Case Studies of MAS in Industrial Production:

AARIA & The M. Fox Scheduling System

CS525M, Spring 2002

Presented by:

Edwin P. Jacques

AARIA Introduction Vision: Interconnect manufacturing processes

implemented as agents to outperform current, centrally-controlled manufacturing systems. This is done by allowing quick reconfiguration to optimize profits and allow customization in a dynamic business climate.

AARIA• Autonomous Agents at Rock Island Arsenal

• Agent research conducted at University of Cincinnati.

• Rock Island Arsenal is a military production facility used to demonstrate results of the research.

AARIA: What Does It Do?

Dialog with customers and suppliers Allocate resources to new jobs as they are

introduced into system Optimize schedules across resources Recover from faults in the factory Dispatch work according to a schedule Report Results Designed to be used in real life or simulation

AARIA: How is the system decomposed? Persistent agents

• Change little over time. Do not enter or leave the system without human involvement.

• Resource broker agents manage capacity-constrained resources, e.g.– people, machines & facilities

• Part broker agent manage material handling and inventory

• Unit-process broker agents build products from parts Transient agents

• Rate of change comparable to part processing times.

• For example, materials produced and sold by part broker go through states of birth, inquiry, commitment, availability, activity & death.

AARIA: Collaboration Diagram

AARIA: How many agents are there?

Later stages of production are customers to earlier stages of production.

Stages can be added to simulate a supply chain of any depth. Each stage contains part, unit processor and resource brokers.

Final customer is viewed as just another customer in the supply chain.

AARIA: What is inside the agents?

AARIA is focused on agent organization, not implementation of specific agents.

Agents are viewed simply as problem-solvers.

The internal implementation is borrowed from agent infrastructure software, Cybele. e.g.– including discovery of trading partners & negotiation protocols.

AARIA: Agent Negotiations Architecture calls for agent negotiation along axes of

possible production (e.g.- price, quality, delivery time, product features & speed of answers)

Prototype only considers price and delivery time. Sequence:

• Customer orders product

• Available Unit Process Brokers receive order and solicit bids from part brokers.

• Continues up to end of the chain, and works its way back down as bids are returned.

• If customer accepts bid, commitments are made in a similar manner.

AARIA: Scheduling

Problem:• Determining if agent can perform a task based on

parameters (e.g.– time & cost) is not hard.

• Determining optimal sequence in which to arrange tasks in all agents throughout system is NP-complete problem.

Solution:• When order presented, a possible schedule is

developed for every possible delivery time.

• When schedule is accepted, it is optimized as a background task.

AARIA: Maturity

Performance:• Inventory costs reduced as much as 47%

• Lead times cut as much as 59%

• Schedule reducible time down 93%• Overtime, inventory holding charges, etc.

Deployment:• Prototyped. Capable of simulation.

• Being productized under brand name eFactory.

• The Army might be using it, but that’s privileged information.

AARIA: References

1. Baker, Albert D, et al. Agents and the Internet: Infrastructure for Mass Customization. IEE Internet Computing. September, 1999.

2. Baker, Albert D, et al. Internet-based Manufacturing: A Perspective from the AARIA Project. Working Paper, Enterprise Action Group, Cincinnati, Ohio. August, 1999.

3. http://www.aaria.uc.edu/

FOX Introduction

Vision• Agents are a natural way of decomposing a supply

chain.

• Develop an architecture for agent-oriented supply-chain management.

FOX Scheduling System• Generic building shell that implements generic,

reusable components for communicative-act based communication, conversational coordination & role-based organization modeling.

FOX: What Does It Do?

Provide mechanism for distributing supply chain activities to agents.

Provide coordination among agents.• Communicates plans/requests.

• Communicates changes in plans (delays) Handling responsiveness constraints

(solution quality improves if more time given). Allow information within agents available to

be made available to other interested agents.

FOX: What are the agents? System provides a shell that can be used to implement any

distribution of supply-chain intelligence. Decomposition is often at management (organizational) level. Typical decomposition:

• Order acquisition agent: negotiates with customers about orders.• Logistics agent: Coordinates plants, suppliers and distribution

centers to achieve most on-time deliver and cost minimization.• Transportation agent: Assignment and scheduling of transportation

resources.• Scheduling agent: Scheduling and rescheduling activities in the

factory.• Resource agent: Manages inventory and purchases, with goal of

minimizing cost and maximizing delivery.• Dispatching agent: Performs the order release and real-time floor

control as directed by scheduling agent. Operates within degree of freedom allowed by scheduling agent.

FOX: Agent Shell Overview Communication Service allows information

exchange:• Domain-independent speech acts• Domain-dependent content

COOrdination Language (COOL), based on the conversation metaphor.• Express shared conventions• Capture coordination and social knowledge

Generic models of actions and behavior.• Not well specified. No detailed information available

on components at this level.

FOX: Agent Shell

FOX: Communication Language

Based on KQML(propose ;; communicative action

:sender A:receiver B:language list:content (or (produce 200 widgets)

(produce 400 widgets)):conversation C1:intent (explore fabrication possibility)).

FOX: Conversation Plans Rule-based descriptions of how an agent acts and reacts in certain situations. Defines a state machine that provides skeleton for conversation.(def-conversation-plan 'customer-conversation

:content-language 'list:speech-act-language 'kqml:initial-state 'start:final-states '(rejected failed satisfied):control 'interactive-choice-control-ka:rules '((start cc-1) (proposed cc-13 cc-2) (working cc-5 cc-4 cc-3)

(counterp cc-9 cc-8 cc-7 cc-6) (asked cc-10) (accepted cc-12 cc-11))).

FOX: Conversation Plans (cont)

FOX: Conversation Rules Describe actions to be performed when conversation is in a

given state. This rule says that when logistics is in the start state, and a

proposal for an order is received, tell the customer it’s being worked on.(def-conversation-rule 'lep-1

:current-state 'start:received '(propose :sender customer

:content(customer-order :has-line-item ?li))

:next-state 'order-received:transmit '(tell :sender ?agent

:receiver customer :content '(working on it) :conversation ?convn)

:do '(update-var ?conv '?order ?message)).

FOX: Control Architecture

Each agent operates in a loop where:• Events are sensed (such as messages from

other agents)

• Current situation is evaluated, • updating or creating beliefs

• updating or creating conversations

• Entry is selected from agenda• If new conversation, retrieve plan and instantiate

• If existing conversation, apply conversation rules

FOX: Learning

When entering new state• Special rules dynamically estimate how well

system does in respect to certain criteria• e.g.– time, quality, etc.

• Rules look at underachieving criteria and compute new global criterion to correct it.• e.g.– give time greater weight in planning process

FOX: Logistics Execution Conversation Plan

FOX: Small Team Formation

FOX: Implementation Details

COOL specification contains:• 12 conversation plans

• 200 rules and utility functions

• 2600 lines of COOL code to describe (concise!) In a three-tier supply chain example,

• overstock of raw inventory in case of breakdown reduced 26%.

• 7500 lines of COOL code (plus 2000 for GUI)

• Written by one author with no CS background in 3 months.

FOX: References

1. Fox, Mark S. Agent-Oriented Supply-Chain Management. The International Journal of Flexible manufacturing Systems. December, 2000.

2. http://www.eil.utoronto.ca/iscm/papers/index.html

Appendix A: When will agents be implemented? (Trudeau, 1996)