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ObjectiveAs a critical component of agricultural product supply chain management, cold chain logistics demand prediction encounters challenges such as inadequate feature extraction, high nonlinearity of data, and the propensity for algorithms to become trapped in local optima during the digital transformation process. To address these issues and enhance the accuracy of demand prediction, achieve intelligent management of the agricultural product supply chain, a combined forecasting model that integrates grey relational analysis (GRA), the wild horse optimizer (WHO), and temporal convolutional networks (TCN) is proposed in this research.MethodsFirstly, a cold chain logistics indicator system was established for the data of Zhejiang province, China, spanning the years 2000 to 2020. This system covered four key aspects: the economic scale of agricultural products, logistics transportation, digital technology, and agricultural product supply. Then, the GRA was applied to identify relevant indicators of cold chain logistics for agricultural products in Zhejiang province, with 17 indicators selected that had a correlation degree higher than 0.75. Sliding window technology, a problem-solving approach for data structures and algorithms, suitable for reducing the time complexity of data to a better level and improving the execution efficiency of algorithms, was used to partition the selected indicators. Secondly, the TCN model was employed to extract features of different scales by stacking multiple convolutional layers. Each layer utilized different-sized convolutional kernels to capture features within different time ranges. By utilizing the dilated convolutional module of TCN, temporal and spatial relationships within economic data were effectively mined, considering the temporal characteristics of socio-economic data and logistics information in the agricultural supply chain, and exploring the temporal and spatial features of economic data. Simultaneously, the WHO algorithm was applied to optimize five hyperparameters of the TCN model, including the number of TCN layers, the number of filters, residual blocks, Dense layers, and neurons within the Dense layer. Finally, the optimized GRA-WHO-TCN model was used to extract and analyze features from highly nonlinear multidimensional economic data, ultimately facilitating the prediction of cold chain logistics demand.Results and DiscussionsFor comparative analysis of the superiority of the GRA-WHO-TCN model, the 17 selected indicators were input into long short-term memory (LSTM), TCN, WHO-LSTM, and WHO-TCN models. The parameters optimized by the WHO algorithm for the TCN model were set respectively: 2 TCN layer was, 2 residual blocks, 1 dense layer, 60 filters, and 16 neurons in the dense layer. The optimized GRA-WHO-TCN temporal model can effectively extract the temporal and spatial features of multidimensional data, fully explore the implicit relationships among indicator factors, and demonstrating good fitting effects. Compared to GRA-LSTM and GRA-TCN models, the GRA-TCN model exhibited superior performance, with a lower root mean square error of 37.34 and a higher correlation coefficient of 0.91, indicating the advantage of the TCN temporal model in handling complex nonlinear data. Furthermore, the GRA-WHO-LSTM and GRA-WHO-TCN models optimized by the WHO algorithm had improved prediction accuracy and stability compared to GRA-LSTM and GRA-TCN models, illustrating that the WHO algorithm effectively optimized model parameters to enhance the effectiveness of model fitting. When compared to the GRA-WHO-LSTM model, the GRA-WHO-TCN model displayed a lower root mean square error of 11.3 and an effective correlation coefficient of 0.95, predicting cold chain logistics demand quantities in Zhejiang province for the years 2016-2020 as 29.8, 30.46, 24.87, 26.45, and 27.99 million tons, with relative errors within 0.6%, achieving a high level of prediction accuracy. This achievement showcases a high level of prediction accuracy and underscores the utility of the GRA-WHO-TCN model in forecasting complex data scenarios.ConclusionsThe proposed GRA-WHO-TCN model demonstrated superior parameter optimization capabilities and predictive accuracy compared to the GRA-LSTM and GRA-TCN models. The predicted results align well with the development of cold chain logistics of agricultural products in Zhejiang province. This provides a scientific prediction foundation and practical reference value for the development of material flow and information flow in the agricultural supply chain under the digital economy context.

