Research on cold chain logistics optimization model considering low-carbon emissions

Cold chain logistics distribution orders have increased due to the impact of COVID-19. In view of the increasing difficulty of route optimization and the increase of carbon emissions in the process of cold chain logistics distribution, a mathematical model for route optimization of cold chain logistics distribution vehicles with minimum comprehensive cost is established by considering the cost of carbon emission intensity comprehensively in this paper. The main contributions of this paper are as follows: 1) An improved hybrid ant colony algorithm is proposed, which combined simulated annealing algorithm to get rid of the local optimal solution. 2) Chaotic mapping is introduced in pheromone update to accelerate convergence and improve search efficiency. The effectiveness of the proposed method in optimizing cold chain logistics distribution path and reducing costs is verified by simulation experiments and comparison with the existing classical algorithms. © 2023 The Author(s). Published by Oxford University Press.

Research on Location Selection of Dairy Cold Chain Logistics Distribution Center

With the development of economy and the gradual improvement of material living standards, people's demand for nutritious and fresh products such as fresh, dairy products, fruits and vegetables is also increasing. Under the influence of the COVID-19, people put forward higher requirements for the distribution timeliness and radiation radius of cold chain logistics enterprises. Based on the existing research, this paper conducts an optimization study on the location selection of the distribution center of cold chain logistics for large-scale enterprises with self-operated operation mode. Moreover, a location model based on cluster analysis is proposed. The clustering results are corrected by gravity center method. Secondly, the optimization model of cycle picking is used to consider transportation cost, cooling cost, time constraint and so on. A better distribution center node is selected from the alternative points. Finally, the validity of the model is verified by case analysis. © 2023 SPIE.

Optimizing the COVID-19 cold chain vaccine distribution network with medical waste management: A robust optimization approach.

This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vaccines. In this context, a novel multi-period multi-objective mixed-integer linear programming model is initially presented over a 12-month planning horizon for solving the deterministic distribution problem. The model includes newly structured constraints due to the feature of COVID-19 vaccines, which must be administered in two doses at specified intervals. Then, the presented model is tested for the province of Izmir with deterministic data, and the results show that the demand can be satisfied and community immunity can be achieved in the specified planning horizon. Moreover, for the first time, a robust model is created using polyhedral uncertainty sets to manage uncertainties related to supply and demand quantities, storage capacity, and deterioration rate, and it has been analyzed under different uncertainty levels. Accordingly, as the level of uncertainty increases, the percentage of meeting the demand gradually decreases. It is observed that the biggest effect here is the uncertainty in supply, and in the worst case, approximately 30% of the demand cannot be met.

Immune Response in Chickens Vaccinated with Freeze-Thawed or Warmed Water-in-Oil Vaccine.

This study investigated whether freezing or warming water-in-oil (W/O) vaccines affected the immune responses of chickens. One of the conditions affecting the efficacy of commercially available animal vaccines is the storage temperature range. Previous studies have shown that the properties of some inactivated vaccines change owing to freezing, leading to reduced immune responsiveness after inoculation. In this study, we first determined the freezing temperatures of a commercial W/O vaccine using freezers maintained at -10, -13, -15, and -20°C. The results showed that the W/O vaccine froze from -10 to -12°C. Next, we evaluated the effect on antibody level transitions (sample-to-positive ratio) in 46-day-old broiler chickens vaccinated with the W/O vaccine that was maintained at -20°C, 5°C, and -10°C, in that order. In addition, the effect on antibody value transitions was evaluated in 45-day-old broiler chickens vaccinated with the W/O vaccines that were frozen and thawed between -20°C and 5°C repeatedly or warmed to 45°C. In these experiments, no remarkable effect of the freeze-thawing or warming treatments on antibody value transitions was observed. These results suggested that the efficacy of the W/O vaccine was not significantly affected when placed in a frozen environment or left in a room temperature environment of 42°C or lower for approximately 5 d. These data indicate the possibility of expanding the temperature range for handling W/O vaccines.

