Cold Chain Network Research Articles

Optimal multimodal multi-echelon vaccine distribution network design for low and medium-income countries with manufacturing infrastructure during healthcare emergencies

In the event of routine immunizations for containing disease outbreaks, the Expanded Programme on Immunization (EPI) led Low and Medium-Income Countries (LMICs) follow a four-tier distribution network. Such a network is suitable for LMICs importing vaccines rather than manufacturing and may not be suitable for healthcare emergencies. Recent shift from routine immunizations to pandemic-orientation, calls for large-scale multimodal distribution models. This paper proposes a cold chain network for LMICs with manufacturing hubs during health emergencies, integrating vaccine manufacturers and incorporating multiple transportation modes. The problem is formulated as a Mixed Integer Quadratically Constrained Programming (MIQCP) problem, to minimize ‘Cumulative Transportation Time’ (CTT) with the minimal number of trips. To solve large-scale problems the paper also proposes a novel algorithm that divides certain parts of the network and retains the edges that are likely to give better solutions. The proposed model extends the existing body of the literature by introducing features such as vehicle choice between locations, generalized hierarchical structure, network decomposition, and appropriateness with LMIC networks. We conduct numerical experiments to validate the proposed algorithm using data from India. The results show that the proposed model saves cumulative transportation time along the optimally generated network when compared with the actual vaccine supply network in India during healthcare emergencies. The proposed algorithm is also faster than the formulation giving comparable results while solved in a commercial solver.

Developing a vaccine distribution network between county towns

Without the cold-chain network, it would be impossible in today's world to transport large quantities of perishable goods, primarily food and medicine, over long distances, but it is still very important that the necessary materials reach their destination on time and with adequate cooling. This is absolutely true for the vaccines created to stop the Covid-19 pandemic, which had to be distributed quickly and at certain storage temperatures to all parts of the world. This is a rather difficult task with NP-hard complexity. In this research, we are investigating the optimal way to get from a distribution center in Budapest to the county towns with transport vehicles, so that neither the vaccines deteriorate, nor we violate regulations. For this, we used vehicle route planning principles and Excel-Solver software.

Estimation of the Network Reliability for a Stochastic Cold Chain Network with Multi-State Travel Time

Estimation of the Network Reliability for a Stochastic Cold Chain Network with Multi-State Travel Time

A decision support framework for optimal vaccine distribution across a multi-tier cold chain network

In this paper, we present a decision support framework for optimizing multiple aspects of vaccine distribution across a multi-tier cold chain network. We propose two multi-period optimization formulations within this framework: first to minimize inventory, ordering, transportation, personnel and shortage costs associated with a single vaccine; the second for the case when multiple vaccines with differing efficacies and costs are available for the same disease. We use the case of the Indian state of Bihar and COVID-19 vaccines to illustrate the implementation of the framework. We present computational experiments including what-if scenario and sensitivity analyses to demonstrate: (a) the organization of the model outputs; (b) how the models can be used to assess the impact of cold chain point storage capacities, transportation vehicle capacities, and manufacturer capacities on the optimal vaccine distribution pattern; and (c) the impact of vaccine efficacies and associated costs such as ordering and transportation costs on the vaccine selection decision informed by the model. We then demonstrate how robust optimization versions of the single vaccine model, with box and budgeted uncertainty sets, outperform the deterministic version under multiple levels of uncertainty in key model parameters. Finally, we also consider the computational expense of the framework for realistic problem instances, and suggest multiple preprocessing techniques to reduce computational runtimes. Our study presents public health authorities and other stakeholders with a vaccine distribution and capacity planning tool for multi-tier cold chain networks.

