20IO International Conference on Computer Application and System Modeling (ICCASM 2010)

Research on Multi-agent-basedSimulation Platform for Production Scheduling

Wang Feng

The Department of Business Administration

Shanghai Lixin University of Commerce

Shanghai, 201620, China

RubyI231@sohu.com

Abstract-Simulation has become one of the main means for

solving production scheduling problem. This paper analyzed

the basic characteristics of production scheduling system and

all kind of production factors. Then, the correspondingAgent model of many production factors was established , and a multi - agent - system- based Architecture of production

scheduling simulation system was brought up. At last, this

paper developed a distributed simulation platform based on

all these study, which would provide decision-making supports

for the production scheduling.

Key words-production scheduling, multi-agent, system

simulation

I. INTRODUCTION

With emergence of the virtual enterprise and

competition between supply chains, the cross-enterprise

scheduling has become a more prominent problem.

lob-shop scheduling, which has many uncertainties and

random factors involved, is one of the most important and

the most complex problem in the production stage, and is

a well-known NP-hard problem[I). The traditional

strategy for solving planning and scheduling problems is

to follow a hierarchical approach in which the planning

problem is solved first to define the production targets.

The scheduling problem is solved next to meet these

targets and there is no interaction between the two

decision levels. In such a traditional strategy, the planning

model is typically a linear and simplified representation,

which is used to predict production targets and material

flow over several months (up to 1 year)[2), but it is very

difficult to describe and effectively solve the dynamic

scheduling in a multi-stage, multi-level and multi-subject

setup. With the developments in computer technology,

simulation has become a very powerful tool in solving the

production scheduling problem[3).

In recent years, many production quality simulation

platforms have been developed and some of them have

wide range of applications, for example, Repast[4), Extand,

and Arena[5). While these simulation platforms solve the

problem of production scheduling in an undivided

homogeneous system, in reality, various supply chain

enterprises and virtual enterprise alliances often are

distributed, and the subsystems often are heterogeneous.

These differences in the simulation platform and actual

environment reduce their applicability. Thus, it is

necessary to research on a distributed simulation platform,

that integrates information from various heterogeneous

subsystems in real-time; that has distributed decision

making capabilities; and, that has centralized control

mechanisms. We use multi-agent technology[6) to design

such a distributed, inter operable, real-time simulation

platform.

The objective of our research is to put forward a

multi-agent-based simulation model for production

scheduling system, then to design and develop the

distributed simulation platform.

II. INTRODUCTION OF MULTI-AGENT

Agent is generally thought as an independent entity

which has the abilities of perceiving, solving problem and

communicating with the outside world, and could decide

its own behavior in accordance with the internal

knowledge and external motivation[7). Agent also could

be seen as an autonomous process, which could control

their own decision-making and behavior through its

understanding of the environment so that it could achieve

one or more goals.

Because the capacity of single agent to solve the

problem is limited, Multi-Agent System (MAS) has gotten

rapid development, which solves complex problems by

means of the mutual cooperation of many agents, at the

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2010 International Conference on Computer Application and System Modeling (lCCASM 2010)

same time, every agent keep the capacity to solve problem

independently. A lot of research show that there are some

advantages when you model a system with MAS, as

follows: Firstly, the model has the powerful abilities to

describe model and solve the distributed problem;

Secondly, the model has a satisfactory response time via

the distributed computing; Thirdly, MAS is a good natural

and suitable vector of the artificial intelligence algorithms,

such as genetic algorithm, immune algorithm, in particular,

swarm intelligence algorithms, for example, ant colony

algorithm, particle swarm optimization and so on.

The intelligent agents in the MAS are an independent

entity which constantly interacts with the environment.

The research on the MAS touch upon how to coordinate

intelligent behaviors of a group of agents, that is,

knowledge, objectives, intentions and planning, so as to

union them together to take action and solve the problems.

