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Structural Property and Meta-heuristic for the Flow Shop Scheduling Problem

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Computational Intelligence in Flow Shop and Job Shop Scheduling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 230))

Summary

According to the No Free Lunch Theorem, all algorithms equal to the randomly blind search if no problem information is known. Therefore, it is very important to study the problem properties (especially structural properties) and introduce them into algorithms so as to improve the algorithm performance (both solution quality and computational effort). For the flow shop scheduling problem (FSP) with makespan criterion, structural properties are wildly used in the existing literature, but there is no systematic review on it. This chapter surveys the existing structural properties, which are divided into two types: neighborhood properties (such as the famous block property) and solution space properties (such as the big-valley phenomenon).

This chapter also shows how to introduce the structural properties into meta-heuristic algorithms like tabu search (TS). By comparing the performance of structural property based TS with the simple version of TS, it is shown how much the meta-heuristic algorithm can benefit from the structural properties.

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Author information

Authors and Affiliations

  1. Shanghai Baosight Software Co., Ltd., Shanghai, 201203, China

    Feng Jin

  2. Department of Automation, Tsinghua University, Beijing, 100084, China

    Feng Jin, Shiji Song & Cheng Wu

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  1. Feng Jin
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  2. Shiji Song
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  3. Cheng Wu
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Editors and Affiliations

  1. Mathematics & Computer Science Department, University of Missouri, 63121, Saint Louis, MO, USA

    Uday K. Chakraborty

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Jin, F., Song, S., Wu, C. (2009). Structural Property and Meta-heuristic for the Flow Shop Scheduling Problem. In: Chakraborty, U.K. (eds) Computational Intelligence in Flow Shop and Job Shop Scheduling. Studies in Computational Intelligence, vol 230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02836-6_1

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  • DOI: https://doi.org/10.1007/978-3-642-02836-6_1

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