Application of neural networks to heuristic scheduling algorithms


Akyol D.

COMPUTERS & INDUSTRIAL ENGINEERING, vol.46, no.4, pp.679-696, 2004 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 4
  • Publication Date: 2004
  • Doi Number: 10.1016/j.cie.2004.05.005
  • Journal Name: COMPUTERS & INDUSTRIAL ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.679-696
  • Keywords: artificial neural networks, multilayered perceptron, heuristic scheduling, flowshop scheduling problems, fuzzy membership functions, FLOW-SHOP, M-MACHINE, N-JOB
  • Dokuz Eylül University Affiliated: Yes

Abstract

This paper considers the use of artificial neural networks (ANNs) to model six different heuristic algorithms applied to the n job, m machine real flowshop scheduling problem with the objective of minimizing makespan. The objective is to obtain six ANN models to be used for the prediction of the completion times for each job processed on each machine and to introduce the fuzziness of scheduling information into flowshop scheduling. Fuzzy membership functions are generated for completion, job waiting and machine idle times. Different methods are proposed to obtain the fuzzy parameters. To model the functional relation between the input and output variables, multilayered feedforward networks (MFNs) trained with error backpropagation learning rule are used. The trained network is able to apply the learnt relationship to new problems. In this paper, an implementation alternative to the existing heuristic algorithms is provided. Once the network is trained adequately, it can provide an outcome (solution) faster than conventional iterative methods by its generalizing property. The results obtained from the study can be extended to solve the scheduling problems in the area of manufacturing. (C) 2004 Elsevier Ltd. All rights reserved.