2,050 research outputs found
A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems
High-rise buildings require the installation of complex elevator group control
systems (EGCS). In vertical transportation, when a passenger makes a hall call by pressing a
landing call button installed at the floor and located near the cars of the elevator group, the
EGCS must allocate one of the cars of the group to the hall call. We develop a Particle Swarm
Optimization (PSO) algorithm to deal with this car-call allocation problem. The PSO algorithm
is compared to other soft computing techniques such as genetic algorithm and tabu search
approaches that have been proved as efficient algorithms for this problem. The proposed PSO
algorithm was tested in high-rise buildings from 10 to 24 floors, and several car configurations
from 2 to 6 cars. Results from trials show that the proposed PSO algorithm results in better
average journey times and computational times compared to genetic and tabu search
approaches
Vertical transportation in buildings
Nowadays, the building industry and its associated technologies are
experiencing a period of rapid growth, which requires an equivalent growth regarding
technologies in the field of vertical transportation. Therefore, the installation of
synchronised elevator groups in modern buildings is a common practice in order to
govern the dispatching, allocation and movement of the cars shaping the group. So,
elevator control and management has become a major field of application for Artificial
Intelligence approaches. Methodologies such as fuzzy logic, artificial neural networks,
genetic algorithms, ant colonies, or multiagent systems are being successfully proposed
in the scientific literature, and are being adopted by the leading elevator companies as
elements that differentiate them from their competitors. In this sense, the most relevant
companies are adopting strategies based on the protection of their discoveries and
inventions as registered patents in different countries throughout the world. This paper
presents a comprehensive state of the art of the most relevant recent patents on
computer science applied to vertical transportationConsejería de Innovación, Ciencia y Empresa, Junta de Andalucía P07-TEP-02832, Spain
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
Evolutionary Networks for Multi-Behavioural Robot Control : A thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science Massey University, Albany, New Zealand
Artificial Intelligence can be applied to a wide variety of real world problems, with
varying levels of complexity; nonetheless, real world problems often demand for
capabilities that are difficult, if not impossible to achieve using a single Artificial
Intelligence algorithm. This challenge gave rise to the development of hybrid systems
that put together a combination of complementary algorithms. Hybrid approaches
come at a cost however, as they introduce additional complications for the developer,
such as how the algorithms should interact and when the independent algorithms
should be executed. This research introduces a new algorithm called Cascading
Genetic Network Programming (CGNP), which contains significant changes to the
original Genetic Network Programming. This new algorithm has the facility to
include any Artificial Intelligence algorithm into its directed graph network, as either
a judgement or processing node. CGNP introduces a novel ability for a scalable
multiple layer network, of independent instances of the CGNP algorithm itself. This
facilitates problem subdivision, independent optimisation of these underlying layers
and the ability to develop varying levels of complexity, from individual motor control
to high level dynamic role allocation systems. Mechanisms are incorporated to
prevent the child networks from executing beyond their requirement, allowing the
parent to maintain control. The ability to optimise any data within each node
is added, allowing for general purpose node development and therefore allowing
node reuse in a wide variety of applications without modification. The abilities
of the Cascaded Genetic Network Programming algorithm are demonstrated and
proved through the development of a multi-behavioural robot soccer goal keeper, as
a testbed where an individual Artificial Intelligence system may not be sufficient.
The overall role is subdivided into three components and individually optimised
which allow the robot to pursue a target object or location, rotate towards a target
and provide basic functionality for defending a goal. These three components are
then used in a higher level network as independent nodes, to solve the overall multi-
behavioural goal keeper. Experiments show that the resulting controller defends the
goal with a success rate of 91%, after 12 hours training using a population of 400
and 60 generations
Submodular Function Maximization for Group Elevator Scheduling
We propose a novel approach for group elevator scheduling by formulating it
as the maximization of submodular function under a matroid constraint. In
particular, we propose to model the total waiting time of passengers using a
quadratic Boolean function. The unary and pairwise terms in the function denote
the waiting time for single and pairwise allocation of passengers to elevators,
respectively. We show that this objective function is submodular. The matroid
constraints ensure that every passenger is allocated to exactly one elevator.
We use a greedy algorithm to maximize the submodular objective function, and
derive provable guarantees on the optimality of the solution. We tested our
algorithm using Elevate 8, a commercial-grade elevator simulator that allows
simulation with a wide range of elevator settings. We achieve significant
improvement over the existing algorithms.Comment: 10 pages; 2017 International Conference on Automated Planning and
Scheduling (ICAPS
Genetic and tabu search approaches for optimizing the hall call-car allocation problem in elevator group systems
The most common problem in vertical transportation using elevator group appears when a
passenger wants to travel from a floor to other different floor in a building. The passenger
makes a hall call by pressing a landing call button installed at the floor and located near the
cars of the elevator group. After that, the elevator controller receives the call and identifies
which one of the elevators in the group is most suitable to serve the person having issued
the call. In this paper, we have developed different elevator group controllers based on
genetic and tabu search algorithms. Even though genetic algorithm has been previously
considered in vertical transportation problems, the use of tabu search approaches is a
novelty in vertical transportation and has not been considered previously. Tests have been
carried out for high-rise buildings considering diverse sizes in the group of cars. Results
indicate that the waiting time and journey time of passengers were significantly improved
when dealing with such soft computing approaches. Also, a quickly evaluable solution
quality function in the algorithms allows suitable computational times for industry
implementation
Genetic algorithm for controllers in elevator groups: analysis and simulation during lunchpeak traffic
The efficient performance of elevator group system controllers
becomes a first order necessity when the buildings have a high utilisation ratio
of the elevators, such as in professional buildings. We present a genetic
algorithm that is compared with traditional controller algorithms in industry
applications. An ARENA simulation scenario is created during heavy
lunchpeak traffic conditions. The results allow us to affirm that our genetic
algorithm reaches a better performance attending to the system waiting times
than THV algorithm
A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings
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.Plan Estatal de Investigación Científica y Técnica y de Innovación (España
A brief review on vertical transportation research and open issue
Book of Proceedings of the International Joint Conference-CIO-ICIEOM-IIE-AIM (IJC 2016), "XX Congreso de Ingeniería de Organización", "XXII International Conference on Industrial Engineering and Operations Management, "International IISE Conference 2016, "International AIM Conference 2016". Donostia-San Sebastian (Spain), July 13-15, 2016Vertical transportation refers to the movements of people in buildings.
High-rise buildings have emerged as a common construction nowadays. In such
buildings, the vertical transportation is extremely difficult to manage, specially,
when the people arrive at the same time at specific floors wanting to travel to other
floors. To solve such situations, the installation of elevator group control systems
(EGCS) is a usual practice. EGCS are used to manage multiple elevators in a building
to efficiently transport passengers. EGCSs need to meet the demands by assigning
an elevator to each landing call while optimizing several criteria. This paper
reviews the most relevant contributions in vertical transportation industr
Applications of Soft Computing in Mobile and Wireless Communications
Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications
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