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Minimizing Energy Usage and Makespan of Elevator Operation in Rush Hour Using Multi-Objective Variable Neighborhood Strategy Adaptive Search with a Mobile Application

Author

Listed:
  • Rojanee Homchalee

    (Applied Statistics Research Unit, Department of Mathematics, Mahasarakham University, Maha Sarakham 44150, Thailand)

  • Rapeepan Pitakaso

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

  • Orawich Kumphon

    (Applied Statistics Research Unit, Department of Mathematics, Mahasarakham University, Maha Sarakham 44150, Thailand)

Abstract

The purpose of this study is to address two major issues: (1) the spread of epidemics such as COVID-19 due to long waiting times caused by a large number of waiting for customers, and (2) excessive energy consumption resulting from the elevator patterns used by various customers. The first issue is addressed through the development of a mobile application, while the second issue is tackled by implementing two strategies: (1) determining optimal stopping strategies for elevators based on registered passengers and (2) assigning passengers to elevators in a way that minimizes the number of floors the elevators need to stop at. The mobile application serves as an input parameter for the optimization toolbox, which employs the exact method and multi-objective variable neighborhood strategy adaptive search (M-VaNSAS) to find the optimal plan for passenger assignment and elevator scheduling. The proposed method, which adopts an even-odd floor strategy, outperforms the currently practiced procedure and leads to a 42.44% reduction in waiting time and a 29.61% reduction in energy consumption. Computational results confirmed the effectiveness of the proposed approach.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1948-:d:1128733
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    References listed on IDEAS

    as
    1. Yongming Zhang & Zhe Yan & Li Li & Jiawei Yao, 2018. "A Hybrid Building Power Distribution System in Consideration of Supply and Demand-Side: A Short Overview and a Case Study," Energies, MDPI, vol. 11(11), pages 1-19, November.
    2. 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.
    3. Lutfi Al-Sharif & Ahmad Tayseer Hammoudeh, 2014. "Evaluating the elevator round trip time for multiple entrances and incoming traffic conditions using Markov chain Monte Carlo," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 18(1), pages 51-64.
    4. Chuleeporn Kusoncum & Kanchana Sethanan & Rapeepan Pitakaso & Richard F. Hartl, 2021. "Heuristics with novel approaches for cyclical multiple parallel machine scheduling in sugarcane unloading systems," International Journal of Production Research, Taylor & Francis Journals, vol. 59(8), pages 2479-2497, April.
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