Fuzzy multi-project rough-cut capacity planning

Malek Masmoudi, Elias W. Hans, Roel Leus, Alain Hait

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

This chapter studies the incorporation of uncertainty into multi-project rough-cut capacity planning. We use fuzzy sets to model uncertainties, adhering to the so-called possibilistic approach. We refer to the resulting proactive planning environment as Fuzzy Rough Cut Capacity Planning (FRCCP). Uncertain workloads are modeled as fuzzy numbers, and a Simulated Annealing (SA) procedure is proposed to solve the underlying optimization problem. The performance of the SA algorithm is studied in comparison to an existing deterministic Branch&Price algorithm and to an LP-based heuristic, which are both generalized to handle fuzzy parameters. Computational experiments are performed on instances from a shipyard maintenance centre, in which we examine the computational performance of the algorithms and we also study to what extent multi-objective optimization techniques can be applied.
Original languageEnglish
Title of host publicationSequencing and scheduling with inaccurate
EditorsYuri N. Sotskov, Frank Werner
PublisherNova Science
Pages-
Number of pages16
ISBN (Print)978-1-61209-579-0
Publication statusPublished - 2014

Publication series

Name
PublisherNova Science Publishers

Keywords

  • METIS-297231
  • IR-86956

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  • Cite this

    Masmoudi, M., Hans, E. W., Leus, R., & Hait, A. (2014). Fuzzy multi-project rough-cut capacity planning. In Y. N. Sotskov, & F. Werner (Eds.), Sequencing and scheduling with inaccurate (pp. -). Nova Science.