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Survey of Job Shop Scheduling Techniques

Published

Author(s)

Albert T. Jones, Luis C. Rabelo

Abstract

A large number of approaches to the modeling and solution of job shop scheduling problems have been reported in the Operations Research (OR) literature, with varying degrees of success. These approaches revolve around a series of technological advances that have occurred over the last 35 years. These include mathematical programming, dispatching rules, expert systems, neural networks, genetic algorithms, fuzzy logic, and inductive learning. In this chapter, we will focus on dynamic, job shop problems that are both deterministic and stochastic. We will take anevolutionary view and describe how these technologies have been applied to these problems. To do this, we discuss a few of the most important contributions in each of these technology areas and the most recent trends.
Citation
Encyclopedia of Electrical and Electronics Engineering

Keywords

manufacturing, operations management, simulation, supply chain management, system integration

Citation

Jones, A. and Rabelo, L. (1998), Survey of Job Shop Scheduling Techniques, Encyclopedia of Electrical and Electronics Engineering, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=821200 (Accessed February 23, 2024)
Created July 31, 1998, Updated October 12, 2021