Your browser doesn't support javascript.
Optimal Covid-19 vaccine stations location and allocation strategies
Benchmarking: An International Journal ; 2022.
Article in English | Web of Science | ID: covidwho-2032211
ABSTRACT
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.
Keywords

Full text: Available Collection: Databases of international organizations Database: Web of Science Topics: Vaccines Language: English Journal: Benchmarking: An International Journal Year: 2022 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Databases of international organizations Database: Web of Science Topics: Vaccines Language: English Journal: Benchmarking: An International Journal Year: 2022 Document Type: Article