0 Item

Control of Traffic Systems in Buildings

By: Sandor A. Markon, Hajime Kita, Hiroshi Kise

Write A Review

Hardcover | 7 July 2006

At a Glance

Hardcover
304 Pages

Dimensions(cm)
23.39 x 15.6 x 1.75

Hardcover

$210.72

or 4 interest-free payments of $52.68 with

Aims to ship in 7 to 10 business days

When will this arrive by?
Enter delivery postcode to estimate

Control of Traffic Systems in Buildings, focusing on elevator groups, presents the state of the art in their analysis and control. It covers the theory and design of passenger and cargo traffic systems with actual operational examples and topics of current interest such as: noisy, on-line and algorithmic optimization; identification and tracking of loads; integration of control with security and management; medium- and wide-area networked control; deployment and testing of transport systems.

Workers in elevator control have pioneered the development of many modern control systems for use in all sorts of traffic and scheduled systems so this exposition of recent work in in-building transport control will be of interest to researchers and engineers in many areas of control, particularly in optimal or supervisory control, urban transportation systems and intelligent transport systems.

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.

Defective items

If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.

For more info please visit our Help Centre.

Other Editions and Formats

Paperback

Published: 21st October 2010

View Product

Transportation Systems
Introduction	p. 3
Passenger Transportation Systems	p. 7
Elevators	p. 9
Construction and Operation	p. 10
Safety	p. 10
Modern Technology	p. 10
Control	p. 10
Other Passenger Transportation Equipment	p. 11
Escalators	p. 11
Moving Walkways	p. 13
Horizontal Elevators	p. 13
Cargo Transportation Systems	p. 15
Freight Elevators	p. 15
Conveyors	p. 15
Automated Guided Vehicles	p. 17
Stacker Cranes	p. 18
External Connections and Related Systems	p. 19
External Connections	p. 19
Pedestrian Connections	p. 19
Freight Connections	p. 19
Related Systems	p. 19
Factory Automation	p. 20
Warehouse Automation	p. 20
Hospital Automation	p. 20
Modeling and Simulation
General Modeling Concepts	p. 23
Components and Topology	p. 23
Vehicles	p. 23
Guideways	p. 24
Signal Systems	p. 26
Zones and Banks	p. 28
Nodes and Links	p. 29
Human-machine Interaction and Control Objectives	p. 30
Modeling of the Traffic	p. 30
Human-machine Interface of Elevators	p. 31
Human-machine Interface of Escalators and Other Equipment	p. 32
Control Objectives	p. 32
Queuing Models	p. 33
General Overview of Queuing Models	p. 33
Queuing Models for Elevator Systems	p. 34
The Simplest Case: M/M/1 Model	p. 34
A More General Model: M/G/1	p. 36
Modeling Techniques for Discrete Event Systems	p. 39
Field Studies	p. 39
Monte-Carlo Modeling	p. 41
Simulation Techniques	p. 41
Modeling by ESM-based Methodology	p. 41
The ESM Framework for Simulations	p. 43
The ESM Model for Discrete Event Simulation	p. 43
Communication Between ESMs	p. 45
Tools for Defining the ESM Model	p. 47
Implementation of the Simulation Program	p. 48
Modeling Cooperating Elevators and AGVs by the ESM Methodology	p. 50
Traffic Survey as the Starting Point for Simulations	p. 51
A Simplified Model of the Traffic in the Building	p. 52
Scheduling Models with Transportation	p. 55
Jobshop Scheduling Problems	p. 55
Classification of Jobshop Scheduling Problems	p. 61
Computational Complexity and Optimization Methods for JSP	p. 62
Robotic Cell Scheduling Problems	p. 64
Intelligent Control Methods for Transportation Systems
Analytical and Heuristic Control of Transportation Systems	p. 69
Evolution of Control Methods	p. 69
Analytical Approaches	p. 70
Heuristic Rules	p. 71
Algorithmic Control	p. 72
Fuzzy AI Group Control	p. 73
Early Approaches to Optimal Control	p. 74
Adaptive Control by Neural Networks and Reinforcement Learning	p. 79
Information Processing by Neural Networks	p. 79
Multilayer Perceptrons	p. 80
Model of the Processing Units	p. 80
Structure and Operation of the Multilayer Perceptron	p. 80
Expressive Power of the MLP	p. 82
Learning as an Optimization Problem	p. 83
Nonlinear Optimization by the Gradient Method	p. 84
Derivation of the Learning Rule	p. 85
Hints for the Implementation and Use of the BP Method	p. 87
Using More Refined Optimization Methods	p. 89
Learning and Generalization by MLPs	p. 91
Learning and Generalization	p. 91
Generalization in the Case of MLPs	p. 91
Testing MLPs	p. 91
Learning by Direct Optimization	p. 92
Forward-Backward Modeling	p. 92
Learning with Powell's Conjugate Direction Method	p. 93
Learning by Genetic Algorithms	p. 93
Reinforcement Learning	p. 94
Markov Decision Processes	p. 94
Dynamic Programming (DP)	p. 96
The Value Iteration Method	p. 97
Q-learning	p. 98
Genetic Algorithms for Control-system Optimization	p. 103
Stochastic Approach to Optimization	p. 103
Genetic Algorithm	p. 104
Combinatorial Optimization with GA	p. 105
Nonlinear Optimization with GA	p. 107
GA as the Evolution of Distributions	p. 108
