Applying Market-Oriented Programming to Product Routing Christopher J. Hazard April 9 th, 2007 North...
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![Page 1: Applying Market-Oriented Programming to Product Routing Christopher J. Hazard April 9 th, 2007 North Carolina State University.](https://reader035.fdocuments.us/reader035/viewer/2022062907/5a4d1b897f8b9ab0599bdf0d/html5/thumbnails/1.jpg)
Applying Market-Oriented Programming to Product Routing
Christopher J. HazardApril 9th, 2007
North Carolina State University
![Page 2: Applying Market-Oriented Programming to Product Routing Christopher J. Hazard April 9 th, 2007 North Carolina State University.](https://reader035.fdocuments.us/reader035/viewer/2022062907/5a4d1b897f8b9ab0599bdf0d/html5/thumbnails/2.jpg)
April 9, 2007
Christopher J. Hazard North Carolina State University 2
Motivation from Warehouses
• Buffer and regroup• Methodologies
– People gather items– Conveyers and Carrousels– Items bundled on movable shelves
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April 9, 2007
Christopher J. Hazard North Carolina State University 3
Outline
• Alphabet Soup• Representing MOP Economies• Implications of Market Segmentation• Effective Valuation Functions
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April 9, 2007
Christopher J. Hazard North Carolina State University 4
What is Alphabet Soup?
LetterStation Word
Station
LetterStation
WordStation
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April 9, 2007
Christopher J. Hazard North Carolina State University 5
Alphabet Soup Testbed Mechanics
• Speed & acceleration clamped • Collisions bad• “Perfect sensing”• Throughput & utilization
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April 9, 2007
Christopher J. Hazard North Carolina State University 6
Demo
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April 9, 2007
Christopher J. Hazard North Carolina State University 7
Alphabet Soup Research Challenges
• Which letters in which buckets?• Bucket specializations?• Which buckets to fulfill words?• Which stations to assign words and
letters?• Which bucketbots for which buckets?
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April 9, 2007
Christopher J. Hazard North Carolina State University 8
Market-Oriented Programming
• Resource allocation– Agents– Markets/Auctions– Resources– Valuations
• Transform optimization problems• Interface: price & resource• Sometimes altruistic or honest agents
[Wellman ‘96]
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April 9, 2007
Christopher J. Hazard North Carolina State University 9
Representing an Economy
Item Type I
Agent Type A Agent Type
Item Type
Item Can Be Sold By
Auction
Auction with Multiple Item or Agent Types
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April 9, 2007
Christopher J. Hazard North Carolina State University 10
Representing an Economy (2)
arrow anonymous price? linear price? representationno yes {A+, I}no no {A+, I*}yes yes {A, I}yes no {A, I*}
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April 9, 2007
Christopher J. Hazard North Carolina State University 11
Economy 1
Word
Letter Bundle
Letter
Bucket
Letter Station
Transportation
Bucketbot
Storage Right
StorageLetter Bundle
Letter Builder
Word Station
Word Queue
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April 9, 2007
Christopher J. Hazard North Carolina State University 12
Economy 2
Letter Bundle
Storage RightLetter Bundle
Letter
Bucket
Letter Station
Letter Builder
Transportation
Storage
Bucketbot
Word Station
Word Word Queue
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April 9, 2007
Christopher J. Hazard North Carolina State University 13
Economy 3
Letter BundleBundle Slot
Letter
Word
Bucket
Letter Builder
Transportation
Word Queue
Bucketbot
Storage Right
Storage
Letter Station
Word Station
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April 9, 2007
Christopher J. Hazard North Carolina State University 14
TAC SCM Example
PC
Agent & Factory
Motherboard
MotherboardSupplier
RFQ (Customer)
CPU
CPUSupplier
Memory
MemorySupplier
Hard Disk
Hard DiskSupplier
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April 9, 2007
Christopher J. Hazard North Carolina State University 15
Segmenting the Market
A X
B X'
C
X
A
B
X'
C
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April 9, 2007
Christopher J. Hazard North Carolina State University 16
Communicating Price Information Between Market Segments
• Information channel– Auctioneers– “Middleman” agents
• Information timing– Tâtonnement-like– Reactive
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April 9, 2007
Christopher J. Hazard North Carolina State University 17
Auctioneer Communication
• Tâtonnement-like [Cheng & Wellman ’98]
– DCOP [Modi et al. ’03 and Petcu & Faltings ’05]
– Artificially harder problem?• Inefficient “discovery” of valuations• Constraints between agents on both sides
• Reactive– Exposure problem
• No free disposal– Sub-problems unaccounted for (e.g. TSP)
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April 9, 2007
Christopher J. Hazard North Carolina State University 18
“Middleman” Agents as Information Channel
• Exposure problem– No free disposal
• Learning market prices– Speculators– Specialization
• Propagate demand for goods not in market• Leverage uncertainty models• Tâtonnement issues
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April 9, 2007
Christopher J. Hazard North Carolina State University 19
Valuation Functions
• Time, energy, external costs• Opportunity cost• Balancing & utilization• Agency assignment• Challenge of mapping to global optimum
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April 9, 2007
Christopher J. Hazard North Carolina State University 20
Conclusions
• Market-Oriented Programming:– Distributed– Subsystem Performance– Heuristics (valuations)– (Some) Protection from self-interested agents
• Many design choices– Uncertainty