A MULTIAGENT SYSTEM FOR JOB-SHOP SCHEDULING

Claudio Cubillos, Leonardo Espinoza and Nibaldo Rodriguez

Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2241, Valparaíso, Chile

Keywords: AOSE, PASSI, Planning & Scheduling, Agent System, JSSP.

Abstract: The present work details the design and implementation of a multiagent system devoted to the dynamic Job

Shop Scheduling problem. The agent system tackles the planning and scheduling of jobs and their

corresponding operations on a set of available machines. The system has been modeled with the PASSI

agent-based software development methodology and implemented over the JADE agent platform.

1 INTRODUCTION

In a multiagent system (MAS) diverse agents

communicate and coordinate generating synergy to

pursue a common goal. Hence as modeling artifact,

agent-based systems borrow their key characteristics

from us, humans, and our societies.

Therefore, multiagent systems can be seen as a

natural evolution from Distributed Artificial

Intelligence (DAI) and Distributed Computing (DC).

This higher level of abstraction has allowed agents

to tackle the increasing complexity of nowadays

open software systems where integration,

transparency and interoperation among

heterogeneous components are a must.

For this technology to get more mature and

widespread, the use of agent-oriented software

engineering (AOSE) methodologies and tools are a

key factor of success.

Hence, the present work describes the design of a

multiagent system using a particular AOSE

methodology called PASSI (Cossentino et al., 2003).

The chosen domain for the system corresponds

to the job-shop scheduling problem under a dynamic

scenario in which job requests coming from clients

must be processed on-the-fly and where changes can

occur due to changes in the environment.

2 THE JSSP PROBLEM

The traditional Job-Shop Scheduling Problem

(JSSP), can be described by a set of n jobs {J

}

1≤j≤n

which is to be processed on a set of m machines

}

1≤r≤m

. Each job has a technological sequence of

machines to be processed.

The processing of job J

on machine M

is called

the operation O

. Operation O

requires the

exclusive use of M

for an uninterrupted duration p

its deterministic processing time, and each operation

has pre-assigned materials {W

}

1≤i≤k

. In addition,

each job has a due-date {D

}

1≤j≤n

A schedule is a set of completion times for each

operation {c

}

1≤j≤n;1≤r≤m

that satisfies those

constraints. The considered JSSP involves the

scheduling of n jobs J on the m machines M and

consuming k materials W while minimizing the total

tardiness regarding the due-dates.

On the other hand, the dynamic variant of the

problem adds the fact that the jobs to be processed

are not known in advance and that they must be

scheduled as they arrive.

It is one of the most hard NP-complete

combinatorial optimization problems.

2.1 Related Work

Diverse proposals of agent-based systems can be

found in literature tackling the job-shop or

production scheduling problem.

In (Saad et al., 1995) a Production Reservation

approach was proposed by using a bidding

mechanism based on the Contract Net Protocol -

CNP (Smith, 1978) to generate the production plan.

In AARIA (Parunak et al, 1997), the

manufacturing capabilities (e.g. people, machines,

and parts) are encapsulated as autonomous agents

and use a mixture of heuristic scheduling techniques:

148

Cubillos C., Espinoza L. and Rodriguez N. (2008).

A MULTIAGENT SYSTEM FOR JOB-SHOP SCHEDULING.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - SAIC, pages 148-153

DOI: 10.5220/0001705301480153

 SciTePress

forward/backward scheduling simulation scheduling,

and intelligent scheduling.

In (Maturana et al., 1999) the adaptive multi-

agent manufacturing system architecture called

MetaMorph combined the CNP with mediator-

centric federation architecture was presented.

Other recent CNP-based solutions can be found

in (Váncza, 2000) (Maturana et al., 1999) (Lim,

2002) and (Usher, 2002).

One of the contributions of the present work is to

provide a more formal design of multiagent system

devoted to Job-shop scheduling using the PASSI

methodology.

3 PASSI METHODOLOGY

PASSI is a step-by-step methodology for designing

and developing multi-agent societies. Its name

stands for a Process for Agent Societies

Specification and Implementation. PASSI integrates

design models and concepts from both OO software

engineering and artificial intelligence approaches

using the UML notation.

