Jones, A.
, Rabelo, L.
and Yih, Y.
(1996),
Job Shop Scheduling, Encyclopedia of Operations Research, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=821199
(Accessed November 30, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Job Shop Scheduling
Published
Author(s)
, Luis C. Rabelo,
Abstract
A large number of approaches to the modeling and solution of job shop scheduling problems have been reported in the OR literature, with varying degrees of success. These approaches revolve around a series of technological advances that have occurred over that last 30 years. These include mathematical programming, dispatching rules, expert systems, neural networks, genetic algorithms, and inductive learning. In this article, we take evolutionary view in describing how these technologies have been applied to job shop scheduling problems. To do this, a few of the most important contributions in each of these technology areas are discussed. We close by looking at the most recent trend which combines several of these technologies into a single hybrid system.Citation
Encyclopedia of Operations Research
Pub Type
Journals
Download Paper
Keywords
Artificial Intelligence Math Programming, scheduling, sequencing simulation
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created April 30, 1996, Updated October 12, 2021