Confirmation of a measurement model for hospital supply chain resilience DOI Creative Commons
Baoyang Ding,

Xiaohan Yang,

Tiantian Gao

и другие.

Frontiers in Public Health, Год журнала: 2024, Номер 12

Опубликована: Май 22, 2024

Background The hospital supply chain has revealed increasing vulnerabilities and disruptions in the wake of COVID-19 pandemic, threatening healthcare services patient safety. resilience chains emerged as a paramount concern within system. However, there is lack systematic research to develop an instrument tailored industry that both valid reliable for measuring resilience. Therefore, this study aims construct validate comprehensive scale assessing resilience, based on dynamic capability theory. Methods This followed rigorous development steps, starting with literature review 15 semi-structured interviews generate initial items. These items were then refined through expert panel feedback three rounds Delphi studies. Using data from 387 hospitals Province S, mainland China, underwent testing validation using structural equation modeling. To ensure most effective model, five alternative models examined determine suitable parsimonious model. Results produced 26-item captures dimensions line theory: anticipation, adaptation, response, recovery, learning, all showing satisfactory consistency, reliability validity. Conclusion multi-dimensional offers managers valuable tool identify areas needing attention improvement, benchmark against their counterparts, ultimately strengthen unexpected risks.

Язык: Английский

A Multi-stage Machine Learning Model to Design a Sustainable-Resilient-Digitalized Pharmaceutical Supply Chain DOI
Mostafa Jafarian,

Iraj Mahdavi,

Ali Tajdin

и другие.

Socio-Economic Planning Sciences, Год журнала: 2025, Номер unknown, С. 102165 - 102165

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Facility location and capacity planning for sampling and testing processes under demand uncertainty DOI

Zhongbao Zhou,

Sun Wenting, Tiantian Ren

и другие.

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 111094 - 111094

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
A High-Accuracy and Low-Power Emerging Technology-Based Associative Memory DOI
Mahan Rezaei, Abdolah Amirany, Mohammad Hossein Moaiyeri

и другие.

IEEE Transactions on Nanotechnology, Год журнала: 2024, Номер 23, С. 293 - 298

Опубликована: Янв. 1, 2024

Associative memory (AM) is a subcategory of neural networks (NNs) inspired by human memory. Over time, the need to process complex tasks has increased, leading development intelligent processors. Most NN circuits have been implemented using complementary metal-oxide-semiconductor (CMOS) technologies. However, some adverse effects become more apparent with scaling transistors. Several emerging technologies, such as magnetic tunnel junctions (MTJ) and carbon nanotube field-effect transistors (CNTFET), introduced address these issues. This paper proposes novel, robust AM design based on CNTFETs MTJs. The use MTJs in proposed motivated their reliable reconfigurability nonvolatility. Moreover, overcome limitations conventional CMOS technology. main goal method increase voltage swing synapse output, reducing impact variations increasing accuracy. Simulation results indicate that offers up 50% fewer recall attempts at least 15% 9% lower average static energy consumption than state-of-the-art counterparts.

Язык: Английский

Процитировано

3
Two-stage approach for COVID-19 vaccine supply chain network under uncertainty using the machine learning algorithms: A case study DOI
Mahdyeh Shiri, Parviz Fattahi, Fatemeh Sogandi

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 135, С. 108837 - 108837

Опубликована: Июнь 17, 2024

Язык: Английский

Процитировано

3
Comparative analysis of lean and agile supply chain strategies for effective vaccine distribution in pandemics: A case study of COVID-19 in a densely populated developing region DOI Creative Commons

Kasuni R.R. Gomes,

H. Niles Perera, Amila Thibbotuwawa

и другие.

Supply Chain Analytics, Год журнала: 2023, Номер 3, С. 100022 - 100022

Опубликована: Июнь 16, 2023

Mass vaccination programs should employ effective strategies to design a resilient vaccine supply chain for immunizing populations quickly and efficiently. The need more flexible responsive is highlighted during the pandemic, where authorities are required effectively execute distribution. Our study proposes scientifically driven approach identify suitable distribution, enhancing effectiveness of mass vaccination. We propose two-stage identifying best strategy that supports faster rollouts, reducing infections deaths pandemic. optimize distribution network under both using Mixed Integer Programming (MIP) four disruption scenarios in first stage. Second, we have used systems dynamics simulation Susceptible-Exposed-Infectious-Recovered (SEIR) model pandemics impact In all scenarios, Lean less costly, Agile reduces lead time rollout. show achieving cost-saving or lead-time saving either becomes increasingly difficult when severity disruptions at storage increases. suggests novel methodology determines most which minimizes several scenarios. decision-makers can appropriate delivery densely populated developing regions, proposed framework compares strategies' on design.

Язык: Английский

Процитировано

7
Current trends in scientific research on managerial innovations in healthcare supply chain: A bibliometric analysis DOI

Nouhaila Ben Khizzou,

Mourad Aarabe, Lhoussaine Alla

и другие.

