A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Abstract

Abstract.-Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.Keywords: Elevator; lift; viral system; bio-inspired algorithms; elevator group control system; vertical transportation AcknowledgmentsThe authors acknowledge the financial support provided by the Spanish Plan for Research and Development Technology and Innovation Plan through its Programme Industrial Production and Design, project Ref. DPI2010-15352. A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings Abstract.-Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.

A modified genetic algorithm applied to the elevator dispatching problem

“…Cortés et al (2012b) use a viral system algorithm that is based on a virus infection analogy. In his work, he compares this approach to genetic and tabu search algorithms.…”

Immunity‐Based Optimal Estimation Approach for a New Real Time Group Elevator Dynamic Control Application for Energy and Time Saving

“…However, the width of the optimal way space makes this problem hard to solve in the best way. Because there are the option of n p  different ways in a building having n storey and p cabin [2, 3]. Actually, what is expected from the group elevator systems is the average performance of such cabins instead of individual performance of elevator.…”

“…Actually, what is expected from the group elevator systems is the average performance of such cabins instead of individual performance of elevator. In addition, the basic criteria at such performances are to minimize the average waiting time of passengers and energy consumption values of cabins [3, 4]. Generally, by considering such parameters, the most optimum way is selected from the way space, and these are applied to the system.…”

“…In addition, such calculations can be made in shortest time with advanced computer systems, and the most optimum car can be allocated to the users [3]. …”

Dynamic Fuzzy Logic Elevator Group Control System for Energy Optimization

“…Therefore, modern heuristics usually has to be employed in order to solve the problem (sometimes helped by technological measures [10] that contribute to reduce uncertainty): Algorithms based on Prioritized A* Search (pruning a solution tree) [11] or the employment of Linear Programming [12] have shown certain accurate performance. Usually, artificial intelligence like genetic algorithms [13] - [17], immune algorithms [18], particle swarm [19] - [20] or viral systems [21] show acceptable results, but the time employed in obtaining a solution or convergence problems do not make them efficient enough solutions. Other techniques like neural networks [22] need too much training time to work properly, as well as they are sometimes difficult to implement and do not show desirable results at all when adapting to fast unforeseen variations.…”