Paper Info
Title
Machine learning techniques for scheduling jobs with incompatible families and unequal ready times on parallel batch machines
Abstract
This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication facilities, where the machines can be modeled as parallel batch processors. Total weighted tardiness on parallel batch machines with incompatible job families and unequal ready times of the jobs is attempt to minimize. Given that the problem is NP hard, a simple heuristic based on the Apparent Tardiness Cost (ATC) Dispatching Rule is suggested. Using this rule, a look-ahead parameter has to be chosen. Because of the appearance of unequal ready times and batch machines it is hard to develop a closed formula to estimate this parameter. The use of inductive decision trees and neural networks from machine learning is suggested to tackle the problem of parameter estimation. The results of computational experiments based on stochastically generated test data are presented. The results indicate that a successful choice of the look-ahead parameter is possible by using the machine learning techniques.
Year
DOI
Venue
2006
10.1016/j.engappai.2005.10.001
Eng. Appl. of AI
Keywords
Field
DocType
unequal ready time,look-ahead parameter,parallel batch processor,parallel batch machine,apparent tardiness cost,scheduling problem,parameter estimation,dispatching rule,scheduling,machine learning,batching,incompatible family,batch machine,look ahead,neural network,decision tree,computer experiment
Decision tree,Heuristic,Mathematical optimization,Job shop scheduling,Tardiness,Computer science,Scheduling (computing),Test data,Artificial intelligence,Estimation theory,Artificial neural network,Machine learning
Journal
Volume
Issue
ISSN
19
3
Engineering Applications of Artificial Intelligence
Citations 
PageRank 
References 
17
0.91
11
Authors
3
Name
Order
Citations
PageRank
Lars Mönch11034124.98
Jens Zimmermann2677.68
Peter Otto3171.25