Priority rules for job shops with weighted tardiness costs
Management Science
Scheduling jobs on parallel machines applying neural network and heuristic rules
Computers and Industrial Engineering
Scheduling a batch processing machine with incompatible job families
Computers and Industrial Engineering
Minimizing the makespan on a batch machine with non-identical job sizes: an exact procedure
Computers and Operations Research
A genetic algorithm to minimize maximum lateness on a batch processing machine
Computers and Operations Research
Incremental Induction of Decision Trees
Machine Learning
Computers and Operations Research
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Expert Systems with Applications: An International Journal
A GRASP approach for makespan minimization on parallel batch processing machines
Journal of Intelligent Manufacturing
Computers and Industrial Engineering
Journal of Intelligent Manufacturing
Computers and Industrial Engineering
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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.