Abstract
The Covid-19 vaccination process faced many problems; “who gets vaccinated” and “where is the nearest vaccination center” are two questions that entail a multi-Criteria problem. Such problems arise in the most inopportune time of the Covid-19 pandemic. The matching process depended on many variables: the current health state of the person, insurance company, location, and residency. Such variables are not static and differ from one country to another. Hence, this paper presents a new Multi-Criteria Hybrid Matching Method (MCHMM). MCHMM is a hybrid matching method extended from three multi-Criteria decision-making methods. MCHMM uses the strengths of three methods: Analytic Hierarchy Process (AHP), and Gale-Shapley algorithm. To fully automate the decision-making process of matching the MCHMM, which relies on CRITIC to develop the weight of Criteria, the weights are then used in AHP to develop the preference list of the Gale-Shapley algorithm (GS). Then the GS algorithm will produce the matched sets, eliminating the human factor and interference like favoritism in deciding. Such elimination is essential to reduce long waiting queues, proclivities, tendencies, biases, preconceptions, prejudices, and predispositions. Hence, reason and logic will guide the decision process, ridding it of impurities. MCHMM will match the person with the vaccination center most suited to her/his case, not based on distance only but also based on the Criteria s/he chooses. MCHMM is a unique hybrid algorithm that solves a multi-Criteria problem taking advantage of other algorithms. Furthermore, MCHMM can be generalized and can be used in any similar situation. Keywords : Covid-19, Gale-Shapley Algorithm, Analytic Hierarchy Process, Criteria Importance through Inter Criteria Correlation, Multi-Criteria Hybrid Matching Method DOI： https://doi.org/10.35741/issn.0258-2724.58.3.29
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