An Optimization Design for a Half-Closed-Loop Logistics Network with Uncertainty

A two-echelon half-closed-loop logistics network system is founded for the issue of manufacturing logistics network design, including supply and return channels. Considering the integration of the uncertainty and cycle time of the demand quantities of products and the return rates of used products, sites selection of potential logistics centers, plants and recycling plants, and transshipment problem, a stochastic, multi-time-step, capacitated mixed integer linear programming model is presented, the objective function makes total costs including fixed costs, transportation costs and storage costs in logistics centers minimize. Since such network design problems belong to a class of NP hard problems, a genetic algorithm approach-based heuristic to this model is presented. Finally a numerical example is used to prove the model and genetic algorithm validity.