A new neuro-dominance rule for single machine tardiness problem with unequal release dates

Cakar T.

ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2, vol.4132, pp.963-973, 2006 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 4132
  • Publication Date: 2006
  • Journal Name: ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, EMBASE, MathSciNet, Philosopher's Index, zbMATH
  • Page Numbers: pp.963-973
  • Istanbul Technical University Affiliated: No

Abstract

We present a neuro-dommance rule for single machine total weighted tardiness problem with unequal release dates. To obtain the neuro-dominance rule (NDR), backpropagation artificial neural network (BPANN) has been trained using 10000 data and also tested using 10000 another data. The proposed neuro-dommance rule provides a sufficient condition for local optimality. It has been proved that if any sequence violates the neuro-dominance rule then violating jobs are switched according to the total weighted tardiness criterion. The proposed neuro-dominance rule is compared to a number of competing heuristics and meta heuristics for a set of randomly generated problems. Our computational results indicate that the neuro-dominance rule dominates the heuristics and meta heuristics in all runs. Therefore, the neuro-dominance rule can improve the upper and lower bounding schemes.