부산대학교 도서관

Increases in demand for a greater variety of products help companies gain more shares of growing competitive markets but, in contrast, lead to an increase in production processes and, therefore, higher costs and longer lead times. Although several techniques for platform formations and assembly lines have been introduced to enable more varieties of goods to be produced, this also makes a system more complex and less cost-efficient. This paper proposes a differential evolution (DE) approach that incorporates a new heuristic method, improved solution representation and enhanced crossover and mutation operators for solving the modular-based product family design problem in a reconfigurable manufacturing system. The heuristic is applied to repair some solutions in the initial population by replacing eligible components with packages to provide near-optimal solutions in the initial stage and enable DE to find the optimal solution quickly. The proposed crossover is designed to further use the repaired solutions to produce new individuals with better qualities. Finally, a case study of a kettle family is conducted to validate this heuristic method, with the experimental results showing that it saves 57.5% of the purchasing costs of components and, on average, 41.35% of setup costs compared with those of median-joining phylogenetic network- and non-platform-based heuristics. Moreover, the proposed DE achieves improved performances with average errors of 63.34% and 38.52% from those of the standard versions of DE and a genetic algorithm, respectively, in terms of the total production costs of producing the same variants.