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Dynamic modeling of environmental systems
Deaton, Michael L.
اطلاعات کتابشناختی
Dynamic modeling of environmental systems
Author :
Deaton, Michael L.
Publisher :
Springer,
Pub. Year :
2000
Subjects :
Environmental sciences-- Computer simulation. Environmental sciences-- Mathematical...
Call Number :
GE 45 .D37 .D43 2000
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Chapter 1. Modeling and simulation modeling
(18)
1.1. Types of models
(19)
1.2. Analytical vs. Simulation modeling
(19)
The limits of analytical modeling: queuing theory
(21)
Advantages of simulation modeling
(24)
1.3. Applications of simulation modeling. Level of abstraction. Methods
(25)
Chapter 2. The three methods in simulation modeling
(27)
2.1. System dynamics
(27)
Example 2.1: New product diffusion
(28)
Underlying mathematics and simulation engine
(32)
Abstraction level
(33)
Software tools
(33)
2.2. Discrete event modeling
(33)
Example 2.2: Bank
(34)
Abstraction level
(35)
Underlying mathematics and simulation engine
(36)
Software tools
(36)
2.3. Agent based modeling
(36)
Example 2.3: Agent based epidemic model
(37)
Abstraction level
(40)
For those who have read books and papers on agent based modeling
(41)
Underlying mathematics and simulation engine
(41)
Software tools
(42)
Chapter 3. Agent based modeling. Technology overview
(43)
3.1. Who are the agents?
(43)
Who are the agents in an American automotive market model?
(43)
3.2. Agent based modeling and object-oriented design
(45)
OO modeling in AnyLogic
(47)
3.3. Time in agent based models
(49)
3.4. Space in agent based models
(51)
3.5. Discrete space
(52)
Example 3.1: Schelling segregation
(55)
Example 3.2: Conway’s Game of Life
(57)
Example 3.3: Wildfire
(58)
Discrete space API
(64)
3.6. Continuous 2D and 3D space
(65)
Movement in continuous space
(67)
Example 3.4: Air defense system
(68)
Example 3.5: Agent leaving a movement trail
(80)
Continuous space API
(83)
3.7. Networks and links
(84)
Standard networks
(84)
Example 3.6: Periodic repair of a standard network
(86)
Example 3.7: Custom network built using standard connections
(88)
Fully connected networks
(90)
Network and layout-related API
(90)
Unidirectional, temporary, and other custom types of links
(91)
Example 3.8: Kinship modeled using custom links
(92)
A note on vertical links in hierarchical models
(96)
Using ports to connect agents
(96)
3.8. Communication between agents. Message passing
(97)
Synchronous and asynchronous communication
(97)
API for message passing
(98)
Message handling
(99)
Other types of inter-agent communication
(100)
3.9. Dynamic creation and destruction of agents
(100)
3.10. Statistics on agent populations
(102)
Example 3.9: Kinship model with standard statistics
(102)
Example 3.10: Kinship model with dynamic histograms
(104)
Customized high performance statistics
(105)
Example 3.11: Kinship model with customized statistics
(106)
3.11. Condition-triggered events and transitions in agents
(107)
Chapter 4. How to build agent based models. Field service example
(109)
4.1. The problem statement
(109)
4.2. Phase 1. Can be done on paper
(111)
Who are the agents?
(111)
Equipment unit agent
(111)
Service crew agent
(113)
Agent communication. Message sequence diagrams
(115)
Space and other things shared by all agents
(117)
4.3. Phase 2. The model in AnyLogic. The first run
(118)
The model structure and the top level object Main
(118)
The EquipmentUnit agent
(119)
The ServiceCrew agent
(121)
Animation
(123)
The first run
(123)
Discussion and next steps
(124)
4.4. Phase 3. The missing functionality
(124)
Maintenance, age, and failure rate
(125)
Scheduling maintenance. Handling requests of two types
(126)
Discussion. Code in the model
(129)
4.5. Phase 4. Model output. Statistics. Cost and revenue calculation
(130)
Equipment availability and service crew utilization
(131)
Cost and revenue
(135)
4.6. Phase 5. Control panel. Running the flight simulator
(137)
Design of control panel
(137)
Changing the number of service crews
(138)
Equipment replacement policy
(139)
Running the flight simulator
(139)
4.7. Phase 6. Using the optimizer to find the best solution
(141)
Preparing the model for optimization
(141)
Setting up the optimization experiment
(142)
Optimization run
(143)
4.8. Assumptions
(145)
4.9. Bonus phase. 3D animation
(146)