The models and phases of PASSI encompass

anthropomorphic representation of system

requirements, social viewpoint, solution architecture,

code production and reuse, and deployment

configuration supporting mobility of agents. The

design process with PASSI is supported by the

PASSI ToolKit (PTK, 2005) to be used as an add-in

for Rational Rose.

Figure 1 shows PASSI methodology consisting

of five models plus twelve steps in the process of

building multi-agent. These are briefly described in

the following. Please refer to (Burratato, 2002) for a

more detailed description.

System Requirements Model. Corresponds to an

anthropomorphic model of the system requirements

in terms of agency and purpose. It involves 4 steps:

a Domain Description (D.D.), an Agent

Identification (A.Id.), a Role Identification (R.Id.),

and a Task Specification (T.Sp.),

Agent Society Model. It considers the social

interactions and dependencies among the agents

involved. It considers 3 additional steps: an

Ontology Description (O.D.), a Role Description

(R.D.), and a Protocol Description (P.D.).

Agent Implementation Model. Provides the

solution architecture in terms of classes and methods

and considers: an Agent Structure Definition

(A.S.D.) and an Agent Behavior Description

(A.B.D.)

Deployment Model. Describes a model of the

distribution of the parts of the system across

hardware processing units and the migration

between processing units.

4 THE AGENT SYSTEM

The multiagent job-shop scheduling system stands

over the Jade Agent Platform (Bellifemine et al.,

1999), which provides a full environment for agents

to work.

In the following subsections, the agent system is

described making reference to the most relevant

PASSI steps and artefacts, while considering space

restrictions.

Figure 1: PASSI diagram showing Models and steps required (Burrafato, 2002).

A MULTIAGENT SYSTEM FOR JOB-SHOP SCHEDULING

149

4.1 Agent Identification (A.Id.)

In this step the use cases capturing the system

requirements are grouped together to conform an

agent. The diagram in Figure 2 shows the identified

use cases for this job-shop system and the leveraged

agents.

Firstly, the Client agent is a GUI agent in charge

of the communication between an actual client and

the rest of the system, providing the possibility of

generating a job order, and to communicate

inbound/outbound eventualities regarding such order

due to changes in the environment (e.g. order

modification/cancellation from client, order

delay/reject from the system).

Machine agents encapsulate each real machine,

being primarily in charge of its schedule

management. This involves processing requests

coming from Order agents and performing the

scheduling process.

For this, it carries out a search in the solutions

state space by implementing an optimization

heuristic. In the actual system, a search algorithm

presented by (Yoo et al., 2002), inspired in

simulated annealing was implemented.

On its turn, Order agents are devoted to the job

order management, its breakdown into operations,

the request of necessary materials for each operation

execution to Stock agents, and the request to

Machine agents for the scheduling of each operation.

For the interaction with the Stock agents the

FIPA Query Interaction Protocol (FIPA. 2002b)

standard is used. In the latter case, the FIPA Request

Interaction Protocol is used (FIPA, 2002a).

4.2 Task Specification (T. Sp.)

In this phase the scope is to focus on each agent’s

behavior, decomposing it into tasks, which usually

capture some functionality that forms a logical unit

of work and generating cohesion. Therefore for each

agent an activity diagram is developed containing

what that agent is capable of along the diverse roles

it performs. In general, an agent will be requiring

one task for handling each incoming and outgoing

message.

As example, a portion of the tasks of the Order

agent are depicted in Figure 3. The diagram shows

five tasks on the right that constitute the Order agent

capabilities. The ReceiveOrder task handles Client

Order Agent

<<Agent>>

Client Agent

<<GUI Agent>>

Stock Agent

<<Agent>>

Machine Agent

<<Agent>>

Stock Administrato

(from 01-Domain Des...

