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Davoodi M, Batista A, Senapati A, Calabrese JM. Personnel Scheduling during the COVID-19 Pandemic: A Probabilistic Graph-Based Approach. Healthcare (Basel) 2023; 11:1917. [PMID: 37444751 DOI: 10.3390/healthcare11131917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Effective personnel scheduling is crucial for organizations to match workload demands. However, staff scheduling is sometimes affected by unexpected events, such as the COVID-19 pandemic, that disrupt regular operations. Limiting the number of on-site staff in the workplace together with regular testing is an effective strategy to minimize the spread of infectious diseases like COVID-19 because they spread mostly through close contact with people. Therefore, choosing the best scheduling and testing plan that satisfies the goals of the organization and prevents the virus's spread is essential during disease outbreaks. In this paper, we formulate these challenges in the framework of two Mixed Integer Non-linear Programming (MINLP) models. The first model aims to derive optimal staff occupancy and testing strategies to minimize the risk of infection among employees, while the second is aimed only at optimal staff occupancy under a random testing strategy. To solve the problems expressed in the models, we propose a canonical genetic algorithm as well as two commercial solvers. Using both real and synthetic contact networks of employees, our results show that following the recommended occupancy and testing strategy reduces the risk of infection 25-60% under different scenarios. The minimum risk of infection can be achieved when the employees follow a planned testing strategy. Further, vaccination status and interaction rate of employees are important factors in developing scheduling strategies that minimize the risk of infection.
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Affiliation(s)
- Mansoor Davoodi
- Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden Rossendorf (HZDR), 01328 Görlitz, Germany
| | - Ana Batista
- Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden Rossendorf (HZDR), 01328 Görlitz, Germany
| | - Abhishek Senapati
- Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden Rossendorf (HZDR), 01328 Görlitz, Germany
| | - Justin M Calabrese
- Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden Rossendorf (HZDR), 01328 Görlitz, Germany
- Department of Ecological Modelling, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany
- Department of Biology, University of Maryland, College Park, MD 20742, USA
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Lan S, Fan W, Yang S, Pardalos PM. Physician scheduling problem in Mobile Cabin Hospitals of China during Covid-19 outbreak. ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2023; 91:349-372. [PMID: 36721866 PMCID: PMC9880358 DOI: 10.1007/s10472-023-09834-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/16/2023] [Indexed: 05/27/2023]
Abstract
In this paper, we investigate a novel physician scheduling problem in the Mobile Cabin Hospitals (MCH) which are constructed in Wuhan, China during the outbreak of the Covid-19 pandemic. The shortage of physicians and the surge of patients brought great challenges for physicians scheduling in MCH. The purpose of the studied problem is to get an approximately optimal schedule that reaches the minimum workload for physicians on the premise of satisfying the service requirements of patients as much as possible. We propose a novel hybrid algorithm integrating particle swarm optimization (PSO) and variable neighborhood descent (VND) (named as PSO-VND) to find the approximate global optimal solution. A self-adaptive mechanism is developed to choose the updating operators dynamically during the procedures. Based on the special features of the problem, three neighborhood structures are designed and searched in VND to improve the solution. The experimental comparisons show that the proposed PSO-VND has a significant performance increase than the other competitors.
