ProMAIS: A MULTI-AGENT MODEL FOR PRODUCTION

INFORMATION SYSTEMS

Lobna Hsairi, Khaled Ghédira

Laboratoire URIASIS, Institut Supérieur de Gestion

41, rue de la liberté-Cité Bouchoucha-Tunis, CP 2000 le Bardo, Tunisie

Faiez Gargouri

Laboratoire LARIS, Faculté des Sciences Economiques et de Gestion de

Sfax, Route de l'aérodrome km 4,Bp 1088-3018 Sfax, Tunisie

Keywords: Information System (IS), Cooperative Information System (CIS), Agent, Multi-Agent System (MAS),

Production system, ProMAIS, Manufacturing system.

Abstract: In the age of information proliferation and communication advances, Cooperative Information System (CIS)

technology becomes a vital factor for production system design in every modern enterprise. In fact, current

production system must hold to new strategic, economic and organizational structures in order to face new

challenges. Consequently, intelligent software based on agent technology emerges to improve system design

on the one hand, and to increase production profitability and enterprise competitive position on the other

hand. This paper starts with an analytical description of logical and physical flows dealt with manufacturing,

then proposes a Production Multi-Agent Information System (ProMAIS). ProMAIS is a collection of

stationary and intelligent agent-agencies with specialized expertises, interacting to carry out the shared

objectives: cost-effective production in promised delay and adaptability to the changes. In order to bring

ProMAIS’s dynamic aspect out, interaction protocols are specially zoomed out by cooperation, negotiation

and Contract Net protocols.

1 INTRODUCTION

The use of the Information System (IS) technology

today has already imbedded into every modern

enterprise’s core as a result of the quantity of

information and the need of sharing them.

Recent developments, driven by the proliferation

of parallel architecture, the advances in wide area

net-working and the Internet democratization, have

made the information systems parts of increasingly

complex systems. That’s how the classic IS are

becoming hard to generate necessitating to break

them up into sub-systems and distribute them. This

results in the birth of the Cooperative Information

Systems (CIS) technology. The CIS aim at

continued cooperativity among user groups through

componentized networks of information systems.

One of the most important classes of IS are

production systems. Such system consists in an

important domain from strategic, economic and

organizational points of view. A number of new

concepts of such systems has been proposed and

developed in the past few years, e.g., Virtual

Organizations, Virtual Enterprise, Supply Chain

Management and Electronic Commerce, etc. These

concepts are similar or overlapping.

Currently, the world is undergoing a Hi-Tech

industrial revolution with information technology as

its main feature. The production systems will be

characterized by intensively concurrent engineering

based on information technologies such as

digitalization, computer network, artificial

intelligence (Qiao et al, 1999).

To compete effectively in today’s markets,

manufacturers must be moving towards an open and

297

Hsairi L., Ghédira K. and Gargouri F. (2004).

ProMAIS: A MULTI-AGENT MODEL FOR PRODUCTION INFORMATION SYSTEMS.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 297-302

DOI: 10.5220/0002602502970302

 SciTePress

distributed architecture. Increasingly, traditional

centralized and sequential manufacturing or

production system decisions are being found

insufficiently flexible to respond to changing

production styles and highly dynamic variations in

production requirements.

The intelligent software agents technology

provides a natural way to overcome such problems.

In sharp contrast to traditional software programs,

software agents are programs that help people to

solve problems by collaborating with other software

agents and other resources in the network. That’s

how, the use of the agent-oriented approach is

imposed in contrast to the object-oriented approach.

Recently, agent technology has been considered

as an important approach for developing intelligent

manufacturing and production systems. Agents help

to capture individual interests, local decision-making

using incomplete information, autonomy,

responsiveness, robustness and modular, distributed,

reconfigurable organizational structures.

A Multi-Agent System (MAS), as a society of

autonomous agents, is an inherently open and

distributed system. It is formed by a group of agents

combined with each other through a network to

cooperatively solve a common problem. The

system’s inter-agent communication capability

provides the essential means for agent collaboration

that aids interoperability of the system.

There is a multifold advantage in using an

agent-based approach for manufacturing and

production systems. First, information for

production system is stored and processed in a

distributed manner in contrast to that stored in one

large program. Second, the agent-based approach

makes the incremental improvement of the

production system possible through learning and

cooperation between agents. Third, it provides a

promising method for enterprise integration

(Qiao et al 1999).

In this paper we present our research efforts and

experiences in analysing the information (logical)

and physical flows between the various divisions of

the enterprise, modeling an intra-organization

cooperative information system based on

decomposition into recursive agents, developing a

MAS architecture named Production Multi-Agent

Information Systems (ProMAIS). ProMAIS is an

ongoing project. It is an organizational structure

which draws descriptions of flows between actors in

the production systems. It allows the actors involved

to communicate with each other, to conserve the

autonomy of every one in making local decision, to

guarantee the adaptability and ability to quickly

respond to the changes and the new conditions of

markets and to react quickly to unexpected risks.

