Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events

[EN] Agri-food supply chains (AFSCs) are very vulnerable to high risks such as pandemics, causing economic and social impacts mainly on the most vulnerable population. Thus, it is a priority to implement resilient strategies that enable AFSCs to resist, respond and adapt to new market challenges. At the same time, implementing resilient strategies impact on the social, economic and environmental dimensions of sustainability. The objective of this paper is twofold: analyze resilient strategies on AFSCs in the literature and identify how these resilient strategies applied in the face of high risks affect the achievement of sustainability dimensions. 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Designing supplier selection strategies under COVID-19 constraints for industrial environments

COVID-19 has been impacting worldwide supply chains causing interruption, closure of production and distribution. This impact has been drastic on the supplier side and, as a consequence of disruptions, strong reductions of production have been estimated. Such a circumstance forces companies to propose innovative best practices of supply chain risk management aimed at facing vulnerability generated by COVID-19 and pursuing industrial improvements in manufacturing and production environments. As a part of supply chain strategy, supplier selection criteria should be revised to include pandemic-related risks. This article aims to propose an answer to such a problem. In detail, a comprehensive tool designed as a hybrid combination of multi-criteria decision-making (MCDM) methods is suggested to manage important stages connected to the production development cycle and to provide companies with a structured way to rank risks and easily select their suppliers. The main criteria of analysis will be first identified from the existent literature. Risks related to COVID-19 will be then analysed in order to elaborate a comprehensive list of potential risks in the field of interest. The Best Worst Method (BWM) will be first used to calculate criteria weights. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) will be then applied to rank and prioritize risks affecting suppliers. The effectiveness of the approach will be tested through a case study in the sector of automotive industry. The applicability of the designed MCDM framework can be extended also to other industrial sectors of interest

Literature Review - the vaccine supply chain

Vaccination is one of the most effective ways to prevent the outbreak of an infectious disease. This medical intervention also brings about many logistical quest

Core Allocations for Cooperation Problems in Vaccination

Vaccination is a very effective measure to fight an outbreak of an infectious disease, but it often suffers from delayed deliveries and limited stockpiles. To use these limited doses of vaccine effectively, health agencies can decide to cooperate and share their doses. In this study, we analyze this type of cooperation. Typically cooperation leads to an increased total return, but cooperation is only plausible when this total return can be distributed in a stable way. This makes cooperation a delicate matter. Using cooperative game theory, we derive theoretical sufficient conditions under which cooperation is plausible (i.e., the core is non-empty) and we show that the doses of vaccine can be traded for a market price in those cases. We perform numerical analyses to generalize these findings and we derive analytical expressions for market prices that can be used in general for distributing the total return. Our results demonstrate that cooperation is most likely to be plausible in case of severe shortages and in case of sufficient supply, with possible mismatches between supply and demand. In those cases, trading doses of vaccine for a market price often results in a core allocation of the total return. We confirm these findings with a case study on the redistribution of influenza vaccines

Supply Chain Operations Management in Pandemics: A State-of-the-Art Review Inspired by COVID-19

Pandemics cause chaotic situations in supply chains (SC) around the globe, which can lead towards survivability challenges. The ongoing COVID-19 pandemic is an unprecedented humanitarian crisis that has severely affected global business dynamics. Similar vulnerabilities have been caused by other outbreaks in the past. In these terms, prevention strategies against propagating disruptions require vigilant goal conceptualization and roadmaps. In this respect, there is a need to explore supply chain operation management strategies to overcome the challenges that emerge due to COVID-19-like situations. Therefore, this review is aimed at exploring such challenges and developing strategies for sustainability, and viability perspectives for SCs, through a structured literature review (SLR) approach. Moreover, this study investigated the impacts of previous epidemic outbreaks on SCs, to identify the research objectives, methodological approaches, and implications for SCs. The study also explored the impacts of epidemic outbreaks on the business environment, in terms of effective resource allocation, supply and demand disruptions, and transportation network optimization, through operations management techniques. Furthermore, this article structured a framework that emphasizes the integration of Industry 4.0 technologies, resilience strategies, and sustainability to overcome SC challenges during pandemics. Finally, future research avenues were identified by including a research agenda for experts and practitioners to develop new pathways to get out of the crisis.</jats:p

