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Innovative Artificial Intelligence Approaches for Effective Healthcare Logistics and COVID-19 Response

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 23540

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Special Issue Editors

Dr. Maxim A. Dulebenets

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Guest Editor

College of Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310-6046, USA
Interests: operations research; simulation modeling; optimization; NP-hard problems; liner shipping scheduling; evolutionary computation; mathematical programming; hybrid algorithms; metaheuristics; transportation engineering; GPS data processing
Special Issues, Collections and Topics in MDPI journals

Dr. Zhiwu Li

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Guest Editor

Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao
Interests: petri net theory and application; supervisory control of discrete event systems; workflow analysis; system reconfiguration; game theory; production scheduling and planning; data and process mining
Special Issues, Collections and Topics in MDPI journals

Dr. Alireza Fallahpour

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Guest Editor

University of Technology Malaysia, 81310 Skudai, Malaysia
Interests: supply chain management; machine learning; mathematical modeling; multi-criteria decision making; expert systems

Dr. Amir M. Fathollahi-Fard

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Guest Editor

Peter B. Gustavson School of Business, University of Victoria, P.O. Box 1700, Victoria, BC V8P5C2, Canada
Interests: supply chain management; healthcare systems; sustainable logistics and production management; optimization algorithms; heuristics; metaheuristics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, hospital managers and healthcare practitioners have started paying ever more attention to logistics activities. In todays’ world, different logistics-based decisions in the supply chains of hospitals and homecare services have to be made. These decisions are related to the healthcare facility’s location, the routing and scheduling of workers and patients, stock management, purchasing, and all the supply chain activities for the healthcare systems and other relevant systems. It is very difficult to simulate and model such practical systems with different real-life constraints. Moreover, the COVID-19 pandemic has imposed additional challenges for healthcare supply chain stakeholders that have to be accounted for when planning different supply chain operations for healthcare. Developing intelligence-based approaches, such as computer-aided techniques, could be very useful for managers and practitioners in this domain. Predictive methods, on the other hand, can assist relevant healthcare stakeholders who often deal with various sources of uncertainty in their operations.

The intelligence-based, computer-aided approaches documented in the literature include, but are not limited to, optimization with the use of operations research techniques and metaheuristic algorithms (such as genetic algorithms, particle swarm optimization, ant colony optimization, artificial bee colonies, the whale optimization algorithm, the red deer algorithm, etc.), predictive methods (e.g., genetic programming, gene expression programming, artificial neural networks and the adaptive neuron fuzzy inference system), and expert systems, simulation and big data optimization methods, among others.

This Special Issue aims to collect recent developments in and applications of the aforementioned intelligence-based approaches for the concepts of supply chains and healthcare logistics. The scope of this Special Issue includes, but is not limited to, the following topics:

Healthcare systems and emergency management;
Predictive models;
Optimization models and algorithms;
Multi-attribute optimization and simulation;
Novel heuristics and metaheuristics;
Home healthcare services;
Reverse logistics;
Medical waste management;
Fuzzy logic and expert systems;
Metaheuristic algorithms and computational intelligence;
Large-scale optimization problems;
Big data optimization for supply chains;
Addressing the COVID-19 challenges in healthcare systems via applied intelligence.

Dr. Maxim A. Dulebenets
Dr. Zhiwu Li
Dr. Alireza Fallahpour
Dr. Amir M. Fathollahi-Fard
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Healthcare systems
Artificial Intelligence
Predictive models
Supply chains
COVID-19 challenges
Sustainablility

Published Papers (7 papers)

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Research

16 pages, 557 KiB

Open AccessArticle

A Bi-Objective Field-Visit Planning Problem for Rapid Needs Assessment under Travel-Time Uncertainty

by Mohammadmehdi Hakimifar, Vera C. Hemmelmayr and Fabien Tricoire

Sustainability 2022, 14(5), 3024; https://doi.org/10.3390/su14053024 - 04 Mar 2022

