Scheduling jobs on parallel machines applying neural network and heuristic rules

Youngshin Park, Sooyoung Kim, Young Hoon Lee

Research output: Contribution to journalArticle

73 Citations (Scopus)

Abstract

The problem of scheduling jobs on identical parallel machines is investigated. The jobs are assumed to have sequence dependent setup times independent of the machine. Each job has a processing time, a due date, and a weight for penalizing tardiness. The objective of scheduling is to fine a sequence of the jobs which minimizes the sum of weighted tardiness. An extension of the ATCS (Apparent Tardiness Cost with Setups) rule which utilizes some look-ahead parameters for calculating the priority index of each job is proposed. An additional factor for measuring the problem characteristics is introduced and a neural network is utilized to get more accurate values of the look-ahead parameters.

Original languageEnglish
Pages (from-to)189-202
Number of pages14
JournalComputers and Industrial Engineering
Volume38
Issue number1
DOIs
Publication statusPublished - 2000 Jan 1

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Scheduling
Neural networks
Processing
Costs

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

Cite this

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Scheduling jobs on parallel machines applying neural network and heuristic rules. / Park, Youngshin; Kim, Sooyoung; Lee, Young Hoon.

In: Computers and Industrial Engineering, Vol. 38, No. 1, 01.01.2000, p. 189-202.

Research output: Contribution to journalArticle

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