Robert T. Sumichrast is a professor of management science and information technology and associate dean at Virginia Polytechnic Institute and State University. He is currently serving as director of the Pamplin MBA Program. He received a PhD in management science from Clemson University and a BS in physics from Purdue University. Dr. Sumichrast's primaryresearch and teaching interests are in the areas of decision support systems with applications to production/operations scheduling. He has served as a consultant for several corporations, as the president of the Southwest Virginia Chapter of APICS, and as president of the southeastern Chapter of INFORMS.
An Evolutionary Algorithm for Sequencing Production on a Paced Assembly Line
Article first published online: 7 JUN 2007
Volume 31, Issue 1, pages 149–172, March 2000
How to Cite
Sumichrast, R. T., Oxenrider, K. A. and Clayton, E. R. (2000), An Evolutionary Algorithm for Sequencing Production on a Paced Assembly Line. Decision Sciences, 31: 149–172. doi: 10.1111/j.1540-5915.2000.tb00928.x
Keith Oxenrider is a biochemist/MBA who has developed a strong interest in utilizing cutting edge programming techniques for manufacturing optimization. He has participated in most phases of manufacturing from daily work on an assembly line to managing a manufacturing plant. Recently he has combined his interest in programming and manufacturing, and is aproject nager for a polyester fiber company's IS department. He is always eager to apply his diverse background to solving difficult problems.
Edward R. Clayton is the Lenz Professor of Management Science and Information Technology at Virginia Polytechnic Institute and State University in Blacksburg, Virginia. He received a PhD from Clemson University and a BS from the University of Florida. He is a co-author of the book GERT Modeling and Simulation: Fundamentals and Applications and has published numerous articles insuch journals as Management Science, Omega, AIIE Transactions, Computers and Operations Research, European Journal of Operations Research, IIE Transactions, and others. He is a charter member of the Decision Sciences Institute, and past president of Southeast DSI. He has served as a paper referee for Decision Sciences and Computers and Operations Research. He has also served as a consultant to the U.S. Army, Piper Aircraft Corporation, A. T. Massey Coal Company, and several other firms on computer modeling and optimization problems.
- Issue published online: 7 JUN 2007
- Article first published online: 7 JUN 2007
- Received: October 2, 1996. Accepted: March 4, 1999
- Evolutionary Algorithm;
- Genetic Algorithm;
- Operations and Logistics Management: Assembly Systems;
- and Simulation
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.