Viewed by 1283

Abstract

After a sudden-onset disaster strikes, relief agencies usually dispatch assessment teams to the affected region to quickly investigate the impacts of the disaster on the affected communities. Within this process, assessment teams should compromise between the two conflicting objectives of a “faster” assessment, which covers the needs of fewer community groups, and a “better” assessment, i.e., covering more community groups over a longer time. Moreover, due to the possible effect of the disaster on the transportation network, assessment teams need to make their field-visit planning decisions under travel-time uncertainty. This study considers the two objectives of minimizing the total route duration and maximizing the coverage ratio of community groups, as well as the uncertainty of travel times, during the rapid needs assessment stage. In particular, within our bi-objective solution approach, we provide the set of non-dominated solutions that differ in terms of total route duration and the vector of community coverage ratio at different levels of travel-time uncertainty. Moreover, we provide an in-depth analysis of the amount of violation of maximum allowed time for decision makers to see the trade-offs between infeasibility and solution quality. We apply the robust optimization approach to tackle travel-time uncertainty due to its advantages in requiring fewer data for uncertain parameters and immunizing a feasible solution under all possible realizations. Full article

(This article belongs to the Special Issue Innovative Artificial Intelligence Approaches for Effective Healthcare Logistics and COVID-19 Response)

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33 pages, 4177 KiB

Open AccessArticle

Multi-Objective Optimization of Home Healthcare with Working-Time Balancing and Care Continuity

by Amir M. Fathollahi-Fard, Abbas Ahmadi and Behrooz Karimi

Sustainability 2021, 13(22), 12431; https://doi.org/10.3390/su132212431 - 10 Nov 2021

Cited by 35 | Viewed by 2780

Abstract

The ageing population in most parts of the world becomes a grand challenge for healthcare decision-makers. The care of elderly persons and general hygienic care at patients’ homes are two main reasons to motivate an optimization problem, namely, home healthcare (HHC). A robust plan for caregivers to have sustainable HHC operations management is to consider working-time balancing of caregivers, care continuity and uncertainties, e.g., the uncertainty of patients’ availability in addition to service and travel times as well as the regulations of companies to meet the standards of high-quality home care services. Based on these motivations and challenges to this field, this study firstly established a multi-objective robust optimization of the HHC which is multi-depot, multi-period and multi-service. The demand of each patient in each period may be different due to promptness of services. Each caregiver plays one of the roles of nurses, doctors, physiotherapists and nutritionists. The types of services are directly related to these roles. The objectives were optimizing the total cost of logistic activities as well as the total unemployment time of caregivers and care continuity. As a complicated optimization problem, this study innovated efficient heuristics and an enhanced nature-inspired metaheuristic. Finally, an extensive comparison with regards to the criteria of the multi-objective algorithms’ assessment was conducted. Some sensitivity analyses were conducted to conclude some practical insights. Full article

(This article belongs to the Special Issue Innovative Artificial Intelligence Approaches for Effective Healthcare Logistics and COVID-19 Response)

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13 pages, 1116 KiB

Open AccessArticle

Green Closed-Loop Supply Chain Network under the COVID-19 Pandemic

by Lily Poursoltan, Seyed-Mohammad Seyed-Hosseini and Armin Jabbarzadeh

Sustainability 2021, 13(16), 9407; https://doi.org/10.3390/su13169407 - 21 Aug 2021

Cited by 7 | Viewed by 2928

Abstract

The closed-loop supply chain considers conceptually the possibility of reverse logistics with the use of recycling, remanufacturing and disposal centers. This study contributes for the first time a green closed-loop supply chain framework for the ventilators, which are highly important in the case of the COVID-19 pandemic. The proposed model simulates a case study of Iranian medical ventilator production. The proposed model includes environmental sustainability to limit the carbon emissions as a constraint. A novel stochastic optimization model with strategic and tactical decision making is presented for this closed-loop supply chain network design problem. To make the proposed ventilator logistics network design more realistic, most of the parameters are considered to be uncertain, along with the normal probability distribution. Finally, to show the managerial dimensions under the COVID-19 pandemic for our proposed model, some sensitivity analyses are performed. Results confirm the high impact of carbon emissions and demand variations on the optimal solution in the case of COVID-19. Full article

(This article belongs to the Special Issue Innovative Artificial Intelligence Approaches for Effective Healthcare Logistics and COVID-19 Response)

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17 pages, 5777 KiB

Open AccessArticle

Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

by Patricia Melin and Oscar Castillo

Sustainability 2021, 13(15), 8295; https://doi.org/10.3390/su13158295 - 24 Jul 2021

