An Improved Phenotype-Genotype Mapping for Solving Selective Assembly Problem Using Evolutionary Optimization Algorithms

Selective assembly is an assembly technique for producing high-quality assemblies from relatively lower quality mating parts. Developing the application of this technique to sheet metal assemblies in the automotive industry can improve the geometrical quality and reduce production costs significantly. Nevertheless, the required calculation time is the main obstacle against this development. To apply a selective assembly technique, an optimization problem of finding the optimal combination of mating parts should be solved. This problem is an MINLP optimization problem for selective assembly of sheet metals. This paper demonstrates that the phenotype-genotype mapping commonly used in most conventional selective assembly studies enlarges the search domain of the optimization. Thereafter, a new approach that makes the mapping one-to-one is proposed and applied to three selective assembly sample cases from the literature. Moreover, it is indicated that meta-heuristic methods are superior to MILP and MINLP methods in solving this problem, particularly for assemblies of more than two components and relatively large batch sizes. The results evidence that using the new method improves the convergence rate of meta-heuristics in solving the problem by reducing the number of cost function evaluations to 45% for sheet metal assemblies. This means reducing up-till 26 h of the optimization time for the presented sample cases.