Hierarchical planning method for product supply based on multi objective Genetic Algorithm

Manufacturers have to make dexterous supply plans in order to cope with the demand fluctuations. The demands will change day to day, and also the predicted demand values should be modeled by stochastic variables. Under these uncertain demand conditions, we formulate product supply planning into multi-objective problem pursuing profit maximization and risk minimization. We will propose a method that can generate Pareto optimal solutions based on multi-objective Genetic Algorithm and Monte Carlo simulation. The method adopts searching mode switching strategy to obtain quality Pareto optimal solution, and the method consists of two layers; the upper layer is a global search stage and the lower one is a local search stage to search better solutions around global solutions. Since the local search stage is time consuming, we also propose a fast calculation method of evaluating individuals for Genetic Algorithm.