4.10. Bonus discussion. Could we model this in discrete event style?
(148)
Chapter 5. System dynamics and dynamic systems
(152)
5.1. How to draw stock and flow diagrams
(152)
Drawing stocks and flows
(152)
Drawing variables, dependency links, polarities, and loop types
(154)
Naming conventions for system dynamics variables
(155)
Layout of large models. "Sectors" and shadow variables
(155)
5.2. Equations
(156)
Using Java in SD equations
(159)
"Constant variables" and parameters
(159)
Units and unit checking
(160)
5.3. Example: Population and carrying capacity
(161)
Phase 1: Unlimited resources. Positive feedback. Exponential growth
(161)
Customizing the dataset collection
(165)
Phase 2: Crowding affects lifetime. Negative feedback. S-shaped growth
(165)
Phase 3: Crowding affects births
(167)
Phase 4: Negative feedback with delay. Overshoot and oscillation
(169)
Specifying units and performing unit checking
(170)
5.4. Other types of experiments. Interactive games
(171)
Example 5.1: New product diffusion - compare runs
(171)
Example 5.2: New product diffusion - sensitivity analysis
(173)
Example 5.3: Epidemic model – calibration
(174)
Example 5.4: Epidemic model - instant charts
(177)
Example 5.5: Stock management game
(179)
5.5. Exporting the model and publishing it on the web
(183)
Chapter 6. Multi-method modeling
(186)
6.1. Architectures
(186)
The choice of model architecture and methods
(188)
6.2. Technical aspect of combining modeling methods
(189)
Examples 5.1 - 5.21: Combining modeling methods
(189)
System dynamics -> discrete elements
(189)
Discrete elements -> system dynamics
(191)
Agent based <-> discrete event
(197)
Referencing model elements located in different active objects
(200)
The simulation performance of multi-method models
(201)
6.3. Examples
(202)
Example 6.22: Epidemic and clinic
(202)
Example 6.23: Consumer market and supply chain
(208)
Example 6.24: Product portfolio and investment policy
(212)
6.4. Discussion
(223)
Chapter 7. Designing state-based behavior: statecharts
(224)
7.1. What is a statechart?
(224)
Example 7.1: A laptop running on a battery
(224)
How do statecharts differ from action charts and flowcharts?
(225)
7.2. Drawing statecharts
(226)
Simple states
(226)
Transitions
(227)
Statechart entry point
(227)
Composite states
(228)
History state
(228)
Final state
(229)
7.3. State transitions: triggers, guards, and actions
(230)
Which transitions are active?
(230)
Trigger types
(230)
Timeout expressions
(231)
Transitions triggered by messages
(232)
Sending messages to a statechart
(233)
Guards of transitions
(234)
Transitions with branches
(235)
Internal transitions
(235)
Order of action execution
(236)
Synchronous vs. asynchronous transitions
(237)
7.4. Statechart-related API
(238)
7.5. Viewing and debugging the statecharts at runtime
(239)
7.6. Statecharts for people’s lives and behavior
(239)
Example 7.2: Life phases
(239)
Example 7.3: Adoption and diffusion
(240)
Example 7.4: Disease diffusion
(241)
Example 7.5: Purchase behavior with a choice of two competing products
(242)
7.7. Statecharts for physical objects
(243)
Example 7.6: Generic resource with breakdowns and repairs
(243)
Example 7.7: Delivery truck
(244)
Example 7.8: Aircraft maintenance checks
(245)
7.8. Statecharts for products and projects
(246)
Example 7.9: Product life cycle, including NPD
(246)
Example 7.10: Pharmaceutical NPD pipeline
(247)
7.9. Statecharts for timing
(248)
Example 7.11: Statechart for shop working hours
(248)
Chapter 8. Discrete events and Event model object
(250)
8.1. Discrete events
(250)
The terminology
(250)
Discrete events: approximation of real world continuous processes
(250)
Discrete event management inside AnyLogic engine
(251)
8.2. Event – the simplest low level model object
(252)
Example 8.1: Event writes to the model log every time unit
(254)
Example 8.2: Event generates new agents
(255)
Events triggered by a condition
(256)
Example 8.3: Event waits on a stock reaching a certain level
(257)
Example 8.4: Automatic shutdown after a period of inactivity
