A SIMULATION BASED GENETIC ALGORITHM FOR RISK-BASED PARTNER SELECTION IN NEW PRODUCT DEVELOPMENT

摘要

In this paper, we investigate the problem of partner selection in new product development. First, we give a formal description of the problem and model it as a 0-1 integer programming problem with non-linear objective function and stochastic constraints. Because of the complexity of the constraints, a Monte Carlo method is used to measure the probabilities of stochastic constraints satisfaction. Then, we develop a simulation based genetic algorithm approach to find the optimal solution for partner selection. The approach is demonstrated by some numerical examples. The results show that the suggested approach has high efficiency and the model has potential to practical applications.