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Affiliation(s)
- Shaowen Lan
- School of Management, Hefei University of Technology, Hefei, 230009 China
- Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei, 230009 China
| | - Wenjuan Fan
- School of Management, Hefei University of Technology, Hefei, 230009 China
- Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei, 230009 China
| | - Shanlin Yang
- School of Management, Hefei University of Technology, Hefei, 230009 China
- Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei, 230009 China
| | - Panos M. Pardalos
- Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611-6595 USA
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Malekian S, Komijan AR, Shoja A, Ehsanifar M. New nurse scheduling model considering nurses’ seniority and the possibility of consecutive shifts under COVID-19 (A real case study). INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2134639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Saman Malekian
- Department of Industrial Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
| | - Alireza Rashidi Komijan
- Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
| | - Ahmad Shoja
- Department of Mathematics Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
| | - Mohammad Ehsanifar
- Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran
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Moosavi A, Ozturk O, Patrick J. Staff scheduling for residential care under pandemic conditions: The case of COVID-19. OMEGA 2022; 112:102671. [PMID: 35530747 PMCID: PMC9065499 DOI: 10.1016/j.omega.2022.102671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 04/30/2022] [Indexed: 06/14/2023]
Abstract
The COVID-19 pandemic severely impacted residential care delivery all around the world. This study investigates the current scheduling methods in residential care facilities in order to enhance them for pandemic conditions. We first define the basic problem that addresses decisions associated with the assignment and scheduling of staff members, who perform a set of tasks required by residents during a planning horizon. This problem includes the minimization of costs associated with the salary of part-time staff members, total overtime, and violations of service time windows. Subsequently, we adapt the basic problem to pandemic conditions by considering the impacts of communal spaces (e.g., shared rooms) and a cohorting policy (classification of residents based on their risk of infection) on the spread of infectious diseases. We introduce a new objective function that minimizes the number of distinct staff members serving each room of residents. Likewise, we propose a new objective function for the cohorting policy that aims to minimize the number of distinct cohorts served by each staff member. A new constraint is incorporated that forces staff members to serve only one cohort within a shift. We present a population-based heuristic algorithm to solve this problem. Through a comparison with two benchmark solution approaches (a mathematical programme and a non-dominated archiving ant colony optimization algorithm), the superiority of the heuristic algorithm is shown regarding solution quality and CPU time. Finally, we conduct numerical analyses to present managerial implications.
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Affiliation(s)
- Amirhossein Moosavi
- University of Ottawa, Telfer School of Management, 55 Laurier Avenue East, Ottawa, Ontario K1N 6N5, Canada
| | - Onur Ozturk
- University of Ottawa, Telfer School of Management, 55 Laurier Avenue East, Ottawa, Ontario K1N 6N5, Canada
| | - Jonathan Patrick
- University of Ottawa, Telfer School of Management, 55 Laurier Avenue East, Ottawa, Ontario K1N 6N5, Canada
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Chaieb M, Sassi DB, Jemai J, Mellouli K. Challenges and solutions for the integrated recovery room planning and scheduling problem during COVID-19 pandemic. Med Biol Eng Comput 2022; 60:1295-1311. [PMID: 35316468 PMCID: PMC8938740 DOI: 10.1007/s11517-022-02513-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 01/10/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Marouene Chaieb
- College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Building N3963, 16273 Saudi Arabia
- LARODEC, Institut Supérieur de Gestion de Tunis, Université de Tunis, 41 Ave de la Liberte, Tunis, 2000 Tunisia
| | - Dhekra Ben Sassi
- College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Building N3963, 16273 Saudi Arabia
- RIADI, École Nationale des Sciences de l’Informatique (ENSI), Campus Universitaire de la Manouba, 2010 Manouba, Tunisia
| | - Jaber Jemai
- Computer and Information Systems Division, Higher Colleges of Technology, Abu Dhabi, United Arab Emirates
| | - Khaled Mellouli
- LARODEC, Institut Supérieur de Gestion de Tunis, Université de Tunis, 41 Ave de la Liberte, Tunis, 2000 Tunisia
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Alo UR, Nkwo FO, Nweke HF, Achi II, Okemiri HA. Non-Pharmaceutical Interventions against COVID-19 Pandemic: Review of Contact Tracing and Social Distancing Technologies, Protocols, Apps, Security and Open Research Directions. SENSORS (BASEL, SWITZERLAND) 2021; 22:280. [PMID: 35009822 PMCID: PMC8749862 DOI: 10.3390/s22010280] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 12/17/2022]
Abstract
The COVID-19 Pandemic has punched a devastating blow on the majority of the world's population. Millions of people have been infected while hundreds of thousands have died of the disease throwing many families into mourning and other psychological torments. It has also crippled the economy of many countries of the world leading to job losses, high inflation, and dwindling Gross Domestic Product (GDP). The duo of social distancing and contact tracing are the major technological-based non-pharmaceutical public health intervention strategies adopted for combating the dreaded disease. These technologies have been deployed by different countries around the world to achieve effective and efficient means of maintaining appropriate distance and tracking the transmission pattern of the diseases or identifying those at high risk of infecting others. This paper aims to synthesize the research efforts on contact tracing and social distancing to minimize the spread of COVID-19. The paper critically and comprehensively reviews contact tracing technologies, protocols, and mobile applications (apps) that were recently developed and deployed against the coronavirus disease. Furthermore, the paper discusses social distancing technologies, appropriate methods to maintain distances, regulations, isolation/quarantine, and interaction strategies. In addition, the paper highlights different security/privacy vulnerabilities identified in contact tracing and social distancing technologies and solutions against these vulnerabilities. We also x-rayed the strengths and weaknesses of the various technologies concerning their application in contact tracing and social distancing. Finally, the paper proposed insightful recommendations and open research directions in contact tracing and social distancing that could assist researchers, developers, and governments in implementing new technological methods to combat the menace of COVID-19.
