Designing a Sustainable Model for Providing Health Services Based on the Internet of Things and Meta-Heuristic Algorithms

Document Type : SI: SD of ISC

Authors

1 Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Graduated Student, University of Houston, Texas, USA

4 Lecturer, Department of Business and Communications, Faculty of Business and Communications, INTI International University, Malaysia

5 Lecturer, Malaysia Institute of Transportation (MITRANS), Faculty of Business and Management, Department of Operations Management, University Teknologi MARA, Cawangan Selangor, Kampus Puncak Alam, MALAYSIA

Abstract

In this article, a health service delivery model based on the Internet of Things (IoT) under uncertainty is presented. The considered model includes a set of patients, doctors, vehicles, and services that should be provided in the shortest time and cost. The most important decisions of the network include the allocation of specialist doctors to patients, the routing of vehicles, and doctors to provide health services. The dataset of the problem has been provided to the hospital and centers using IoT tools and an integration framework has been designed for this problem. The results of solving the numerical examples show that to reduce the service delivery time and the distance traveled by vehicles, the design costs of the model should be increased. Also, the increase in the rate of uncertainty during service delivery leads to an increase in total costs in the health system. In this article, Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-objective imperialist Competitive algorithm (MOICA) were proposed to solve the model, and the results showed that the proposed methods are more efficient than the exact methods. These algorithms have achieved close to optimal results in the shortest possible time. Also, the calculation results in large numerical examples show the high efficiency of the MOICA.

