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Job Shop Scheduling

Published

Author(s)

Albert T. Jones, Luis C. Rabelo, Yuehwern Yih

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

Keywords

Artificial Intelligence Math Programming, scheduling, sequencing simulation

Citation

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 April 17, 2024)
Created April 30, 1996, Updated October 12, 2021