Elevator Optimization With Real-World Constraint: Weight-Aware Algorithm

Authors

  • Valluri Keerthi Ram Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India
  • Kamma Ajay Nageswara Rao Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India
  • Gokavarapu Abhay Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India
  • Mamatha T M Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India

Keywords:

Elevator Control Systems, Real-Time Optimisation, Heuristic Algorithms, Passenger Flow Management, Energy Efficiency, Load Balancing

Abstract

In the present day, efficient elevator management is critical for modern high-rise buildings, particularly in reducing energy consumption and wait times while enhancing passenger safety. Our study proposes a weight-aware optimisation algorithm using heuristic methods like greedy search and nearest neighbour. By incorporating a centralised control panel and real-time weight management, the system reduces operational complexity, minimises overload risks, and ensures balanced load distribution. These enhancements lead to lower energy consumption, lower maintenance costs, and improved system reliability. The simulation results highlight significant reductions in waiting time and energy consumption, aligning the solution with sustainable building practices. This research offers a scalable framework for smarter, more efficient elevator systems in urban infrastructure.

DOI: https://doi.org/10.24321/2582.5607.202603

References

Uimonen, S., Tukia, T., Doghi, C., Siikonen, M.L., Lehtonen, M.: High-resolution model- ling of elevator power consumption. Journal of Building Engineering 18, (2018).

Tukia, T., Uimonen, S., Siikonen, M.L., Donghi, C., Lehtonen, M.: Modelling the aggre- gated power consumption of elevators – the New York city case study. Applied Energy 2019

Published

2026-05-14