Room-temperature-storable chemiluminescence freeze-drying mixes for detection of SARS-CoV-2 neutralizing antibody

As the vaccine was successfully developed, the spread of the epidemic (COVID-19) was effectively controlled. But there are still thousands of people affected COVID-19 after being vaccinated. Neutralizing activity has become a critical method for quantifying neutralizing antibody against SARS-CoV-2. However, limited to the strict conditions of cold chain transportation, the neutralizing activity test has not been widely promoted. In this study, a room-temperature-storable chemiluminescence freeze-drying mixes for SARS-CoV-2 neutralizing antibody detection was developed to decrease the cost of lyophilization step for promoting its application in third world countries. Several freeze concentrated solutions were used to protect the antigen bioactivity. The mixes can be stored at room temperature over 12 months and still exhibited great accuracy and precision. Thus, the proposed room-temperature-storable chemiluminescence freeze-drying mixes offers a cheap and stable storage method for SARS-CoV-2 neutralizing antibody detection and shows a great potential for promoting the neutralizing activity test.

Logistics of Covid-19 vaccines: main challenges in theory and practice

Paper aims: This paper presents the literature findings of the Covid-19 vaccines supply chain, its main challenges and best practices, which are compared and verified empirically. Originality: The questionnaire developed in this study provides new empirical data about the Covid-19 vaccines supply chain, especially regarding the impacts of the Covid-19 pandemic in the chain, as these events are still recent. Research method: A questionnaire was sent by e-mail to specialists working with the Covid-19 vaccine supply chain in South America. Each response was compared to the literature findings. Main findings: Despite the challenges faced by vaccination programs, some countries have achieved good results due to strategies adopted at the beginning of their immunization campaigns. The empirical research confirmed that literature findings match business reality, although some empirical results vary depending on the scenario of the country regarding the impacts of the pandemic. Implications for theory and practice: This paper summarizes the Covid-19 vaccine supply chain and its challenges, best practices of the most successful countries regarding the immunization process, providing a better understanding of the pandemic scenario. Some empirical data corroborate the literature, and some discrepancies allow the formulation of suppositions that may be tested in future studies © This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Stability of SARS-CoV-2 in cold-chain transportation environments and the efficacy of disinfection measures.

Background: Low temperature is conducive to the survival of COVID-19. Some studies suggest that cold-chain environment may prolong the survival of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and increase the risk of transmission. However, the effect of cold-chain environmental factors and packaging materials on SARS-CoV-2 stability remains unclear. Methods: This study aimed to reveal cold-chain environmental factors that preserve the stability of SARS-CoV-2 and further explore effective disinfection measures for SARS-CoV-2 in the cold-chain environment. The decay rate of SARS-CoV-2 pseudovirus in the cold-chain environment, on various types of packaging material surfaces, i.e., polyethylene plastic, stainless steel, Teflon and cardboard, and in frozen seawater was investigated. The influence of visible light (wavelength 450 nm-780 nm) and airflow on the stability of SARS-CoV-2 pseudovirus at -18°C was subsequently assessed. Results: Experimental data show that SARS-CoV-2 pseudovirus decayed more rapidly on porous cardboard surfaces than on nonporous surfaces, including polyethylene (PE) plastic, stainless steel, and Teflon. Compared with that at 25°C, the decay rate of SARS-CoV-2 pseudovirus was significantly lower at low temperatures. Seawater preserved viral stability both at -18°C and with repeated freeze-thaw cycles compared with that in deionized water. Visible light from light-emitting diode (LED) illumination and airflow at -18°C reduced SARS-CoV-2 pseudovirus stability. Conclusion: Our studies indicate that temperature and seawater in the cold chain are risk factors for SARS-CoV-2 transmission, and LED visible light irradiation and increased airflow may be used as disinfection measures for SARS-CoV-2 in the cold-chain environment.

Needs, Challenges and Countermeasures of SARS-CoV-2 Surveillance in Cold-Chain Foods and Packaging to Prevent Possible COVID-19 Resurgence: A Perspective from Advanced Detections.