)

Material check-out

(from Stock Agent)

Stock Lev el Determining

(f rom St ock Agent )

Material Request

(from Stock Agent)

Stock Management

(f rom Stock Agent)

<<extend>>

<<include>>

<<extend>>

Inf orm Assignment

(from Client Agent)

Generate Material

(from Stock Agent)

<<ex tend>>

Orden Deletion

(from Client Agent)

Client

(from 01-Dom

...)

Div ide Job Order

(from Order Agent)

Request Materials

(from Order Agent)

<<communicate>>

Generate Order

(f rom Client Agent)

<<communicate>>

Inf orm Operations Plan

(from Machine Ag...

Machine's Operations Plan

Management

(from Machine Ag...

<<include>>

Order Modif ication

(from Client Agent)

<<extend>>

Send Processing Request

(f rom O r der Agent)

Perf orm Modif ication

(from Machine Ag...

<<include>>

Operation Assignment

(from Machine Ag...

<<include>>

Prov ide Order Inf ormation

(from Order Agent)

Manage Order Ev ents

(from Order Agent)

<<comm unicate>>

Request s Processing

(from Machine Ag...

<<comm unicate>>

<<extend>>

<<comm unicate>>

Schedule Changes

Notification

(from Machine Ag...

Operator GUI

(from 01-Dom

...)

Machine Ev ents

Management

(from Machine Ag...

<<comm unicate>>

<<extend>>

Schedule Changes

Communication

(from Machine Ag. ..

<<extend>>

Scheduled Operations'

Management

(from Order Agent)

<<comm unicate>>

Manage Job Order

(from Order Agent)

<<include>>

<<communicate>>

Figure 2: Agent Identification Diagram for the Job-shop scheduling system.

ICEIS 2008 - International Conference on Enterprise Information Systems

150

messages which request for an order to be processed.

This one calls the DivideOrderIntoOperations task

which splits the order into its corresponding set of

operations on different machines. Each operation is

forwarder to the SendOperation in charge of

constructing and sending the request message to the

Machine agent. The message is handled by the

OperationScheduling task of the Machine agent.

In the following, once the machine has found a

scheduling position of that operation, it answers

back the Order through a ReportOperationSchedule

task. Such a message is handled by the

ReceiveResponseFromMachine task in the Order

side. In this step an evaluation is performed in order

to check whether if the operation corresponds to the

last one within the order. In such a case, the order

has been scheduled completely; otherwise still

remain operations to be scheduled on machines.

After processing the last operation, the

ReportingOrderStatus task is called, being in charge

of collecting all the schedules of the order’s

operation set and informing the Client the actual

schedule of its order. Finally, the Client handles the

above message through its ReceiveOrderInformation

task.

4.3 Ontology Description (O.D.)

In this step the agent society is described from an

ontological perspective, providing them with a

common knowledge of the job shop domain, and

thus, enabling communication among involved

actors and agents.

The following Figure 4 shows a portion of the

Domain Ontology Description providing the

concepts necessary for mutual agent understanding

within the society.

On the center the JobOrder is depicted, which is

decomposed into Operations. Other related concepts

are MaterialList, Stock and StockLevel.

In the middle-upper part of the diagram we can

identify on the left the Machine concept and on the

right the Client concept. Both have Events and a

Utility Function associated.

5 IMPLEMENTATION

As stated before, a system was implemented over the

Jade Framework. In addition to the described agents,

other simulation agents were created in order to

coordinate the correct creation, execution and

destroy of the agents along the diverse experiments

and runs.

Other implementation issue regards the

development of GUIs for Machine and Order agents.

The above Figure 5 shows a screenshot of two

Machine agent GUI’s. The machines 1 and 11 are

depicted in the foreground while having the Jade

platform GUI at the background.

The GUI of each Machine agent shows a grid

indicating the Job Order ID (e.g. Orden_C_8) and

the task ID, that is, the relative order of the operation

within the set of operations of the job order.

The grid also details the starting and processing

times together with the Early and Latest Start Times

(EST & LST respectively).

In the case of the Order GUI a similar approach

was taken. In this case the grid shows the list of all

the tasks (or operations) for the given Job order

indicating times and the corresponding machines on

each case.

