首页|OPTIMIZING MULTILEVEL DECENTRALIZED PROBLEMS WITH A NOVEL APPLICATION OF GABASOV’S ADAPTIVE METHOD: HIGHLIGHTING APPLICATIONS IN COVID-19 VACCINE DISTRIBUTION
OPTIMIZING MULTILEVEL DECENTRALIZED PROBLEMS WITH A NOVEL APPLICATION OF GABASOV’S ADAPTIVE METHOD: HIGHLIGHTING APPLICATIONS IN COVID-19 VACCINE DISTRIBUTION
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NETL
NSTL
Edp Sciences S A
This paper presents a novel algorithm for solving multilevel monoobjective decentralized linear programming problems (ML-MO-DLPPs). Our approach represents a refined adaptation of Sinha and Sinha’s linear programming method, enhanced by the development of an interval reduction map, which dynamically refines the decision variable intervals based on the influence of decisions made at preceding levels. Each stage of the algorithm’s construction is thoroughly analyzed. The algorithm’s effectiveness is demonstrated through a comprehensive numerical example, highlighting its practical applicability to resource management problems. Particular emphasis is placed on its application to vaccination planning in long-term care facilities during the COVID-19 pandemic, addressing the optimization of resource allocation with a strong focus on the equitable distribution of COVID-19 vaccines.
Multilevel linear programmingGabasov’s adaptive methodsimplex methodresource allocationinterval reduction map
MUSTAPHA KACI
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Laboratory of Mathematics, Djillali Liabes University of Sidi Bel-Abbes, P.O. Box 89, 22000 Sidi Bel-Abbes, Algeria
NETL
NSTL
Edp Sciences S A
