Decision Sciences Journal
Volume 31, Number 1
Winter 2000
An Evolutionary Algorithm for Sequencing Production on
a Paced Assembly Line
Robert T. Sumichrast
Pamplin College of Business (0209), Virginia Polytechnic Institute
and State University, Blacksburg, VA 24061, email: sumichrast@vt.edu
Keith A. Oxenrider
1007 Tarlton Drive, Shelby, NC 28150, email: mitakeet@sol-system.com
Edward R. Clayton
1007 Pamplin Hall (0235), Virginia Polytechnic Institute and
State University, Blacksburg, VA 24061, email: eclayton@vt.edu
ABSTRACT. A new sequencing method for mixed-model assembly
lines is developed and tested. This method, called the Evolutionary
Production Sequencer (EPS) is designed to maximize production
on an assembly line. The performance of EPS is evaluated using
three measures: minimum cycle time necessary to achieve 100%
completion without rework, percent of items completed without
rework for a given cycle time, and sequence “smoothness.”
The first two of these measures are based on a simulated production
system. Characteristics of the system, such as assembly line
station length, assembly time and cycle time, are varied to better
gauge the performance of EPS. More fundamental variation is studied
by modeling two production systems. In one set of tests, the
system consists of an assembly line in isolation (i.e., a single-level
system). In another set of tests, the production system consists
of the assembly line and the fabrication system supplying components
to the line (i.e., a two-level system). Sequence smoothness is
measured by the mean absolute deviation (MAD) between actual
component usage and the ideal usage at each point in the production
sequence.
The performance of EPS is compared to those of well-known
assembly line sequencing techniques developed by Miltenburg (1989),
Okamura and Yamashina (1979), and Yano and Rachamadugu (1991).
EPS performed very well under all test conditions when the criterion
of success was either minimum cycle time necessary to achieve
100% production without rework or percent of items completed
without rework for a given cycle time. When MAD was the criterion
of success, EPS was found inferior to the Miltenburg heuristic
but better than the other two production-oriented techniques.
Subject Areas: Evolutionary Algorithm, Genetic Algorithm,
Heuristics, Manufacturing, Operations and Logistics Management:
Assembly Systems, and Simulation. |