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E-raamat: Control of Traffic Systems in Buildings

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Control of Traffic Systems in BuildingsSuurem pilt
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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.
Part I Transportation Systems
1 Introduction
3(4)
2 Passenger Transportation Systems
7(8)
2.1 Elevators
9(2)
2.1.1 Construction and Operation
10(1)
2.1.2 Safety
10(1)
2.1.3 Modern Technology
10(1)
2.1.4 Control
10(1)
2.2 Other Passenger Transportation Equipment
11(4)
2.2.1 Escalators
11(2)
2.2.2 Moving Walkways
13(1)
2.2.3 Horizontal Elevators
13(2)
3 Cargo Transportation Systems
15(4)
3.1 Freight Elevators
15(1)
3.2 Conveyors
15(2)
3.3 Automated Guided Vehicles
17(1)
3.4 Stacker Cranes
18(1)
4 External Connections and Related Systems
19(4)
4.1 External Connections
19(1)
4.1.1 Pedestrian Connections
19(1)
4.1.2 Freight Connections
19(1)
4.2 Related Systems
19(4)
4.2.1 Factory Automation
20(1)
4.2.2 Warehouse Automation
20(1)
4.2.3 Hospital Automation
20(3)
Part II Modeling and Simulation
5 General Modeling Concepts
23(10)
5.1 Components and Topology
23(7)
5.1.1 Vehicles
23(1)
5.1.2 Guideways
24(2)
5.1.3 Signal Systems
26(2)
5.1.4 Zones and Banks
28(1)
5.1.5 Nodes and Links
29(1)
5.2 Human—machine Interaction and Control Objectives
30(3)
5.2.1 Modeling of the Traffic
30(1)
5.2.2 Human—machine Interface of Elevators
31(1)
5.2.3 Human—machine Interface of Escalators and Other Equipment
32(1)
5.2.4 Control Objectives
32(1)
6 Queuing Models
33(6)
6.1 General Overview of Queuing Models
33(1)
6.2 Queuing Models for Elevator Systems
34(5)
6.2.1 The Simplest Case: M/M/1 Model
34(2)
6.2.2 A More General Model: M/G/1
36(3)
7 Modeling Techniques for Discrete Event Systems
39(16)
7.1 Field Studies
39(2)
7.2 Monte-Carlo Modeling
41(2)
7.2.1 Simulation Techniques
41(1)
7.2.2 Modeling by ESM-based Methodology
41(2)
7.3 The ESM Framework for Simulations
43(7)
7.3.1 The ESM Model for Discrete Event Simulation
43(2)
7.3.2 Communication Between ESMs
45(2)
7.3.3 Tools for Defining the ESM Model
47(1)
7.3.4 Implementation of the Simulation Program
48(2)
7.4 Modeling Cooperating Elevators and AGVs by the ESM Methodology
50(5)
7.4.1 Traffic Survey as the Starting Point for Simulations
51(1)
7.4.2 A Simplified Model of the Traffic in the Building
52(3)
8 Scheduling Models with Transportation
55(14)
8.1 Jobshop Scheduling Problems
55(6)
8.2 Classification of Jobshop Scheduling Problems
61(1)
8.3 Computational Complexity and Optimization Methods for JSP
62(2)
8.4 Robotic Cell Scheduling Problems
64(5)
Part III Intelligent Control Methods for Transportation Systems
9 Analytical and Heuristic Control of Transportation Systems
69(10)
9.1 Evolution of Control Methods
69(1)
9.2 Analytical Approaches
70(1)
9.3 Heuristic Rules
71(3)
9.3.1 Algorithmic Control
72(1)
9.3.2 Fuzzy AI Group Control
73(1)
9.4 Early Approaches to Optimal Control
74(5)
10 Adaptive Control by Neural Networks and Reinforcement Learning
79(24)
10.1 Information Processing by Neural Networks
79(1)
10.2 Multilayer Perceptrons
80(3)
10.2.1 Model of the Processing Units
80(1)
10.2.2 Structure and Operation of the Multilayer Perceptron
80(2)
10.2.3 Expressive Power of the MLP
82(1)
10.3 Learning as an Optimization Problem
83(8)
10.3.1 Nonlinear Optimization by the Gradient Method
84(1)
10.3.2 Derivation of the Learning Rule
85(2)
10.3.3 Hints for the Implementation and Use of the BP Method
87(2)
10.3.4 Using More Refined Optimization Methods
89(2)
