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Recent Advances in Evolutionary Algorithms for Job Shop Scheduling

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Automated Scheduling and Planning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 505))

Abstract

Scheduling decides the order of tasks to efficiently use resources considering criteria such as minimization of the number of late tasks, minimization of the completion time, minimization of the idle times of the machines, etc. Approaches for solving scheduling problems can be divided into three broad groups: (a) exact methods that produce exact optimal solutions, (b) approximation methods that find high quality near optimal, and (c) hybrid methods based on the first two. Approximate methods can be easily combined with other types of heuristics and can be applied to a wide range of problems.

In the category of approximation algorithms, evolutionary algorithms (EAs) are very promising tools for the problems with dynamic characteristics, contradicting multi-objectives and highly nonlinear constraints. For EAs to be effective and efficient for a combinatorial optimisation problem like scheduling, the structure of an EA needs to be designed carefully to exploit the problem structures. An appropriate representation for the problem and the type of search operators suitable for the representation should be studied because they directly affect the search efficiency of the EA.

In this chapter, our focus will be on EAs for job shop scheduling problems (JSP). First, JSP will be formulated as an optimization problem and approaches for JSP will be given briefly. Second, EAs will be introduced and the key issues in the application of EAs for JSP will be emphasized. Third, various representations used in EAs for handling JSP will be described and advantages and drawbacks of different representations will be described based on the results from the literature. Forth, crossover and mutation operators designed for particular representations will be illustrated and their strength and limitations will be discussed. Almost all successful applications of evolutionary combinatorial optimisation include some kind of hybrid algorithms, where both EAs and local search were used. The seventh topic of this chapter is devoted to local search strategies which are frequently integrated into EAs.

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Author information

Authors and Affiliations

  1. Dept. of Computer Engineering, Erciyes University, 38039, Melikgazi, Kayseri, Turkey

    Bahriye Akay

  2. Center of Excellence for Research in Computational Intelligence and Applications, School of Computer Science, University of Birmingham, Birmingham, B15 2TT, U.K

    Xin Yao

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  1. Bahriye Akay
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  2. Xin Yao
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Correspondence to Bahriye Akay .

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Editors and Affiliations

  1. , Elektrik-Elektronik Fakultesi, Istanbul Teknik Universitesi, Istanbul, 34469, Turkey

    A. Sima Uyar

  2. , School of Computer Science, University of Nottingham, Wollaton Road, Nottingham, NG8 1BB, United Kingdom

    Ender Ozcan

  3. , Centre for Emergent Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, United Kingdom

    Neil Urquhart

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Akay, B., Yao, X. (2013). Recent Advances in Evolutionary Algorithms for Job Shop Scheduling. In: Uyar, A., Ozcan, E., Urquhart, N. (eds) Automated Scheduling and Planning. Studies in Computational Intelligence, vol 505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39304-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-39304-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39303-7

  • Online ISBN: 978-3-642-39304-4

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