Special Obligations And Emergency Conditions

Each paper can be read independently, but the general problem that’s at the focus of this dissertation is the following. On the one hand, morality and justice appear to impose the same requirements on all. According to some of the most influential moral and political theories, what we owe to each other should be informed by ‘impartial’ requirements of fairness or respect for persons—requirements that apply regardless of whether the people you interact with happen to be relatives, friends, or members of the same society. Yet many would balk at the notion that we cannot permissibly favor some—they would charge that these sorts of views of morality or justice don’t do justice to the importance of ‘special’ relationships. This dissertation focuses on how we should think about resolving this tension between requirements, especially when it comes to national or democratic ties in emergency contexts. The first paper offers a critical analysis of the special relationship that nationalists claim we hold to co-nationals. The second paper assesses the limits of this relationship under the emergency conditions provided by the pandemic. The third investigates our special relationship to democratic society and the limits of action under the climate emergency. In ‘Against Cultural Identity as Grounds for the Intrinsic Value of Self-Determination’ I argue against the liberal nationalist claim that national self-determination is intrinsically valuable because it’s grounded in national cultural identity. In ‘Vaccine Nationalism and Basic Rights’ I argue that the case against COVID-19 vaccine nationalism is robustly overdetermined because it violates duties we have to uphold a basic subsistence right to health. In ‘Eco-Sabotage as Defensive Activism’ I argue that we can do justice to our commitments to democratic society and yet still engage in illegal and coercive property destruction with environmental aims

Spontaneous changes of human behaviors and intervention strategies: human and animal diseases

Doctor of PhilosophyDepartment of Industrial & Manufacturing Systems EngineeringChih-Hang WuThe topic of infectious disease epidemics has recently attracted substantial attentions in research communities and it has been shown that the changes of human behaviors have significant impacts on the dynamics of disease transmission. However, the study and understanding of human reactions into spread of infectious disease are still in the very beginning phase and how human behaviors change during the spread of infectious disease has not been systematically investigated. Moreover, the study of human behaviors includes not only various enforced measures by public authorities such as school closure, quarantine, vaccination, etc, but also the spontaneous self-protective actions which are triggered by risk perception and fear of diseases. Hence, the goal of this research is to study the impacts of human behaviors to the epidemic from these two perspectives: spontaneous behavioral changes and public intervention strategies. For the sake of studying spontaneous changes of human behaviors, this research first time applied evolutionary spatial game into the study of human reactions to the spread of infectious disease. This method integrated contact structures and epidemics information into the individuals’ decision processes, by adding two different types of information into the payoff functions: the local information and global information. The new method would not only advance the field of game theory, but also the field of epidemiology. In addition, this method was also applied to a classic compartmental dynamic system which is a widely used model for studying the disease transmission. With extensive numerical studies, the results first proved the consistency of two models for the sake of validating the effectiveness of the spatial evolutionary game. Then the impacts of changes of human behaviors to the dynamics of disease transmission and how information impacts human behaviors were discussed temporally and spatially. In addition to the spontaneous behavioral changes, the corresponding intervention strategies by policy-makers played the key role in process of mitigating the spread of infectious disease. For the purpose of minimizing the total lost, including the social costs and number of infected individuals, the intervention strategies should be optimized. Sensitivity analysis, stability analysis, bifurcation analysis, and optimal control methods are possible tools to understand the effects of different combination of intervention strategies or even find an appropriate policy to mitigate the disease transmission. One zoonotic disease, named Zoonotic Visceral Leishmaniasis (ZVL), was studied by adopting different methods and assumptions. Particularly, a special case, backward bifurcation, was discussed for the transmission of ZVL. Last but not least, the methodology and modeling framework used in this dissertation can be expanded to other disease situations and intervention applications, and have a broad impact to the research area related to mathematical modeling, epidemiology, decision-making processes, and industrial engineering. The further studies can combine the changes of human behaviors and intervention strategies by policy-makers so as to seek an optimal information dissemination to minimize the social costs and the number of infected individuals. If successful, this research should aid policy-makers by improving communication between them and the public, by directing educational efforts, and by predicting public response to infectious diseases and new risk management strategies (regulations, vaccination, quarantine, etc.)

