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Achieving the coordination and symbiosis of cold chain logistics and green finance is notably critical for promoting regional green and sustainable development. However, The existing research on the coupling coordination relationship between cold chain logistics and green finance, as well as its driving factors, remains limited and lacks in-depth analysis. This study portrays the coupling coordination degree (CCD) from the perspectives of measurement, spatial patterns, and driving factors in China with multi-source data and the optimal parameters-based geographical detector. Results show that the CCD in China demonstrates an overall increasing trend of fluctuations, along with obvious regional differences. The spatial distribution of the CCD demonstrates a positive correlation, characterized by H-H and L-L clustering. The spatial pattern of the CCD is high in the eastern, southern regions and low in the western, northern regions, this gap is gradually narrowing between the east and west, south and north gap is widening. This spatial pattern is marked by infrastructure, economic factors, human capital, energy intensity, technological factors, and natural factors. Notably, the interactive impact among human capital, financial markets, and digital intelligence technology contributes to further integration, with the impact of individual factors ranging from 7.11 to 632.79%. It offers valuable implications for policymakers and logistics companies for sustainable development, and contributes empirical insights to emerging countries. [ABSTRACT FROM AUTHOR]

To ensure the safety of fresh food, the cold chain logistics is widely utilised, which can provide a low temperature environment. However, due to the high energy consumption, how to reduce carbon emission of cold chain logistics to develop sustainable distribution has become the research focus. In response, this study proposes an intelligent distribution system for cold chain logistics to achieve green operations. Specifically, the framework of the intelligent system based on blockchain technology is proposed to improve operational efficiency and enhance food traceability. Meanwhile, an electric vehicle routing optimisation model taking into account carbon emission is deployed in the intelligent system to plan the distribution routes. The suggested optimisation model seeks to reduce the total cost, including fixed, spoilage, refrigeration, punishment, queuing, charging and carbon emission costs. Furthermore, an ant colony algorithm is embedded in the intelligent system to help with distribution routes design. Finally, this study combines a real case to discuss the performance of the intelligent system, and the results show that the proposed system can significantly reduce distribution cost and carbon emission. Managers can use the suggested intelligent system to design distribution routes and monitor the distribution process. [ABSTRACT FROM AUTHOR]

As a high-value agricultural product, the cold chain logistics management of Gannan navel oranges has global significance. Especially in Africa and Europe, food safety and supply chain quality are critical issues. However, the existing Gannan navel orange logistics information management system relies on traditional databases and information systems, with insufficient data sharing and transparency, affecting the overall coordination of the supply chain and customer satisfaction. In addition, the traditional system is vulnerable to hacker attacks and malicious tampering of data by insiders, resulting in serious economic losses and reputation damage. So the blockchain platform constructed in this study improves the transparency and traceability of logistics data through the SMART-PBFT algorithm of the alliance chain, which optimizes the logistics management process, improves the quality of logistics services, and reduces operating costs. It improves the logistics efficiency and data transparency of local agricultural products, and also provides a demonstrative case for the cold chain management of other high-value agricultural products around the world, with broad application potential and reference value. [ABSTRACT FROM AUTHOR]