The pandemic caused by SARS-CoV-2 has a huge impact on the global economy. SARS-CoV-2 could possibly and potentially be transmitted to humans through cold-chain foods and packaging (namely good-to-human), although it mainly depends on a human-to-human route. It is imperative to develop countermeasures to cope with the spread of viruses and fulfil effective surveillance of cold-chain foods and packaging. This review outlined SARS-CoV-2-related cold-chain food incidents and current methods for detecting SARS-CoV-2. Then the needs, challenges and practicable countermeasures for SARS-CoV-2 detection, specifically for cold-chain foods and packaging, were underlined. In fact, currently established detection methods for SARS-CoV-2 are mostly used for humans; thus, these may not be ideally applied to cold-chain foods directly. Therefore, it creates a need to develop novel methods and low-cost, automatic, mini-sized devices specifically for cold-chain foods and packaging. The review intended to draw people's attention to the possible spread of SARS-CoV-2 with cold-chain foods and proposed perspectives for futuristic cold-chain foods monitoring during the pandemic.

Efficient COVID-19 Identification Using Deep Learning for IoT

Internet of Things (IoT) has made it possible to diagnose and treat patients remotely, as well as to expedite the transportation of essential drugs and medical equipment to locations that are geographically separated. This has occurred at a time when society has become more socially distant. During the Ebola and COVID-19 outbreaks, the Internet of Things (IoT) technology was put to use in remote patient monitoring and the management of the vaccine cold chain. Concurrently, this study reflects on the variables that are required for IoT to scale. Since December 2019, the COVID-19 outbreak on a worldwide scale has developed into a significant problem. In order for medical treatment to be successful, it is essential to make a prompt and accurate diagnosis of persons who may be infected with the COVID-19 virus. In order to put a halt to the spread of COVID-19, it is important to construct an automated system that is based on deep transfer learning and is capable of detecting the virus based on chest X-rays. The authors of this study present an internet-of-things (IoT) system that makes use of ensemble deep transfer learning to diagnose COVID-19 patients at an earlier stage. It is feasible to keep an eye on potentially hazardous COVID-19 incidents as they occur so long as suitable procedures are adhered to. Inceptions A variety of different deep learning models are included into the framework that has been proposed for the Internet of Things. According to the findings of the study, the method that was suggested assisted radiologists in accurately and quickly identifying patients who could have COVID-19. The proposed effort focuses on developing an effective identification system based on the COVID-19 standard for use in an IoT setting. © 2022 IEEE.

Online retailer cold chain physical distribution service quality and consumers: evidence from China during the COVID-19 pandemic

In order to improve the satisfaction of online retail consumers and explore the process of forming consumer loyalty during the COVID-19 pandemic, this research combines the theories of perceived value and affect-as-information, taking cold chain logistics services (PDS) as an example to discuss the influence of PDS quality (PDSQ) of online retail cold chain on consumers' psychological emotion (satisfaction and psychological distress), attitude, and behaviour (loyalty). We collected 350 valid responses from an online response team in Wuhan, China, and analysed the data using exploratory factor analysis and structural equation modelling. Results reveal that the general psychological distress of consumers plays an intermediary role in the influence path of PDSQ on consumer loyalty during the pandemic. Therefore, online retailers should combine logistics services with measures to alleviate consumer psychological distress in disaster situations, which can increase consumers' loyalty to online retailers.

A cold chain logistics traceability system framework based on the identification and resolution system for Industrial Internet

With the global outbreak of COVID-19, hundreds of pneumonias caused by cold chain products occurred worldwide, which seriously threatened the safety of people's lives and properties. To effectively prevent product quality problems caused by cold chain logistics, it is urgent to establish a cold chain logistics traceability system with interoperability of heterogeneous systems, to record, share and track the temperature, location, time, and other specific information. The traditional cold chain logistics traceability systems have many problems, such as broken cold chains, untrustworthy data, and data tampering and sharing, which hinder the coordination and interaction efficiency of cold chain logistics traceability data. This paper creatively proposes a cold chain logistics traceability system framework based on the identification and resolution system for the Industrial Internet. It establishes a general cold chain logistics traceability identification data model. The system framework and data model can effectively solve the difficulties of multi-code identification and multi-source heterogeneous system interaction, to improve the efficiency of cold chain logistics traceability, and ensure the quality of cold chain logistics products. © 2022 ACM.

