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Differential Evolution Algorithm for Optimizing the Energy Usage of Vertical Transportation in an Elevator (VTE), Taking into Consideration Rush Hour Management and COVID-19 Prevention

Author

Listed:
  • Surajet Khonjun

    (Artificial Intelligence Optimization Laboratory, Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand)

  • Rapeepan Pitakaso

    (Artificial Intelligence Optimization Laboratory, Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand)

  • Kanchana Sethanan

    (Research Unit on System Modeling for Industry, Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Natthapong Nanthasamroeng

    (Artificial Intelligence Optimization Laboratory, Department of Engineering Technology, Faculty of Industrial Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand)

  • Kiatisak Pranet

    (Artificial Intelligence Optimization Laboratory, Department of Logistics Management, Faculty of Industrial Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand)

  • Chutchai Kaewta

    (Artificial Intelligence Optimization Laboratory, Department of Computer Science, Faculty of Computer Science, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand)

  • Ponglert Sangkaphet

    (Artificial Intelligence Optimization Laboratory, Department of Computer Science, Faculty of Computer Science, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand)

Abstract

This research aimed to develop an effective algorithm to minimize the energy use of vertical transportation in elevators while controlling the number of passengers in the elevator waiting area and the number of passengers in the elevator during rush hour, thus maintaining social distancing to limit the spread of COVID-19. A mobile application and Internet of Things (IoT) devices were used to electronically communicate between the elevator’s control system and the passengers. IoT devices were used to reduce the number of passengers waiting for an elevator and passengers’ waiting time, while the energy consumption of the lift was reduced by using passenger scheduling and elevator stopping strategies. Three mathematical models were formulated to represent the different strategies used to cause the elevator to stop. These strategies were normal (allowing the elevator to stop at every floor), odd–even (some elevators are allowed to stop at odd floors and others are allowed to stop at even floors of the building), and high–low (some elevators are allowed to stop at high floors and others are allowed to stop at low floors of the building). Lingo v.11 and the differential evolution algorithm (DE) were used to address the optimal scheduling of the passengers and the elevators. The computational results show that the odd–even strategy had a 13.91–23.71% lower energy consumption compared with the high–low and normal strategies. Furthermore, the use of DE consumed 6.67–7.99% less energy than the use of Lingo.v11. Finally, the combination of DE and the designed application reduced the number of waiting passengers, the average passenger waiting time, and the total energy consumption by 74.55%, 75.12%, and 45.01%, respectively.

Suggested Citation

  • Surajet Khonjun & Rapeepan Pitakaso & Kanchana Sethanan & Natthapong Nanthasamroeng & Kiatisak Pranet & Chutchai Kaewta & Ponglert Sangkaphet, 2022. "Differential Evolution Algorithm for Optimizing the Energy Usage of Vertical Transportation in an Elevator (VTE), Taking into Consideration Rush Hour Management and COVID-19 Prevention," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2581-:d:756675
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    References listed on IDEAS

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    1. Rojanee Homchalee & Rapeepan Pitakaso & Orawich Kumphon, 2023. "Minimizing Energy Usage and Makespan of Elevator Operation in Rush Hour Using Multi-Objective Variable Neighborhood Strategy Adaptive Search with a Mobile Application," Mathematics, MDPI, vol. 11(8), pages 1-22, April.
    2. Yanfang Dong & Caihang Liang & Lili Guo & Xiaoliang Cai & Weipeng Hu, 2023. "Life Cycle Carbon Dioxide Emissions and Sensitivity Analysis of Elevators," Sustainability, MDPI, vol. 15(17), pages 1-23, August.

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