The remainder of this paper is structured as

follows: section 2 reviews research literature,

section 3 presents ProMAIS the Multi-Agent model

for Production Information Systems, section 4

presents a prototype implementation, section 5 gives

concluding remarks and perspectives.

2 RESEARCH LITERATURE OF

AGENT TECHNOLOGY IN THE

INDUSTRY

Recently, agent technology has been considered as

an important approach for developing industrial

distributed systems. A number of researchers have

attempted to play agent technology to industrial

enterprise integration. In this section, we briefly

review some interesting projects in this domain such

as MetaMorph II (Shen et al, 1998) and an Agent-

Based Intelligent Manufacturing System for the 21

Century (Qiao et al, 1999).

At the university of Calgary, the MetaMorph II

project start at the beginning of 1997. The aim of

this project was to integrate the manufacturing

enterprise’s activities such as design, marketing,

planning, scheduling, execution and product

distribution, with those of its suppliers, customers

and partners into an open distributed intelligent

environment. To do this, the researchers propose an

Agent-Based Manufacturing Enterprise

Infrastructure (ABMEI) combining and extending

the architecture used in previous projects

MetaMorph I (Maturana et al, 1996), ABCDE

(Balasubramanian et al, 1996) and DIDE

(Shen et al, 1996). In this Infrastructure, the system

is primarily organized at the highest level through

sub-system mediators which are connected via the

Internet/Intranet. Each sub-system is connected to

the system through a special mediator. Each

manufacturing enterprise must have at least one

Enterprise Mediator. It can be considered as the

administration center of the enterprise. All other

mediators should register with it.

Qiao shows in (Qiao et al, 1999) that the

intelligent manufacturing system is a solution of the

problems of the 21

century manufacturing industry.

In order to establish an Agent-Based Manufacturing

System, they propose an intelligent object called

Manufacturing Agent (MA). According to the

general structure of the manufacturing enterprise,

they propose the Agent-Based Manufacturing

System architecture, in which the whole operation

logic of a manufacturing enterprise is divided into

four parts: central part, management, planning and

production. Each part consists of a group of MAs.

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All the MAs are connected to the distributed

enterprise network or Intranet. This MA network is

heterarchical in nature.

3 PRODUCTION MULTI-AGENT

INFORMATION SYSTEMS

(ProMAIS)

In order to present our ProMAIS model, we must

first exhibit how to ‘agentify’ the IS.

3.1 Information Systems as MASs

The definition of Hayes-Roth (Hayes-Roth, 1995)

advocates the most important features of an agent.

Therefore, intelligent agents continuously perform

three functions. First, the ability of perception of

dynamic conditions in the environment. Second, the

capability of action to affect conditions in the

environment. Finally, the ability of reasoning to

interpret perceptions, solve problems, draw

inferences, and determine actions. Autonomous

agents have the opportunity and ability to make

decisions of their own.

Nevertheless, in the case of an agentified IS, the

perception of dynamic conditions in the environment

refers to different kinds of incoming messages that

perform a communication event (such as receiving

an acknowledgement of a sales order or receiving a

request for a sales quotation) and environment

events (such as receiving a payment). The action to

affect conditions in the environment refers to

communication acts of the agentified IS (such as

acknowledging a sales order) and to physical acts

(such as making a payment or delivering goods).

Finally, the reasoning to interpret perceptions, solve

problems, draw inferences and determine actions

refers to the computational inference of correct

answers to queries, the proper processing of

incoming messages and the determination of proper

actions (such as checking all sales orders of a

customer whose credibility is in question or using an

alert when the fulfilment of a commitment is

overdue).

Therefore, an IS may be explicitly designed as

an agent (Wagner, 2000) by treating its information

items as its beliefs or knowledge, adding further

mental components like perceptions (as incoming

messages) memory and commitments and providing

support for agent-to-agent communication on the

basis of a standard Agent Communication Language

(ACL) (Labrou et al, 1999).

3.2 System design

The actors of an enterprise are collectively

responsible for carrying out a common objective.

The common objective consists in making cheaper

and more cost-effective products. However, ‘cost-

effective’ means the production with the best

quality, in the promised delay and with a much

lower price. The entities are very interdependent and

interact to respond to the same objective and to

assure the improvement of its performance. Each

entity operates under a number of constraints and

objectives and is responsible for its process and its

production. Hence, the performance of each entity

depends on the performance of the others and of

their good cooperation. Thus, each entity has its own

mission to contribute to the global goal of the net

and it is connected to other entities defined with hard

collabration relationship. Modeling such entities

with agent technologies provides a natural way to

cover such features. Our proposed model ProMAIS

is a way of modeling such entities.