The study first proposes a heterogeneous fleet, multi-compartment electric vehicle routing problem for perishable products (MCEVRP-PP). We capture a lot of practical demands and constraints of the MCEVRP-PP, such as multiple temperature zones, the hard time window, charging more than once during delivery, various power consumption per unit of refrigeration, etc. We model the MCEVRP-PP as a mixed integer program and aim to optimize the total cost including vehicle fixed cost, power cost, and cooling cost. A hybrid ant colony optimization (HACO) is developed to solve the problem. In the transfer rule, the time window is introduced to improve flexibility in route construction. According to the features of multi-compartment electric vehicles, the capacity constraint judgment algorithm is developed in route construction. Six local search strategies are designed with time windows, recharging stations, etc. Experiments based on various instances validate that HACO solves MCEVRP-PP more effectively than the ant colony optimization (ACO). Compared with fuel vehicles and single-compartment vehicles, electric vehicles and multi-compartment electric vehicles can save the total cost and mileage, and increase utilization of vehicles.

The integration of advanced predictive models is pivotal for optimizing demand forecasting and inventory management in cold chain logistics. This study evaluates the application of machine learning techniques—ARIMA (Auto-Regressive Integrated Moving Average) and Multiple Linear Regression (MLR)—to forecast demand trends and analyze key drivers in a mid-sized cold chain operation. Trained on a multi-year sales dataset, the ARIMA model excelled in capturing seasonal patterns, while the MLR model effectively incorporated multivariable factors such as temperature, product type, and promotional activity. Both models demonstrated strong predictive accuracy, with low Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), offering reliable and computationally efficient solutions for mid-sized operations. The findings underscore the novelty of combining ARIMA’s time-series capabilities with MLR’s multivariable analysis to address complex demand drivers. By aligning with Resource-Based View (RBV) and Supply Chain Resilience Theory, this research advances the understanding of AI-driven predictive models as strategic tools for enhancing operational efficiency, reducing waste, and promoting sustainability in cold chain logistics. This work sets the stage for future innovations in AI-driven supply chain optimization.

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Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates the application of thermoelectric power generation devices in conjunction with cold chain logistics transport vehicles, focusing on their efficiency and performance. Our experimental results highlight the impact of thermoelectric module characteristics, such as thermal conductivity and the filling thickness of copper foam, on the energy utilization efficiency of the system. The specific experimental setup involved a simulated logistics cold chain transport vehicle exhaust waste heat recovery thermoelectric power generation system, consisting of a high-temperature exhaust heat exchanger channel and two side cooling water tanks. Thermoelectric modules (TEMs) were installed between the heat exchanger and the water tanks to use the temperature difference and convert heat energy into electrical energy. The analysis demonstrates that using high-performance thermoelectric modules with a lower thermal conductivity results in better utilization of the temperature difference for power generation. Additionally, the insertion of porous metal copper foam within the heat exchanger channel enhances convective heat transfer, leading to an improved performance. Furthermore, the study examines the concepts of exergy and entropy generation, providing insights into the system energy conversion processes and efficiency. Overall, this research offers valuable insights for optimizing the design and operation of thermoelectric generators in cold chain logistics transport vehicles to enhance energy utilization and sustainability. [ABSTRACT FROM AUTHOR]

The natural attributes of perishable and vulnerable cold chain products make the cold chain have more risks than the general supply chain. The attribute characteristics and internal relations of various risks increase the complexity of risk analysis. The purpose of this paper is to study the horizontal and vertical assessment of various risk factors. The multi‐dimensional risk measurement model is used to integrate the assessment of multiple risk factors of human, machine, environment, and management, and the cold chain risk management is discussed from the perspective of risk factor classification. The root‐state risk identification (RSRI) method was used to identify potential risks. Based on the double standard filtering and multiple criteria, the filtering of irrelevant risks and screening of uncontrollable risks were evaluated, and the triangular fuzzy number method was used to quantitatively evaluate the controllable risk factors. A total of 223 potential risks, 18 important risks, and 6 key risks were identified, followed by inspection and quarantine reports, pesticide residues, improper loading and unloading operations, unqualified centralized environment, unqualified pre‐cooling technology of carriages, and unreasonable storage temperature. According to the analysis results, targeted control measures are proposed to better prevent risks and reduce the probability of cold chain accidents. The traditional risk assessment method can only assess the impact of a single risk factor on the system. This assessment method overcomes this limitation and provides a new perspective for cold chain risk management. Practical Application: This study laid the foundation for further risk safety management of cold chain logistics. [ABSTRACT FROM AUTHOR]

