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GLOBAL JOB SHOP SCHEDULING WITH A GENETIC ALGORITHM
Author(s) -
HERRMANN JEFFREY W.,
LEE CHUNGYEE,
HINCHMAN JIM
Publication year - 1995
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/j.1937-5956.1995.tb00039.x
Subject(s) - computer science , job shop scheduling , scheduling (production processes) , flow shop scheduling , schedule , job shop , genetic algorithm , fair share scheduling , operations research , industrial engineering , distributed computing , operations management , machine learning , operating system , engineering
This paper describes a global job shop scheduling procedure that uses a genetic algorithm to find a good schedule. Unlike previously considered algorithms, this procedure has been implemented in the scheduling system for a manufacturing facility and has led to improved scheduling. This facility is a semiconductor test area. The test area is a job shop and has sequence‐dependent setup times at some operations. The concern of management is to meet their customer due dates and to increase throughput. This requires the coordination of many resources, a task beyond the ability of simple dispatching rules. We discuss a centralized procedure that can find a good schedule through the use of a detailed scheduling model and a genetic algorithm that searches over combinations of dispatching rules. We discuss our effort in developing a system that models the shop, creates schedules for the test area personnel, and makes a number of contributions to test area management.