Experimental study of the absorption refrigeration using ocean thermal energy and its under-lying prospects

The construction of a maritime cold chain network with islands as node is an important avenue for the economic development of the island, in which efficient refrigeration technology is essential to the development of the marine industry. Motivated by the substantial resources of tropical ocean, an absorption-compression refrigeration device is constructed and its technical feasibility is firstly demonstrated by experiment, achieving a refrigerating capacity of 6.8 kW and 10.2 kW at the evaporation temperature of −24.5 °C and −18 °C, respectively. The pressure rise by the compressor has been reduced by 185 kPa when compared to traditional vapor-compression refrigeration. The energy-saving potential of different refrigeration methods is also summarized, with the proposed refrigeration achieving the highest level of energy-efficient ratio, EER = 6.57. Especially, the proposed refrigeration can be cascaded with power generation, desalination, chilled-soil agriculture, and aquaculture to realize the gradient utilization of ocean thermal energy and hence improve the overall economy. This work opens up a new path for promoting and developing the use of ocean thermal energy for blue ocean economy in tropical oceans with bathymetry depths of 300 m or higher.

Two‐stage distributionally robust optimization model for a pharmaceutical cold supply chain network design problem

AbstractPharmaceutical safety has received increasing attention from governments and corporations, and building a safe and effective pharmaceutical cold supply chain network has become an important issue. This paper proposes a two‐stage pharmaceutical cold supply chain network design problem considering drug safety, in which the drug demand, transportation costs, and drug safety risk costs are assumed to be random variables. Due to the presence of uncertainty and the fact that information about the distribution of uncertain parameters is often only partially known, a distributionally robust optimization method is used to handle the uncertainty. A two‐stage distributionally robust optimization model is constructed, in which the reliability of the estimation of the demand necessary to satisfy the entire cold chain network is ensured to be greater than a certain predetermined level by introducing an ambiguous joint chance constraint. The decision process for the problem can be divided into strategic and operational decisions with the goal of minimizing the total costs related to facility construction, the purchase of raw materials, drug production, transportation, and safety risks. By introducing an ambiguity set with mean and covariance information to describe the uncertain parameters, the two‐stage model is eventually reformulated as a standard second‐order cone program, thus making it computationally tractable. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed models and optimization methods.

Analysis of network disruption evolution of Chinese fresh cold chain under COVID-19.

The spread of the global COVID-19 epidemic, home quarantine, and blockade of infected areas are essential measures to prevent the spread of the epidemic, but efforts to prevent and control the outbreak lead to the disruption of fresh and cold chain agricultural products in the region. Based on the multi-layer management model of non-scale agricultural households in China, we applied the complex network theory to construct an evolutionary model of the Chinese fresh cold chain network with adaptation degree priority connection, dual local world considering transport distance connection relationship, and superiority and inferiority mechanism. Based on this model, we studied the evolution of fresh cold chain disruption, and puts forward the optimal design of fresh cold chain network disruption and reconnection from the perspective of practicality and economy.

A mathematical model for vaccine cold chain network design considering social sustainability

Breakages that may occur in the cold chain cause serious economic, environmental, and social costs, as well as a substantial risk for human and public health. Therefore, it is necessary to design an effective, robust, and strong vaccine cold chain network. Sustainable Development Goal 3 titled “Good Health and Well Being” emphasizes children's health and specifies reducing the mortality rate for under five ages. In this study, we consider the Expanded Programme on Immunization (EPI) vaccine cold chain in Türkiye and develop a linear programming model for a multi-product, multi-period, multi-stage vaccine cold chain network in light of Sustainable Development Goal 3. The study aims to maximize fully immunized children for up to five years and propose a framework for a vaccine cold chain network design. The proposed model is applied to a real case. Finally, various scenario analyzes are applied to show the results of the model under different conditions.

