A green stochastic open location-routing problem (GSOLRP) is generally a combined location-routing problem and set of green vehicles (e.g., hybrid vehicles corresponding to customers’ demands and traffic limitations) under uncertainty. The environmental topics in production systems strive to reduce their costs by finding efficient locations for depots and optimize routing vehicles to transport manufactured goods to depots with the least CO2 pollution possible. In this model, the stochastic locations are assumed as an effective parameter on the cost of the supply chain and its rival. The proposed method determines depots opening decision, vehicle allocation, and path design in such a stochastic location and minimizes the quantity of CO2 emissions by supply chain management. A robust optimization approach is used for stochastic customers’ location. Then, a hybrid meta-heuristic method of combining the imperialist competitive algorithm (ICA) and variable neighborhood search (VNS) is developed to solve large-scale problems.