Scheduling jobs on parallel machines applying neural network and heuristic rules

Youngshin Park; Sooyoung Kim; Young Hoon Lee

doi:10.1016/S0360-8352(00)00038-3

Scheduling jobs on parallel machines applying neural network and heuristic rules

Youngshin Park, Sooyoung Kim, Young Hoon Lee

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-review

74 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 language	English
Pages (from-to)	189-202
Number of pages	14
Journal	Computers and Industrial Engineering
Volume	38
Issue number	1
DOIs	https://doi.org/10.1016/S0360-8352(00)00038-3
Publication status	Published - 2000 Jan 1

All Science Journal Classification (ASJC) codes

Computer Science(all)
Engineering(all)

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Scheduling jobs on parallel machines applying neural network and heuristic rules

Abstract

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