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International Journal of Production Research

Artificial Intelligence Applications in Healthcare Supply Chain Networks under Disaster Conditions

Submission Window: July 24th, 2022 - Jan 24th, 2023

AIMS AND SCOPE

Disasters are events that cause disruption in the normal functioning of society and if not controlled, cause financial and human losses (Ghasemi et al., 2020; Zhang et al. 2021). Due to their nature, these events can be divided into two natural types such as earthquakes, volcanoes, and man-made such as the COVID-19 pandemic (Goodarzian et al., 2021a). Disaster support and logistics are also the main pillars of disaster management (Shirazi et al., 2021; Zhan et al. 2021; Qin et al. 2021). Logistics has a fundamental and decisive role in the supply and support chain of disaster management, and if this role is not fulfilled, the whole disaster management process will face problems and will be disrupted. One of the most important strategies to control disorders in the health care system is resilience (Goodarzian et al., 2021b and 2021d). Resilience is the ability of the supply chain to overcome unpredictable events. The purpose of creating resilience in the healthcare supply chain is to prevent the chain from moving towards adverse conditions. During the outbreak of COVID-19, many factories, leisure, and sports schools were utilized for either treating patients or manufacturing vital Personal Protective Equipment (PPE). For example, the Shanghai-GM-Wuling automobile factory rapidly changed products and produced face masks (Betti and Ni, 2020). Healthcare supply chain management/logistic network considering resilience must therefore be the focus of an integral approach that looks at all the links in the sequence and never loses sight of their interdependence. Therefore, there is a need to pay attention to a healthcare supply chain that can effectively manage the situation. Also, quantitative concepts and models have rarely been used in the healthcare supply chain (Guven-Uslu et al. 2014; Gupta et al., 2014; Gupta and Dutta, 2016; Gupta et al., 2018; Ghasemi et al., 2019). The lack of efficient and effective models in the field has resulted in inefficient handling of healthcare supply chains (Goodarzian et al. 2021e, Mathur et al. 2018).

Artificial Intelligence (AI) has recently emerged as an important tool to manage the complexities of the common business challenges (Mitra, Kapoor and Gupta, 2020). AI and related technologies such as machine learning, robotics, stochastic optimization are increasingly prevalent in business and life and are gradually being used in the field of healthcare (Ghasemi et al., 2021; Goodarzian et al., 2021c; Dutta et al., 2018; Mitra, Kapoor and Gupta, 2022). This technology is used for various therapeutic and research purposes, including diagnosis, management of chronic diseases, medical services, and drug discovery. AI technology has better patient-care potential, health system logistics processes, and the ability to diagnose disease than humans. Moreover, AI systems increase the quality of life of people. For example, today AI has made great strides in the diagnosis and treatment of cancer, especially breast cancer (Huang et al. 2020 and Coccia, 2020). Hence, hospitals, NGOs and humanitarian organizations in disaster situations are looking for AI solutions to increase cost savings, improve patient satisfaction, and meet the needs of staff and manpower. In addition to existing approaches such as quantitative predictive models (such as fuzzy inference system, regression, machine learning and neural network), computer-aided approaches, meta-heuristic algorithms (such as genetic, ant colony optimization algorithm, particle swarm optimization algorithm, etc.), simulation approaches (such as discrete simulation and agent-based), optimization based decision support systems approaches (mixed integer linear programs, stochastic programming) there is always a need for new methods to solve problems faster and more efficiently. Therefore, this SI seeks new AI-based approaches to solve the mentioned problems.

As recent disaster (COVID-19 pandemic) and disruption in healthcare supply chains is causing a shortage of medicines, vaccines, and high demand of patients, it seems that AI methods are needed to solve the problems in the healthcare supply chain networks. Therefore, this special issue seeks to invite papers on the application of AI methods in addressing the healthcare supply chain challenges.

 

TOPICS COVERED

The following topics are included with respect to the application of the AI approaches in healthcare supply chain networks under disaster conditions, but are not limited to:
 

  • Global impact of the COVID-19 pandemic on public healthcare supply chains;

  • AI role in disaster management and healthcare management;

  • Closed-loop and reverse supply chain management;

  • Home healthcare services and medical waste management;

  • Resilience to manage pandemic risks supply chain;

  • Exact and Heuristic/meta-heuristic algorithms in healthcare supply chains;

  • Machine Learning (ML) techniques, Fuzzy logic and fuzzy inference system for time series forecasting in healthcare supply chains;

  • Multi-Criteria Decision-Making (MCDM) methods and game theory in healthcare supply chain network;

  • Deterministic, stochastic, and robust optimization model in the healthcare supply chain.

 

SUBMISSION GUIDELINES AND TIMELINE

Instructions for authors can be found at:

https://www.tandfonline.com/tprs

 

  • First date for manuscript submission: 24 July 2022

  • Last date for manuscript submission: 24 January 2023

  • Notification to authors: 24 March 2023

  • Revised Manuscript Due: 24 May 2023

  • Decision Notification: 24 July 2023

 

GUEST EDITORS

 

REFERENCES

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