Phase 1 Problems
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1 Phase 1 Problems ● Small size – 20 to 150 methods – 10 to 20 agents ● Problem Classes – One static + one dynamically arriving problem – Uncertainty in duration/quality – Too much work to do (only class with more than 70 methods) – Non Local Effects (hard enables + soft facilitates) – Syncronization 1
description
Phase 1 Problems. Small size 20 to 150 methods 10 to 20 agents Problem Classes One static + one dynamically arriving problem Uncertainty in duration/quality Too much work to do (only class with more than 70 methods) Non Local Effects (hard enables + soft facilitates) Syncronization. 1. - PowerPoint PPT Presentation
Transcript of Phase 1 Problems
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Phase 1 Problems
● Small size– 20 to 150 methods– 10 to 20 agents
● Problem Classes– One static + one dynamically arriving problem– Uncertainty in duration/quality– Too much work to do (only class with more than 70
methods)– Non Local Effects (hard enables + soft facilitates)– Syncronization
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Phase 2
● Much more complex scenario generator– mix and match various problem classes, now called
templates– better control of the assignment of agents to
methods ● within a template & between problems
– More dynamics (both new tasks & changes to old)– New NLEs (hinder, disable)– New QAFs (Sum-And, Exactly-One)
● Much larger problems– 10–100 agents– 725–10,000 methods
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Experimental Environment
● Scenario Generator generates random scenarios.
● Each scenario contains one or more problems.● Problems are independent, except that they
may overlap in time. Agents can only do one thing at a time.
● Problems consist of Template Instances.● Templates comprise tasks and methods.● Methods are individual executable actions.
Experimental Environment
● Each problem, and some complex templates, are broken into a sequential set of time windows.
● Each window has an earliest start time and a deadline.
● We can independently control how tight these start time and deadlines are relative to method execution time and how much windows overlap with one another.
● Non-local effects (NLEs) also are limited to be from earlier windows to later windows.
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Templates
● Simple– Static single-window problems from Phase 1
● Syncronization– Single sinch point from Phase 1
● Dynamic Simple– Dynamically arriving simple single-window
● NLE Chain– Multiple windows chained together with enables or
facilitates– Recursive template for each window
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Templates
● Multi-Synch – Multiple sych points under a single window
● Enables Tracks– Multiple “tracking” tasks, each with a series of
linearly enabled subtasks● Contingency
– multiple “preparation” methods enable multiple “completion” methods.
– A crucial task with multiple outcomes determines what “completion” method is actually needed at runtime
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Templates
● Second Chance Dynamic– “preparation” task almost impossible, enables
“completion” task.– an “easier” preparation task arrives, allowing an
easier second chance at completion● Circular Soft NLE
– Soft NLEs (facilitates, hinders) are set up in cycles, creating a hard optimization problem
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Simple Templates
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Syncronization Template
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Dynamic Template
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NLE Chain Template
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Multi-Synch Template
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Enables Tracks Template
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Contingency Template
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Second Chance Template
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Circular Soft NLE Template
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Experimental Classes [Phase 2]
● General Mix● Negative Interdependence● Very Dynamic● Circular Soft Interdependencies● Tight Deadlines● Uncertainty● Contingency● Big Interdependence● 100 Agent Mix
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General Mix
● Basic template mix for most problems● 10–70 Agents● 400–3000 Methods● 1 fast fallback methods● 2 redundant methods● Template Mix
– Simple[Sum]– Simple[SumAnd]– Dynamic– NLEChain
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- Multi Synch
- Enables Tracks
- Contingency
- CircularSoftNLE[facilitates]
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General Mix, 10 Agents, Actual
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General Mix, 10 Agents, Detail
Circular Soft NLE Template
Enables Tracks TemplateNLE Chain (start)
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Negative Interdependence
● Simple Template with lots of hinders and disables
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Very Dynamic
● Dynamic Template with changes to deadlines, release times, and quality/duration distributions
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Random changes to:Release TimesDeadlinesQuality DistributionsDuration Distributions
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Circular Soft Interdependencies
● Circular facilitation creates hard optimization problem
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Tight Deadlines
● Simple Templates with Sum and tight deadlines (must use alternative fallback or redundant methods)
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Uncertainty
● Simple and Dynamic Templates; high uncertainty in duration/quality; 20% method failure
Uncertainty make scheduling difficult
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Contingency
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Big Interdependence● NLE Template Mix: NLE Chains, Enables Tracks,
Circular Soft NLEs plus random NLEs
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100 Agent Mix (actual)
● Similar to General Mix without Dynamic changes
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