Selection and optimization of alternative modular products using evolutionary computing

In most configurations of modular structures, products are assumed to have a unique modular structure. However, it is well known that alternatives for constructing modular structures may exist in any level of abstraction. Explicit considerations of alternative structures invoke changes in the number of module instances so that lower costs, more independency of structures and higher efficiency can be achieved. Relatively few research papers deal with the optimization of modular structures problem with alternative assembly combinations aiming at minimization of module investments was found in the literature. A genetic algorithm (GA) was applied to solve the optimization problem of selecting and combining the alternative of modular structures to create a set of modular structures minimizing the cost involved in its implementation. Test results are presented and the performance of the proposed GA and compared to Simulated Annealing technique and compared to solutions obtained from total enumeration tests. Finally an evaluation of the performance of the proposed G.A. in a set of large problems is presented and discussed.