This paper studies the time-dependent cold chain logistics vehicle routing problem considering both traffic congestion and carbon emissions. A cold chain logistics model with time-dependent green vehicle paths with time windows (TDGVRPTW) was developed to fulfil the demands of green logistics and to take comprehensive account of consideration should be given to factors such as road congestion and carbon emissions. The objective of the model is to minimise total costs, which include carbon emission costs, penalty costs, fuel consumption costs, fixed costs, damage costs and refrigeration costs. Two-phase hybrid search algorithm was developed to solve this model. During the initial stage of the algorithm, a dual-population ant colony optimization (DACO) algorithm sharing the optimal individual is employed to optimize the distribution route of the vehicle. During the second phase, an adaptive golden section search (AGSS) algorithm is used to optimise the departure time of the vehicle from the distribution centre to avoid traffic congestion time periods. To validate the effectiveness of the suggested two-phase hybrid search algorithm, it is applied to the improved Solomon benchmark test set. The experimental findings demonstrate that the two-phase hybrid search algorithm can reasonably plan the driving routes and departure times for each vehicle, effectively avoiding peak traffic congestion periods in the city, and reducing the overall delivery cost.

Medicine occupies an important position in people’s lives, and its safety and effectiveness are directly related to people’s lives and health. In recent years, with the frequent occurrence of unqualified vaccines, quality control in drug transportation has been paid more and more attention. Therefore, this paper constructs a tripartite evolutionary game model composed of government, pharmaceutical enterprises, and logistics enterprises and models behavioral strategies. The interaction between the two and the influence of different parameters on the evolution are simulated and analyzed. The results show that implementing a government punishment mechanism, inter-firm revenue sharing and drug failure rate will have different impacts on the outcome of the evolutionary stability strategy of pharmaceutical enterprises and logistics enterprises. These conclusions not only enrich the theoretical literature on factors affecting the decision-making factors of pharmaceutical enterprises and logistics enterprises to ensure drug quality but also provide useful references for improving government policies.

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

Cold chain logistics is an industry that generates high levels of carbon emissions. In the context of a low-carbon economy, it is crucial to recognize the low-carbon competitiveness of regional cold chain logistics and to implement effective measures to guide the development and improvement of their low-carbon competitiveness. This is essential for transitioning the economic development model and promoting low-carbon economic growth. This article proposes a low-carbon competitiveness evaluation model known as the Triangular Fuzzy–Gray Correlation Evaluation Model. This model is based on the Triangular Fuzzy Theory and Gray System Theory. According to the calculated logistics low-carbon competitiveness index, a scatter plot is used to rank and classify the evaluation objects. This method utilizes triangular fuzzy numbers as evaluation levels and further expands upon them by introducing the concept of gray correlation in group decision making. By constructing relative closeness based on curve similarity, the improved method possesses a strong ability to capture information and objectivity compared to traditional models. The selected critical indicators cover four significant aspects: low-carbon environment, low-carbon flow service capability, energy consumption in cold chain logistics, and low-carbon energy transition. Empirical research is being conducted using relevant data from Henan in 2022. The measured results are divided into four levels of competition. Using the diamond model, this study analyzes the development of low-carbon cold chain logistics at different levels in each city and provides corresponding recommendations. [ABSTRACT FROM AUTHOR]

The research in this article aims to consider low-carbon factors, through reasonable vehicle allocation and optimization of distribution routes, to achieve high satisfaction and low total cost, and to provide an optimized solution for fresh food distribution companies. In this article, cargo damage cost, energy cost, and carbon emission cost are added to the total cost, and customer satisfaction constraints based on time and quality are added, respectively, to construct a multi-vehicle cold chain VRP model under the low-carbon perspective. In order to obtain a good initial path method, a good chromosome is generated and added to the initial chromosome population according to the constraints of the vehicle type and time window, and the local elite retention strategy is combined to speed up the population convergence. Finally, taking the data of A Fresh Food Company as an example, the MATLAB software is used to realize the programming, which verifies the validity and superiority of the multi-vehicle cold chain VRP model under the low-carbon perspective. [ABSTRACT FROM AUTHOR]

