research-article

A decomposition-based two-stage online scheduling approach and its integrated system in the hybrid flow shop of steel industry

Authors:

Sheng-Long Jiang,

Yong MaAuthors Info & Claims

Volume 213, Issue PC

https://doi.org/10.1016/j.eswa.2022.119200

Published: 01 March 2023 Publication History

Highlights

•

Dynamic scheduling is a challenge for implementing smart manufacturing in the steel industry.

•

A novel online scheduling model based on two-stage uncertain optimization is formulated.

•

A decomposition-based algorithm combined MILP and CP is presented.

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An integrated scheduling system is developed and implemented in a real-world steel plant.

Abstract

Steelmaking-continuous casting (SCC) is one of the most critical building blocks in the modern steel industry. Many random events occur in the real-world SCC production system. In this paper, we propose a two-stage online scheduling policy that protects the baseline schedule by the slacks provided by intra-flow times and casting speeds. The main task of the two-stage online scheduling model is: (1) to make “here-and-now” decisions for minimizing economic costs and penalties caused by constraint violations; (2) make “wait-and-see” decisions for online scheduling. Afterward, we propose a decomposition-based optimization algorithm that divides the online scheduling problem into a master problem (MP) to seek partial solutions at the last processing stage and a slave problem (SP) to check optimal solutions for upstream processing stages. Then, we employ the IBM ILOG CPLEX to solve MP and use the constraint programming (CP) optimizer to solve SP. Sensitivity analyses and algorithm comparisons are conducted on a set of well-synthetic and realistic instances to validate the proposed model and algorithm. The results show that the proposed online scheduling model and algorithm can solve realistic industrial case studies. Finally, we also develop a scheduling system integrating the proposed model and algorithm.

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Cited By

Li PXue QZhang ZChen JZhou D(2023)Multi-objective energy-efficient hybrid flow shop scheduling using Q-learning and GVNS driven NSGA-IIComputers and Operations Research10.1016/j.cor.2023.106360159:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.cor.2023.106360

Index Terms

A decomposition-based two-stage online scheduling approach and its integrated system in the hybrid flow shop of steel industry
1. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Scheduling algorithms
    2. Online algorithms
      1. Online learning algorithms
        Scheduling algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning
        Sequential decision making

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 213, Issue PC

Mar 2023

1402 pages

ISSN:0957-4174

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2023

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Cited By

Li PXue QZhang ZChen JZhou D(2023)Multi-objective energy-efficient hybrid flow shop scheduling using Q-learning and GVNS driven NSGA-IIComputers and Operations Research10.1016/j.cor.2023.106360159:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.cor.2023.106360

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