Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model

• Monte Carlo simulation embedded hybrid genetic algorithm is effective.
• The above mentioned algorithm can obtain exact solutions at any confidence level.
• Hybrid GA has higher accuracy and efficiency than hybrid PSO.
• The overall profits of CLSC network increase with the increasing of confidence level.

The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment.