Industrial Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/45

Browse

Browsing by Rights "Attribution 4.0 International"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ArticlePublicationOpen Access
    A mathematical model for equitable in-country COVID-19 vaccine allocation
    (Taylor and Francis, 2022) Koyuncu, Burcu Balçık; Yücesoy, Ecem; Akça, Berna; Karakaya, Sırma; Kaplan, Asena Ayse; Baharmand, H.; Sgarbossa, F.; Industrial Engineering; KOYUNCU, Burcu Balçık; Yücesoy, Ecem; Akça, Berna; Karakaya, Sırma; Kaplan, Asena Ayse
    Given the scarcity of COVID-19 vaccines, equitable (fair) allocation of limited vaccines across the main administrative units of a country (e.g. municipalities) has been an important concern for public health authorities worldwide. In this study, we address the equitable allocation of the COVID-19 vaccines inside countries by developing a novel, evidence-based mathematical model that accounts for multiple priority groups (e.g. elderly, healthcare workers), multiple vaccine types, and regional characteristics (e.g. storage capacities, infection risk levels). Our research contributes to the literature by developing and validating a model that proposes equitable vaccine allocation alternatives in a very short time by (a) minimising deviations from the so-called ‘fair coverage’ levels that are computed based on weighted pro-rata rations, and (b) imposing minimum coverage thresholds to control the allocation of vaccines to higher priority groups and regions. To describe the merits of our model, we provide several equity and effectiveness metrics, and present insights on different allocation policies. We compare our methodology with similar models in the literature and show its better performance in achieving equity. To illustrate the performance of our model in practice, we perform a comprehensive numerical study based on actual data corresponding to the early vaccination period in Turkey.
  • Thumbnail Image
    ArticlePublicationOpen Access
    Robust reformulations of ambiguous chance constraints with discrete probability distributions
    (Balikesir University, 2019) Yanıkoğlu, İhsan; Industrial Engineering; YANIKOĞLU, Ihsan
    This paper proposes robust reformulations of ambiguous chance constraints when the underlying family of distributions is discrete and supported in a so-called ``p-box'' or ``p-ellipsoidal'' uncertainty set. Using the robust optimization paradigm, the deterministic counterparts of the ambiguous chance constraints are reformulated as mixed-integer programming problems which can be tackled by commercial solvers for moderate sized instances. For larger sized instances, we propose a safe approximation algorithm that is computationally efficient and yields high quality solutions. The associated approach and the algorithm can be easily extended to joint chance constraints, nonlinear inequalities, and dependent data without introducing additional mathematical optimization complexity to that of the original robust reformulation. In numerical experiments, we first present our approach over a toy-sized chance constrained knapsack problem. Then, we compare optimality and computational performances of the safe approximation algorithm with those of the exact and the randomized approaches for larger sized instances via Monte Carlo simulation.