By expanding the theory of planned behavior with Structural Equation Modeling, the objective of the study is to investigate consumer behaviors in the purchasing of fresh food through fresh-food apps and cold chain logistics services usage in Shanghai and Beijing, China. The results showed that the usefulness of the fresh-food apps has a positive impact on consumers’ attitudes to enjoying apps’ cold chain logistics services. However, the ease of use of apps has never had a positive impact on consumers’ attitudes towards enjoying cold chain logistics services. Furthermore, consumers’ attitudes, perceived behavioral control and subjective norm have a positive impact on their intention to use cold chain logistics services via fresh food apps. Findings confirmed that attitude plays a part of mediating role in usefulness and behavioral intention. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The distribution center is a transit place for goods in the logistics network, used to achieve the distribution of goods. Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness due to the low temperature or ultra-low temperature requirements of transport objects. Aiming at the problems of high cost and low efficiency of cold chain distribution center location, a new location model of cold chain distribution center is developed. The Affinity Propagation (AP) clustering algorithm is used to simplify location selection. And combine the binary semantics with entropy to reduce the subjectivity of the binary semantics in the process of experiment. The results show that the location selection using the research method is optimal, and the problem that multiple secondary distribution centers are the same retailer will not appear. The research method is more objective and scientific for the location of cold chain distribution centers. Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness. Distribution center is the transfer place of goods in the logistics network, which plays an important role in the logistics supply chain system. A new location model of cold chain distribution center is developed. Using Affinity Propagation (AP) clustering algorithm to simplify location selection and combining binary semantics with entropy method can further improve the objectivity of influencing factor index weights. The results show that the problem of multiple secondary distribution centers providing logistics services for the same retailer will not occur in the research method, and the location selection results are optimal. And the logistics cost can be reduced by 0.042%. This study improves the distribution efficiency, enhances the customer experience of cold chain logistics distribution, and provides certain technology and reference value for the development of cold chain logistics distribution. [ABSTRACT FROM AUTHOR]

Cold-chain logistics presents significant challenges to sustainable development in terms of economics, society, and the environment. To address this issue, this paper focuses on solving a real-world problem—the multipath location-routing problem-based cold-chain logistics (MPLRPCCL), which considers soft time windows and heterogeneous fleets. The mathematical model aims to optimize the total logistics costs, including fuel consumption, carbon emissions, cargo deterioration, penalties, operating depots, and renting fleets. To effectively solve this problem, the paper proposes three versions of a memetic algorithm (MA) that incorporate control modules for monitoring the MA's performance and improving its global optimization performance. Computational evaluations were conducted, and the results show that the proposed algorithms are effective and outperform the classic MA framework. Furthermore, the proposed model and algorithms were applied to real-world cases in Hangzhou city, China, demonstrating their practical applicability. Overall, this paper offers a promising approach to tackling the complex challenges posed by cold-chain logistics and contributes to the sustainable development of this important field. [ABSTRACT FROM AUTHOR]

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With the continuous development of the global logistics industry, cold chain transportation and joint distribution, as critical strategies in supply chain management, are gradually becoming key means to ensure the safe transportation of perishable goods, pharmaceuticals, and other temperature-sensitive commodities. The present study is dedicated to an in-depth exploration of cold chain logistics and joint distribution, with a particular focus on a review of fresh food logistics modes, aiming to comprehensively understand their operational modes, advantages, challenges, and future development trends. The present study elucidates the basic concepts of fresh food logistics and underscores its significance in supply chain management. Through comparative analysis of different operational modes, it reveals their advantages in enhancing efficiency, reducing costs, and mitigating environmental impacts. The present study focuses on the operational mode of joint distribution, discussing its application in cold chain logistics and its differences from traditional logistics modes. Through case studies and empirical analysis, it evaluates the impact of joint distribution on logistics efficiency and costs, as well as its potential to enhance transportation efficiency and reduce carbon emissions. Lastly, the present study provides an outlook on the future development trends of cold chain logistics and joint distribution, discussing the influences of technological innovation, policy support, and industry collaboration and offering recommendations and prospects to drive the sustained development of the industry. Through a comprehensive summary of fresh food logistics, cold chain logistics operational modes, and joint distribution operational modes, this paper aims to provide in-depth theoretical support and practical guidance for related research and practices.