COVID-19 and Environment: Impacts of a Global Pandemic

This is a reprint of the MDPI IJERPH Special Issue entitled "COVID-19 and Environment: Impacts of a Global Pandemic". The reprint consists of 17 papers with different topics related to the COVID-19 pandemic and environmental impacts using data from different countries all over the globe

Use of Markov Decision Process Models in Preventive Medicine

The biggest trade-off when proposing health care policies is about balancing the effectiveness and the practicality of the policies. The optimal policies providing benchmark performances can be driven through using operations research tools; however, they usually have complex structures that are necessary to sufficiently represent various aspects of the system being modeled. There are also policies either proposed in guidelines or followed in practice but they often vary with the system characteristics, i.e., preferences of the clinicians, available resources of the clinics, etc. Therefore, standardized, simple yet effective policies are needed for many healthcare applications, including preventive medicine. At this point, we study developing health care delivery policies that maximize the effect of the preventive interventions, while providing applicable policy structures that can be easily followed by health practitioners in practice. We focus on two applications of preventive medicine: childhood vaccine administration practices in developing countries; and colorectal cancer screening and surveillance. Vaccine administration practices in developing countries suffer from open-vial wastage. Doses remaining from opened vials are disposed at the end of a day, due to lack of appropriate cold storage conditions. We propose administering vaccines from different sizes of multi-dose vials to address the open-vial wastage problem. We utilize a Markov decision process model to maximize the expected total number of doses administered via reducing vaccine wastage. The model dynamically decides which size of a multi-dose vial to open next, and when to terminate vaccination service for the day, given the time remaining in the replenishment cycle and available vaccine stocks. We show that the optimal policies are of control-limit type. Using data for routine pediatric vaccines, we show that the proposed optimal policies could cost-effectively reduce open-vial wastage and significantly improve the covered vaccine demand. We also analyze the initial vaccine inventory composition that specifies how many vials of each size should be kept in stock. We show that the optimal policy for the right vaccine inventory composition may improve the expected vaccine demand covered up to target levels without early termination of vaccination service while realizing reasonably small or no additional cost. Although the number of system variables being tracked in our state space is manageable, the optimal policies still require significant effort to be adopted in practice. That is especially challenging in developing countries, where the resources, e.g., clinic staff, are limited. Therefore, we introduce simple vaccine administration policies that are developed with the guidance of the insights from our numerical and structural analyses. Our insights on the simple vaccine administration policies show that these policies can provide promising performance, in terms of costs and expected vaccine demand covered, compared to the optimal policies while requiring only a single system variable, i.e., time of a decision, to be monitored. Colonoscopy screening prevents, and early-detects colorectal cancer (CRC), one of the most common and deadliest cancers in the world. Considering that the risk of developing CRC significantly increases after age 50, and that the North American population is aging, the colonoscopy screening and follow-up policies employed by gastroenterologists play a vital role in the well-being of the population. Existing clinical guidelines recommend colonoscopy screening policies that are shown to be cost-effective in CRC prevention and early detection. Nevertheless, almost half the practitioners do not follow these guidelines, indicating controversy around the best CRC screening practices. Several studies analyze alternative CRC screening policies using simulation and mathematical models. Especially, dynamic alternative policies, derived by a stochastic dynamic programming approach, can significantly increase health outcome improvements due to CRC screening and follow-up. However, under dynamic policies, colonoscopy screening and surveillance intervals significantly vary in factors such as age, gender, and personal history, which are harder to implement for clinicians. Our study on this second application aims at deriving efficient and simpler-to-implement colonoscopy screening and follow-up policies, but that perform closely to the optimal policies. We employ a patient-level discrete-event simulation model, built and validated using real data, to mimic CRC progression in asymptomatic and higher-risk individuals. We estimate the expected life-years, age-based risk of having CRC, CRC mortality, costs associated with CRC screening, and the number of required colonoscopies for a large set of screening policies. We evaluate the performances of all relevant simpler-to-implement colonoscopy policies, including the periodic screening policies currently used by practitioners, and all feasible periodic policies with n-period switch times (for n=0,1,2). Our analysis identifies under the parameter settings under which alternative and simpler policies are sufficient to provide close-to-optimal performance. These results provide insights on the types of policies on which to focus in future studies, for researchers from both medical and operational research fields