This study presents a bi-objective optimization model for the Green Vehicle-Routing Problem in cold chain logistics, with a focus on symmetric distance matrices, aiming to minimize total costs, including carbon emissions, while maximizing customer satisfaction. To address this complex challenge, we developed a Stage-Specific Multi-Objective Five-Element Cycle Optimization algorithm (MOFECO-SS), which dynamically adjusts optimization strategies across different stages of the process, thereby enhancing overall efficiency. Extensive comparative analyses with existing algorithms demonstrate that MOFECO-SS consistently outperforms in solving the multi-objective optimization model, particularly in reducing total costs and carbon emissions while maintaining high levels of customer satisfaction. The symmetric nature of the distance matrix further aids in achieving balanced and optimized route planning. The results highlight that MOFECO-SS offers decision-makers flexible route planning options that balance cost efficiency with environmental sustainability, ultimately improving the effectiveness of cold chain logistics operations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Maintaining the quality and integrity of frozen goods throughout the supply chain necessitates a robust and efficient cold chain logistics network. This research proposes a machine learning-based method for optimizing such networks, resulting in significant cost reduction and resource utilization improvement. The method employs a three-phase approach. First, K-means clustering groups sellers based on their geographical proximity, simplifying the problem and enabling more accurate demand prediction. During the second phase of the proposed method, Gaussian Process Regression models predict future sales volume for each seller cluster, leveraging historical sales data. Finally, the Capuchin Search Algorithm simultaneously optimizes distributor location and resource allocation for each cluster, minimizing both transportation and holding costs. This multi-objective approach achieved a 34.76% reduction in costs and a 15.6% reduction in resource wastage compared to the existing system. This novel method offers a valuable tool for frozen goods distribution networks, with advantages such as considering multiple goals for optimization, focusing on demand prediction, potential for reduced complexity, and focusing on managerial insights over compared methods. [ABSTRACT FROM AUTHOR]

Purpose – Assessing the efficiency of fresh food cold chain logistics as accurately as possible is essential for industry development planning. This study was designed to analyze the efficiency of fresh food cold chain logistics in China. Design/methodology/approach – A three-stage data envelopment analysis (DEA) model was used to analyze the efficiency of fresh food cold chain logistics in 30 provinces of China from 2013 to 2019. The stochastic frontier analysis (SFA) model in the second stage was used to eliminate the influence of external environmental factors and random disturbances on efficiency analysis results. Findings – (1) The overall actual efficiency of fresh food cold chain logistics in China is unsatisfactory, with an average technical efficiency of 0.382 over the 7-year period. (2) The national average technical efficiency and average scale efficiency were overestimated by 29.9% and 40.0%, respectively, compared with the actual values. (3) The efficiency of fresh food cold chain logistics does not align with the level of regional economic development. (4) Distinct regional variations exist in the efficiency of fresh food cold chain logistics in China, with higher efficiencies observed in Northwest China and the Central Yangtze River regions, and the lowest efficiencies in the northeast regions. Originality/value – This study applies a three-stage DEA model to assess the development and regional differences of fresh food cold chain logistics in China, enriching the application of models and empirical analysis in this field. By analyzing the situation in China, it provides ideas and references for other developing countries to develop cold chain logistics.

Abstract Maintaining the quality and integrity of frozen goods throughout the supply chain necessitates a robust and efficient cold chain logistics network. This research proposes a machine learning-based method for optimizing such networks, resulting in significant cost reduction and resource utilization improvement. The method employs a three-phase approach. First, K-means clustering groups sellers based on their geographical proximity, simplifying the problem and enabling more accurate demand prediction. During the second phase of the proposed method, Gaussian Process Regression models predict future sales volume for each seller cluster, leveraging historical sales data. Finally, the Capuchin Search Algorithm simultaneously optimizes distributor location and resource allocation for each cluster, minimizing both transportation and holding costs. This multi-objective approach achieved a 34.76% reduction in costs and a 15.6% reduction in resource wastage compared to the existing system. This novel method offers a valuable tool for frozen goods distribution networks, with advantages such as considering multiple goals for optimization, focusing on demand prediction, potential for reduced complexity, and focusing on managerial insights over compared methods.

Cold chain logistics requires low-temperature transportation, which consumes more energy and has higher distribution costs than ordinary logistics. Moreover, as the scale of cities continues to expand, traffic congestion is becoming more frequent. Therefore, it is particularly important to plan the distribution route reasonably. In this paper, we study the problem of cold chain logistics vehicle path planning based on travel time prediction. First of all, multiple connected routes with real-time changes in traffic conditions between customers in the road network were considered to describe the distribution scene. Second, a genetic algorithm-optimized backpropagation algorithm fused travel time predictions for road segments based on fixed detector technology and floating car technology to improve the accuracy of road segment travel time prediction. Then, based on the prediction of road segment travel time, a method for predicting the travel time of the route is proposed, and the actual road network is transformed into a travel time network for each customer. Finally, the vehicle routing problem in cold chain logistics was investigated using predicted travel time as input. This problem is formulated as a bi-objective model aimed at minimizing costs and carbon emissions. To address this problem, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was proposed. The study provides support for cold chain logistics distribution companies to develop distribution strategies based on local environmental policies and their own operational conditions. [ABSTRACT FROM AUTHOR]