Large yellow croaker (Larimichthys crocea) is one of economic important mariculture fish species with abundant nutritional value, and usually stored at refrigerated temperature. Our study investigated the effects of temperature fluctuation and abuse on the quality of the large yellow croaker and further analyzed the volatile compounds as temperature abuse sensitivity or indicators during temperature fluctuations. We simulated a cold chain model that mapped the temperature of chilled large yellow croaker during land-sea freight. Temperature fluctuation caused a non-negligible effect on the quality of the large yellow croaker. Samples exposed to greater temperature fluctuation had a higher rise in total viable count (TVC), total volatile basic nitrogen content, K value, and thiobarbituric acid reactive substances value. Among them, TVC is the sensitive index to temperature fluctuation. A total of 81 typical target compounds were identified by using headspace-gas chromatography-ion mobility spectrometry. During temperature fluctuations, the concentration of volatile aldehydes, including pentanal, heptanal, nonanal, and octanal decreased, while the concentration of ketones and nitrogenous and heterocyclic compounds increased. Principal component analysis and orthogonal partial least squares discriminant analysis showed that 2-pentanone and ethyl acetate were significantly correlated with the quality changes during temperature fluctuations, which could be the potential indicators for observing quality changes in large yellow croaker during temperature fluctuation.

Climate change significantly disrupts global supply chains, especially those reliant on temperature-controlled cold chains. Shipping containers, pivotal in handling 90% of worldwide trade, face escalated vulnerability during port storage. Consequently, cargo owners often shift these risks to marine cargo insurers for mitigation. Extreme weather and climate shifts are causing substantial global economic losses. Analyzing 20 documents from 1985 to 2021, this study finds a focus on factors disrupting processes, overlooking resulting consequences. Moreover, it highlights a scarcity of peer-reviewed academic work on this crucial topic, stressing the need for expanded research on climate change's impact on marine cargo insurance in cold chains.

Copyright of Storage & Process is the property of Tianjin Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID-19 pandemic has become a global health and humanitarian crisis that catastrophically affects many industries. To control the disease spread and restore normal lives, mass vaccination is considered the most effective way. However, the sustainable last-mile cold chain logistics operations of COVID-19 vaccines is a complex short-term planning problem that faces many practical challenges, e.g., low-temperature storage and transportation, supply uncertainty at the early stage, etc. To tackle these challenges, a two-stage decision-support approach is proposed in this paper, which integrates both route optimization and advanced simulation to improve the sustainable performance of last-mile vaccine cold chain logistics operations. Through a real-world case study in Norway during December 2020 and March 2021, the analytical results revealed that the logistics network structure, fleet size, and the composition of heterogeneous vehicles might yield significant impacts on the service level, transportation cost, and CO2 emissions of last-mile vaccine cold chain logistics operations. [ABSTRACT FROM AUTHOR]

With the continuous improvement of people's income level and consumption level, the demand for fresh products is driven by the strong demand, and at the same time, low-carbon and green development puts forward new requirements for the cold chain logistics industry. Starting from the perspective of considering the green cost, by constructing a distribution path optimization model of fresh cold chain products considering green cost with the optimal total distribution cost as the research objective, energy saving and emission reduction are integrated into the path optimization problem so that we can explore how to protect the environment while realizing the benefits of the enterprise, and the model is solved by using the ant colony algorithm. By observing the cold chain logistics distribution path arrangement before and after optimization, it is found that the fresh cold chain product distribution path optimization considering green cost can effectively reduce the transportation cost, refrigeration cost, carbon emission cost, and cargo damage cost in the distribution process. Under the optimal distribution strategy, the total cost is reduced by about 16.6% compared to the original route, and the environmental cost is reduced while reducing the distribution cost. It shows that this strategy can improve transportation efficiency and customer satisfaction while saving resources and protecting the environment. And this study comprehensively considers the actual operation of logistics enterprises, so this study has a certain significance of reference value for the green transformation of enterprises. It further promotes the sustainable development of the cold chain industry and reduces the distribution costs of cold chain logistics companies. It also provides a certain degree of inspiration and reference for other cold chain logistics companies to realize the unification of economic and environmental benefits in actual operation. [ABSTRACT FROM AUTHOR]

The cold chain logistics (CCL) distribution have been studied by considering customer satisfaction, we propose a multi-objective CCL model with the goals of minimal carbon transaction cost, minimal network cost and maximal customer satisfaction. According to the characteristics of the model, an improved Multi-Objective algorithm was designed, which integrates the dynamic crowding distance and differential mutation operator into non-dominated sorting genetic algorithm II (DDNSGA-II). The DDNSGA-II enhances the diversity of the initial population, the local search ability and search accuracy. Finally, the experimental data show that the proposed approaches effectively increase the customer satisfaction, reduce total distribution costs, and promote energy conservation and emission reduction.