Availability of the Yellow Fever Vaccine in Primary Health Care Services in Brazil

ObjectivesTo analyze the availability of the Yellow Fever (YF) vaccine at Primary Health Care (PHC) services located in areas with vaccine recommendation in Brazil between two different periods.MethodsSecondary data from the Program for Improving Access and Quality in PHC, with 13,666 services in 2013-14, and 19,125 in 2017-18, were used. A structured questionnaire was used for the data collection. Pearson’s chi-square and thematic cartographic maps were used to analyze the frequency of the YF vaccine, and Poisson regression with robust variances was used to analyze the associated factors.ResultsThe overall frequency of PHC facilities with YF vaccine always available increased from 87.0% (CI 95%; 86.4-87.5) in 2013-14 to 89.7% (CI 95%; 89.2-90.1) in 2017-18. Facilities located in the South, Southeast, Midwest and Northern regions and in non-state capitals, as well as centers that had adequate facilities for vaccination actions, as well as an adey -50quate cold chain network, showed a greater prevalence of YF vaccine always available.ConclusionThe frequency of a steady supply of YF vaccine available at PHC facilities, located in Areas with Routine Vaccination Recommendations, increased between 2013-14 and 2017-18. Geographical and structural characteristics related to vaccine actions at PHC facilities influenced access. The effective supply chain networks for the YF vaccine, together with adequate services at PHC facilities are essential to ensuring that the YF vaccine is always available.

Strengthening the immunization supply chain: A time-to-supply based approach to cold chain network optimization & extension in Madhya Pradesh

Expansion of immunization coverage is dependent in part on delivering potent vaccines in an equitable and timely manner to immunization outreach session sites from Cold Chain Points (CCPs). When duration of travel between the last CCP and the session site (Time-to-Supply) is too long, three consequences may arise: decreased potency due to exposure to heat and freezing, beneficiary dropouts due to delayed session starts, and, increased operational costs for the Health Facility (HF) conducting the outreach sessions. Guided by the Government of India’s recommendation on cold chain point expansion to ensure that all session sites are within a maximum of 60 min from the last CCP, CHAI and the State Routine Immunization Cell in the state of Madhya Pradesh collaborated to pilot a novel approach to cold chain network optimization and expansion in eight districts of Madhya Pradesh. Opportunities for realignment of remote sub-health centers (SHCs) and corresponding session sites to alternative existing CCPs or to HFs which could be converted to new CCPs were identified, and proposed using a greedy adding algorithm-based optimization which relied on health facility level geo-location data. Health facility geo-coordinates were collected through tele-calling and site visits, and a Microsoft Excel based optimization tool was developed.This exercise led to an estimated reduction in the number of remote SHCs falling beyond the permissible travel time from CCPs by 56.89 percent (132 remote sites), from 232 to 100. The 132 resolved sites include 73 sites realigned to existing CCPs, and 59 sites to be attached to 22 newly proposed CCPs. Both the network optimization approach and the institutional capacity built during this project will continue to be useful to India’s immunization program. The approach is replicable and may be leveraged by developing countries facing similar challenges due to geographical, institutional, and financial constraints.

Interruption Risk Assessment and Transmission of Fresh Cold Chain Network Based on a Fuzzy Bayesian Network

The fresh cold chain network is complex, and the interruption risk can significantly impact it. Based on the Bayesian theory, we constructed a fresh cold chain network interruption risk topology structure. The probability of each root node was predicted and calculated based on the fuzzy set theory. The evaluation model was then validated and improved through the virus transmission model based on risk transmission. Sensitivity analysis was used to determine significant risk factors. Several strategies for minimizing interruption risks were identified.

A Hybrid Chemical Based Metaheuristic Approach for a Vaccine Cold Chain Network

One year ago, the coronavirus (COVID-19) virus began spreading around the world. Vaccines have now been developed by a variety companies from different countries. Vaccines must be stored in good condition within cold boxes designed to keep vaccines within acceptable temperature ranges. This paper proposes a hybrid algorithm based on a chemical reaction called Hybrid Artificial Chemical Reaction Optimization Algorithm (HACROA) which has been used to design a vaccine cold chain network in northern Thailand. The scope of this research is to focus on the Office of Disease Prevention and Control Area 1 (Chiang Mai, Thailand). The objective of this research is to rearrange routes to minimize total travel distances. Experiments were conducted to compare the efficiency of the hybrid algorithm with other algorithms in term of the distance. The optimal parameters of the algorithm employed a statistical experiment theory namely full factorial design. Experiment design and analysis were adopted to investigate the factors that affect the performance of this algorithm. HACROA was able to rearrange routes and found a better solution than the other algorithms.

Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology

PurposeIn the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.Design/methodology/approachA novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.FindingsThroughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.Originality/valueA novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

Research on double-layer model of cold chain Network based on Genetic Algorithm

Based on the self-run cold chain logistics model dominated by large chain supermarkets, this paper establishes a double-layer programming model of fresh agricultural products cold chain logistics network optimization model of large chain supermarkets. The upper model is the total cost optimization model of cold chain logistics network, which solves the location problem of distribution points and the optimization problem of vehicle path. The lower level model is the optimized model of customer time satisfaction, which solves the problem of supermarket flow distribution. Finally, an example is given and an improved genetic algorithm is used to solve the two-layer programming model, which proved the double-layer model has certain theoretical and practical significance.

An Integrated Solution for Solar Cold Chain Portfolio Management using Internet of Things

The Cold Chain system includes a range of systems, such as 'thermal' and 'refrigerated packaging'strategies for moving temperature-sensitive goods along a supply chain, and logistical preparationto ensure shipment integrity. Cold Chain products, including refrigerated trucks and railcars,container ships, reefers, and air freight, are shipped in many different ways. The basic elements of the system are Cooling Systems, Cold, Cold Transport and Cold Processing and Distribution. The'cold' element of the system, i.e. refrigerators powered by gas or kerosene, has been consideredthe most suitable choice in areas without reliable energy sources such as electricity. Nevertheless,numerous problems with these tools have made it both difficult and costly to maintaintemperatures within the safe range. In the 1980s, solar refrigerators powered by batteries wereintroduced as a solution to those problems. But the batteries they relied on required regularmaintenance, had a lifespan of only three to five years, and replacements of quality were costlyand sometimes hard to get. A new solution with solar power has emerged in recent years byavoiding unreliable energy storage which is also expensive. This new solar-powered systemfreezes the material and keeps the tank frozen when there is no sunlight. The new IoT technologyoffers greater visibility and control over the entire cold chain network operated by the solar. Usingdata in real-time, IoT will allow quicker, more appropriate reactions as well as much moreinformed decisions. Nonetheless, the issue is that most IoT solutions aren't considered primetimeready. IoT has tremendous potential to play a pivotal role in turning the Solar Cold Chain Systeminto Smarter one. This literature analysis is created by revising a good number of papers publishedin peer-reviewed journals and online sources using the secondary data obtained. The goal of thestudy is to explain the use of IoT in the Solar-powered Cold Chain System, the future of IoT inthis area, find research gaps and finally list the research agenda.

Artificial Carbon Nanotube Synthesis Optimization to Address the Vaccine Cold Chain Network Problem

Artificial Carbon Nanotube Synthesis Optimization to Address the Vaccine Cold Chain Network Problem

Design of Green Cold Chain Networks for Imported Fresh Agri-Products in Belt and Road Development

The development of Belt and Road has seen a boom of imported fresh agri-products in China. This stimulates the growth of refrigerated transport, which accounts for much more carbon emissions than traditional transport. Designing a sustainable cold chain network is of vital importance from both financial and environmental perspectives. In this research, a multi-objective linear programming model is proposed for green cold chain design for multiple imported fresh agri-products in China to balance between the two competing goals—the total cost and carbon emissions. The effect of the outdoor air temperature on the carbon emissions of transportation and maintaining distribution centers is considered. By applying the ε-constraint method, the multi-objective model is solved. Numerical examples derived from the scenario of imported fresh-agri products in China are conducted to shed light on green cold chain design under Belt and Road development.

