A Coevolutionary Algorithm for Balancing and Sequencing in Mixed Model Assembly Lines

A mixed model assembly line is a production line where a variety of product models are produced. Line balancing and model sequencing problems are important for an efficient use of such lines. Although the two problems are tightly interrelated with each other, prior researches have considered them separately or sequentially. This paper presents a new method using a coevolutionary algorithm that can solve the two problems at the same time. In the algorithm, it is important to promote population diversity and search efficiency. We adopt a localized interaction within and between populations, and develop methods of selecting symbiotic partners and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. Also presented are the experimental results that demonstrate the proposed algorithm is superior to existing approaches.

[1]  Richard K. Belew,et al.  New Methods for Competitive Coevolution , 1997, Evolutionary Computation.

[2]  Alan S. Perelson,et al.  Searching for Diverse, Cooperative Populations with Genetic Algorithms , 1993, Evolutionary Computation.

[3]  Emanuel Falkenauer,et al.  A New Representation and Operators for Genetic Algorithms Applied to Grouping Problems , 1994, Evolutionary Computation.

[4]  Yeongho Kim,et al.  Sequencing in mixed model assembly lines: A genetic algorithm approach , 1996, Comput. Oper. Res..

[5]  Mitchell A. Potter,et al.  The design and analysis of a computational model of cooperative coevolution , 1997 .

[6]  A. Chakravarty,et al.  Balancing Mixed Model Lines with In-Process Inventories , 1985 .

[7]  Candace Arai Yano,et al.  Sequencing to minimize work overload in assembly lines with product options , 1991 .

[8]  George L. Nemhauser,et al.  An Algorithm for the Line Balancing Problem , 1964 .

[9]  L. Tsai Mixed-model sequencing to minimize utility work and the risk of conveyor stoppage , 1995 .

[10]  Yeongho Kim,et al.  Genetic algorithms for assembly line balancing with various objectives , 1996 .

[11]  Kenjiro Okamura,et al.  A heuristic algorithm for the assembly line model-mix sequencing problem to minimize the risk of stopping the conveyor , 1979 .

[12]  Gregor von Laszewski,et al.  Intelligent Structural Operators for the k-way Graph Partitioning Problem , 1991, ICGA.

[13]  Helio J. C. Barbosa A Coevolutionary Genetic Algorithm for a Game Approach to Structural Optimization , 1997, ICGA.

[14]  Yuval Davidor,et al.  A Naturally Occurring Niche and Species Phenomenon: The Model and First Results , 1991, ICGA.

[15]  Nick T. Thomopoulos,et al.  Mixed Model Line Balancing with Smoothed Station Assignments , 1970 .

[16]  Risto Miikkulainen,et al.  Forming Neural Networks Through Efficient and Adaptive Coevolution , 1997, Evolutionary Computation.

[17]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.

[18]  E. M. Dar-El,et al.  A mixed-model sequencing application , 1981 .

[19]  Mary Lou Maher,et al.  Modeling design exploration as co-evolution , 1996 .

[20]  W. Daniel Hillis,et al.  Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .

[21]  J. L. C. Macaskill,et al.  Production-Line Balances for Mixed-Model Lines , 1972 .

[22]  Alan S. Perelson,et al.  Using Genetic Algorithms to Explore Pattern Recognition in the Immune System , 1993, Evolutionary Computation.

[23]  T. Fogarty,et al.  Artificial symbiogenesis , 1995 .

[24]  Nick T. Thomopoulos,et al.  Line Balancing-Sequencing for Mixed-Model Assembly , 1967 .

[25]  Avraham Shtub,et al.  An analytic framework for sequencing mixed model assembly lines , 1992 .