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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. Motivation Requirements Model Implementation Demo.

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Towards automated procurement via agent-aware negotiation support

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  1. 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

  2. Agenda Motivation Requirements Model Implementation Demo

  3. 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

  4. 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

  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. Agenda Motivation Requirements Model Implementation Demo

  8. 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.

  9. 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.

  10. Agenda Motivation & Goal Requirements Model Agent Service Description Demo

  11. 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).

  12. 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

  13. 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

  14. Agenda Motivation Requirements Model Implementation Demo

  15. Service Architecture RFQ RFQ’ RFQ’ RFQ’

  16. Service Architecture SOLUTION SOLUTION PROBLEM PROPOSE (BIDS) PROPOSE (BIDS)

  17. AUML Interaction protocol IP-CFP IP-RFQ IP Request Solution Protocols implemented as JADE behaviours (extensions of the FSMBehaviour class) IP-AWARD

  18. Service Ontology (I) RFQ ProviderResponse Buyer’s Constraints Providers’ Constraints

  19. Service Ontology (II) Bid Solution Problem

  20. 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

  21. iBundler @ work TRANSLATOR BUYER RFQ ProviderResponse

  22. iBundler @ work TRANSLATOR BUYER Problem Solution

  23. Agenda Motivation & Goal Requirements Model Agent Service Description Demo

  24. 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

  25. iBUNDLER DEMO

  26. Demo Contract Allocation. Unconstrained RFQ Ignoring business rules may lead to inefficient allocations of products/services!!! Unbalanced allocation Unsafe allocation Unsafe allocation

  27. Demo Contract Allocation. Constrained RFQ Balanced allocation Safe allocation Safe allocation

  28. Demo Conclusion 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?

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