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

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 a...

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Published inOptimization Vol. 74; no. 4; pp. 843 - 869
Main Authors Wei, Yanshu, Su, Ke, Zhao, Bing, Shang, Tianyou
Format Journal Article
LanguageEnglish
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
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Abstract 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.
AbstractList 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.
Author Wei, Yanshu
Shang, Tianyou
Zhao, Bing
Su, Ke
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SubjectTerms 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
Title Nonlinear robust distribution planning model for perishable products based on sustainable development
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