On optimization of stochastic max–min-plus-scaling systems: An approximation approach

Samira S. Farahani, Ton van den Boom, Bart De Schutter

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

A large class of discrete-event and hybrid systems can be described by a max–min-plus-scaling (MMPS) model, i.e., a model in which the main operations are maximization, minimization, addition, and scalar multiplication. Accordingly, optimization of MMPS systems appears in different problems defined for discrete-event and hybrid systems. For a stochastic MMPS system, this optimization problem is computationally highly demanding as often numerical integration has to be used to compute the objective function. The aim of this paper is to decrease such computational complexity by applying an approximation method that is based on the moments of a random variable and that can be computed analytically.

Original languageEnglish
Pages (from-to)20-27
JournalAutomatica
Volume83
DOIs
Publication statusPublished - 2017

Keywords

  • Discrete event systems
  • Max–min-plus-scaling systems
  • Moments
  • Optimization
  • Stochastic disturbance

Fingerprint Dive into the research topics of 'On optimization of stochastic max–min-plus-scaling systems: An approximation approach'. Together they form a unique fingerprint.

Cite this