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 |
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Engineering(all)
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Park, Y., Kim, S., & Lee, Y. H. (2000). Scheduling jobs on parallel machines applying neural network and heuristic rules. Computers and Industrial Engineering, 38(1), 189-202. https://doi.org/10.1016/S0360-8352(00)00038-3