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Towards automated procurement via agent-
aware negotiation support
Andrea Giovannucci, Juan A. Rodríguez-Aguilar
Antonio Reyes, Jesus Cerquides, Xavier Noria
Ljubljana March 1st 2005
Artificial Intelligence Research Institute
2
Motivation
Requirements
Model
Implementation
Demo
Agenda
3
Motivation. Parts purchasingFRONT SUSPENSION, FRONT WHEEL BEARING ACQUISITION
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
GOAL: BUY PARTS TO
PRODUCE 200 CARS
4
Motivation
Typical negotiation (sourcing) event in industrial procurement
PART DESCRIPTION UNITS
1 FRONT HUB 2
7 LOWER CONTROL ARM BUSHINGS
3
8 STRUT 4
9 COIL SPRING 2
14 STABILIZER BAR 1
5
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.
6
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.
7
Motivation
Requirements
Model
Implementation
Demo
Agenda
8
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.
RequirementsBuyer side
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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.
RequirementsProvider side
10
Motivation & Goal
Requirements
Model
Agent Service Description
Demo
Agenda
11
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, , ........ Vm bid valuation functions per item • qi
j decision variable on the number of units selected from j-th offer for i-th item
• pij unitary prices per item
• Δij = <δi1
j,…, δ ikj> bid values offered by j-th bid for i-th item
Realised as a variation of MDKP (multi-dimensional knapsack problem).
nj mi
ji
ji
jiiij pqVwy
1 1
),,(
Model
12
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
13
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
Model
14
Motivation
Requirements
Model
Implementation
Demo
Agenda
15
Service Architecture
RFQ
RFQ’RFQ’
RFQ’
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Service Architecture
PROPOSE (BIDS)
PROPOSE (BIDS)PROBLEM
SOLUTIONSOLUTION
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AUML Interaction protocol
Protocols implemented as
JADE behaviours (extensions of the
FSMBehaviour class)
IP-RFQ IP-CFP
IP Request Solution
IP-AWARD
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Service Ontology (I)RFQ
Buyer’s Constraints
ProviderResponse
Providers’ Constraints
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Service Ontology (II)
ProblemBid Solution
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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
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iBundler @ workBUYERTRANSLATOR
ProviderResponse
RFQ
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iBundler @ workTRANSLATOR BUYER
Problem
Solution
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Motivation & Goal
Requirements
Model
Agent Service Description
Demo
Agenda
24
FRONT SUSPENSION, FRONT WHEEL BEARING
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
DemoParts acquisition
GOAL: BUY PARTS TO
PRODUCE 200 CARS
25
iBUNDLER DEMO
26
CONTRACT ALLOCATION
RFQ LINE
CONTRACTEE ALLOCATION
1 Alfa Ricambi
UK Parts Ltd.
50%
50%
2 Alfa Ricambi
GHL Motor
75%
25%
3 Alfa Ricambi
GHL Motor
8%
92%
4 UK Parts Ltd. 100%
5 UK Parts Ltd. 100%
DemoContract Allocation. Unconstrained RFQ
Unbalanced
allocation
Unsafe
allocation
Unsafe
allocation
Ignoring business rules may lead to inefficient allocations of products/services!!!
27
DemoContract Allocation. Constrained RFQ
CONTRACT ALLOCATION
(CONSTRAINED)
RFQ LINE
CONTRACTEE ALLOCATION
1 Alfa Ricambi
UK Parts Ltd.
75%
25%
2 Alfa Ricambi 100%
3 Alfa Ricambi
GHL Motor
33%
67%
4 UK Parts Ltd.
GHL Motor
50%
50%
5 UK Parts Ltd.
GHL Motor
75%
25%
Balanced
allocation
Safe
allocation
Safe
allocation
28
DemoConclusion
CONTRACT ALLOCATION
(CONSTRAINED)
RFQ LINE
CONTRACTEE ALLOCATION
1 Alfa Ricambi
UK Parts Ltd.
75%
25%
2 Alfa Ricambi 100%
3 Alfa Ricambi
GHL Motor
33%
67%
4 UK Parts Ltd.
GHL Motor
50%
50%
5 UK Parts Ltd.
GHL Motor
75%
25%
CONTRACT ALLOCATION
(UNCONSTRAINED)
RFQ LINE
CONTRACTEE ALLOCATION
1 Alfa Ricambi
UK Parts Ltd.
50%
50%
2 Alfa Ricambi
GHL Motor
75%
25%
3 Alfa Ricambi
GHL Motor
8%
92%
4 UK Parts Ltd. 100%
5 UK Parts Ltd. 100%
iBundler helps buyers & providers to reach better agreeements
29
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?
30
Thank you ... Any questions?