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 | |
| Publication status | Published - 2000 Jan 1 |
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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. / 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 journal › Article
TY - JOUR
T1 - Scheduling jobs on parallel machines applying neural network and heuristic rules
AU - Park, Youngshin
AU - Kim, Sooyoung
AU - Lee, Young Hoon
PY - 2000/1/1
Y1 - 2000/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0033717569&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0033717569&partnerID=8YFLogxK
U2 - 10.1016/S0360-8352(00)00038-3
DO - 10.1016/S0360-8352(00)00038-3
M3 - Article
AN - SCOPUS:0033717569
VL - 38
SP - 189
EP - 202
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
SN - 0360-8352
IS - 1
ER -