(258)
Example 8.5: Event slows down the simulation on a particular date
(259)
Event API
(260)
8.3. Dynamic events
(261)
Example 8.6: Product delivery
(262)
API related to dynamic events
(264)
Chapter 9. Rails and trains
(265)
9.1. Defining the rail topology
(265)
Example 9.1: A very simple rail yard
(266)
3D animation of rail yards
(268)
Creating rail yards programmatically
(269)
Example 9.2: Creating a rail yard by code
(269)
Java class Track
(271)
Java class Switch
(272)
9.2. Defining the operation logic of the rail model
(273)
Example 9.3: Train stop
(274)
Example 9.4: Ensuring safe movement of trains
(279)
Example 9.5: Simple classification yard
(283)
Example 9.6: Airport shuttle train (featuring AnyLogic Pedestrian Library)
(290)
Java class Train (subclass of Entity)
(293)
Java class RailCar
(295)
Chapter 10. Java for AnyLogic users
(298)
10.1. Primitive data types
(298)
10.2. Classes
(299)
Class as grouping of data and methods. Objects as instances of class
(300)
Inheritance. Subclass and super class
(301)
Classes and objects in AnyLogic models
(302)
10.3. Variables (local variables and class fields)
(302)
Local (temporary) variables
(303)
Class variables (fields)
(303)
10.4. Functions (methods)
(304)
Standard and system functions
(305)
Functions of the model elements
(306)
Defining your own function
(308)
10.5. Expressions
(310)
Arithmetic expressions
(310)
Relations and equality
(311)
Logical expressions
(311)
String expressions
(312)
Conditional operator ?:
(312)
10.6. Java arrays and collections
(313)
Arrays
(314)
Collections
(316)
Replicated active objects are collections too
(318)
10.7. Naming conventions
(319)
10.8. Statements
(321)
Variable declaration
(322)
Function call
(322)
Assignment
(323)
If-then-else
(323)
Switch
(324)
For loop
(325)
While loop
(326)
Block {…} and indentation
(327)
Return statement
(328)
Comments
(328)
10.9. Where am I and how do I get to…?
(330)
10.10. Viewing Java code generated by AnyLogic
(332)
10.11. Creating your Java classes within AnyLogic model
(333)
Inner classes
(335)
10.12. Linking external Java modules (JAR files)
(336)
Chapter 11. Exchanging data with external world
(339)
11.1. Text files
(339)
Example 11.1: Using text file as a log
(340)
Example 11.2: Reading table function from a text file
(341)
Example 11.3: Reading agent parameters from a CSV file
(343)
11.2. Excel spreadsheets
(345)
Example 11.4: Reading data of various types from fixed cells in Excel
(346)
Example 11.5: Reading model parameters from Excel using Java reflection
(348)
Example 11.6: Displaying the model output as a chart in Excel
(350)
11.3. Databases
(352)
SQL queries
(353)
AnyLogic database connectivity objects
(356)
Example 11.7: Loading data from a database and using ResultSet
(357)
Example 11.8: Creating agent populations parameterized from a database
(359)
Example 11.9: Dumping simulation output into a database table
(362)
Example 11.10: Using prepared statement when writing to databases
(364)
11.4. Working with the clipboard
(365)
Example 11.11: Working with clipboard
(365)
11.5. Standard output, the model log, and command line arguments
(367)
Chapter 12. Presentation and animation: working with shapes, groups, colors
(368)
12.1. Drawing and editing shapes
(368)
Polylines and curves
(369)
Arcs
(370)
Text
(370)
Images
(371)
Z-Order
(372)
Selecting hidden shapes
(373)
Coordinates and the grid
(373)
Copying shapes
(374)
Locking shapes – preventing selection by mouse
(375)
General properties of graphical shapes
(376)
Advanced properties of graphical shapes
(376)
12.2. Grouping shapes
(377)
3D Groups
(380)
Working with the group contents dynamically using API
(381)
On draw extension point – execute custom code on each frame
(381)
Groups in the project tree
(382)
Top level groups for active object presentation and icon
(382)
12.3. Animation principles. Dynamic properties of shapes
(382)
Dynamic properties of shapes
(383)
Example 12.1: Commodity price change animation
(383)
Example 12.2: Elevator doors animation