Keywords


Abolghasemian, M., Ghane Kanafi, A., & Daneshmandmehr, M. (2020). A two-phase simulation-based optimization of hauling system in open-pit mine. Iranian journal of management studies, Vol. 13(4), pp. 705-732.
Abolghasemian, M., Kanafi, A. G., & Daneshmand-Mehr, M. (2022). Simulation-based multiobjective optimization of open-pit mine haulage system: a modified-NBI method and meta modeling approach. Complexity, 2022.
Bahadori-Chinibelagh, S., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2022). Two constructive algorithms to address a multi-depot home healthcare routing problem. IETE Journal of Research, 68(2), pp. 1108-1114.
Ben-Tal, A., & Nemirovski, A. (2002). Robust optimization–methodology and applications. Mathematical programming, Vol. 92, pp. 453-480.
Ben-Tal, A., Goryashko, A., Guslitzer, E., & Nemirovski, A. (2004). Adjustable robust solutions of uncertain linear programs. Mathematical programming, Vol. 99(2), pp. 351-376.
Chobar, A. P., Adibi, M. A., & Kazemi, A. (2022). Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability, pp. 1-28.
Euchi, J. (Ed.). (2019). Transportation, logistics, and supply chain management in home healthcare: emerging research and opportunities: emerging research and opportunities.
Fathollahi-Fard, A. M., Ahmadi, A., & Karimi, B. (2021). Multi-objective optimization of home healthcare with working-time balancing and care continuity. Sustainability, Vol. 13(22), 12431.
Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., Tavakkoli-Moghaddam, R., & Smith, N. R. (2022). Bi-level programming for home health care supply chain considering outsourcing. Journal of Industrial Information Integration, Vol. 25, 100246.
Ghasemi, P., Khalili, H. A., Chobar, A. P., Safavi, S., & Hejri, F. M. (2022). A new multiechelon mathematical modeling for pre-and postdisaster blood supply chain: robust optimization approach. Discrete Dynamics in Nature and Society, 2022, pp. 1-10.
Ghiasvand Ghiasi, F., Yazdani, M., Vahdani, B., & Kazemi, A. (2021). Multi-depot home health care routing and scheduling problem with multimodal transportation: Mathematical model and solution methods. Scientia Iranica.
Goodarzian, F., Abraham, A., & Fathollahi-Fard, A. M. (2021). A biobjective home health care logistics considering the working time and route balancing: a self-adaptive social engineering optimizer. Journal of Computational Design and Engineering, Vol. 8(1), pp. 452-474.
Goodarzian, F., Taleizadeh, A. A., Ghasemi, P., & Abraham, A. (2021). An integrated sustainable medical supply chain network during COVID-19. Engineering Applications of Artificial Intelligence, Vol. 100, 104188.
Issabakhsh, M., Hosseini-Motlagh, S. M., Pishvaee, M. S., & Saghafi Nia, M. (2018). A vehicle routing problem for modeling home healthcare: a case study. International Journal of Transportation Engineering, Vol. 5(3), pp. 211-228.
Jahangiri, S., Abolghasemian, M., Ghasemi, P., & Chobar, A. P. (2023). Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions. International journal of industrial and systems engineering, Vol. 43(1), pp. 1-19.
Jahangiri, S., Abolghasemian, M., Pourghader Chobar, A., Nadaffard, A., & Mottaghi, V. (2021). Ranking of key resources in the humanitarian supply chain in the emergency department of iranian hospital: a real case study in COVID-19 conditions. Journal of applied research on industrial engineering, Vol. 8 (Special Issue), pp. 1-10.
Legato, P., Mazza, R. M., & Fortino, G. (2022). A multi-level simulation-based optimization framework for IoT-enabled elderly care systems. Simulation Modelling Practice and Theory, Vol. 114, 102420.
Luo, H., Dridi, M., & Grunder, O. (2019, October). Ant colony optimization algorithm for a transportation problem in home health care with the consideration of carbon emissions. In International Conference on Artificial Evolution (Evolution Artificielle) (pp. 136-147). Springer, Cham.
Maadanpour Safari, F., Etebari, F., & Pourghader Chobar, A. (2021). Modelling and optimization of a tri-objective Transportation-Location-Routing Problem considering route reliability: using MOGWO, MOPSO, MOWCA and NSGA-II. Journal of optimization in industrial engineering, Vol. 14(2), pp. 83-98.
Nasir, J. A., & Kuo, Y. H. (2020). A decision support framework for home health care transportation with simultaneous multi-vehicle routing and staff scheduling synchronization. Decision Support Systems, Vol. 138, 113361.
Nikzad, E., Bashiri, M., & Abbasi, B. (2021). A matheuristic algorithm for stochastic home health care planning. European Journal of Operational Research, Vol. 288(3), pp. 753-774.
Olsen, C. F., Bergland, A., Debesay, J., Bye, A., & Langaas, A. G. (2019). Striking a balance: health care providers’ experiences with home-based, patient-centered care for older people—a meta-synthesis of qualitative studies. Patient Education and Counseling, Vol. 102(11), pp. 1991-2000.
Ratta, P., Kaur, A., Sharma, S., Shabaz, M., & Dhiman, G. (2021). Application of blockchain and internet of things in healthcare and medical sector: applications, challenges, and future perspectives. Journal of Food Quality, 2021.
Salehi-Amiri, A., Jabbarzadeh, A., Hajiaghaei-Keshteli, M., & Chaabane, A. (2022). Utilizing the Internet of Things (IoT) to address uncertain home health care supply chain network. Expert Systems with Applications, Vol. 208, 118239.
Shi, Y., Boudouh, T., & Grunder, O. (2019). A robust optimization for a home health care routing and scheduling problem with consideration of uncertain travel and service times. Transportation Research Part E: Logistics and Transportation Review, Vol. 128, pp. 52-95.
Sixsmith, J., Sixsmith, A., Fänge, A. M., Naumann, D., Kucsera, C. S. A. B. A., Tomsone, S., ... & Woolrych, R. (2014). Healthy ageing and home: The perspectives of very old people in five European countries. Social science & medicine, Vol. 106, pp. 1-9.
Umair, M., Cheema, M. A., Cheema, O., Li, H., & Lu, H. (2021). Impact of COVID-19 on IoT adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Sensors, Vol. 21(11), 3838.
Zahedi, M., & Nahr, J. (2020). Designing a hub covering location problem under uncertainty conditions. Management Science Letters, Vol. 9(3), pp. 477-500.
Zhou, X., Yu, Z., Yuan, L., Wang, L., & Wu, C. (2020). Measuring accessibility of healthcare facilities for populations with multiple transportation modes considering residential transportation mode choice. ISPRS International Journal of Geo-Information, Vol. 9(6), 394.