Two-Stage Robust Optimization Model for Fresh Cold Chain considering Carbon Emissions and Uncertainty

Deqiang Qu,Zhong Wu

doi:10.1155/2021/5556707

Abstract

Sustainable development is an everlasting theme and lasting strategy in today’s era. Low-carbon economy is an inevitable approach to the implementation of sustainable development. Cold chain logistics has become one of the main sources of carbon emissions. However, in the research on location planning of cold chain logistics, the costs of carbon emissions have not been taken into consideration in previous studies. The two-stage stochastic optimization (TSSO) model was established based on the comprehensive consideration of transportation costs, time penalty costs, and carbon emission costs. In this case, it is extremely difficult to deal with uncertainty in TSSO model. Therefore, this paper constructs a two-stage robust optimization (TSRO) model using data-driven method and robust optimization theory and verifies the validity of this model through an actual case. The application of this method to a cold chain logistics enterprise showed that the service level of logistics cannot be guaranteed by stochastic optimization model. In the TSRO model, the costs increase by 2.18% at the price of robustness, whereas logistics service level shows an upward trend (from 85.83% to 92.75%). In the TSRO model, enterprises are forced to choose a better distribution path when carbon tax increases, which not only helps enterprises save costs but also achieves low-carbon environmental benefits.

Highlights

Sustainable development is a long-standing concept of human society. e deteriorating environment is constantly challenging national sustainable development strategies
Results of Data-Driven Two-Stage Robust Optimization Model. e existence of big data service platform provides a strong guarantee for the specific demand value of samples collected before the route planning, so that the two-stage stochastic programming problem in this paper can be transformed into a more practical decision-making problem
E specific data processing steps are shown in Table 6. rough the above steps, this paper obtains the basic sample data set of the region. e fitting interval shown in Table 7 can be obtained by normalizing the sample data. e validity of the interval is represented by the coverage of sample requirements. e range of fluctuation parameters of the sampled data is used as the classification basis, and the coverage rate is used to measure the advantages and disadvantages. rough MATLAB programming, the following results are obtained

Summary

Introduction

Sustainable development is a long-standing concept of human society. e deteriorating environment is constantly challenging national sustainable development strategies. E carbon emission studied in this paper is an extension of traditional vehicle routing problem, and the low carbon cold chain logistics transportation planning problem in two stages is studied. E extensive application of robust optimization model in different fields makes scholars pay more attention to its expansibility It is rarely found in the previous literature that robust optimization is used to study the sustainable development of cold chain. In the research of fresh product cold chain logistics, only a few scholars use stochastic probability model, and no other scholars use two-stage robust optimization theory to study. There are few papers on the research of cold chain logistics in low-carbon economy mode using robust optimization theory in methodology.

Problem Description

Large refrigerated vehicle

Model Establishment

D6 D7 D8

Conclusion