Investigation of Optimization Techniques on the Elevator Dispatching Problem

TL;DR

This paper compares several optimization algorithms to improve elevator dispatching by reducing passenger wait and journey times, using a case study to identify the most effective method.

Contribution

It evaluates and compares four optimization algorithms for real-time elevator dispatching, identifying the most effective approach for minimizing passenger wait times.

Findings

The algorithms successfully reduce average passenger wait times.

Performance indices indicate the most efficient algorithm among those tested.

The proposed method enhances elevator efficiency and passenger experience.

Abstract

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.