An Online Learning-Based mACO Approach for Hot Rolling Scheduling Problems Involving Dynamic Order Arrivals

The hot rolling scheduling problem involving dynamic order arrivals (HRSP-DOA) is pivotal in promoting Industry 4.0 initiatives in steel companies. This paper introduces a novel multi-objective ant colony optimization (mACO) algorithm enhanced with online learning strategies to address the HRSP-DOA....

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Bibliographic Details
Published in:IEEE transactions on automation science and engineering 2025, Vol.22, p.12322-12335
Main Authors: Jiang, Sheng-Long, Liu, Qie, Cao, Ling-Ling, Sun, Liangliang
Format: Article
Language:English
Subjects:
ACO
Costs
Dynamic scheduling
Genetic algorithms
Heuristic algorithms
hot rolling
Job shop scheduling
Multi-objective
online learning
Slabs
Steel
Uncertainty
Vehicle dynamics
Vehicle routing
VRP
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