ABSTRACT
The COVID-19 pandemic has significantly disrupted global supply chains (SCs), emphasizing the importance of SC resilience, which refers to the ability of SCs to return to their original or more desirable state following disruptions. This study focuses on collaboration, a key component of SC resilience, and proposes a novel collaborative structure that incorporates a fictitious agent to manage inventory transshipment decisions between retailers in a centralized manner while maintaining the retailers' autonomy in ordering. The proposed collaborative structure offers the following advantages from SC resilience and operational perspectives: (1) it facilitates decision synchronization for enhanced collaboration among retailers, and (2) it allows retailers to collaborate without the need for information sharing, addressing the potential issue of information sharing reluctance. Additionally, this study employs non-stationary probability to capture the deeply uncertain nature of the ripple effect and the highly volatile customer demand caused by the pandemic. A new reinforcement learning (RL) algorithm is developed to handle non-stationary environments and to implement the proposed collaborative structure. Experimental results demonstrate that the proposed collaborative structure using the new RL algorithm achieves superior SC resilience compared with centralized inventory management systems with transshipment and decentralized inventory management systems without transshipment using traditional RL algorithms. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd 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 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 . (Copyright applies to all s.)
ABSTRACT
At the onset of the COVID‐19 pandemic, hospitals were in dire need of data‐driven analytics to provide support for critical, expensive, and complex decisions. Yet, the majority of analytics being developed were targeted at state‐ and national‐level policy decisions, with little availability of actionable information to support tactical and operational decision‐making and execution at the hospital level. To fill this gap, we developed a multi‐method framework leveraging a parsimonious design philosophy that allows for rapid deployment of high‐impact predictive and prescriptive analytics in a time‐sensitive, dynamic, data‐limited environment, such as a novel pandemic. The product of this research is a workload prediction and decision support tool to provide mission‐critical, actionable information for individual hospitals. Our framework forecasts time‐varying patient workload and demand for critical resources by integrating disease progression models, tailored to data availability during different stages of the pandemic, with a stochastic network model of patient movements among units within individual hospitals. Both components employ adaptive tuning to account for hospital‐dependent, time‐varying parameters that provide consistently accurate predictions by dynamically learning the impact of latent changes in system dynamics. Our decision support system is designed to be portable and easily implementable across hospital data systems for expeditious expansion and deployment. This work was contextually grounded in close collaboration with IU Health, the largest health system in Indiana, which has 18 hospitals serving over one million residents. Our initial prototype was implemented in April 2020 and has supported managerial decisions, from the operational to the strategic, across multiple functionalities at IU Health.
ABSTRACT
Purpose: This study aims to focus on building a conceptual closed-loop vaccine supply chain (CLVSC) to decrease vaccine wastage and counterfeit/fake vaccines. Design/methodology/approach: Through a focused literature review, the framework for the CLVSC is described, and the system dynamics (SD) research methodology is used to build a causal loop diagram (CLD) of the proposed model. Findings: In the battle against COVID-19, waste management systems have become overwhelmed, which has created negative environmental and extremely hazardous societal impacts. A key contributing factor is unused vaccine doses, shown as a source for counterfeit/fake vaccines. The findings identify a CLVSC design and transshipment operations to decrease vaccine wastage and the potential for vaccine theft. Research limitations/implications: This study contributes to establishing a pandemic-specific VSC structure. The proposed model informs the current COVID-19 pandemic as well as potential future pandemics. Social implications: A large part of the negative impact of counterfeit/fake vaccines is on human well-being, and this can be avoided with proper CLVSC. Originality/value: This study develops a novel overarching SD CLD by integrating the epidemic model of disease transmission, VSC and closed-loop structure. This study enhances the policymakers' understanding of the importance of vaccine waste collection, proper handling and threats to the public, which are born through illicit activities that rely on stolen vaccine doses. © 2023, Esen Andiç-Mortan and Cigdem Gonul Kochan.
