Merging artificial immune system and ordinal optimization for solving the optimal buffer resource allocation of production line

Choosing suitable buffer setups for network-flow production lines of automated manufacturing systems to augment throughputs is a pragmatic issue. In this work, an approach merging artificial immune system (AIS) and ordinal optimization (OO) is developed to determine an optimal buffer resource allocation of a network-flow production line for maximizing the throughput. The proposed approach consists of two levels. The first level is using AIS assisted by a rough estimation to select a candidate subset of solutions. The second level is to identify an excellent solution among the solutions obtained from the first level using optimal computing budget allocation (OCBA) technique. We have tested the proposed approach on a three-stage ten-node network-flow production line and compared the test results with those obtained by three swarm intelligence methods with accurate estimation. Test results demonstrate that the proposed approach can obtain an excellent solution within a reasonable computing time and outperforms the three swarm intelligence methods.