Towards automated procurement via agent-aware negotiation support
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Towards automated procurement via agent-aware negotiation support Andrea Giovannucci, Juan A. Rodríguez-Aguilar Antonio Reyes, Jesus Cerquides, Xavier Noria. Artificial Intelligence Research Institute. Ljubljana March 1st 2005. Agenda. Motivation Requirements Model Implementation Demo.

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Towards automated procurement via agent-aware negotiation support

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Towards automated procurement via agent-aware negotiation support

Andrea Giovannucci, Juan A. Rodríguez-Aguilar

Antonio Reyes, Jesus Cerquides, Xavier Noria

Artificial Intelligence Research Institute

Ljubljana March 1st 2005


Agenda

Motivation

Requirements

Model

Implementation

Demo


PART NUMBER

DESCRIPTION

UNITS

1

FRONT HUB

2

7

LOWER CONTROL ARM BUSHINGS

3

8

STRUT

4

9

COIL SPRING

2

14

STABILIZER BAR

1

Motivation. Parts purchasing

FRONT SUSPENSION, FRONT WHEEL BEARINGACQUISITION

GOAL: BUY PARTS TO

PRODUCE 200 CARS


PART

DESCRIPTION

UNITS

1

FRONT HUB

2

7

LOWER CONTROL ARM BUSHINGS

3

8

STRUT

4

9

COIL SPRING

2

14

STABILIZER BAR

1

Motivation

Typical negotiation (sourcing) event in industrial procurement


Motivation

  • Multi-item, multi-unit, multi-attribute negotiations in industrial procurement pose serious challenges to buying agents when trying to determine the best set of providing agents’ offers.

  • A buying agent’s decision involves a large variety of preferences expressing his business rules.

  • Providers require to express their business rules over their offering.


Goal

  • To provide a negotiation service for buying agents to help them determine the optimal bundle of offers based on a large variety of constraints and preferences.

    • assistance to buyers in one-to-many negotiations; and

    • automated winner-determination in combinatorial auctions.

  • To relieve buying agents with the burden of solving too hard a problem (NP problem) and concentrate on strategic issues.


Agenda

Motivation

Requirements

Model

Implementation

Demo


Requirements

Buyer side

  • Negotiation over multiple items.

  • “Fuzzy” expressiveness to compose demands(e.g. quantity requested per item lies within some range).

  • Safety constraints. Establish minimum/maximum percentage of units per item that can be allocated to a single provider.

  • Capacity constraints. Allocated units cannot excede providers’ capacities.

  • Item constraints. Capability of imposing constraints on the values a given item’s attributes take on.

  • Inter-item constraints. Capability of imposing relationship on different items’ attributes.


Requirements

Provider side

  • Multiple bids/offers per provider

  • Offers expressed over quantity ranges in batch sizes (e.g. Provider P offers Buyer B from 100 to 200 3-inches screws in 25-unit buckets)

  • Offers over bundles of items

  • Types of offers over bundles

    • XOR. Exclusive offers that cannot be simultaneously accepted.

    • AND. Useful for providers whose pricing expressed as a combination of basis price and volumen-based price (e.g. Provider P’s unit price is $2.5 and different discounts are applied depending on volume of required items: 1-10 units (2%), 10-99 (3%), 100-1000 (5%)).

  • Homogeneous offers that enforce buyers to select equal number of units per offer item.


Agenda

Motivation & Goal

Requirements

Model

Agent Service Description

Demo


Model

  • Modelled as a combinatorial problem defined as the optimisation(maximisation or minimisation) of:

    • yj.(binary) decision variable on for the submitted bids

    • 0≤wj≤1 degree of importance assigned by the buyer to item i-th

    • V1, , ........ Vmbid valuation functions per item

    • qijdecision variable on the number of units selected from j-th offer for i-th item

    • pijunitary prices per item

    • Δij = <δi1j,…, δ ikj> bid values offered by j-th bid for i-th item

  • Realised as a variation of MDKP (multi-dimensional knapsack problem).


Model

SIDE CONSTRAINTS

FORMALISATION

  • Units allocated to each provider falls within his offer

  • Allocated units per bid multiple of bid’s batch

  • Aggregation of selected bids’ units lies within requested ranges of units

  • Units allocated to a single provider do not exceed his capacity

  • Percentage of units allocated to a single provider does not exceed safety constraints


Model

SIDE CONSTRAINTS

FORMALISATION

  • Homogeneous combinatorial bids must be satisfied

  • Providers per item must comply with saftey constraints

  • AND bids must be satisfied

  • XOR bids must be satisfied

  • Intra-item constraints must be satisfied

  • Inter-item constraints must be satisfied


Agenda

Motivation

Requirements

Model

Implementation

Demo


Service Architecture

RFQ

RFQ’

RFQ’

RFQ’


Service Architecture

SOLUTION

SOLUTION

PROBLEM

PROPOSE (BIDS)

PROPOSE (BIDS)


AUML Interaction protocol

IP-CFP

IP-RFQ

IP Request Solution

Protocols implemented as

JADE behaviours (extensions of the

FSMBehaviour class)

IP-AWARD


Service Ontology (I)

RFQ

ProviderResponse

Buyer’s Constraints

Providers’ Constraints


Service Ontology (II)

Bid Solution

Problem


Implementation features

  • All agents in the agency implemented in JADE

  • FIPA as ACL (agent communication language)

  • Two implementations of SOLVER

    • ILOG CPLEX + SOLVER

    • MIP modeller based on GNU GLPK library

  • Ontology editor: Protegé2000

  • Ontology generator: The Beangenerator Protege2000 plugin to generate ready-to-use Java classes


iBundler @ work

TRANSLATOR

BUYER

RFQ

ProviderResponse


iBundler @ work

TRANSLATOR

BUYER

Problem

Solution


Agenda

Motivation & Goal

Requirements

Model

Agent Service Description

Demo


PART NUMBER

DESCRIPTION

UNITS

1

FRONT HUB

2

7

LOWER CONTROL ARM BUSHINGS

3

8

STRUT

4

9

COIL SPRING

2

14

STABILIZER BAR

1

Demo

Parts acquisition

FRONT SUSPENSION, FRONT WHEEL BEARING

GOAL: BUY PARTS TO

PRODUCE 200 CARS


iBUNDLER DEMO


Demo

Contract Allocation. Unconstrained RFQ

Ignoring business rules may lead to inefficient allocations of products/services!!!

Unbalanced

allocation

Unsafe

allocation

Unsafe

allocation


Demo

Contract Allocation. Constrained RFQ

Balanced

allocation

Safe

allocation

Safe

allocation


Demo

Conclusion

iBundler helps buyers & providers to reach better agreeements


Summary and future works

  • iBundler is an agent-aware negotiation service to help buying agents to determine the optimal bundle of offers based on a large variety of constraints and preferences. It provides:

    • assistance to buyers in one-to-many negotiations; and

    • automated winner-determination in combinatorial auctions.

  • What happens if all constraints cannot be met?

  • Empirical evaluation of the agentified service vs web service

  • How to support bidders?


Thank you ... Any questions?


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