The relations of intelligent agents may be collaboration,

some time it is competition. The MAS releases the

restraint of centralized planning and order controlling, and

it provides distributed controlling, emergency and parallel

processing. In addition, it could reduce the cost of

software or hardware and provide a more rapid means to

solving problems.

At present, the research on MAS is very active and is

growing to be maturity, usually used in more complex,

high flexible, dynamic, open and distributed environment.

With applying of artificial intelligence and computer

technology in more wide range of manufacturing industry,

The Multi-Agent technology offers intelligent methods for

the product designing and manufacturing, as well as

coordination and cooperation between many domains

throughout the life cycle of products, also provides a more

effective way for system integration, concurrent design,

intelligent manufacturing, and agile manufacturing.

III. MULTI-AGENT-BASED MODEL OF PRODUCTION

SCHEDULING SYSTEM

A. Analysis a/production scheduling system

There are all kinds of robots, intelligent machine and

other resources, in the point of intuitive view; they are the

most suitable deputy of the multi-agent system. When a

manufacturing system is running, because that it is

impossible to grasp the overall situation (production task,

process planning, the status of shop and machine, etc.)

accurately and fully in advance, almost every functional

node needs to make local-decision independently and

autonomy. Then, it has become impossible to make a

long-term planning or scheduling, and the methods which

intent to have a comprehensive pre-analysis also lost their

practical significance. Local reasoning and

decision-making of the nodes on behalf of different

function departments in different manufacturing systems

have become very important, for this reason, it would be

helpful and feasible to employ agents in these

decision-making nodes. Each function of the

manufacturing system could be simulated with single

agent or a group of well-organized agents.

It is easy to divided whole manufacturing process into

a number of interactions between agents, and also is easy

to reorganize, increase or decrease. Modeling the

decision-making nodes with agents brings about that all

kinds of events are solved independently, so that it is

possible to solve the problem of dynamic adaptability in

product manufacturing. The scale of the MAS, that is the

number of agents, should be corresponding with the

flexibility of the manufacturing line. Because of using the

local-deciding principle, every agent could be modified

independently. When the production masks or

manufacturing resources change, new decision-making

nodes or agents could be easily increased or deleted

correspondingly. The basic principle of modeling

manufacturing system with the MAS is to simplity the

system as possible as we can so that it is easy to control

the system. The interactions of agents is helpful to

simplity greatly the complexity of the manufacturing

system, thus the MAS is straightforward, and could be use

to model the manufacturing system with high flexibility

and adaptability, whose production masks and production

process are difficult to be predict and describe.

Thus, it is feasible in theory that we abstract and divide

the manufacturing workshop into many function units, and

establish the multi-agent-based production scheduling

system. In order to do this, we should firstly analyze all

kinds of production elements, and build the model of

single agent, and then build the MAS model for the whole

manufacturing system.

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2010 International Conference on Computer Application and System Modeling (ICCASM 2010)

B. Main Agents

Production scheduling systems, which are different in

different enterprises, have some common elements, such

as equipments, material-handling tools, warehouse, order

or planning, management centers. The process of

production often is same in a great degree; of course,

sometime it is started by orders, sometime by planning,

and sometime by both. Both orders and planning could be

thought as an output, which are described as orders.

In general, there are these entities (showed in table 1)

III a production scheduling system, we build

corresponding agent models for all these elements, some

common entities are show in Table 1.

TABLE l. MAIN AGENTS

element name descriptions agent name

Processing Machines or other

machines manufacturing equipments Machine-agent

Including a group of same Processing

machines Workcenter -agent center

buffers Temporary storing materials Buffer-agent

�aLerial

warehouse SLoring material Material-agent

Product

warehouse Storing end products Production-agent

Scheduling ln charge or scheduling and

management managing SMC-agent

center

Transport Some vehicles or equipments for

equipments transporting Transport-agent

workers Worker-agent

In multi-Agent system, single Agent has its own life

cycle, it should be different states in different stages of the

life cycle, these states are basic global states that every

agent has. In Jade, the global states include starting,

suspending, activating, deleting and freeing, and so on.

