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Automation and Remote Control

, Volume 75, Issue 7, pp 1203–1220 | Cite as

A neural network algorithm for servicing jobs with sequential and parallel machines

  • O. GholamiEmail author
  • Yu. N. Sotskov
Topical Issue

Abstract

We consider the FJC max problem of optimal servicing with respect to performance for a given set of jobs by sequential and parallel machines. The problem FJC max is a generalization of the classical JC max problem for the case when the servicing system has not only sequential but also parallel (identical) machines. We propose a two-stage algorithm for a heuristic solution of problem FJC max On the first stage, we solve the problem JC max, i.e., we assume that the servicing system does not have parallel machines. On the second stage, operations are distributed over parallel machines. On both stages of the algorithm, we use neural network decision making models. The efficiency of a neural network algorithm for the problem JC max and problem FJC max was evaluated on 20 test examples obtained from 20 known JC max problems by including into the servicing system a random number of copies of sequential machines.

Keywords

Schedule Problem Remote Control Parallel Machine Priority Rule Heuristic Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Pleiades Publishing, Ltd. 2014

Authors and Affiliations

  1. 1.Department of Computer Science, Nour Department, Mahmudabad CenterIslam University of AzadMahmudabadIran
  2. 2.United Institute of Informatics ProblemsNational Academy of Sciences of BelarusMinskBelarus

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