10.4 Learning and Generalization by MLPs
91(3)
10.4.1 Learning and Generalization
91(1)
10.4.2 Generalization in the Case of MLPs
91(1)
10.4.3 Testing MLPs
91(1)
10.4.4 Learning by Direct Optimization
92(1)
10.4.5 Forward-Backward Modeling
92(1)
10.4.6 Learning with Powell's Conjugate Direction Method
93(1)
10.4.7 Learning by Genetic Algorithms
93(1)
10.5 Reinforcement Learning
94(9)
10.5.1 Markov Decision Processes
94(2)
10.5.2 Dynamic Programming (DP)
96(1)
10.5.3 The Value Iteration Method
97(1)
10.5.4 Q-learning
98(5)
11 Genetic Algorithms for Control-system Optimization
103(18)
11.1 Stochastic Approach to Optimization
103(1)
11.2 Genetic Algorithm
104(7)
11.2.1 Combinatorial Optimization with GA
105(2)
11.2.2 Nonlinear Optimization with GA
107(1)
11.2.3 GA as the Evolution of Distributions
108(2)
11.2.4 GA and Estimation of Distributions Algorithms
110(1)
11.3 Optimization of Uncertain Fitness Functions by Genetic Algorithms
111(10)
11.3.1 Introduction to GA for Optimization with Uncertainty
111(1)
11.3.2 Optimization of Noisy En mess Functions
112(1)
11.3.3 Adaptation to Changing Environment
112(1)
11.3.4 Discussion from the Application Side
113(1)
11.3.5 Approach to Uncertain Optimization by GA
114(1)
11.3.6 GA for Optimizing a Fitness Function with Noise
115(1)
11.3.7 GA for Varying Environments
116(2)
11.3.8 MFEGA and an Example of its Application
118(3)
12 Control System Optimization by ES and PSO
121(22)
12.1 Evolution Strategies
121(7)
12.1.1 Framework of Evolution Strategies
121(7)
12.1.2 Algorithm Designs for Evolutionary Algorithms
128(1)
12.2 Optimization of Noisy Fitness with Evolution Strategies
128(9)
12.2.1 Ways to Cope with Uncertainty
129(2)
12.2.2 Optimal Computing Budget Allocation
131(1)
12.2.3 Threshold Selection
132(5)
12.3 Particle Swarm Optimization
137(4)
12.3.1 Framework of Particle Swarm Optimization
137(2)
12.3.2 PSO and Noisy Optimization Problems
139(2)
12.4 Summary
141(2)
13 Intelligent Control by Combinatorial Optimization
143(8)
13.1 Branch-and-Bound Search
143(2)
13.2 Tabu Search
145(6)
13.2.1 Definition of the Problem
145(1)
13.2.2 Local Search
145(2)
13.2.3 Basic Structure of Tabu Search
147(4)
Part IV Topics in Modern Control for Transportation Systems
14 The S-ring: a Transportation System Model for Benchmarking
151(12)
14.1 The Kac Ring
151(2)
14.2 Definition of the S-ring Model
153(3)
14.3 Control of the S-ring
156(2)
14.3.1 Representations of the Policy
156(1)
14.3.2 Policy Examples
157(1)
14.3.3 Extensions
157(1)
14.4 A Prototype S-ring
158(1)
14.5 Solution by Dynamic Programming
158(1)
14.5.1 Formulation
158(1)
14.5.2 Solution
159(1)
14.6 Solution by Numerical Methods
159(2)
14.6.1 Kiefer—Wolfowitz Stochastic Approximation
160(1)
14.6.2 Q-learning and Evolutionary Strategies
160(1)
14.6.3 Results of the Optimization Experiments
161(1)
14.7 Conclusions
161(2)
15 Elevator Group Control by NN and Stochastic Approximation
163(24)
15.1 The Elevator Group Control as an Optimal Control Problem
164(1)
15.2 Elevator Group Control by Neural Networks
165(4)
15.2.1 State Representation for Elevator Group Control
166(3)
15.3 Neurocontroller for Group Control
169(8)
15.3.1 Structure of the Neurocontroller for Elevator Group Control
171(3)
15.3.2 Initial Training of the Neurocontroller
174(3)
15.4 Adaptive Optimal Control by the Stochastic Approximation
177(9)
15.4.1 Outline of the Basic Adaptation Process
177(2)
15.4.2 Sensitivity of the Controller Network
179(3)
15.4.3 Simulation Results for Adaptive Optimal Group Control
182(4)
15.5 Conclusions
186(1)