Cited by 15 | Viewed by 2242

Abstract

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach. Full article

(This article belongs to the Special Issue Innovative Artificial Intelligence Approaches for Effective Healthcare Logistics and COVID-19 Response)

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16 pages, 1195 KiB

Open AccessArticle

An Assessment of Social Distancing Obedience Behavior during the COVID-19 Post-Epidemic Period in China: A Cross-Sectional Survey

by Jinghan Yuan, Hansong Zou, Kefan Xie and Maxim A. Dulebenets

Sustainability 2021, 13(14), 8091; https://doi.org/10.3390/su13148091 - 20 Jul 2021

Cited by 24 | Viewed by 3804

Abstract

Social distancing plays a critical role in reducing the disease diffusion risk during the COVID-19 pandemic and post-pandemic period. In order to explore the social distancing obedience behavior, a comprehensive survey was conducted in this study by collecting data from 1064 Chinese residents in January 2021 by means of a questionnaire. Structural equation modeling (SEM) and hierarchical linear regression (HLR) analyses were employed to investigate the research hypotheses considered, testing the three influencing factors of social distancing obedience behavior: public guidance, risk perception, and regulation punishment. The reliability and validity of the measurements are demonstrated. The outcomes from the conducted analyses show that the public guidance significantly affects risk perception of individuals, while risk perception imposes a positive impact on social distancing obedience behavior. Moreover, risk perception serves a mediating role in the relationship between the public guidance and social distancing obedience behavior. In addition, regulation punishment positively predicts social distancing obedience behavior and could even have a greater effect by enhancing risk perception. Hence, this study suggests that the relevant authorities and agencies implement strong social distancing policies during the COVID-19 post-pandemic period from the perspective of promoting the public guidance, risk perception, and regulation punishment. Full article

(This article belongs to the Special Issue Innovative Artificial Intelligence Approaches for Effective Healthcare Logistics and COVID-19 Response)

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30 pages, 4577 KiB

Open AccessArticle

Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

by Mohd Khanapi Abd Ghani, Nasir G. Noma, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Begonya Garcia-Zapirain, Mashael S. Maashi and Salama A. Mostafa

Sustainability 2021, 13(10), 5406; https://doi.org/10.3390/su13105406 - 12 May 2021

Cited by 9 | Viewed by 3646

Abstract

Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively. Full article

(This article belongs to the Special Issue Innovative Artificial Intelligence Approaches for Effective Healthcare Logistics and COVID-19 Response)

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13 pages, 573 KiB

Open AccessArticle

Integrated Healthcare and the Dilemma of Public Health Emergencies

by Krzysztof Goniewicz, Eric Carlström, Attila J. Hertelendy, Frederick M. Burkle, Mariusz Goniewicz, Dorota Lasota, John G. Richmond and Amir Khorram-Manesh

Sustainability 2021, 13(8), 4517; https://doi.org/10.3390/su13084517 - 19 Apr 2021

Cited by 28 | Viewed by 4948

Abstract

Traditional healthcare services have demonstrated structural shortcomings in the delivery of patient care and enforced numerous elements of integration in the delivery of healthcare services. Integrated healthcare aims at providing all healthcare that makes humans healthy. However, with mainly chronically ill people and seniors, typically suffering from numerous comorbidities and diseases, being recruited for care, there is a need for a change in the healthcare service structure beyond direct-patient care to be compatible in peacetime and during public health emergencies. This article’s objective is to discuss the opportunities and obstacles for increasing the effectiveness of healthcare through improved integration. A rapid evidence review approach was used by performing a systematic followed by a non-systematic literature review and content analysis. The results confirmed that integrated healthcare systems play an increasingly important role in healthcare system reforms undertaken in European Union countries. The essence of these changes is the transition from the episodic treatment of acute diseases to the provision of coordinated medical services, focused on chronic cases, prevention, and ensuring patient continuity. However, integrated healthcare, at a level not yet fully defined, will be necessary if we are to both define and attain the integrated practice of both global health and global public health emergencies. This paper attains the necessary global challenges to integrate healthcare effectively at every level of society. There is a need for more knowledge to effectively develop, support, and disseminate initiatives related to coordinated healthcare in the individual healthcare systems. Full article

(This article belongs to the Special Issue Innovative Artificial Intelligence Approaches for Effective Healthcare Logistics and COVID-19 Response)

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