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Optimizing Resource Allocation with Machine Learning

EasyChair Preprint no. 12914

14 pagesDate: April 5, 2024


Efficient resource allocation is a critical challenge in various domains, including transportation, finance, healthcare, and manufacturing. Traditional resource allocation methods often rely on manual heuristics and are limited in their ability to adapt to dynamic environments and complex decision-making scenarios. In recent years, machine learning techniques have emerged as powerful tools for optimizing resource allocation, leveraging their ability to learn patterns from data and make intelligent decisions.

This abstract provides an overview of the application of machine learning in optimizing resource allocation. We discuss key challenges in resource allocation and highlight how machine learning algorithms can address these challenges. Specifically, we explore three key aspects: demand prediction, allocation optimization, and real-time adaptation.

Firstly, accurate demand prediction is crucial for efficient resource allocation. Machine learning algorithms can analyze historical data and extract patterns to forecast future demand, considering various factors such as seasonality, trends, and external events. By accurately predicting demand, organizations can strategically allocate resources to meet anticipated needs, reducing wastage and improving operational efficiency.

Secondly, allocation optimization techniques utilize machine learning algorithms to dynamically assign resources based on various constraints and objectives. These algorithms can consider multiple factors, such as resource availability, cost, time constraints, and service-level agreements. By formulating resource allocation as an optimization problem and leveraging machine learning techniques, organizations can find optimal solutions that maximize resource utilization while minimizing costs and meeting specific performance targets.

Keyphrases: efficiency, Improving, operational

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Favour Olaoye and Kaledio Potter and Lucas Doris},
  title = {Optimizing Resource Allocation with Machine Learning},
  howpublished = {EasyChair Preprint no. 12914},

  year = {EasyChair, 2024}}
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