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Optimization Strategies for Restricted Candidate Lists in Field Service Scheduling

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Intelligent Computational Optimization in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 366))

Abstract

Field service scheduling (FSS) is a large class of practical optimization problems combining features of the vehicle routing problem (VRP), scheduling problems and the general assignment problem (GAP). In some cases the problem reduces to well known variants of VRP, while other, more common circumstances give rise to a distinct set of optimization problems that have so far received very little attention in the literature. In this chapter we show how strategies for restricted candidate lists (RCL) – methods for pre-calculating and contextualizing the candidate list reduction procedures within a context of a generic optimization framework, can be used to efficiently solve a wide spectrum of FSS instances in a real-life industrial environment. A comparison of results obtained using a greedy randomized adaptive search procedure (GRASP) meta-heuristic with and without the use of certain RCL strategies is presented as it applies to specific variants of the problem.

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Author information

Authors and Affiliations

  1. University of Southampton, UK

    Marko Žerdin

  2. ClickSoftware Ltd., Israel

    Alexander Gibrekhterman, Uzi Zahavi & Dovi Yellin

Authors
  1. Marko Žerdin
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  2. Alexander Gibrekhterman
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  3. Uzi Zahavi
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  4. Dovi Yellin
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Editor information

Editors and Affiliations

  1. Dept. Artificial Intelligence, Kyushu Institute of Technology, 680-4 Kawazu, 820-8502, Izuka-Shi, Fukuoka, Japan

    Mario Köppen

  2. School of Engineering and Applied Science, Aston University, Aston Triangle, B4 7ET, Birmingham, U.K.

    Gerald Schaefer

  3. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

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Žerdin, M., Gibrekhterman, A., Zahavi, U., Yellin, D. (2011). Optimization Strategies for Restricted Candidate Lists in Field Service Scheduling. In: Köppen, M., Schaefer, G., Abraham, A. (eds) Intelligent Computational Optimization in Engineering. Studies in Computational Intelligence, vol 366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21705-0_3

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  • DOI: https://doi.org/10.1007/978-3-642-21705-0_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21704-3

  • Online ISBN: 978-3-642-21705-0

  • eBook Packages: EngineeringEngineering (R0)

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