Optimization of a platform configuration with generational changes

Abstract

Platform is an established strategy for producing customized products while managing the economy of scale. Innovation in various areas makes different components in a platform outdated or redundant within a short span of time. This poses severe challenge to the robustness of the platform configuration that efficiently satisfies the volatile needs of the customers from various segments. Therefore, deciding the platform configuration that can adequately accommodate generational changes in the product design is emerging as a new challenge. This paper deals with optimization of a platform configuration through a couple of product generations. For this, specifications from different customers and their probable attribute changes are mapped to product׳s utility, which signifies importance of each component through a period of time. Utility by cost ratio for different products forms the basic variable for optimizing the configuration of a platform. An illustrative example is detailed to demonstrate the methodology adopted in exploring the optimal platform configuration. This paper incorporates an intelligent DNA-based technique to reach the optimal configuration. The results of simulated DNA computation are compared with that of genetic algorithm (GA). The results show significant improvement in the number of objective function evaluations before reaching the optimal result, against that of GA thus establishing its superiority in numerical optimization.