An intelligent model for assuring food quality in managing a multi-temperature food distribution centre

In the globalized cold chain network, the effective distribution of perishable food is of utmost importance when transporting multiple types of food with different handling requirements, such as temperature and humidity, for minimizing the food spoilage rate during transportation. Currently, mismanagement of premium fruit and vegetables leads to a huge amount of capital loss such that logistics service providers (LSPs) apply refrigerated trucks to deliver them for the sake of minimizing the food spoilage rate during transportation. Since different types of food have their own different handling requirements, traditional refrigerated distribution management at a fixed environmental condition is insufficient. Without considering such requirements, traditional route planning by merely minimizing the travelling distance is ineffective in maintaining food quality, resulting in an increased likelihood of food deterioration and food chilling injury. In addition, there is a lack of real-time product monitoring to control violations of the required handling requirements in order to prevent delivery of spoilt food to customers. In this paper, an internet of things (IoT)-based route planning system (IRPS) is proposed (i) to design a multi-temperature packaging model, (ii) to develop real-time product monitoring during transportation, and (iii) to optimize routing solutions. Under the IoT framework, the ambient environmental information can be collected automatically by building a wireless sensor network so as to develop total product monitoring during the distribution process. Experiments using the Taguchi method are conducted to examine the most effective packaging model for various products in terms of maximizing duration of optimal environment conditions in tertiary packaging. By integrating the above results and travelling constraints, the optimal delivery routes can be formulated by using genetic algorithms (GAs). With the aid of IRPS, the food spoilage rate during transportation and the time needed in route planning and in the delivery of deteriorated food can be reduced, while customer satisfaction is enhanced.

Modeling Barriers of Green Cold Chain Management in India

In this twenty-first century, our country is still incurring significant post-harvest losses. Most of the cold storage facilities are based on conventional energy intensive process with outdated technologies. Improper storage and cold treatment infrastructure reduces the life and deteriorates the quality of perishable products. Supply chain practitioners adopted the green cold chain management practices to overcome numerous challenges associated with developing the cold chain network. Literature review reported that green cold chain management is under-developing phase in India, but these practices have gained huge popularity in the western world. The purpose of the paper is to identify necessary barriers, determine hierarchical and contextual relationships between variables influencing green cold chain practices in India. Here researcher used interpretive structural methodology that uses systematic application of some elementary notions of graph theory approach and Boolean algebra to construct directed graph. Further the dependence and driving power of variables are categorized using MICMAC analysis. The formulation of strategy considers barriers of green cold chain management, i.e.: (1) High capital costs of renewable projects, (2) Outdated technology, (3) Poor cold chain network, (4) Lack of skilled manpower, (5) Poor cold storage Infrastructure, (6) Cold chain sterilization with chemicals, (7) Lack of reverse logistics, (8) Lack of information system infrastructure, (9) Lack of government support, and (10) Non-recyclable packaging. The findings suggest that cold chain infrastructure and government support are key drivers of green cold chain practices.

Delayed Newcastle disease virus replication using RNA interference to target the nucleoprotein

Each year millions of chickens die from Newcastle disease virus (NDV) worldwide leading to severe economic and food losses. Current vaccination campaigns have limitations especially in developing countries, due to elevated costs, need of trained personnel for effective vaccine administration, and functional cold chain network to maintain vaccine viability. These problems have led to heightened interest in producing new antiviral strategies, such as RNA interference (RNAi). RNAi methodology is capable of substantially decreasing viral replication at a cellular level, both in vitro and in vivo. In this study, we utilize microRNA (miRNA)-expressing constructs (a type of RNA interference) in an attempt to target and knockdown five NDV structural RNAs for nucleoprotein (NP), phosphoprotein (P), matrix (M), fusion (F), and large (L) protein genes. Immortalized chicken embryo fibroblast cells (DF-1) that transiently expressed miRNA targeting NP mRNA, showed increased resistance to NDV-induced cytopathic effects, as determined by cell count, relative to the same cells expressing miRNA against alternative NDV proteins. Upon infection with NDV, DF-1 cells constitutively expressing the NP miRNA construct had improved cell survival up to 48 h post infection (h.p.i) and decreased viral yield up to 24 h.p.i. These results suggest that overexpression of the NP miRNA in cells and perhaps live animal may provide resistance to NDV.