2001) Competitive neural network to solve scheduling problems. Neurocomputing pp 177−196

  10. Johnson SM (1954) Optimal two and three stage production schedules with setup times included. Naval Res Logist Q 1(1)

  11. Palmer D (1965) Sequencing jobs through a multi-stage. Process in the minimum total time – a quick method of obtaining a near optimum. Oper Res Q 16:45−61

    Google Scholar 

  12. Gupta J (1971) A functional heuristic algorithm for the flow shop scheduling problem. Oper Res Q 22:27−39

    Article  Google Scholar 

  13. Dannenbring D (1977) An evolution of flowshop scheduling heuristics. Manage Sci 23:174−1182

    Google Scholar 

  14. Enscore NME, Ham I (1983) A heuristic algorithm for the m-machine, n-jobs flow shop scheduling problem. Omega II:11−95

    Google Scholar 

  15. Baker KR (1974) Introduction to sequencing and scheduling Wiley, New York

  16. Zurada JM (1992) Introduction to artificial neural systems. Jaico, Mumbai

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Correspondence to A. Noorul Haq.

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Noorul Haq, A., Radha Ramanan, T. A bicriterian flow shop scheduling using artificial neural network. Int J Adv Manuf Technol 30, 1132–1138 (2006). https://doi.org/10.1007/s00170-005-0135-5

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  • DOI: https://doi.org/10.1007/s00170-005-0135-5

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