FCM based hybrid evolutionary computation approach for optimization power consumption by varying cars in EGCS

Ta Cheng Chen*, An-Chen Lee, Shih Lun Huang

*Corresponding author for this work

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Elevators are the essential transportation tools in high buildings so that elevator group control system (EGCS) is developed to dynamically layout the schedule of elevators in a group. In this study, a fuzzy cognitive map (FCM) based computation approach by using particle swarm optimization (PSO) has been applied for estimating the minimum required elevators in EGCS so as to minimize the electricity consumption with predefined service quality. In literature, most of the studies were mostly focused on the scheduling strategy in order to have more efficient elevator dispatching or energy saving. However, the minimum numbers of elevators should be activated to sustain the required service quality. In other words, the maximum average waiting time for customers should be less than the predefined length of time while the minimum numbers of elevators are working in EGCS. The experimental results show that the performance of the proposed FCM based approach is feasible to estimate the required power consumption and average waiting time so as to decide the optimal numbers of elevators in EGCS.

Original languageEnglish
Pages (from-to)5917-5924
Number of pages8
JournalApplied Mathematical Modelling
Volume39
Issue number19
DOIs
StatePublished - 1 Oct 2015

Keywords

  • Elevator group control system
  • Fuzzy cognitive map
  • Particle swarm optimization

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