A decision support system for scheduling a vaccination campaign during a pandemic emergency: The COVID-19 case

Fabbri, C; Ghedini, Pierfrancesco; Leonessi, Marco; Malaguti, Enrico

doi:10.1016/j.cie.2023.109068

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Its objective is to maximise the vaccination profit, which consists of successful vaccinations with minimum delays, unsatisfied schedules, and wastage, while minimising the total cost, which includes facility costs, drone deployment costs, vaccination costs, and other inventory costs. Fabbri et al [54] similarly used the term for determining the calendar for stock availability and forecast, vaccine types for each target cohort, and sub-district demands while assuring that vaccination sites are within the desired coverage. The objective of their model is to serve all the demand at the shortest time possible.…”

Section: Vaccination Schedulingmentioning

confidence: 99%

“…They however included a minimum and maximum number of weeks for the dosing interval. Fabbri et al [54] also left out the single-dose vaccine in their vaccine scheduling model but included mixed vaccination wherein patients get a different vaccine in the second dose from that given in the first dose. Wang et al [37] also only took the multiple doses into account in their drone-based robust location and scheduling model.…”

Section: Multiple Vaccinesmentioning

confidence: 99%

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Multiperiod vaccination facility planning

Tacadao,

Simonis,

Quesada

et al. 2024

Preprint

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We propose a decision support tool, based on Mixed Integer Programming, to help with the planning of national vaccination campaigns, and test it on data and scenarios obtained from the publicly available information about the Irish COVID-19 vaccination program. We show that a large variety of constraints can be included in the tool, as well as combinations of different objective functions for the different stakeholders. The tests show that the multi-period/multi-vaccine problem can be effectively solved by the given model, while allowing to consider the impact of potential changes in the input data. We also present a number of specific visualizations that help the user understand the plan created by the application.

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Section: Vaccination Schedulingmentioning

confidence: 99%

Section: Multiple Vaccinesmentioning

confidence: 99%

Multiperiod vaccination facility planning

Tacadao,

Simonis,

Quesada

et al. 2024

Preprint

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show abstract

“…Finally, the OR tools have been shown to be essential for the design of a vaccination campaign in a recent paper by Fabbri et al (2023), in which a fast multiscenario decision support system for campaign planning is devised. The tool considers the different types of vaccines and the corresponding target population, and it has been tested on a case study from Northern Italy.…”

Section: Recent Contributions Of Operations Research In Healthcare Ma...mentioning

confidence: 99%

Hybridizing a matheuristic with ALNS for the optimal collection and delivery of medical specimens

Ferone,

Festa,

Fugaro

et al. 2023

Int Trans Operational Res

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The past few years of the COVID‐19 pandemic outbreak have shown that optimal management of medical specimens is a key aspect of healthcare logistics, both for addressing the delivery of perishable items such as vaccines, and for ensuring the timely analysis of swabs and samples. Accordingly, recent optimization literature described the problem of optimal collection and delivery of medical specimens, modeled as a multitrip vehicle routing problem with time windows and a completion time objective function. Aiming to achieve good‐quality solutions in short computational times, this work describes a hybrid approach, combining a matheuristic construction phase with an adaptive large neighborhood search (ALNS). Our matheuristic relies on a clustering algorithm to yield subsets of the medical specimens that are optimally served by single vehicles of the fleet. The solutions of the matheuristic phase serve as starting points for the ALNS intensification phase. Extensive experimentation on both new and established benchmark problem instances shows that our hybrid method is able to match the optimality of the state of the art on small instances and outperforms the existing exact method by one order of magnitude on larger problems.

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