A Digital Twins Model for Analyzing and Simulating Cold Chain Risks

Due to the outbreak of COVID-19, increasing attention has been paid to designing a cold chain logistics mechanism to ensure the quality of vaccine delivery. In this study, a cold chain digital twins-based risk analysis model is constructed to handle and monitor the vaccine delivery process with a high level of reliability and traceability. The model integrates the Internet of Things (IoT) and digital twins to acquire data on environmental conditions and shipment movements and connect physical cold chain logistics to the digital world. Through the simulation of cold chain logistics in a virtual environment, the risk levels relating to physical operations at a certain forecast horizon can be predicted beforehand, to prevent a 'broken' cold chain. The result of this investigation will reshape the cold chain in the digital age, benefit society in terms of sustainability and environmental impact, and hence contribute to the development of cold chain logistics in Hong Kong. © 2023 IEEE.

Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19.

As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably.

Real-Time Cold Chain Forecasting Model for Agricultural Logistic Transportation using Feature Centric Deep Neural Classification for Seasonal Recommendation

In recent days, cold chain logistic progression has been affected due to covid quarantine because real-time human resources being affected. Agriculture transportation and food safety are essential for human lives to avoid wastage of product. Analyzing the stock hold management needs more prediction accuracy in the seasonal recommendations for producing Agri-products. Increasing information and collaborative approaches in big data leads to more dimensions to analyze the prediction leads to inaccuracy for a recommendation. To improve the cold chain process, intend a Real-time Cold chain forecasting model for agricultural logistic transportation using feature centric deep neural classification for a seasonal recommendation. Initially, the preprocess was carried out to reduce the noise present in the seasonal collective and cold chain logistic dataset, which contains information about agriculture in stock detail, production, seasonal, and daily requirement ratio. The cold chain recommendation big data analytics estimate the seasonal productive margin factor (SPMF) and Stock hold production hit rate (SPHR) for feature logistic margins. Then selects the features using Intensive Agro feature successive rate (IAFSR) be grouped into clusters. Then the selected features are trained with Multi-objective Deep sub spectral neural network (MODS2NN) to categorize the needs of classes for recommendation. This cold chain process improves the prediction accuracy as well than other methods to recommendation the logistic stock hold management by right seasonal recommendation. © 2022 IEEE.

Research on Cold Chain Logistics Service Quality Evaluation Considering Product Freshness under Community Group Buying

In 2019, The outbreak of Corona Virus Disease 2019 (named COVID-19) has caused great changes in the living habits of residents, and the community group buying model has re-emerged. Under the background of community group buying mode, combined with the characteristics of fresh products, and based on the SEVRQUAL model and the national standard of "Logistics Enterprise Cold Chain Service Requirements and Capability Evaluation Indicators", an evaluation index system of cold chain logistics service quality for community group purchase of fresh products with 5 dimensions and 29 indicators is constructed from the perspective of users. Then a 5-level Likert scale was used to design relevant questionnaires, and Xingsheng Youxuan and Meituan Youxuan were used as empirical cases for sample research. Combined collected sample data, the validity and rationality of the index system were tested through reliability, validity testing and factor analysis. The data analysis also shows the problems and influencing factors of Xingsheng Youxuan and Meituan Youxuan in terms of fresh food cold chain logistics service quality, and further suggestions for the development of cold chain logistics services considering product freshness under community group buying is also provided. © 2022 ACM.

Analysis and Forecast of Demand for Cold Chain Logistics of Agricultural Products in Shanghai in the Context of The Covid Pandemic

Forecasting the demand for cold chain logistics of agricultural products will help to achieve the balance between supply and demand of agricultural products and promote the healthy development of the cold chain logistics industry of agricultural products. This paper collects relevant data from 2015 to 2020 in Shanghai, and uses grey correlation analysis to conduct correlation analysis on the factors influencing the demand for cold chain logistics of fresh agricultural products. The traditional GM (1,1) model, the new information GM (1,1) model and the metabolism GM (1,1) model are used to forecast the demand for cold chain logistics of agricultural products in Shanghai in the next five years respectively. The grey correlation analysis shows that the employees of the tertiary industry and the total import and export of goods have the greatest impact on the market demand of cold chain logistics and the results of the three GM (1,1) models show that the sum of squared errors of using the new information GM (1,1) model is smaller. Finally, using the new information GM (1,1) model to forecast the demand for cold chain logistics of agricultural products in Shanghai from 2021 to 2025, and it is found that the overall demand for agricultural cold chain in Shanghai is on an upward trend. © 2022 IEEE.