16 Optimal Control by Evolution Strategies and PSO
187(24)
16.1 Sequential Parameter Optimization
188(7)
16.1.1 SPO as a Learning Tool
188(2)
16.1.2 Tuning
190(1)
16.1.3 Stochastic Process Models as Extensions of Classical Regression Models
191(4)
16.1.4 Space-filling Designs
195(1)
16.2 The S-ring Model as a Test Generator
195(3)
16.3 Experimental Results for the S-ring Model
198(10)
16.3.1 Evolution Strategies
198(5)
16.3.2 Particle Swarm Optimization on the S-ring Model
203(5)
16.4 Classical Algorithms on the S-ring Model
208(1)
16.5 Criteria for Choosing an Optimization Algorithm
209(2)
17 On Adaptive Cooperation of AGVs and Elevators
211(10)
17.1 Introduction
211(1)
17.2 Material Handling System for High-rise Buildings
212(1)
17.3 Contract Net Protocol
213(1)
17.4 Intrabuilding Traffic Simulator
214(2)
17.4.1 Outline of the Simulator
214(1)
17.4.2 Performance Index of Control
214(2)
17.5 Cooperation based on Estimated Processing Time
216(2)
17.5.1 Control Using Minimal Processing Time for Bidding
216(1)
17.5.2 Estimation of Process rime by a Neural Network
216(1)
17.5.3 Numerical Example
217(1)
17.6 Optimization of Performance
218(1)
17.6.1 Bidding Function to be Optimized
218(1)
17.6.2 Application of Genetic Algorithm
218(1)
17.6.3 Numerical Example
219(1)
17.7 Conclusion
219(2)
18 Optimal Control of Multicar Elevator Systems by Genetic Algorithms
221(14)
18.1 Introduction
221(1)
18.2 Multicar Elevator Systems and Controller Optimization
222(4)
18.2.1 Multicar Elevator Systems
222(1)
18.2.2 Controllers for MCE
223(1)
18.2.3 Discrete Event Simulation of MCE
223(1)
18.2.4 Simulation-based Optimization
224(1)
18.2.5 Problems in Optimization
225(1)
18.2.6 Acceleration of Computation
225(1)
18.2.7 Re-examination of Configuration of Simulation
226(1)
18.3 A Genetic Algorithm for Noisy Fitness Function
226(1)
18.4 Comparison of GAs for Noisy Fitness
227(3)
18.4.1 Setup of Experiments
227(1)
18.4.2 Results of Experiment
228(2)
18.5 Examination of Control Strategy
230(2)
18.5.1 Examination of Zone Boundary
230(1)
18.5.2 Effect of Weight Extension
230(2)
18.6 Conclusion
232(3)
19 Analysis and Optimization for Automated Vehicle Routing
235(16)
19.1 Introduction
235(1)
19.2 Basic Assumptions and Basic Analysis
236(5)
19.2.1 Parallel and Bottleneck-Free PCVRS
236(1)
19.2.2 Interferences and Steady State
237(2)
19.2.3 One Lap Behind Interference
239(1)
19.2.4 Throughput and Mean Interference Time
240(1)
19.3 Two Basic Vehicle Routings
241(3)
19.3.1 Random Rule
242(1)
19.3.2 Order Rule
242(2)
19.4 Optimal Vehicle Rules
244(3)
19.4.1 Exchange-Order Rule
244(3)
19.4.2 Dynamic Order Rule
247(1)
19.5 Numerical Simulation
247(2)
19.6 Concluding Remarks
249(2)
20 Tabu-based Optimization for Input/Output Scheduling
251(6)
20.1 Introduction
251(1)
20.2 Optimal Input/Output Scheduling Problem
251(1)
20.3 Computational Complexity
252(1)
20.4 Approximation Algorithm
253(2)
20.5 Numerical Experiment
255(1)
20.6 Concluding Remarks
255(2)
Program Listings 257(4)
References 261(14)
Index 275


Sandor Markon has 25 years experience as a designer, researcher and manager dealing with control systems with major elevator manufacturers. He is also active in publishing papers and abstracts in related fields. He has been involved in the installation of four generations of elevator systems in buildings world-wide. The current volume would gathers that knowledge together with that of other researchers currently scattered in short articles, patent disclosures, etc., into one place.
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