ABSTRACT
Blood platelets are a typical instance of perishable age-differentiated products with a shelf life of five days (on average), which may lead to significant wastage of some collected samples. At the same time, a shortage of platelets may also be observed because of emergency demands and the limited number of donors, especially during disasters such as wars and the COVID-19 pandemic. Therefore, developing an efficient blood platelet supply chain management model is highly necessary to reduce shortage and wastage. In this research, an integrated resilient-sustainable supply chain network of perishable age-differentiated platelets considering vertical and horizontal transshipment is designed. In order to achieve sustainability, economic cost, social cost (shortage), and environmental cost (wastage) are taken into account. A reactive resilient strategy utilizing lateral transshipment between hospitals is adopted to make the blood platelet supply chain powerful against shortage and disruption risks. The presented model is solved using a metaheuristic based on a local search-empowered grey wolf optimizer. The obtained results demonstrate the efficiency of the proposed vertical-horizontal transshipment model in reducing total economic cost, shortage, and wastage by 3.61%, 30.1%, and 18.8%, respectively.
Subject(s)
Blood Platelets , COVID-19 , Humans , Pandemics , Hospitals , Tissue DonorsABSTRACT
Vaccination is one of the most efficient ways to restrict and control the spread of epidemic outbreaks such as COVID-19. Due to the limited COVID-19 vaccine supply, an equitable and accessible plan should be prepared to cope with. This research focuses on designing a vaccine supply chain while aiming to achieve an equitable and accessible network. We present a novel mathematical formulation that helps to optimize vaccine distribution to inoculate people with various priority levels to achieve an equitable plan. The transshipment strategy is also incorporated into the model to enhance the accessibility of COVID-19 vaccine types between health facilities. The nature of COVID-19 is dynamic over time due to mutations, and the protection level of each vaccine type against this disease is not exact. Besides, complete information about the demand for different vaccine types is not available. Hence, we use Multi-Stage Stochastic Programming as a reliable strategy that is organized to manage stochastic data in a dynamic environment for the first time in the vaccine supply chain network. The scenarios in this approach are generated using a Monte Carlo simulation method, and then a forward scenario reduction technique is conducted to construct a suitable scenario tree. The practicality and capability of the model are shown in a real-life case of Iran. The results show that the performance of the Multi-Stage Stochastic Programming is significantly improved compared with the two-stage stochastic programming regarding the total cost of the vaccine supply chain and the number of the shortage units.
ABSTRACT
COVID-19 is spread mainly through respiratory droplets. With the development of COVID-19 worldwide, international airports are facing unprecedented imported risks, becoming the forefront of overseas epidemic prevention. The transmission mechanism of the disease is easy to implement due to the general human susceptibility. Despite the ongoing development of COVID-19 vaccines, the public health community still needs to establish nonpharmaceutical interventions to mitigate the spread of COVID-19 in the population, especially among individuals in close contact with confirmed cases. Since the outbreak of COVID-19, relevant authorities in China have taken active prevention and control measures, strictly tracked down and isolated those involved, and effectively contained the spread of the epidemic. Medical workers have played an important role in epidemic prevention and control. Medical workers are putting their lives and health at risk because of a lack of knowledge about COVID-19. This review summarizes the work of preventing cross-infection in the transport of high-risk groups by ambulance in primary hospitals in Jiangsu province during the COVID-19 outbreak. Through standardized management, the cross infection caused by ambulance has been effectively prevented. Therefore, during the COVID-19 outbreak, establishing a safe disinfection management system, strengthening the disinfection management of ambulance transport, and training personnel in personal protection, work requirements and emergency response skills can effectively prevent the spread of the COVID-19. © 2022 Journal of Innovation Management. All rights reserved.