Abstract The road network in rural areas is complex and the infrastructure is relatively backward. The multi-temperature zone cold chain logistics involves agricultural products with different temperature requirements, which requires considering the transportation cost, carbon emission cost, refrigeration cost, and time cost of different temperature zones during path planning, thereby increasing the difficulty of path planning. Therefore, a multi-objective optimization-based algorithm for selecting the optimal path of rural multi-temperature zone cold chain dynamic logistics intermodal transportation is proposed. Based on the analysis of the multi-temperature cold chain collection and distribution model based on multimodal transportation, a multi-objective optimization model is constructed. This model aims to minimize transportation costs, carbon emission costs, refrigeration costs, time costs, and maximize logistics quality, while satisfying constraints such as transfer schedule times, the number of transport mode conversions, transport mode selection, and time continuity. To solve this model, an improved NSGA-II algorithm is adopted, which combines an improved mutation operator, congestion distance calculation, and the C-W saving algorithm to achieve the optimal transport path solution. Additionally, ArcGIS software is used to implement the shortest path planning based on real road networks. The experimental results show that by selecting the road-rail combined transport mode and adopting the D1–D6–D10 transport path, it is possible to transport fresh agricultural products from location A to the distribution center at location B, with the lowest Pareto fitness value. Furthermore, the algorithm's effectiveness is further verified by completing the end-of-life fresh agricultural product distribution task with four multi-temperature refrigerated vehicles. The study also finds that extending or shortening the latest service time window for customers, although it leads to a decrease or increase in the optimal value of the algorithm's objective function, has little impact on the average distribution time and transport vehicles. These findings provide new theoretical and practical guidance for the path selection of multimodal transportation in multi-temperature cold chain logistics, with significant theoretical and application value.

With the rapid development of cold chain logistics, the carbon emissions from fuel-powered refrigerated vehicles are increasingly exacerbated. However, the poor timeliness of cold chain distribution is resulted from the range limit of electric refrigerated vehicles, thereby enterprise development is impacted. In order to reduce distribution costs for cold chain logistics enterprises while saving energy and reducing emissions, the problem of multi-energy vehicle-based cold chain distribution and optimal charging and changing modes for electric refrigerated vehicles were investigated by constructing a cold chain path optimization model which focused on selecting multi-energy vehicles and optimizing charging and changing methods. To effectively solve the model, a modified adaptive large neighborhood search (MALNS) algorithm was proposed along with relevant damage and repair operators. The experimental results demonstrate that the model not only effectively reduces carbon emissions, but also optimizes distribution costs, thereby facilitating the sustainable development of enterprises, and optimal costs can be searched efficiently by the MALNS algorithm. The research results hold significant guiding implications for cold chain logistics enterprise distribution. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Intelligent Agriculture Mechanization is the property of Nanjing Institute of Agricultural Mechanization 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 development of cold chain logistics for seafood plays a pivotal role in guaranteeing food safety, promoting economic progress, reducing losses, and fostering sustainable development, thereby enhancing the overall efficiency of the seafood supply chain. This study conducted a comprehensive investigation into the primary research focuses of the seafood cold chain logistics field using the literature on visualization analysis software (CiteSpace (6.2.R6), VOSviewer (1.6.20), and RStudio Bibliometrix (4.4.0)). A total of 1787 articles were collected and further analyzed from the China National Knowledge Infrastructure (CNKI), Web of Science (WOS), and Google databases over 12 years, establishing a knowledge framework for research in seafood cold chain logistics. Through the utilization of keyword clustering and emerging analysis techniques, the study constructed a knowledge map that intuitively describes the emerging trends and key hotspot in this field. The results indicate a growing trend in the seafood cold chain logistics field, with disciplines such as mathematics, systems, and physics being notably prominent. Key terms such as "cold chain logistics", "highlighted supply chain management", "frozen storage techniques", "cold storage practices", "post-harvest loss prevention strategies", and "optimization of the cold chain" frequently appear in the literature, highlighting the importance of interdisciplinary academic research in these areas. By exploring the current development of the seafood cold chain logistics field, we strengthen the research gaps in the literature and propose future research directions. Therefore, well-conducted bibliometric studies can play a crucial role in advancing the field by providing comprehensive insights, facilitating scholarly discussions, identifying knowledge gaps, generating new research ideas, and showcasing their intended contributions to the field. [ABSTRACT FROM AUTHOR]

