Applying Market-Oriented Programming to Product Routing

Christopher J. HazardApril 9th, 2007

North Carolina State University

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Motivation from Warehouses

• Buffer and regroup• Methodologies

– People gather items– Conveyers and Carrousels– Items bundled on movable shelves

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Outline

• Alphabet Soup• Representing MOP Economies• Implications of Market Segmentation• Effective Valuation Functions

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What is Alphabet Soup?

LetterStation Word

Station

LetterStation

WordStation

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Alphabet Soup Testbed Mechanics

• Speed & acceleration clamped • Collisions bad• “Perfect sensing”• Throughput & utilization

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Demo

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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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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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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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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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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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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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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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TAC SCM Example

PC

Agent & Factory

Motherboard

MotherboardSupplier

RFQ (Customer)

CPU

CPUSupplier

Memory

MemorySupplier

Hard Disk

Hard DiskSupplier

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Segmenting the Market

A X

B X'

C

X

A

B

X'

C

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Communicating Price Information Between Market Segments

• Information channel– Auctioneers– “Middleman” agents

• Information timing– Tâtonnement-like– Reactive

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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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“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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Valuation Functions

• Time, energy, external costs• Opportunity cost• Balancing & utilization• Agency assignment• Challenge of mapping to global optimum

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Conclusions

• Market-Oriented Programming:– Distributed– Subsystem Performance– Heuristics (valuations)– (Some) Protection from self-interested agents

• Many design choices– Uncertainty