Optimization for Automobile Remanufacturing Production Planning Based on Stochastic Expected Value Model

Remanufacturing is an effective way to realize the circular economy. It has been received great attention from the government, business circles and academia. There are multiple uncertainties during the implementing of automobile remanufacturing, which bring forward challenges for making production planning. The features and flow of automobiles' remanufacturing were analysed, the recovery tactic, the number of remanufactured products under stochastic requirement condition were selected as decision objects, the objective function is to maximize the remanufacturing profit, a stochastic expected value model was proposed based on uncertain program theory. To solve this problem, we design a hybrid intelligent algorithm which combines with stochastic simulation, Genetic Algorithm and integer programming, and adopting the encoding of real number suits to the periodic characteristic of variable. A case study of automobile clutch's remanufacturing was given out to validate the feasibility and practicability of the stochastic model and algorithm.