Опубликована: Май 2, 2024

Язык: Английский

Процитировано

2
Innovative Applications of Unsupervised Learning in Uncertainty-Aware Pharmaceutical Supply Chain Planning DOI Creative Commons
Farid Kochakkashani, Vahid Kayvanfar, Roberto Baldacci

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 107984 - 107999

Опубликована: Янв. 1, 2024

The significance of resiliency, reliability, and equity in the pharmaceutical supply chain is often overlooked but becomes evident wake disastrous events. Disruptive incidents underscore critical importance these concepts, necessitating development innovative frameworks to effectively address challenges that emerge their aftermath. This paper introduces a framework specifically designed issues arising from disruptions within chain. A novel mixed-integer nonlinear programming (MINLP) model proposed formulate encompasses distribution both cold non-cold pharmaceuticals vaccines. abundance diverse vaccines, each with its distinct characteristics, presents formidable planning obstacle. noteworthy contribution this study lies innovatively applying AI-driven methodologies chain, employing five pioneering unsupervised learning algorithms for improved inventory management control. model's uncertainty addressed through an joint chance constraint (JCC) formulation. By JCC, ensures high level reliability satisfying uncertain patient demands. MINLP formulation JCCs significant computational complexities intractability. To alleviate issues, state-of-the-art reformulation are provided transform into equivalent linear form. results indicate efficiency techniques illustrate capabilities concerns.

Язык: Английский

Процитировано

2
Supply Chains Problem During Crises: A Data-Driven Approach DOI Creative Commons

Farima Salamian,

Amirmohammad Paksaz,

Behrooz Khalil Loo

и другие.

Modelling—International Open Access Journal of Modelling in Engineering Science, Год журнала: 2024, Номер 5(4), С. 2001 - 2039

Опубликована: Дек. 12, 2024

Efficient management of hospital evacuations and pharmaceutical supply chains is a critical challenge in modern healthcare, particularly during emergencies. This study addresses these challenges by proposing novel bi-objective optimization framework. The model integrates Mixed-Integer Linear Programming (MILP) approach with advanced machine learning techniques to simultaneously minimize total costs maximize patient satisfaction. A key contribution the incorporation Gated Recurrent Unit (GRU) neural network for accurate drug demand forecasting, enabling dynamic resource allocation crisis scenarios. also accounts two distinct destinations—receiving hospitals temporary care centers (TCCs)—and includes specialized chain prevent medicine shortages. To enhance system robustness, probabilistic patterns disruption risks are considered, ensuring reliability. solution methodology combines Grasshopper Optimization Algorithm (GOA) ɛ-constraint method, efficiently addressing multi-objective nature problem. Results demonstrate significant improvements cost reduction, allocation, service levels, highlighting model’s practical applicability real-world research provides valuable insights optimizing healthcare logistics events, contributing both operational efficiency welfare.

Язык: Английский

Процитировано

2
Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus Disease DOI Creative Commons
Donovan Guttieres, Charlot Diepvens, Catherine Decouttere

и другие.

Vaccines, Год журнала: 2023, Номер 12(1), С. 24 - 24

Опубликована: Дек. 25, 2023

Health emergencies caused by epidemic-prone pathogens (EPPs) have increased exponentially in recent decades. Although vaccines proven beneficial, they are unavailable for many pathogens. Furthermore, achieving timely and equitable access to against EPPs is not trivial. It requires decision-makers capture numerous interrelated factors across temporal spatial scales, with significant uncertainties, variability, delays, feedback loops that give rise dynamic unexpected behavior. Therefore, despite progress filling R&D gaps, the path licensure long-term viability of continues be unclear. This paper presents a quantitative system dynamics modeling framework evaluate sustainability vaccine supply under different vaccination strategies. Data from both literature 50 expert interviews used model demand prototypical Ebolavirus Zaire (EBOV) vaccine. Specifically, case study evaluates associated proactive ahead an outbreak similar magnitude as 2018–2020 epidemic North Kivu, Democratic Republic Congo. The scenarios presented demonstrate how uncertainties (e.g., duration vaccine-induced protection) design criteria priority geographies groups, target coverage, frequency boosters) lead important tradeoffs policy aims, public health outcomes, feasibility technical, operational, financial). With sufficient context data, provides foundation apply broad range additional ability identify leverage points preparedness offers directions further research.

Язык: Английский

Процитировано

4
How COVID-19 impacted the temporal and spatial distribution of collision hotspots DOI Creative Commons
Faeze Momeni Rad, Karim El‐Basyouny

Canadian Journal of Civil Engineering, Год журнала: 2024, Номер 51(6), С. 616 - 639

Опубликована: Фев. 5, 2024

This research examines the spatial and temporal shift in collision hotspots caused by COVID-19 pandemic, considering different severities. The Getis-Ord statistic was utilized to create models generate map outputs for 2019 2020. Two distinct approaches were employed: using a census tract shapefile (provided) creating fishnet polygons measuring 500 m m. Results showed fewer outside Edmonton's central core, while fatal collisions concentrated close core. intriguing finding suggests that restrictions led more aggressive driving behaviour near centre, contributing rise numbers. study found significant reduction traffic April 2020, with 58% decrease compared previous year. highlights pandemic's impact on road safety, emphasizing importance of reducing volume advocating control strategies, multi-modal planning, efficient pricing strategies within Vision Zero improved safety.

Язык: Английский

Процитировано

1