ABSTRACT
To assess the resistance of the shipping network of Chinese iron ore imports and the effectiveness of different safeguards, an invulnerability simulation model was established to fill the gap that traditional evaluation methods fail to consider the overall flow and nodes load state of the network. The data of a state-owned enterprise, major export ports in the world and major import ports in China were used to build the initial network. The network with different numbers of import ports was attacked in particular ways. The simulation results show that the network has a certain self-healing ability after the network is attacked, because of the adjusted ability of ship routes and redistribution of cargo load, but the network invulnerability will suddenly change after the key nodes are affected. The improvement of overload capacity has obvious marginal changes in improving network invulnerability if the overload capacity reaches a critical point, and improving the network survivability may aggravate network congestion. Therefore, it is necessary to strengthen the protection of key nodes, increase the number of import ports, and enhance the overload capacity of the ports, to enhance the invulnerability of the Chinese iron ore import shipping network under the COVID-19 and other critical events. Meanwhile, resources such as wharf yards and equipment should be reasonably allocated to prevent and deal with network congestion. Copyright © 2022 by Science Press.
ABSTRACT
A regional healthcare coalition enables its member hospitals to conduct an integrated emergency supply management, which is seldom addressed in the existing literature. In this work, we propose a two-stage stochastic emergency supply planning model to facilitate cooperation and coordination in a regional healthcare coalition. Our model integrates pre-disaster emergency supplies pre-positioning and post-disaster emergency supplies transshipment and procurement and considers two planning goals, i.e., minimizing the expected total cost and the maximum supply shortage rate. With some comparison models and a case study on the West China Hospital coalition of Sichuan Province, China, under the background of the COVID-19 epidemic, we demonstrate the effectiveness and benefits of our model and obtain various managerial insights and policy suggestions for practice. We highlight the importance of conducting integrated management of emergency supplies pre-positioning, transshipment and procurement in the regional healthcare coalition for better preparation and responding to future potential disasters.
ABSTRACT
At the onset of the COVID-19 pandemic, hospitals were in dire need of data-driven analytics to provide support for critical, expensive, and complex decisions. Yet, the majority of analytics being developed were targeted at state- and national-level policy decisions, with little availability of actionable information to support tactical and operational decision making and execution at the hospital level. To fill this gap, we developed a multi-method framework leveraging a parsimonious design philosophy that allows for rapid deployment of high-impact predictive and prescriptive analytics in a time-sensitive, dynamic, data-limited environment, such as a novel pandemic. The product of this research is a workload prediction and decision support tool to provide mission-critical, actionable information for individual hospitals. Our framework forecasts time-varying patient workload and demand for critical resources by integrating disease progression models, tailored to data availability during different stages of the pandemic, with a stochastic network model of patient movements among units within individual hospitals. Both components employ adaptive tuning to account for hospital-dependent, time-varying parameters that provide consistently accurate predictions by dynamically learning the impact of latent changes in system dynamics. Our decision support system is designed to be portable and easily implementable across hospital data systems for expeditious expansion and deployment. This work was contextually grounded in close collaboration with IU Health, the largest health system in Indiana, which has 18 hospitals serving over one million residents. Our initial prototype was implemented in April 2020 and has supported managerial decisions, from the operational to the strategic, across multiple functionalities at IU Health. This article is protected by copyright. All rights reserved
ABSTRACT
The SARS-CoV-2 epidemic starting in Wuhan in December, 2019 has spread rapidly throughout the nation. The control measures to contain the epidemic also produced influences on the transport and treatment process of patients with acute myocardial infarction (AMI), and adjustments in the management of the patients need to be made at this particular time. AMI is characterized by an acute onset with potentially fatal consequence, a short optimal treatment window, and frequent complications including respiratory infections and respiratory and circulatory failure, for which active on-site treatment is essential. To standardize the management and facilitate the diagnosis and treatment, we formulated the guidelines for the procedures and strategies for the diagnosis and treatment of AMI, which highlight 5 Key Principles, namely Nearby treatment, Safety protection, Priority of thrombolysis, Transport to designated hospitals, and Remote consultation. For AMI patients, different treatment strategies are selected based on the screening results of SARS-CoV-2, the time window of STEMI onset, and the vital signs of the patients. During this special period, the cardiologists, including the interventional physicians, should be fully aware of the indications and contraindications of thrombolysis. In the transport and treatment of AMI patients, the physicians should strictly observe the indications for patient transport with appropriate protective measurements of the medical staff.