ProMAIS is based on reflexivity and

heterogeneity. The reflexivity implies that an agent

is itself a MAS and inversely. Agents should have

capability of controling their interaction with the

external environment and their internal decision.

ProMAIS is composed of a number of agents

forming a MAS (see figure 1). The diversity of

agents implies the heterogeneity of the architecture

of knowldge representation and exchanged

messages.

Thus, our approach involves a number of agents

continually in interaction. ProMAIS is built on the

eco-resolution model proposed by

Ferber and Jacopin (Ferber & Jacopin, 1990) and

enriched by Ghedira (Ghedira, 1994). It’s consist in

a MAS in which each agent has a local memory that

consists of the static and the dynamic knowledge,

the acquaintance (the agents that it know and

communicate with) and a mailbox in which it

receives different kinds of messages, that it stores

and treats one by one according to their priority

degree. In addition, each agent has a behaviour

based on satisfaction (see figure 2).

While (MailBox not empty)

Withdraw_And_Treat_First_Message;

If Satisfy

Satisfaction_behaviour;

Else

Unsatisfaction_Behaviour;

EndIf

Figure 2: The general agent behaviour.

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299

In the ProMAIS organizational structure, we can

distinguish two environments: internal and external

one. The external environment includes all kinds of

external actors to the enterprise such as Partner

agents, Supplier agents, Customer agents and

Competitor agents. As part of our study, we take an

interest in Customer and Supplier agents ones. The

internal environment contains all entities of a

manufacturing enterprise. In our proposed model,

agents having a MAS architecture have a common

mailbox beside their individual ones. This common

mailbox belongs to a Messenger agent. This agent

has the role of communicating the received message

to the appropriete agent. Furthermore, it constitues

the interface between the local MAS agents and the

other agents in the global model.

The Monitor, the Design & Engeneering and the

Stock Management & Procurement agents have a

MAS architecture in ProMAIS. The organizational

structure of each one is shown in figures 3, 4 and 5

respectivly.

Figure 1: Organizational structure and Interaction model of ProMAIS.

Planning

Agent

Manufacturing

Agent

Scheduling

Agent

Stock Management

& Supply Agent

Maintenance

Agent

Quality Control

Agent

Design &

Engineering Agent

Internal Environment

External Environment

Caption

Cooperation & communication

Communication

Coordination & communication

Negotiation

Monitor

Agent

Customer Agents

Partner Agents

Competitor Agents

Supplier Agents

Figure 3: Organizational structure of Monitor Agent

Marketing

Agent

Finance &

Account

Agent

Research &

Development

Agent

anageme

Agent

Messenger

Agent

Monitor Agent

Figure 4: Organizational structure of Design &

Engineering Agent.

Design

Agent

ngineerin

Agent

Messenger

Agent

Design &

Engineering Agent

Figure 5: Organizational structure of Stock

Mangement & Procurement Agent.

RM : Raw Material

SFP: Semi-Finished Product

FP : Finished Product

Procurement

Agent

FP Stock

Agent

Messenger

Agent

Stock Management &

Supply Agent

RM & SFP

Stock Agent

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3.3 MAS Dynamic aspect

3.3.1 Communication protocol

A communication act consists of an information

transmission from a sender to a receiver. This

information is encoded into an ACL

(Agent Communication Language), and it is decoded

by the receiver (Labrou et al, 1999).

Our MAS communication protocol is based on

an intentional point-to-point mode (between two

agents), a broadcast mode (one to all agents), or a

multicast mode (to a selected group of agents). It

uses the folowing two message primitives:

SendMsg (Sender, Receiver, “Message”).

GetMsgs (MailBox) to extract all current

messages from the agent mailbox and reconstitue the

message list according to the priority degree of each

message and the agent internal believes.

In order to capture more semantics of MAS

dynamic aspects, it is necessary to make an

ontological distinction between the simple

communication, the coordination, the cooperation

and the negotiation (see figure 1).

3.3.2 Cooperation protocol

Agents involved in ProMAIS take shape of MAS.

They cooperate in order to satisfy the customer, to

be more flexible and open to different kinds of

orders and to respond quickly to the dynamic

changes of external environment to take a position in

the market. Thus, we distinguish the main

cooperation situations between one or several

agencies of agents and we consequently distinguish

the cases of emergency orders and those of

unexpected risks.

In case of emergency orders, we draw apart the

orders hand over by a faithful customer or

characterized by a cash payment and the immediate

orders. In the first case, the cooperation takes place

between the Monitor (in particular the Marketing

agent), the Planning, the Scheduling and the

Manufacturing agents. While, in the case of

immediate orders, in addition to the agents involved

in the last case, the Stock Management &

Procurement (SMP) agent (in particular the Finished

Product Stock agent) is added.

