Evolving Machine-Specific Dispatching Rules for a Two-Machine Job Shop using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Hunt:2014:CEC,
-
title = "Evolving Machine-Specific Dispatching Rules for a
Two-Machine Job Shop using Genetic Programming",
-
author = "Rachel Hunt and Mark Johnston and Mengjie Zhang",
-
pages = "618--625",
-
booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary
Computation",
-
year = "2014",
-
month = "6-11 " # jul,
-
editor = "Carlos A. {Coello Coello}",
-
address = "Beijing, China",
-
ISBN = "0-7803-8515-2",
-
keywords = "genetic algorithms, genetic programming, Evolutionary
Computation for Planning and Scheduling",
-
DOI = "doi:10.1109/CEC.2014.6900655",
-
abstract = "Job Shop Scheduling (JSS) involves determining a
schedule for processing jobs on machines to optimise
some measure of delivery speed or customer
satisfaction. We investigate a genetic programming
based hyper-heuristic (GPHH) approach to evolving
dispatching rules for a two-machine job shop in both
static and dynamic environments. In the static case the
proposed GPHH method can represent and discover optimal
dispatching rules. In the dynamic case we investigate
two representations (using a single rule at both
machines and evolving a specialised rule for each
machine) and the effect of changing the training
problem instances throughout evolution. Results show
that relative performance of these methods is dependent
on the testing instances.",
-
notes = "WCCI2014",
- }
Genetic Programming entries for
Rachel Hunt
Mark Johnston
Mengjie Zhang
Citations