Fuzzy chance constrained programming model for supply chain inventory with PSO algorithm

Abstract

Supply chain inventory optimization problem under uncertain environment is concerned. Fuzzy chance constrained programming model for multi-item joint replenishment is proposed, which can take into account fuzzy demand quantity, as well as the constrained conditions are not satisfied to a certain degree. Demand quantity is a triangular fuzzy number, combined with the possibility measure theory. The objective function is to minimize the total cost of the supply chain. The model is solved by particle swarm optimization (PSO), and the fitness function value of the particle is the objective value of fuzzy chance constrained programming model. The feasibility of the model and the effectiveness of the algorithm are proved by simulation numerical examples, and comparisons of results under different probability level are made.