Assembly planning using a novel immune approach

Inspired by the vertebrate immune system, artificial immune system (AIS) has emerged as a new branch of computational intelligence. This paper explores the application of AIS in the problem of assembly planning and proposes a novel approach, called the immune optimization approach (IOA), to generate the optimal assembly plan. Based on the bionic principles of AIS, IOA introduces manifold immune operations including immune selection, clonal selection, inoculation and immune metabolism to derive the optimal assembly sequence. Maintenance of population diversity, attention to the local as well as the global search, and employment of heuristic knowledge to direct the search of optimized assembly sequences are the major concerns of IOA. The details of IOA are presented and the immune operations are discussed. Two practical products are taken as examples to illustrate the validity of IOA in assembly planning, and encouraging solutions in quality and efficiency are achieved. Comparisons with genetic algorithm demonstrate that IOA finds the optimal assembly solution or near-optimal ones more reliably and more efficiently, indicating that IOA has potential and advantages in dealing with assembly planning.