A scenario-based stochastic programming approach for the product configuration problem under uncertainties and carbon emission regulations

Abstract

To handle the product configuration problem with uncertain supply and demand, stochastic programming approach is applied to formulate the problem as a stochastic mixed-integer programming model. Carbon emission is further integrated into the deployed stochastic model under four different carbon emission regulations. Benders decomposition algorithm is utilized to solve the stochastic model. Computational studies show that the Benders decomposition method can solve large-scale stochastic programming problems with faster convergence rate than commercial solver CPLEX does. The results from the numerical experimental analysis demonstrate the impacts of carbon emission regulations on product configuration decisions.