Regarding the benchmark data for the

experimentations, Figure 6 shows the format of the

plain .txt file used to feed the Order and Machine

agents. The example shows the 15x15 example (15

machines, 15 orders) from (Taillard, 1994).

Figure 3: Part of the Task Specification Diagram for the

Order Agent.

6 CONCLUSIONS

The design of an agent-based software system for

dynamic job shop scheduling was described.

The agent formalization with PASSI promotes

the architecture maintainability, its ability to cope

with newer requirements and the possibility to scale

and integrate other actors and systems.

Next steps consider the testing with benchmark

data tackling diverse scenarios and topologies of

distribution and the implementation of diverse

scheduling algorithms (e.g. Genetic algorithm, tabú

search, sa).

A MULTIAGENT SYSTEM FOR JOB-SHOP SCHEDULING

151

ActualStartTime

<<c oncept>>

MachineSchedule

<<concept>>

OrderStatus

Status : String

<<concept>>

DateTime

Actual Time : Date

<<concept>>

Early StartTime

<<concept>>

Lat eSt artTime

<<concept>>

Early FinishTime

<<concept>>

LateFinis hTime

<<concept>>

ActualFinishTime

<<c oncept>>

JobDate

<<concept>>

StartTime

<<concept>>

FinishTime

<<concept>>

DueDate

<<concept>>

FinishDate

<<concept>>

Mac hineBrea k D own

<<concept>>

OperationCancelled

OperationID : string

<<concept>>

MachineDelay

Mac hineI D : String

Delay Estimate : Date

<<concept>>

OperationDelay

OperationID : String

Delay Estimate : Date

<<concept>>

OperationScheduled

OperationID : String

ActualStartTime : Date

<<concept>>

Ev ent

<<concept>>

Machine Ev ent

MachineID : string

<<concept>>

Client Ev ent

ClientID : String

<<concept>>

Operation

OperationID : String

Step : int

MachineID : St rin g

ProcessingTime : Date

Priority : f loat

<<concept>>

0..*

0..1

0..*

0..1

0..3

Mat erialLis t

<<c oncept>>

CancelOrder

JobOrderID : String

<<concept>>

ChangeDueDate

NewDueDate : Date

<<concept>>

AnticipateDueDate

<<concept>>

Post icipateDueDate

<<c oncep t>>

StockLev el

Quantity : int

Unit : String

<<c oncept>>

JobOrder

JobOrderID : String

Priority : f loat

<<concept>>

1..*

1..2

OperationList

<<c oncep t>>

0..*

0..1

Tardi ne s s

<<concept>>

MeanTardines s

<<concept>>

Non-placed Jobs

<<c onc ept>>

Throughput

<<concept>>

Stock

StockID : String

Mat e rialD : St ring

ProviderID : String

<<c oncep t>>

Prov ider

ProviderID : String

Address : String

PhoneNumber : String

Email : String

<<concept>>

1..*

Client

ClientID : String

Surname : String

Names : String

Address : String

PhoneNumber : String

Email : String

<<concept>>

1..*

0..*

Machine

MachineID : string

Schedule : MachineSchedule

<<concept>>

0..*

Utility Function

<<concept>>

UtilityProperty

Property Ty pe : Propert y

Weight.float

<<c oncept>>

1..*

Property

<<concept>>

Material Entry

<<c onc ept>>

Mat erial Ex it

OperationID : String

<<c oncept>>

Stock Movement

Mat e rial D : St ring

Quantity : int

<<c oncept>>

Mat eri al

MaterialD : String

<<concept>>

1..*

Figure 4: Part of the Domain Ontology Diagram for the Job-shop system.

Figure 5: Screenshot of Order Agents GUI under a JADE Platform.

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152

Figure 6: Part of the Task Specification Diagram for the

Order Agent.

ACKNOWLEDGEMENTS

This work is part of Project No. 209.746/2007

entitled “Coordinación en una sociedad

multiagentededicada a la programación y control

bajo ambiente dinámico”, funded by the Pontifical

Catholic University of Valparaíso (www.pucv.cl).

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