A new methodology for COVID-19 preparedness centers based on a location-allocation platform

Document Type: Research Paper

Authors

School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

COVID-19 disease is spreading all over the planet. It is more than necessary that any researcher does his/her part to control this pandemic disease. Since this virus is infectious, and due to the limitations of hospitals, in different matters, such as human recourses (expertise) and needed equipment, it is reasonable to identify a pre-determined number of hospitals as COVID-19 pandemic centers and trying to equip them as much as possible to treat a relative patient in them. This study proposes a methodology based on a multi-criteria decision making (MCDM) method, namely BWM-WASPAS, for COVID-19 preparedness centers based on a location-allocation problem. This methodology is examined in Tehran city as a real-life case study. We find out that the most important item in this decision making is the ICU capacity. However, ignoring the other criteria is not allowed at all.

Keywords

Main Subjects


Ahmadi, G., Torabi, S.A. and Tavakkoli-Moghaddam‎, R. (2016). A bi-objective location-inventory model with capacitated transportation and lateral transshipments, Int. J. of Production Research, 54(7), 2035-2056.

Cicek, C.T., Gultekin, H. and Tavli, B. (2019). The location-allocation problem of drone base stations. Computers and Operations Research, 111, 155-176.

Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. John Wiley and Sons.

Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.

Kahag, M.R., Akhavan Niaki, S.T., Seifbarghy, M. and Zabihi, S. (2019). Bi-objective optimization of multi-server intermodal hub-location-allocation problem in congested systems: modeling and solution. Journal of Industrial Engineering International, 15(2), 221-248.

Kaya, O. and Urek, B. (2016). A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain. Computers and Operations Research, 65, 93-103.

Mehrabad, M.S., Aazami, A. and Goli, A. (2017). A location-allocation model in the multi-level supply chain with multi-objective evolutionary approach. Journal of Industrial and Systems Engineering, 10(3), 140-160.

Mousavi, S., Alikar, N., Niaki, S. and Bahreininejad, A. (2015). Optimizing a location allocation-inventory problem in a two-echelon supply chain network: A modified fruit fly optimization algorithm. Computers and Ind. Eng., 87, 543-560.

Nobari, A., Kheirkhah, A. and Esmaeili, M. (2019). Considering chain to chain competition in forward and reverse logistics of a dynamic and integrated supply chain network design problem. Journal of Industrial and Systems Engineering,12(1), 147-166.

Puga, M. and Tancrez, J. (2016). A heuristic algorithm for solving large location–inventory problems with demand uncertainty. European Journal of Operational Research, 259(2), 413-423.

Sadati, A., Tavakkoli-Moghaddam, R., Naderi, B. and Mohammadi, M. (2019). A bi-objective model for a scheduling problem of unrelated parallel batch processing machines with fuzzy parameters by two fuzzy multi-objective meta-heuristics. Iranian Journal of Fuzzy Systems, 16(4), 21-40.

Sadeghi, J., Sadeghi, S. and Niaki, S.T.A. (2014). A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parameters. Computers and Operations Research, 41, 53-64.

Salehi, H., Tavakkoli-Moghaddam, R. and Nasiri, G. (2015). A multi-objective location-allocation problem with lateral transshipment between distribution centers. International J. of Logistics Systems and Management, 22(4), 464-482.

Sardou, I. and Ameli, M. (2016). A fuzzy-based non-dominated sorting genetic algorithm-II for joint energy and reserves market clearing. Soft Computing, 20(3), 1161-1177.

Soolaki, M. and Arkat, J. (2018). Supply chain design considering cellular structure and alternative processing routings. Journal of Industrial and Systems Engineering, 11(1), 97-112.

Srinivas, N. and Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), pp. 221-248.

Yadegari, E., Alem-Tabriz, A. and Zandieh, M. (2019). A Memetic Algorithm with a Novel Neighborhood Search and Modified Solution Representation for Closed-loop Supply Chain Network Design. Computers and Industrial Engineering, 128, 418-436.

Zahiri, B., Jula, P. and Tavakkoli-Moghaddam, R. (2018). Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products. Information Sciences, 423, 257-283.

Zhen, L., Wu, Y., Wang, S., Hu, Y. and Yi, W. (2018). Capacitated closed-loop supply chain network design under uncertainty. Advanced Engineering Informatics, 38, 306-315.

Zheng, X., Yin, M. and Zhang. Y. (2019). Integrated optimization of location, inventory and routing in supply chain network design. Transportation Research Part B, 121, 1-20.