GA and Estimation of Distributions Algorithms	p. 110
Optimization of Uncertain Fitness Functions by Genetic Algorithms	p. 111
Introduction to GA for Optimization with Uncertainty	p. 111
Optimization of Noisy Fitness Functions	p. 112
Adaptation to Changing Environment	p. 112
Discussion from the Application Side	p. 113
Approach to Uncertain Optimization by GA	p. 114
GA for Optimizing a Fitness Function with Noise	p. 115
GA for Varying Environments	p. 116
MFEGA and an Example of its Application	p. 118
Control System Optimization by ES and PSO	p. 121
Evolution Strategies	p. 121
Framework of Evolution Strategies	p. 121
Algorithm Designs for Evolutionary Algorithms	p. 121
Optimization of Noisy Fitness with Evolution Strategies	p. 128
Ways to Cope with Uncertainty	p. 129
Optimal Computing Budget Allocation	p. 131
Threshold Selection	p. 132
Particle Swarm Optimization	p. 137
Framework of Particle Swarm Optimization	p. 137
PSO and Noisy Optimization Problem	p. 139
Summary	p. 141
Intelligent Control by Combinatorial Optimization	p. 143
Branch-and-Bound Search	p. 143
Tabu Search	p. 145
Definition of the Problem	p. 145
Local Search	p. 145
Basic Structure of Tabu Search	p. 147
Topics in Modern Control for Transportation Systems
The S-ring: a Transportation System Model for Benchmarking
The Kac Ring	p. 151
Definition of the S-ring Model	p. 151
Control of the S-ring	p. 153
Representations of the Policy	p. 156
Policy Examples	p. 156
Extensions	p. 157
A Prototype S-ring	p. 158
Solution by Dynamic Programming	p. 158
Formulation	p. 158
Solution	p. 159
Solution by Numerical Methods	p. 159
Kiefer-Wolfowitz Stochastic Approximation	p. 160
Q-learning and Evolutionary Strategies	p. 160
Results of the Optimization Experiments	p. 161
Conclusions	p. 161
Elevator Group Control by NN and Stochastic Approximation	p. 163
The Elevator Group Control as an Optimal Control Problem	p. 164
Elevator Group Control by Neural Networks	p. 165
State Representation for Elevator Group Control	p. 166
Neurocontroller for Group Control	p. 169
Structure of the Neurocontroller for Elevator Group Control	p. 171
Initial Training of the Neurocontroller	p. 174
Adaptive Optimal Control by the Stochastic Approximation	p. 177
Outline of the Basic Adaptation Process	p. 177
Sensitivity of the Controller Network	p. 179
Simulation Results for Adaptive Optimal Group Control	p. 182
Conclusions	p. 186
Optimal Control by Evolution Strategies and PSO	p. 187
Sequential Parameter Optimization	p. 188
SPO as a Learning Tool	p. 188
Tuning	p. 190
Stochastic Process Models as Extensions of Classical Regression Models	p. 191
Space-filling Designs	p. 195
The S-ring Model as a Test Generator	p. 195
Experimental Results for the S-ring Model	p. 198
Evolution Strategies	p. 198
Particle Swarm Optimization on the S-ring Model	p. 203
Classical Algorithms on the S-ring Model	p. 208
Criteria for Choosing an Optimization Algorithm	p. 209
On Adaptive Cooperation of AGVs and Elevators	p. 211
Introduction	p. 211
Material Handling System for High-rise Buildings	p. 212
Contract Net Protocol	p. 213
Intrabuilding Traffic Simulator	p. 214
Outline of the Simulator	p. 214
Performance Index of Control	p. 214
Cooperation based on Estimated Processing Time	p. 216
Control Using Minimal Processing Time for Bidding	p. 216
Estimation of Processing Time by a Neural Network	p. 216
Numerical Example	p. 217
Optimization of Performance	p. 218
Bidding Function to be Optimized	p. 218
Application of Genetic Algorithm	p. 218
Numerical Example	p. 219
Conclusion	p. 219
Optimal Control of Multicar Elevator Systems by Genetic Algorithms	p. 221
Introduction	p. 221
Multicar Elevator Systems and Controller Optimization	p. 222
Multicar Elevator Systems	p. 222
Controllers for MCE	p. 223
Discrete Event Simulation of MCE	p. 223
Simulation-based Optimization	p. 224
Problems in Optimization	p. 225
Acceleration of Computation	p. 225
Re-examination of Configuration of Simulation	p. 226
A Genetic Algorithm for Noisy Fitness Function	p. 226
Comparison of GAs for Noisy Fitness	p. 227
Setup of Experiments	p. 227
Results of Experiment	p. 228
Examination of Control Strategy	p. 230
Examination of Zone Boundary	p. 230
Effect of Weight Extension	p. 230
Conclusion	p. 232
Analysis and Optimization for Automated Vehicle Routing	p. 235
Introduction	p. 235
Basic Assumptions and Basic Analysis	p. 236
Parallel and Bottleneck-Free PCVRS	p. 236
Interferences and Steady State	p. 237
One Lap Behind Interference	p. 239
Throughput and Mean Interference Time	p. 240
Two Basic Vehicle Routings	p. 241
Random Rule	p. 242
Order Rule	p. 242
Optimal Vehicle Rules	p. 244
Exchange-Order Rule	p. 244
Dynamic Order Rule	p. 247
Numerical Simulation	p. 247
Concluding Remarks	p. 249
Tabu-based Optimization for Input/Output Scheduling	p. 251
Introduction	p. 251
Optimal Input/Output Scheduling Problem	p. 251
Computational Complexity	p. 252
Approximation Algorithm	p. 253
Numerical Experiment	p. 255
Concluding Remarks	p. 255
Program Listings	p. 257
References	p. 261
Index	p. 275
Table of Contents provided by Publisher. All Rights Reserved.

