research-article

A knowledge-driven constructive heuristic algorithm for the distributed assembly blocking flow shop scheduling problem

Authors:

Xun LiAuthors Info & Claims

Volume 202, Issue C

https://doi.org/10.1016/j.eswa.2022.117269

Published: 15 September 2022 Publication History

Highlights

•

The mixed integer linear programming model (MILP) of DABFSP is designed.

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A knowledge-driven constructive heuristic (KDH) algorithm is proposed to address DABFSP.

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Three different kinds of neighborhood knowledge are refined.

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The quantitative representation of the knowledge is extracted from the feature of DABFSP.

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The results of statistical analysis show the performance of the KDH algorithm.

Abstract

The distributed flow shop scheduling problem (DFSP) has become widespread due to the increasing advantages of multi-factories manufacturing in recent years. The distributed assembly blocking flow shop scheduling problem (DABFSP), which aims at minimizing the maximum assembly completion time, is considered in this paper. A knowledge-driven constructive heuristic (KDH) algorithm is proposed to address the above problem. Three different kinds of neighborhood knowledge are refined by analyzing the characteristic of the problem and quantified to design the KDH algorithm. The jobs belonging to the same production are assigned to the different factories to advance the starting time of assembly, which is quantified as knowledge 1. The assembly sequence of the product is determined according to the processing sequence of the job in the processing factory, which is defined as knowledge 2. The jobs belonging to the same product are distributed together in each processing factory, which is quantified as knowledge 3. The performance of the KDH algorithm is verified on the benchmark instance, which comprises 900 small-scale instances and 810 large-scale instances. The experimental results demonstrate that the KDH algorithm is superior to state-of-the-art algorithms in the aspect of addressing DABFSP.

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Cited By

Zhang BChe AWang Y(2024)Grid-based artificial bee colony algorithm for multi-objective job shop scheduling with manual loading and unloading tasksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123011245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123011
Mraihi TDriss OEL-Haouzi H(2024)Distributed Permutation Flow Shop Scheduling Problem with Worker flexibilityExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121947238:PCOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121947
Zhang YGu X(2023)A biogeography-based optimization algorithm with modified migration operator for large-scale distributed scheduling with transportation timeExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120732231:COnline publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120732
Show More Cited By

Index Terms

A knowledge-driven constructive heuristic algorithm for the distributed assembly blocking flow shop scheduling problem
1. Computing methodologies
2. 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

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 202, Issue C

Sep 2022

1548 pages

ISSN:0957-4174

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 15 September 2022

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Cited By

Zhang BChe AWang Y(2024)Grid-based artificial bee colony algorithm for multi-objective job shop scheduling with manual loading and unloading tasksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123011245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123011
Mraihi TDriss OEL-Haouzi H(2024)Distributed Permutation Flow Shop Scheduling Problem with Worker flexibilityExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121947238:PCOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121947
Zhang YGu X(2023)A biogeography-based optimization algorithm with modified migration operator for large-scale distributed scheduling with transportation timeExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120732231:COnline publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120732
Wang JTang HLei D(2023)A Q-learning artificial bee colony for distributed assembly flow shop scheduling with factory eligibility, transportation capacity and setup timeEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106230123:PAOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106230

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