In recent years, China's cold chain logistics industry has entered a stage of rapid development. At the same time, with the increase of greenhouse gas emissions, green and low-carbon transformation has become a new feature and direction of high-quality and healthy development of the cold chain industry to meet the future development needs of China's low-carbon economy. In view of this, in order to ensure the scientificity of China's low-carbon cold chain logistics evaluation system, in this paper, 30 indicators from the four levels of energy transformation, technological innovation, economic efficiency, and national policy based on different relevant levels were first preliminarily determined, and finally 14 indicators for building China's low-carbon cold chain logistics development evaluation system through consulting experts and the possibility of data acquisition were determined. Data from 2017 to 2021 were selected to conduct a quantitative evaluation of the development level of low-carbon cold chain logistics in China. Firstly, the entropy weight method was used to analyze the weight and obstacle degree of different indicators to explore the impact of different indicators on the development of low-carbon cold chain logistics; Secondly, a weighted decision-making matrix was constructed based on the weights of different indicators, and the technology for order preference by similarity to ideal solution (TOPSIS) evaluation model was used to evaluate the development of low-carbon cold chain logistics in China from 2017 to 2021, in order to determine the development and changes of low-carbon cold chain logistics in China. The research results showed that among the 14 different indicators of the established evaluation system for the development of low-carbon cold chain logistics in China, the growth rate of the use of green packaging materials, the number of low-carbon technical papers published, the proportion of scientific research personnel, the growth rate of cold chain logistics demand for fresh agricultural products, and the reduction rate of hydrochlorofluorocarbon refrigerants account for a relatively large proportion, ranking in the top five, respectively reaching 0.1243, 0.1074, 0.1066, 0.0982, and 0.0716, accounting for more than half of the overall proportion. It has a significant impact on the development of low-carbon cold chain logistics in China. From 2017 to 2021, the development level of China's low-carbon cold chain logistics was scored from 0.1498 to 0.2359, with a year-on-year increase of about 57.5%, indicating that China's low-carbon cold chain logistics development level was relatively fast in the past five years. Although China's low-carbon cold chain logistics development has shown an overall upward trend, it is still in the development stage.

Cold chain logistics is of great significance to improving the supply chain resilience of agricultural product industry chain, enhancing the emergency security capability of agricultural products and promoting the high-quality development of agriculture. Promoting the high-quality development of cold chain logistics of agricultural products in Guangdong-Hong Kong-Macao Greater Bay Area(hereinafter referred to as the GBA) is not only an important part of developing modern service industry and improving urban agricultural industry chain, but also an important support for building a high-quality life circle in the GBA. From the perspective of the unique location and industrial space of the GBA, the study analyzes the characteristics, development space and market demand of the cold chain logistics industry of agricultural products in the GBA based on the whole industrial chain. The cold chain logistics industry chain of agricultural products in the GBA is complete, and the number and scale of enterprises are in the forefront of the country. The rise of new models and new formats, such as "cold chain logistics + cross-border e-commerce", "fresh e-commerce + cold chain home distribution", "cold chain logistics + ready-to-cook food", will bring new impetus to the cold chain logistics industry of agricultural products in the GBA. At the same time, the cold chain logistics industry of agricultural products in the GBA is also faced with key problems such as imperfect standard system, low coverage of the whole process of cold chain, low integration of cold chain resources, urban traffic restriction policy, low informatization level, and great difficulty in the whole process of supervision. Under the new situation and pattern, we should fully understand the strategic position of cold chain logistics in the modern agricultural product supply chain, improve the development environment of cold chain logistics industry by optimizing the industrial layout of cold chain logistics, and strive to make up for the outstanding weaknesses of the "first kilometer" and "last kilometer"of agricultural cold chain logistics. At the same time, we should rely on scientific and technological innovation to promote the intelligent, green and standardized development of agricultural cold chain logistics.