Control of Traffic Systems in Buildings

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

More in Automotive Technology & Trades

Surviving to Drive

A Year Inside Formula 1

Life in the Pitlane

Calum Nicholas

The Truck Book

The Definitive Visual History

Today's Technician: Automotive Electricity and Electronics

8th Edition - Classroom and Shop Manual Pack

The MG Century

100 Years of Safety Fast!

How to Build a Motorcycle

A Nut-and-Bolt Guide to Customizing Your Bike

A+ National Pre-apprenticeship Maths and Literacy for Automotive

A+ PRE-APPRENTICESHIP

How to Repair Briggs and Stratton Engines, 4th Ed.

Mechanical Engineering

Ford Mustang 60 Years

60 Years

Porsche Outlaws

Stuttgart Hot Rods

The Spirit of VW

50 Reasons Why We Love Them

The Complete Book of Porsche 911 4th Edition

Every Model Since 1964

The Story of Aston Martin

A tribute to automotive excellence

The Story of Lamborghini

A tribute to automotive excellence

Bush Mechanics

From Yuendumu to the world

BMW Century

Life in the Pitlane

Calum Nicholas

Mini

60 Years

The Electric Vehicle Revolution

Five Visionaries Leading the Charge

The Complete Book of Corvette 5th Edition

Every Model Since 1953

Epicyclic Gearing

Optimization Techniques

Autonomous Electric Vehicles

Nonlinear Control, Traction, and Propulsion

Knowledge Graph-Based Methods for Automated Driving

Raising and Lowering Vibration Isolator via Asymmetric Damping Adjustment

Application in Vehicle Height and Attitude Control

This product is categorised by