Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines

Manufacturing process planning for multiple parts manufacturing is cast as a hard optimization problem for which a modified genetic algorithm is proposed in this paper. A cyclic crossover operation for an integer-based representation is implemented to ensure that recombination will not result in any violation of processing constraints. Unlike classical approaches, in which the mutation operator alone is used to foil the tendency towards premature convergence, a combination of a neighborhood search based mutation operator and a threshold operator were implemented. This combined approach was designed to; (a) improve the exploring potential and (b) increase population diversity of neighborhoods, in the genetic search process. Capabilities of a modified genetic algorithm method were tested through an application example of a multiple parts reconfigurable manufacturing line. Simulation results show that the proposed modified genetic algorithm method is more effective in generating manufacturing process plans when compared to; a simple genetic algorithm, and simulated annealing. A computational analysis indicates that improved, near optimal manufacturing process planning solutions for multiple parts manufacturing lines can be obtained by using a modified genetic algorithm method.

► This paper considers process planning in multiple parts manufacturing lines.
► The problem is investigated for a reconfigurable manufacturing line.
► An innovative process planning model is devised.
► A modified genetic algorithm is developed and used to solve the model.
► Computational analysis show that the proposed algorithm is efficient and effective.