Nonlinear robust distribution planning model for perishable products based on sustainable development

• Published in: Optimization Vol. 74; no. 4; pp. 843 - 869
• Main Authors: Wei, Yanshu, Su, Ke, Zhao, Bing, Shang, Tianyou
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 12.03.2025; Taylor & Francis LLC
• Subjects: Carbon; Cold chain; Cold storage; Distribution management; distributionally robust optimization; Economic impact; Emissions; Optimization models; Quadratic programming; Risk; risk aversion; Robustness; sequential quadratic programming; Stochastic models; Stochastic programming; Supply chains; Sustainable development
• ISSN: 0233-1934; 1029-4945
• DOI: 10.1080/02331934.2023.2269954

With the development of social economy and the improvement of people living standards, the distribution of cold chain products has become increasingly prominent. In the process of distribution of cold chain products, due to its perishable nature, it is not only necessary to consider the efficiency and economy of transportation, but also to pay attention to risk aversion factors. Therefore, the stochastic programming of cold chain logistics based on risk aversion has become an urgent problem to be solved. Aiming to minimize the economic cost and transform the carbon emission level of cold chain transportation into carbon emission cost, a distributionally robust mean-conditional value-at-risk optimization model for the perishable goods distribution management problem is established, in which the partial distribution information of uncertain demand and transportation environment temperature was known. Then a computationally tractable equivalence model is obtained under the box ambiguity set. For the proposed nonlinear model, a trust region sequential quadratic programming method with filter is proposed to solve it. Finally, through a case study, the relationship between the stochastic programming model and the distributionally robust model is analysed.