Under the condition that the demand for emergency materials is uncertain after the occurrence of an emergency, this paper considers the minimum time and cost of emergency rescue at the same time, studies the location problem of pre disaster emergency materials storage. The box uncertainty set is used to describe the uncertainty of demand, and a robust optimization model with capacity constraints is established. Based on Bertsimas and Sim robust optimization methods, the nonlinear robust optimization model is transformed into a robust corresponding model by introducing auxiliary variables and dual transformation. We use ε constraint method to transform the multi-objective problem into the single objective problem. An immune algorithm is designed to solve the problem. The feasibility of the model and algorithm is verified by computational experiments. The research shows that the robust optimization model has good robustness and can better resist the risk induced by uncertainty. Decision makers should determine the emergency rescue time constraint according to financial level and expected response level; evaluate the risk status, weigh the relationship between reliability and emergency rescue cost, and determine the robust uncertainty parameter for scientific site selection.
Currently, electric vehicles and fuel vehicles coexist in urban distribution. Given this, this paper focuses on the vehicle routing problem with the mixture of electric and fuel vehicles. Then, a green vehicle routing optimization model with a mixed fleet is proposed. The cost of carbon emissions from the fuel is considered in the model. Then, an improved genetic algorithm is designed to solve the problem. Finally, sensitivity analysis is carried out for key factors such as vehicle composition, battery capacity, and charging rate. Numerical experimental results show that the economic power of logistics enterprises to directly upgrade their fleets from all-fuel vehicles to all-electric vehicles is insufficient. The number of electric vehicles kept in the fleet should be determined according to the combination of vehicle cruising range and customer distribution. Logistics enterprises should comprehensively consider the battery capacity and charging rate to reduce the distribution cost of electric vehicles.
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