Machine Learning for Labor Optimization: A Systematic Review of Strategies in Healthcare and Logistics

Clicks: 7
ID: 336661
2025
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #509 of 953 articles by views in Pakistan Social Sciences Review

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 953 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
This study systematically reviews machine learning (ML) based strategies for optimizing labor resources in healthcare and logistics, with particular emphasis on outcome and domain transferability. Demand and constraints of resources vary in healthcare and fulfillment settings over time and hence make those inefficient. Predictive (supervised learning) and adaptive (reinforcement learning) tools of ML can be used to enhance labor allocation and improve operational efficiency. A systematic review covering the period from 2015 to 2024 was carried out according to PRISMA rules and details regarding labor optimization in healthcare and fulfillment centers through ML applications. Using supervised learning and reinforcement learning techniques, overall labor utilization could be enhanced by as much as a 30% reduction in wait and processing times and less costs in both healthcare and logistics industries. The transferability of an intervention across sectors appeared to be effective: the intervention was introduced successfully in one sector and boosted efficiency and quality on the other. The review suggests the adoption of applied ML for optimization in labor services across different sectors coupled with cross-industry collaboration and sharing of data between the industries along with more research into transferable models and ethical practices for maximizing workforce efficiency.
Reference Key
imported_1793230982_6ae288861c747 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ashish Shiwlani
Are you one of these authors? Claim your profile
Journal Pakistan Social Sciences Review
Year 2025
DOI
10.35484/pssr.2025(9-I)48
URL
https://ojs.pssr.org.pk/journal/article/view/947
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.