Development of Reference Process Model and Reference Architecture for Pharmaceutical Cold Chain

Although the competence level in pharmaceutical cold chain logistics is adequate in the current healthcare sector, the future will be more unpredictable, disruptive, and chaotic than the world of today because of rapid technological changes, as well as social disruption. This work introduces and evaluates a new cold chain structure based on the enhanced reference process model (RPM) and reference architecture (RA) for the pharmaceutical cold chain competence design. The modified Delphi technique is used to design a systematic decision context to evaluate the improved RPM and RA and produce a competence design by obtaining agreement from a panel of experts. Ten experts in cold chain logistics took part in the modified Delphi assessment process to describe the model and architecture for an evaluation lead. They defined it as the assessment associated with the professional related to cold chain accreditation. Ten participants from six countries were asked questions regarding their expertise, abilities, and opinions in the first round, and their comments were collected. In the second and third rounds, comments and consensus were collected, which were set at 80% for RPM and RA. This work proposes an innovative scheme for developing occupational standards based on the RPM and RA, as opposed to the traditional method of employing functional analysis to establish occupational standards. This work can be further expanded to develop professional competencies in the pharmaceutical cold chain. © 2023 by the authors.

Cold chain relevance in the food safety of perishable products

The food cold chain is an effective tool that allows food markets to maintain food quality and reduce losses. Poor logistics may result in foodborne disease outbreaks and greenhouse gas emissions caused by organic matter decay. The ongoing pandemic of COVID-19 makes it necessary to study the chances of SARS-CoV-2 transmissions in food products. This study reviews cold chain logistics as a handy tool for avoiding food safety risks, including COVID-19. The cold chain of perishables and its proper management make it possible to maintain quality and safety at any stage of the food supply chain. The technology covers each link of the food chain to prevent microbial spoilage caused by temperature fluctuations and the contamination with SARS-CoV-2 associated with perishable foods. Given the lack of knowledge in this field in Latin America, the region needs new research to determine the impact of the cold chain on perishable foodstuffs. The perishable cold chain is only as strong as its weakest link, and the national and international markets require new traceability protocols to minimize the effect of COVID-19. © 2023, Arriaga-Lorenzo et al

High-Intensity Ultraviolet-C Irradiation Efficiently Inactivates SARS-CoV-2 Under Typical Cold Chain Temperature.

SARS-CoV-2 contaminated items in the cold chain becomes a threat to public health, therefore the effective and safe sterilization method fit for the low temperature is needed. Ultraviolet is an effective sterilization method while its effect on SARS-CoV-2 under low-temperature environment is unclear. In this research, the sterilization effect of high-intensity ultraviolet-C (HIUVC) irradiation against SARS-CoV-2 and Staphylococcus aureus on different carriers at 4 °C and - 20 °C was investigated. The results showed that dose of 15.3 mJ/cm2 achieved more than 3 log reduction of SARS-CoV-2 on gauze at 4 °C and - 20 °C. The vulnerability of coronavirus to HIUVC under - 20 °C was not significantly different than those under 4 °C. Four models including Weibull, biphasic, log-linear tail and log linear were used to fit the survival curves of SARS-CoV-2 and Staphylococcus aureus. The biphasic model fitted best with R2 ranging from 0.9325 to 0.9878. Moreover, the HIUVC sterilization correlation between SARS-CoV-2 and Staphylococcus aureus was established. This paper provides data support for the employment of HIUVC under low-temperature environment. Also, it provides a method of using Staphylococcus aureus as a marker to evaluate the sterilization effect of cold chain sterilization equipment.