(385)
Example 12.3: Stock of money animation
(386)
Example 12.4: Missile attack animation
(388)
Animation frames
(390)
12.4. Replicated shapes
(390)
Example 12.5: Drawing seats in a movie theater
(391)
Example 12.6: Selling seats in the movie theater
(393)
Example 12.7: Drawing a flower
(394)
Example 12.8: Product portfolio bubble chart (BCG chart)
(395)
12.5. Shapes’ API
(397)
Example 12.9: Using color to show the current state of a statechart
(398)
Example 12.10: Show/hide a callout
(399)
Example 12.11: Read graphics from a text file
(400)
Example 12.12: Find all red circles
(402)
Example 12.13: Resize the red circles
(403)
API of non-persistent shapes
(403)
AnyLogic Java class hierarchy for shapes
(403)
12.6. Colors and textures
(404)
Example 12.14: Choosing appropriate colors for an arbitrary number of objects
(405)
Transparency
(405)
Example 12.15: Using transparency to show coverage zone
(406)
Example 12.16: Show population density using color interpolation
(407)
Chapter 13. Designing interactive models: using controls
(409)
Example 13.1: Slider linked to a model parameter
(410)
Example 13.2: Buttons changing the parameter value
(411)
Example 13.3: Edit box linked to a parameter of embedded object
(412)
Example 13.4: Radio buttons changing the view mode
(413)
Example 13.5: Combo box controlling the simulation speed
(414)
Example 13.6: File chooser for text files
(415)
Indivisibility of control actions and model events
(416)
13.1. Dynamic properties of controls
(416)
Example 13.7: Radio buttons enabling/disabling other controls
(416)
Example 13.8: Keeping controls in the top left corner of the window
(417)
Example 13.9: Replicated button
(418)
13.2. Controls' API
(419)
13.3. Handling mouse clicks
(419)
Example 13.10: Hyper link menu to navigate between view areas
(420)
Example 13.11: Creating dots at the click coordinates
(421)
Example 13.12: Catching mouse clicks anywhere on the canvas
(422)
Chapter 14. 3D animation
(423)
Example 14.1: A very simple model with 3D animation
(424)
14.1. Primitive 3D shapes
(425)
14.2. 3D groups and rotation
(428)
Example 14.2: Rotation in 3D – a sign on two posts
(428)
Example 14.3: Bridge crane 3D
(429)
14.3. Standard and imported 3D graphics
(431)
Using standard 3D graphics
(431)
Using external 3D graphics
(431)
14.4. Hierarchical 3D animations. Embedded 3D presentation
(432)
14.5. 3D Windows
(433)
Navigation in the 3D scene at runtime
(434)
Multiple 3D views
(434)
14.6. Cameras
(435)
Example 14.4: A very simple model with multiple 3D windows and cameras
(435)
Example 14.5: Camera on a moving object
(436)
14.7. Lights
(438)
Example 14.6: Examples of Lights in 3D Scene
(440)
Chapter 15. Randomness in AnyLogic models
(444)
15.1. Probability distributions
(444)
Probability distribution functions
(445)
Distribution fitting
(448)
Custom (empirical) distributions
(448)
15.2. Sources of randomness in the model
(450)
Randomness in process models
(451)
Randomness in agent based models
(452)
Example 15.1: Agents randomly distributed within a freeform area
(453)
Example 15.2: Agents randomly distributed over a finite set of locations
(454)
Randomness in system dynamics models
(456)
Example 15.3: Stock price fluctuations in a system dynamics model
(456)
Randomness in AnyLogic simulation engine
(457)
15.3. Random number generators. Reproducible and unique experiments
(457)
Random number generators
(457)
The seed. Reproducible and unique experiments
(459)
Example 15.4: Reproducible experiment with a stochastic process model
(459)
Chapter 16. Model time, date and calendar. Virtual and real time
(462)
16.1. The model time
(462)
Time units
(463)
Developing models independent of time unit settings
(464)
16.2. Date and calendar
(464)
Finding out the current date, day of week, hour of day, etc.
(465)
Constructing dates. Converting the model date to the model time and vice versa
(466)
Specifying timeouts and delays in days, months, years
(467)
16.3. Virtual and real-time execution modes
(468)
Execution mode API
(469)
References
(472)
