Robust Optimization of Urban Cold Chain Logistics Distribution Routing under Uncertain Demand

Abstract

Distribution route planning is the key link of the whole logistics distribution. With the development of social economy, the urban residents' demand for cold chain products is characterized by "small batch, multiple batch, uncertainty". This paper establishes a stochastic programming model with the minimum total cost as the objective function, and introduces feasibility penalty function and risk penalty item into the objective function to construct the robust optimization model based on the stochastic programming model. Based on the characteristics of the model, an improved genetic algorithm is designed to solve the model. Finally, through an analysis of a certain cold chain distribution center in Ningbo, the difference between the optimal solutions of the two optimization models is illustrated. Robust optimization model is superior to stochastic programming model in the stability of model and solution, and there is a positive relationship between risk coefficient of objective function and standard deviation in robust optimization model. So, we research can provide theoretical support for decision makers of vehicle routing in urban cold chain logistics distribution.