Stochastic dependent-chance programming model and hybrid adaptive genetic algorithm for vendor selection problem

This paper proposes a stochastic dependent-chance programming model for vendor selection problem under the condition that the capacity, quality level, service level and lead time of each vendor are considered to be stochastic. Since stochastic programming is hard to solve by traditional methods, a hybrid adaptive genetic algorithm, which embeds the neutral network and stochastic simulation, is presented. To improve the performance of the algorithm, the probability of crossover and mutation will be adjusted according to the stage of evolution and fitness of the population. The solution procedure is tested on several randomly generated problems with varying parameters. The experimental results demonstrate that the hybrid adaptive genetic algorithm has strong adaptability.