接种疫苗
启发式
计算机科学
整数规划
运筹学
免疫
医学
工程类
病毒学
人工智能
免疫学
算法
抗原
作者
Akhilesh Kumar,Gaurav Kumar,Tanaya Vijay Ramane,Gurjot Singh
出处
期刊:Benchmarking: An International Journal
[Emerald (MCB UP)]
日期:2022-09-09
卷期号:30 (9): 3328-3356
被引量:10
标识
DOI:10.1108/bij-02-2022-0089
摘要
Purpose This study proposes strategies for vaccine center allocation for coronavirus disease (COVID) vaccine by determining the number of vaccination stations required for the vaccination drive, location of vaccination station, assignment of demand group to vaccination station, allocation of the scarce medical professional teams to station and number of optimal days a vaccination station to be functional in a week. Design/methodology/approach The authors propose a mixed-integer nonlinear programming model. However, to handle nonlinearity, the authors devise a heuristic and then propose a two-stage mixed-integer linear programming (MILP) formulation to optimize the allocation of vaccination centers or stations to demand groups in the first stage and the allocation of vaccination centers to cold storage links in the second stage. The first stage optimizes the cost and average distance traveled by people to reach the vaccination center, whereas the second stage optimizes the vaccine’s holding and storage and transportation cost by efficiently allocating cold storage links to the centers. Findings The model is studied for the real-world case of Chandigarh, India. The results obtained validate that the proposed approach can immensely help government agencies and policymaking body for a successful vaccination drive. The model tries to find a tradeoff between loss due to underutilized medical teams and the distance traveled by a demand group to get the vaccination. Originality/value To the best of our knowledge, there are hardly any studies on a vaccination program at such a scale due to sudden outbreaks such as Covid-19.
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