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

Rojanee Homchalee, Rapeepan Pitakaso and Orawich Kumphon ()
Additional contact information
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

Mathematics, 2023, vol. 11, issue 8, 1-22

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.

Keywords: vertical transportation system; optimization; multi-objectives variable neighborhood strategy adaptive search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/8/1948/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/8/1948/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:8:p:1948-:d:1128733

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().