An Implementation of the Contract Net Protocol Based on Marginal Cost Calculations

Transcription

1 An Implementation of the Contract Net Protocol Based on Marginal Cost Calculations By Tuomas Sandholm 1993

2 Introduction Formalization of the idding and awarding decision process Formalization ased on marginal cost calculations ased on local agent criteria Network as whole functions more effectively CNP is extended to allow for clustering of tasks, to deal with the possiility of a large numer of announcement and id messages Implementation of TRACONET is asynchronous, distriuted and autonomous

3 Formal model Announcing Bidding Awarding decisions This model covers (differently from CNP) Calculation of marginal costs Clustering tasks into sets to e negotiated over as atomic argaining items Solving the prolem of announcement message congestion

4 Vehicle routing prolem Dispach center 2 Dispach center 3 Dispach center 1 Dispach center 4 Dispach center 5

5 Vehicle routing prolem Geographically dispersed dispatch centers Overlapping working areas Deliveries initiated y factories Certain numer of vehicles Routing prolem Vehicle attriutes: cost per km, max duration, max length, max load weight, max volume Optimization task

6 Solving the prolem Each center intelligent agent Solves its own local routing prolem Negotiates with each other Exchanges with delivery tasks when it is profitale Negotiation is real time No gloal optimization

7 Message passing AGENT 2 AGENT 3 Announcing Looser Bidding Bidding Announcing Winner AGENT 1 Announcing Looser AGENT 4 AGENT 5

8 TRACONET architecture Each involved party can make a id Announcements are not sent to every agent No fixed hierarchy Agent can act oth as a manager and as a contractor

9 Agent structure Bidder Local Optimizer Awarder Local control loop AGENT Annauncer Award taker

10 Agent structure Bargaining system Announcer Bidder Awarder Award taker Local optimizer counting of marginal costs of a set of deliveries (to remove or to add) Optimizing all deliveries Removing and adding sets of deliveries to agent s routing solution

11 Local control Agent s routing solutions are made Start of LCL Invoke of idder, awarder, award taker, announcer Exiting/entering network dynamically Joining Exiting prolems Will not receive award (needs listening phase) Other agents could e making a id and will not receive even looser message (send looser messages)

12 Announcing c rem (T) marginal cost saved if the delivery set T is removed from routing solution c rem (T) heuristic approximation of c rem (T) But (!) c rem rem (T 1 T 2 ) c ) c rem(t 1 ) +c rem rem(t 2 )

13 c c c c Bidding Reads the announcements from other agents Needs to estimate c add (T ) add + add all add non add ( T ) = min f T T T f T T ( T ) = max f T T T f T T B B B B pos pos z ( T ) = f T T Tz f T Tz pos pos ( T ) = f ( T T ) f ( T ) z z z

14 Bidding (semi-opportunistic calculation) heuristic approximation is used for calculation of f', for which f(t) ) <= f'(t) ) for any task set T ) is calculated from f (T ) assuming that none of the agent s unsettled ids are awarded f (T T Evaluation c c add (T (T)) may lead to uneneficial contracts

15 Awarding Award is given for agent with most inexpensive id After awarding, T is removed from agents T and transportation rent solution. If timeout sends «looser» messages to all Agent checks if the awarding still eneficial to itself

16 c c Awarding Needs to estimate c rem (T ) c c + rem rem all rem non rem ( T ) = max f T T f ( T T ) T B B pos z ( T ) = ( ) f T Tz f T T Tz pos pos z ( T ) = min f T T f ( T T ) T B B pos z ( T ) = f ( T ) f ( T T ) z

17 Awarding (semi-opportunistic calculation) heuristic approximation is used for calculation of f, for which f(t) ) <= f (T) ) for any task set T f (T -T) is calculated from f (T ) assuming that none of the agent s unsettled ids are awarded Evaluation c c rem (T (T)) may lead to uneneficial awards Taking awards An agent's award taker reads the awards and inserts the deliveries from the awards to the agent's deliveries T and its transportation solution.

18 Experimental results

19 Questions?