Asymptotic behavior of the expansion method for open finite queueing networks
Computers and Operations Research
On decomposition methods for tandem queueing networks with blocking
Operations Research
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Immune algorithms-based approach for redundant reliability problems with multiple component choices
Computers in Industry - Special issue: Application of genetics algorithms in industry
Architecture for an Artificial Immune System
Evolutionary Computation
Buffer allocation in general single-server queueing networks
Computers and Operations Research
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Computers and Operations Research
Journal of Intelligent Manufacturing
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Adequate allocation of buffers in transfer lines is crucial to the optimization of line throughput and work in process (WIP) inventory. Their optimal allocation is subject to specific constraints, associated costs, and revenue projections. In this paper, we implement a combined artificial immune system optimization algorithm in conjunction with a decomposition method to optimally allocate buffers in transfer lines. The aim of the buffer allocation problem (BAP) is to achieve optimal system performance under buffers space constraints. Maximizing line throughput does not necessarily achieve maximum profit. In this study the immune decomposition algorithm (IDA) is used to determine optimal buffer allocation for maximum line throughput and maximum line economic profit. Results of extensive series of tests carried out to compare, in production lines with different characteristics, the performances of the proposed method and those of other algorithms are presented.