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

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

Agenda

Motivation

Requirements

Model

Implementation

Demo


Motivation parts purchasing

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


Motivation

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


Motivation1

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.


Towards automated procurement via agent aware negotiation support

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.


Agenda1

Agenda

Motivation

Requirements

Model

Implementation

Demo


Towards automated procurement via agent aware negotiation support

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.


Towards automated procurement via agent aware negotiation support

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.


Agenda2

Agenda

Motivation & Goal

Requirements

Model

Agent Service Description

Demo


Model

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).


Model1

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


Model2

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


Agenda3

Agenda

Motivation

Requirements

Model

Implementation

Demo


Service architecture

Service Architecture

RFQ

RFQ’

RFQ’

RFQ’


Service architecture1

Service Architecture

SOLUTION

SOLUTION

PROBLEM

PROPOSE (BIDS)

PROPOSE (BIDS)


Auml interaction protocol

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

Service Ontology (I)

RFQ

ProviderResponse

Buyer’s Constraints

Providers’ Constraints


Service ontology ii

Service Ontology (II)

Bid Solution

Problem


Implementation features

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

iBundler @ work

TRANSLATOR

BUYER

RFQ

ProviderResponse


Towards automated procurement via agent aware negotiation support

iBundler @ work

TRANSLATOR

BUYER

Problem

Solution


Agenda4

Agenda

Motivation & Goal

Requirements

Model

Agent Service Description

Demo


Towards automated procurement via agent aware negotiation support

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


Towards automated procurement via agent aware negotiation support

iBUNDLER DEMO


Towards automated procurement via agent aware negotiation support

Demo

Contract Allocation. Unconstrained RFQ

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

Unbalanced

allocation

Unsafe

allocation

Unsafe

allocation


Towards automated procurement via agent aware negotiation support

Demo

Contract Allocation. Constrained RFQ

Balanced

allocation

Safe

allocation

Safe

allocation


Towards automated procurement via agent aware negotiation support

Demo

Conclusion

iBundler helps buyers & providers to reach better agreeements


Summary and future works

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?


Towards automated procurement via agent aware negotiation support

Thank you ... Any questions?


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