With the rapid advancement of medical technology and the intensification of global aging trends, the health and wellness industry is flourishing, making the location and routing of health resorts increasingly crucial. This study focuses on the location and routing issues of cold chain logistics in health resorts, constructing a location-routing model that minimizes carbon emissions while considering demand uncertainty. The model employs a carbon trading strategy to achieve overall cost minimization, utilizes triangular fuzzy numbers to handle demand uncertainty, and integrates an improved genetic algorithm with the Gurobi solver for efficient optimization. The results reveal the significance of carbon trading and refrigeration costs in cold chain logistics for health resorts, highlighting the importance of reducing carbon emissions and transitioning to green energy. This research provides a comprehensive methodological approach to location-routing decisions in cold chain logistics for health resorts, laying a theoretical and practical foundation for sustainable development in this field. [ABSTRACT FROM AUTHOR]

Rising demand for fresh produce is driving the growth of the perishable food cold chain logistics (PFCCL) market. However, unsustainable practices threaten its expansion. This study addresses this gap by conducting a systematic literature review using a PRISMA framework. By analyzing 80 articles from the Web of Science (2010–2023), this study identifies key challenges to PFCCL sustainability, including infrastructure limitations, high energy use, and workforce skill shortages. Besides, this research highlights a focus on decision-making tools for sustainability and the potential of smart technologies. This study calls for further investigation into social sustainability, theoretical frameworks, empirical studies on sustainable strategies, advanced decision-making techniques, and real-world applications of smart technologies. The study is significant because it clarifies some of the major issues that PFCCL faces, including environmental effects, infrastructure deficiencies, and perishability problems. It educates stakeholders and policymakers about the critical need for sustainable practices (i.e., mitigation strategies, decision-making optimization approaches, and smart technology solutions) in the PFCCL by revealing these insights. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to solve the problem of inaccurate prediction results caused by the excessive impact of downstream on upstream suppliers in the process of cold chain logistics transportation demand prediction, the author proposes a demand prediction system Multi agent based on improved genetic algorithm and deep learning. The system will improve genetic algorithm, combine deep learning with practical problems in cold chain logistics supply chain, and evaluate the improved model through instance simulation. The results are as follows: After optimization, the order quantity of each stratum reduces the influence of retailers on upstream suppliers by more than 70%; As for the overall transportation cost, it shows a continuous upward trend within 20 cycles, while the total cost of each cycle fluctuates in a lower range after optimization, reducing the overall total cost by about 50%. It shows the reliability of the improved demand forecasting system in this study to greatly reduce storage and transportation costs. [ABSTRACT FROM AUTHOR]

