Investigation of Optimization Techniques on the Elevator Dispatching Problem

2022-02-26 · Shaher Ahmed, Mohamed Shekha, Suhaila Skran, Abdelrahman Bassyouny

In the elevator industry, reducing passenger journey time in an elevator system is a major aim. The key obstacle to optimising elevator dispatching is the unpredictable traffic flow of passengers. To address this difficulty, two main features must be optimised: waiting time and journey time. To address the problem in real time, several strategies are employed, including Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization Algorithm (PSO), and Whale Optimization Algorithm (WOA). This research article compares the algorithms discussed above. To investigate the functioning of the algorithms for visualisation and insight, a case study was created. In order to discover the optimum algorithm for the elevator dispatching problem, performance indices such as average and ideal fitness value are generated in 5 runs to compare the outcomes of the methods. The goal of this study is to compute a dispatching scheme, which is the result of the algorithms, in order to lower the average trip time for all passengers. This study builds on previous studies that recommended ways to reduce waiting time. The proposed technique reduces average wait time, improves lift efficiency, and improves customer experience.

📄 PDF Abstract BibTeX arXiv:2202.13092

Code (0)

등록된 구현이 없습니다.

Application of Deep Q Learning with Simulation Results for Elevator Optimization

2022-09-30 · Zheng Cao, Raymond Guo, Caesar M. Tuguinay, Mark Pock 외

This paper presents a methodology for combining programming and mathematics to optimize elevator wait times. Based on simulated user data generated according to the canonical three-peak model of elevator traffic, we firs…

Q-Learning

Elevator-LIO: Robust LiDAR-Inertial Odometry for Multi-Floor Navigation under Elevator-Induced Non-Inertial Motion

2026-05-23 · Yifan Zhang, Yudong Huang, Yuchong Zhang, Changze Li 외 arxiv

This paper presents Elevator-LIO, a LiDAR-inertial odometry framework designed to achieve continuous robot localization during elevator travel, thereby supporting cross-floor robotic tasks. To address the state-estimatio…

Conflict-Based Search for Multi-Agent Path Finding with Elevators

2026-02-24 · Haitong He, Xuemian Wu, Shizhe Zhao, Zhongqiang Ren arxiv

This paper investigates a problem called Multi-Agent Path Finding with Elevators (MAPF-E), which seeks conflict-free paths for multiple agents from their start to goal locations that may locate on different floors, and t…

Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review

2022-05-07 · Ziqing Zhu, Ze Hu, Ka Wing Chan, Siqi Bu 외

The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms. The optimal bidding strategy and disp…

Computational Efficiency Decision Making reinforcement-learning Reinforcement Learning (RL)

Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching

2020-11-09 · Hamed Khorasgani, HaiYan Wang, Chetan Gupta

Dynamic dispatching aims to smartly allocate the right resources to the right place at the right time. Dynamic dispatching is one of the core problems for operations optimization in the mining industry. Theoretically, de…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)