A smart elevator scheduler that considers dynamic changes of energy cost and user traffic

Sungyong Ahn, Soyoon Lee, Hyokyung Bahn

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


With the recent advances in energy-aware building technologies, the electricity usage of a smart building is detected every moment and might have different costs at each time slot of a day. This article presents a new elevator scheduling algorithm for a smart building that considers the dynamic changes of electricity price and passenger traffic. The goal of our algorithm is to minimize the electricity charge without increasing passengers' waiting time. To this end, we use a control parameter to increase the number of working elevator cars when the passenger traffic is heavy or the electricity price becomes low. In contrast, when the electricity price becomes high (i.e., peak time), the system adjusts the control parameter to reduce the number of working elevator cars. This is not a simple issue as the two goals we pursue sometimes conflict. Thus, we use an optimization technique based on genetic algorithms in the design of our scheduler. To evaluate the proposed elevator scheduling system, we conduct experiments under synthetic and realistic workload conditions. The results show that the proposed elevator scheduling system significantly saves the electricity charge of the conventional elevator scheduling system. Specifically, the average reduction in the electricity charge is 68.3% without sacrificing passengers' waiting time.

Original languageEnglish
Pages (from-to)187-202
Number of pages16
JournalIntegrated Computer-Aided Engineering
Issue number2
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© 2017 - IOS Press and the author(s).


  • Elevator scheduling
  • electricity price
  • genetic algorithm
  • group elevator
  • smart building


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