Diverse demands have promoted the rapid development of the cold chain logistics industry. In the paper, a novel approach for calculating the comprehensive carbon emission cost was proposed and the front warehouse mode was analyzed under the background of energy conservation and emission reduction. To solve the two-echelon low-carbon location-routing problem (2E-LCLRP), a mathematical model considering operating cost, total transportation cost, fixed cost, refrigeration cost, cargo damage cost, and comprehensive carbon emission cost was proposed to determine the minimum total cost. A hybrid ant colony optimization (HACO) algorithm based on an elbow rule and an improved ant colony optimization (IACO) algorithm was proposed to solve the 2E-LCLRP. According to the elbow rule, the optimal number of front warehouses was determined and an IACO algorithm was then designed to optimize vehicle routes. An adaptive hybrid selection strategy and an optimized pheromone update mechanism were integrated into the HACO algorithm to accelerate convergence and obtain global optimal solutions. The proposed model and algorithm were verified through the case study of the 2E-LCLRP in Nanjing, China. The HACO algorithm outperformed the original ant colony optimization (ACO) algorithm in terms of convergence rate and solution quality. This study provides significant insights for enhancing heuristic algorithms as well as valuable research methods. Furthermore, the results can help cold chain logistics companies in balancing economic costs and environmental benefits and address cold chain distribution of agricultural products. [ABSTRACT FROM AUTHOR]

With the improvement of the national economic level and the diversity of medical equipment needs, the car refrigerator as an important carrier in the cold chain logistics link is rapidly developed. According to the different applications, there are differences in the refrigeration technologies and cooling capacity of car refrigerators. Around car refrigerators, the development status of car refrigerator refrigeration technologies is reviewed, the characteristics and functions of car refrigerators are introduced, the mechanism and characteristics of several refrigeration technologies under different scenarios are studied, and the potential development and application directions of car refrigerators with different refrigeration technologies are summarized. The research results are of certain practical significance to the improvement of the last-mile of cold chain logistics. [ABSTRACT FROM AUTHOR]

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.)

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.

AbstractThe purpose of this paper is to investigate the impact of sustainability innovation and supply chain resilience on sustainability performance and to predict the interrelationship between economic, environmental, and social performances. Using a cross-sectional quantitative study, an online survey was conducted among the Chinese cold chain logistics companies, and 204 valid responses were collected. Data analysis was conducted using the partial least squares structural equation modelling approach. The results indicated that sustainability innovation and supply chain resilience positively affect three pillars of sustainability performance. Within sustainability performance, economic performance has a significant positive correlation with environmental and social performances, while environmental performance has no effect on social performance. The findings made constructive contributions. Theoretically, this study contributes to dynamic capability theory by predicting the direct impact of sustainability innovation and supply chain resilience on sustainability performance in a model that is relatively new in the literature. In addition, this study examines the relationship between economic, environmental, and social performances in a unique context (i.e. the cold chain logistics industry). Practically, this study will guide practitioners in developing innovative and resilient strategies committed to business sustainability and urge policymakers to develop policies such as subsidies, regulation, and training programmes to promote innovation and collaboration in this industry.

[Objective] As 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. [Methods] Firstly, 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 convolution‐ al 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 Discussions] For 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 GRALSTM 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. [Conclusions] The 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. [ABSTRACT FROM AUTHOR]

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]

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.)

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]

Purpose: Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process. Design/methodology/approach: This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China. Findings: The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions. Research limitations/implications: The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics. Originality/value: In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world. [ABSTRACT FROM AUTHOR]

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.

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.

This study presents a bi-objective optimization model for the Green Vehicle-Routing Problem in cold chain logistics, with a focus on symmetric distance matrices, aiming to minimize total costs, including carbon emissions, while maximizing customer satisfaction. To address this complex challenge, we developed a Stage-Specific Multi-Objective Five-Element Cycle Optimization algorithm (MOFECO-SS), which dynamically adjusts optimization strategies across different stages of the process, thereby enhancing overall efficiency. Extensive comparative analyses with existing algorithms demonstrate that MOFECO-SS consistently outperforms in solving the multi-objective optimization model, particularly in reducing total costs and carbon emissions while maintaining high levels of customer satisfaction. The symmetric nature of the distance matrix further aids in achieving balanced and optimized route planning. The results highlight that MOFECO-SS offers decision-makers flexible route planning options that balance cost efficiency with environmental sustainability, ultimately improving the effectiveness of cold chain logistics operations.

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]