Design and control of manufacturing systems: a discrete event optimisation methodology

Giulia Pedrielli, Andrea Matta, Arianna Alfieri, Mengyi Zhang

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Simulation optimisation has gained a great attention due to its success in the design of complex manufacturing systems. In this paper, we look at manufacturing as a special class of queueing systems and propose the Discrete Event Optimisation (DEO) methodology, which provides a formal way to develop integrated mathematical models for the simultaneous simulation and optimisation. In the case, the obtained model is a mixed integer linear programming model; the methodology provides a formal way to generate approximations of them. The analytical properties of DEO models are analysed for the first time in the framework of sample path optimisation and mathematical programming. The methodology represents a reference for the use of mathematical programming as a way to model simulation optimisation for queueing systems. The applicability of the DEO methodology to complex problems is showed using the task and buffer allocation problem in a production line.

Original languageEnglish (US)
Pages (from-to)1-22
Number of pages22
JournalInternational Journal of Production Research
DOIs
StateAccepted/In press - Dec 20 2017

Keywords

  • manufacturing systems
  • mathematical programming
  • optimisation
  • queueing systems
  • simulation

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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