Heterogeneous fleet recyclables collection routing optimization in a two-echelon collaborative reverse logistics...

In recent years, production and consumption activities carried out in accordance with the "take-make-dispose" process have caused environmental damage and resource waste. Reverse logistics based on the concept of "sustainable development and circular economy" has gained public attention, including the recycling and reuse of recyclable wastes. In order to explore the impact of recyclable wastes transportation on cost, environment and resources, this paper focuses on a vehicle routing problem considering electric heterogeneous fleet for a two-echelon recycling network, recycling stations and recycling centers. With the goal of minimizing the total cost, a recycling heterogeneous fleet electric vehicle routing model with time windows is established under the consideration of vehicle load constraints, vehicle type constraints on the weight of loaded recyclable wastes and customer's allowable service time constraints. For carbon emissions reduction and environment protection, vehicles used in recycling stations are designated as electric vehicles. Then, a hybrid genetic algorithm with a large-scale neighborhood search algorithm is proposed. Finally, numerical experiments are conducted on vehicle types in recycling stations, and the computational results show the use of heterogeneous fleets could reduce costs. By analyzing the relationship between customer service time and total cost, the best service time rule could be obtained in the case of customer satisfaction. Meanwhile, by analyzing the relationship between the number and the loading capacity of electric vehicles in recycling stations and total cost, the best number and the loading capacity of electric vehicles could be obtained. This heterogeneous fleet recyclables collection routing model can greatly support the construction of recycling centers and the planning of recycling tasks, and provide a basis for recycling companies to deal with the relationship between economy, environment and resources.