A hybrid machine learning-optimization approach to pricing and train formation problem under demand uncertainty

Due to the complexity of pricing in service industry, it is important provide an efficient framework for real-life and large-sized applications. To this end, we combined optimization approach with a regression-based machine learning method reliable integrated train formation problem under hybrid uncertainty. do so, firstly, model applied forecast ticket price passenger railway then, obtained used as input model. Further, order deal uncertainty demand parameter, robust fuzzy stochastic programming proposed. Finally, real transportation network from Iran demonstrate efficiency proposed The analysis numerical results indicated that able state optimal less comparison traditional models.