Determination of an optimal assembly sequence using the psychoclonal algorithm

Owing to conflicting objectives, assembly planning has become a difficult task for decision makers to devise an effective plan that can satisfy the majority of system goals. This is a tedious job and its quick and effective solution is the subject of much research. In recent years, artificial immune systems (AISs) have captured the attention of various researchers due to their ability to perform tasks such as learning and memory acquisition. The approach is suitable for solving multi-modal and combinatorial optimization problems. This paper extends the AIS approach by proposing a new methodology, termed the ' psychoclonal algorithm ', to handle the assembly-planning problem. It inherits its traits from Maslow ' s need hierarchy theory and the theory of clonal selection. The special features of this algorithm are the various levels of needs, immune memory, and affinity maturation. Various levels of needs and immune memory help to preserve the feasibility of solution, whereas affinity maturation guides the solution to general rather than local optima. The algorithm has been initially validated on a known data set that had been previously solved using both the genetic algorithm and the immune algorithm approach. Using this data set the new psychoclonal algorithm was shown to provide a significant improvement over the other two approaches.