The current stereo warehouse in the cold chain logistics industry faces problems such as high product loss rate, severe temperature deviations, and low level of informatization. To ensure the food quality and safety of cold chain logistics warehouses, and achieve cost reduction and efficiency improvement, this study focuses on the operational scenarios of stereo warehouses in cold chain logistics, and designs a five-dimensional digital twin model with full mapping and multidimensional simulation. By integrating heterogeneous data from multiple sources, it achieves real-time visualization and monitoring of all elements in warehouses, and proposes a model optimization strategy that supports system-linked decision-making. This strategy intelligently adjusts the load and thermal distribution in the temperature-controlled zones of warehouses to maximize operational efficiency and reduce operating costs. Using this approach, a digital twin system for cold chain logistics warehouses was constructed. By comparing the warehouse operation data before and after optimization, the loss rate of fresh vegetables under the same storage conditions was reduced by 25-30%. This system achieves precise mapping and coordinated operation of real-time data from cold chain logistics warehouses and dynamic virtual models. The practical application of this solution is demonstrated in a cold-chain warehouse of a supply chain company in Guangdong, forming an integrated control model for cold chain logistics warehouses. This model provides a reference for the application of digital twin technology in the operation, scheduling, decision-making, and collaborative operations of cold chain logistics warehouses and offers new possibilities for establishing improved packaging for perishable products and ensuring cold chain food quality and safety.

With the rapid increase in demand for fresh products, cold chain logistics has become an important mode of transportation. Logistics enterprises are faced with the problem of cost control and improving of customer satisfaction. In light of this, we present a bi-objective optimization vehicle routing problem model in cold chain logistics, which aims to reduce the total costs and improve customer satisfaction. To solve the intricate model, we propose a hybrid algorithm called the Simulated Annealing Non-dominated Sorting Genetic Algorithm II (SA-NSGA-II) algorithm, which amalgamates the simulated annealing algorithm with the NSGA-II algorithm, enabling efficient resolution of the bi-objective problem. Extensive numerical experiments validate the effectiveness of the proposed model and algorithm by exhibiting solutions with lower costs and higher satisfaction levels. Furthermore, we conduct a sensitivity analysis to explore the impact of vehicle speed on both costs and satisfaction, shedding light on the trade-offs between various optimization objectives.

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Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cold chain logistics packaging technology is crucial for the operation of perishable food chains （such as meat, fish, dairy products, fruit and vegetable products） and certain products in the pharmaceutical industry （such as vaccines, biopharmaceuticals, and blood products）. In the future, the cold chain logistics packaging of fresh agricultural products will tend towards high-performance, environmentally friendly, and digital development. The article reviews the packaging technologies used in the cold chain logistics of fresh agricultural products in recent years. Analyzes the characteristics, use value, application scope, technical advantages and disadvantages of different packaging materials, and analyzes the potential cold chain logistics packaging technologies and materials for agricultural products, as well as the composite use of different packaging technologies, in order to explore the optimal packaging for different fresh agricultural products. Develop corresponding cold chain logistics packaging tailored to different fresh agricultural products, gradually abandoning packaging technologies with low recycling rates and poor performance. By increasing the frequency of recycling and reducing the cost of using high-performance materials, to reduce losses in the cold chain logistics of fresh agricultural products and achieve the goal of environmental friendliness.

Cold chain logistics(CCL) plays an increasingly important role in modern logistics. In order to achieve efficient and high-quality CCL, providing feasible methods in vehicle transportation and distribution(VCD) is crucial. This article adopts an improved ant colony algorithm(ACA), with transportation cost and time as the main objectives, to optimize common path selection problems in VCD processes, and proves the effectiveness of this algorithm through experiments. The improved ACA is introduced to enhance the search ability, and the pheromone is constantly updated in the search process to find a better path. The experimental results show that the total cost of CCL transportation for agricultural products obtained by improving the ACA is 821 yuan, and customer satisfaction reaches 100%. The advantage of this algorithm is that it can comprehensively consider various factors, search for global optimal solutions, adapt to various complex scenarios, and is not easily trapped in local optima. [ABSTRACT FROM AUTHOR]