In the case of the unexpected risks, we draw

apart the cases of a strike, a delay in carrying out a

task or in the arrival of raw materials, on the one

hand and those of machine breakdown, on the other

hand. All these kinds of unexpected risks necessitate

a cooperation between different agents involved. In

the first case, the cooperation takes place first

between the Manufacturing and Scheduling agents.

If the problem is not solved, the Planning agent is

then involved to cooperate with already montioned

agents. If the problem is still not solved (the

replanning operation introduces a delay in the

deadline of some orders), the Monitor agent is

involved to cooperate with the others, with the aim

to make decisions like the use of the subcontracting,

the additional hours or, if it is necessary, the interim

hours. In such a case, the strategic decisions already

made can be modified. In case of a machine

breakdown, the cooperation takes place between the

Manufacturing, the Maintenance and the Scheduling

agents if the state of the machine is adjustable.

However, if the breakdown is serious and cannot be

adjusted in a convenient time, the Monitor agent is

involved with the aim to make quickly the decision

to overcome this kind of problem like the use of the

subcontracting.

3.3.3 Negotiation protocol

In ProMAIS, we can distinguish two cases of

negotiation. The first one appears at the time of the

hand-over of an order from a Customer agent. The

negotiation will take place between the Customer

agent and the Monitor agent regarding the fixing

order price. The second one appears at the time of

fixing the estimated price of a new product that is

conceived by the Design & Engeneering agent. This

negotiation will take place between the Design &

Engineering agent and the Monitor agent.

3.3.4 Contract Net protocol

The Contract Net is a negotiation protocol proposed

by Smith (Smith, 1980). It facilitates distributing

subtasks among various agents. The agent wanting

to solve the problem broadcasts a call for bids, waits

for a reply for some time, and then awards a contract

to the best offer(s) according to its selection criteria.

The Contract Net protocol used in our proposed

model ProMAIS consists in a process for selecting a

supplier for a purchasing operation. For this, the

SMP agent broadcasts a call for bid for all Supplier

agents. Each Supplier agent thus becoming a bidder

sends its bid as regarding price and delay. Even the

Supplier agents can’t satisfy the order send the same

bid message giving the value of (–1) to the price and

delay variables. This message is necessary for the

learning of the SMP agent. For example, if it

receives at many times a refusal message from the

same Supplier agent, the SMP agent eliminates the

latter from the list of Suppliers.

After receiving the propositions (agreement and

refusal bids) the SMP agent verifies the fund

availability through a cooperation with the Monitor

agent and it will select a Supplier agent to perform

the purchasing operation according to its criteria

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(mainly a price and a fund availability for this

purchasing operation), and award a contract to it.

4 PROTOTYPE

IMPLEMENTATION

The proposed model ProMAIS has been

implemented in a simulation form.

This virtual system incorporates heterogenous

agents. It is implemented within a multi-agent

platform called AgentBuilder Pro 1.3

(Reticular, 2000) which is an integrated tool suit for

constructing intelligent software agents. It consists

of two major components: the Toolkit and the

Run-Time system. All agents are implemented using

Java programming language.

Communication among agents was realized

using RMI protocol and the inter-agent messages

were formatted in KQML (Knowledge Query and

Manipulation Languages) format.

In this simulation, the agent execution cycle

consists of the following steps: processing new

messages, determining which rules are applicable to

the current situation, executing the actions specified

by these rules and updating the mental model in

accordance with these rules.

5 CONCLUSION AND FUTURE

WORKS

Manufacturing systems are organizations composed

of heterogeneous entities involved in the production

and the delivery of finished product or services.

Nowadays, the IS for such organization can be

viewed as a collection of sub-systems distributed

between the different entities. This results in the

cooperative information system technology. The

entities communicate via computerized data. The

manufacturing organization must follow the

dynamic of the market and respond quickly to the

customers requirements.

The choice of intelligent software agent

technology provides a natural way to design such

systems because the intrinsic feature of MAS

correspond to those to be preserved in the hoped

production systems. Autonomy, heterogeneity,

openness, cooperation, dynamicity, commitment

etc… are at the heart of our reflection.

ProMAIS, provides the integration of different

entities (divisions) in manufacturing and production

systems. In fact, a cooperative MAS allows each

agent to communicate and cooperate with others

while conserving its autonomy.

ProMAIS is an ongoing project. A major

short-term research goal is to study the position of

humans in the system. The long-term research goal

tends towards fixing the distribution of the global

databases, study the interaction of agents with

databases, the resolution of different kinds of

conflict resulting from the heterogeneity of data

sources and then to implement this approach in a

real manufacturing enterprise.

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