Reseach Article
A Hybrid Approach for Optimizing Resource Allocation Efficiency by Integrating the Hungarian Algorithm and Linear Programming
| Journal of Advanced Artificial Intelligence |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 1 |
| Year of Publication: 2024 |
| Authors: Johnson Tunde Fakoya, Yetunde Esther Ogunwale, Micheal Olalekan Ajinaja |
10.5120/jaai202404
|
Johnson Tunde Fakoya, Yetunde Esther Ogunwale, Micheal Olalekan Ajinaja . A Hybrid Approach for Optimizing Resource Allocation Efficiency by Integrating the Hungarian Algorithm and Linear Programming. Journal of Advanced Artificial Intelligence. 1, 1 ( Oct 2024), 28-32. DOI=10.5120/jaai202404
Abstract
Efficient resource allocation stands as a cornerstone for numerous sectors, from logistics and transportation to project management and scheduling. The Hungarian Algorithm and Linear Programming (LP) have individually demonstrated prowess in solving resource allocation problems. However, each method bears its limitations when confronted with complex scenarios. This paper presents a novel hybrid approach that integrates the Hungarian Algorithm and LP to capitalize on their respective strengths while mitigating their weaknesses. The Hungarian Algorithm excels in assigning optimal task-worker pairs in bipartite graphs, offering a polynomial-time solution for matching problems. On the other hand, Linear Programming provides a versatile framework for optimization but may face challenges in handling discrete assignments and combinatorial problems. The proposed hybrid approach capitalizes on the efficiency of the Hungarian Algorithm in generating initial feasible solutions and leverages LP to refine and optimize these solutions further. By embedding the Hungarian Algorithm within the LP formulation, the hybrid method inherits its ability to handle discrete variables and combinatorial constraints while harnessing LP's optimization capabilities to enhance resource allocation efficiency. Through a series of computational experiments and case studies, the effectiveness and scalability of the hybrid approach are demonstrated across diverse resource allocation scenarios. Results indicate significant improvements in solution quality and computational efficiency compared to traditional methods. Additionally, sensitivity analyses highlight the adaptability of the hybrid approach to varying problem parameters and constraints. This hybrid approach not only addresses the limitations of individual methods but also unlocks synergies that propel optimization performance to new heights.
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