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Affiliation(s)
- Uzoma Rita Alo
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
| | - Friday Onwe Nkwo
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
| | - Henry Friday Nweke
- Centre for Research in Machine Learning, Artificial Intelligence and Network Systems, Computer Science Department, Ebonyi State University, P.M.B 053, Abakaliki 480211, Ebonyi State, Nigeria;
| | - Ifeanyi Isaiah Achi
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
| | - Henry Anayo Okemiri
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
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Jordan E, Shin DE, Leekha S, Azarm S. Optimization in the Context of COVID-19 Prediction and Control: A Literature Review. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:130072-130093. [PMID: 35781925 PMCID: PMC8768956 DOI: 10.1109/access.2021.3113812] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 09/10/2021] [Indexed: 05/08/2023]
Abstract
This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.
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Affiliation(s)
- Elizabeth Jordan
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
| | - Delia E. Shin
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
| | - Surbhi Leekha
- Department of Epidemiology and Public HealthUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Shapour Azarm
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
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Mao Z, Zhang W, Yang B, Zhang T. Impact of coronavirus pandemic on sharing mode of manufacturer. COMPUTERS & INDUSTRIAL ENGINEERING 2021; 158:107386. [PMID: 35313662 PMCID: PMC8926416 DOI: 10.1016/j.cie.2021.107386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Service platform has developed rapidly in car-sharing, consumers often buy or own cars but not fully utilize and share them. Since the coronavirus pandemic has affected sales and people's attitudes towards car-sharing, which brought both opportunities and challenges to the platform and changed the operating mode of manufacturers, some traditional manufacturers have motivated to cooperate with third-party platform. In this paper, we develop an analytical framework to examine the pricing decisions and optimal mode selection of manufacturer under the COVID-19 epidemic. Considering the supply chain consists of a manufacturer and a third-party sharing platform. We analyze three scenarios including no sharing, customers-to-customers, and mixed sharing, then employ a game theoretic approach to get equilibrium solutions and analytically derive the optimal mode choice. Our analysis shows that when the operation and maintenance cost is low, manufacturer will join the third-party platform, and the sharing price increase in operation and maintenance cost, while the selling price decrease in operation and maintenance cost. When the value perception factor less than the threshold, the manufacturer will retain sales channel, and the selling demand decrease in value perception factor in the growing market, the sharing demand has the same trend, vice versa. Furthermore, we find that if the operation and maintenance cost is low and value perception factor is high, mixed sharing is the best choice for the manufacturer, while the manufacturer will choose no car-sharing when the value perception factor is relatively low.
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Affiliation(s)
- Zhenzhen Mao
- Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China
| | - Weisi Zhang
- Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China
- School of Management, Fudan University, Shanghai 200433, China
| | - Bin Yang
- Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China
| | - Tao Zhang
- Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China
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