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Tractable Risk Measures for the Selective Scheduling Problem with Sequence-Dependent Setup Times

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Operations Research and Enterprise Systems (ICORES 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1162))

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Abstract

Quantifying and minimizing the risk is a basic problem faced in a wide range of applications. Once the risk is explicitly quantified by a risk measure, the crucial and ambitious goal is to obtain risk-averse solutions, given the computational hurdle typically associated with optimization problems under risk. This is especially true for many difficult combinatorial problems, and notably for scheduling problems. This paper aims to present a few tractable risk measures for the selective scheduling problem with parallel identical machines and sequence-dependent setup times. We indicate how deterministic reformulations can be obtained when the distributional information is limited to first and second-order moment information for a broad class of risk measures. We propose an efficient heuristic for addressing the computational difficulty of the resulting models and we showcase the practical applicability of the proposed approach providing computational evidence on a set of benchmark instances.

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Authors and Affiliations

  1. Department of Mechanical, Energy and Management Engineering, University of Calabria, Rende, Italy

    M. E. Bruni & S. Khodaparasti

Authors
  1. M. E. Bruni
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  2. S. Khodaparasti
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Correspondence to M. E. Bruni .

Editor information

Editors and Affiliations

  1. INFORMS, Catonsville, MD, USA

    Greg H. Parlier

  2. Universidad Carlos III de Madrid, Madrid, Spain

    Federico Liberatore

  3. RMIT University, Melbourne, VIC, Australia

    Marc Demange

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Bruni, M.E., Khodaparasti, S. (2020). Tractable Risk Measures for the Selective Scheduling Problem with Sequence-Dependent Setup Times. In: Parlier, G., Liberatore, F., Demange, M. (eds) Operations Research and Enterprise Systems. ICORES 2019. Communications in Computer and Information Science, vol 1162. Springer, Cham. https://doi.org/10.1007/978-3-030-37584-3_4

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  • DOI: https://doi.org/10.1007/978-3-030-37584-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37583-6

  • Online ISBN: 978-3-030-37584-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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