Some agents have their own some states, called as local

states, because they are on behalf of some entities, such as

manufacturing equipments, generally their agents have the

following states: (l)free: well-equipped, no task in queue;

(2)working: running for task; (3) repairing: regularly

maintaining or repairing for a fault; (4)waiting material:

task in queue and waiting material; (5)changing tools:

task in queue, but need to change tools for the task.

C. Multi-Agent-based Simulation Model

Based on the analysis of the system process, this paper

brings up a multi-agent model for the production

scheduling system, as shown in Figure 1.

The scheduling management center (smc-agent) would

inquire the stock after receiving orders, if the inventory

can meet the orders, it would not start producing;

otherwise, it would compute the difference and get a

production planning, then, in accordance with the dates

from the production data center, the smc-agent would

decompensate the planning into process tasks, and get a

job series.

In order to solve the most key problem that is how to

schedule these jobs, this paper provides two methods: one

is to build a scheduling agent that in charge of carry it out,

the scheduling agent could be built according on different

algorithms or rules; another is to assign the jobs in the

light of some rules or protocols that are settled depend on

negotiating between agents.

If the fIrst method is taken, the smc-agent need only to

send the job-order to the relevant agent directly, then the

purpose of the simulating often is to observe the

production course and to fInd the bottleneck. The second

method is a research hot at present, and it need to fully

develop and make use of the intelligence of agents.

Manufacturing agents, including the machine-agent

and workcenter-agent, ask or receive jobs from the

smc-agent, and then they ask and get materials from their

front buffers. After fInishing the task, they send the parts

to buffers where the parts are waiting for next processing.

If the front buffers have no the needed materials, the

manufacturing agents could consider the next task or wait

for the materials according to scheduling results or rules.

It is necessary to transporting materials between

buffers, manufacturing nodes and stocks, it is not

time-consuming in some cases, it needs time and

equipments to do the jobs in another cases. The smc-agent

is also in charge of scheduling these equipments, their

behaviors are similar to the manufacturing agents, and this

paper simplifIes these problems. The agent of worker is

also similar to the manufacturing agent.

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2010 International Conference on Computer Application and System Modeling (ICCASM 2010)

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IV. SYSTEM DEVELOPMENT

The paper selected Jade (Java Agent Development

Framework) to develop the system. Jade is the middleware

developed by TILAB for the development of distributed

multi-agent applications based on the peer-to-peer

communication architecture. All the intelligence, the

initiative, the information, the resources and the control

can be fully distributed on mobile terminals as well as on

computers in the fixed network. The environment can

evolve dynamically with peers, that in Jade are call agents,

that appear and disappear in the system according to the

needs and the requirements of the application environment.

Jade is fully developed in Java and is based of the

following driving principles: interoperability, uniformity

and portability, easy to use, pay-as-you-go philosophy.

A. System Architecture

The system architecture is provided as shown Figure 2

by this paper. Jade includes both the libraries (i.e. the Java

classes) required to develop application agents and the

run-time environment that provides the basic services and

that must be active on the device before agents can be

executed. Each instance of the Jade run-time is called

container (since it "contains" agents). The set of all

containers is called platform, which has only one "main

container". Other containers must enroll in the main

container. In addition, the main container include two

special agents: AMS(Agent Management system) and

DF(Directory Facilitator), the former is in charge of giving

some management function, for example, naming, deleting,

creating agents; the latter gives the yellow-page function,

that is, a agent could find the services that it needs via the

DF. All communications between agents need to get help

from the Message Transport System.

In order to improve the software reusability, the

simulation system makes use of inheriting and builds a

father agent called father-agent that has some basic

common function that all agents should have, for example,

registering, messaging and time advancing. Other agents

could build by inheriting the father-agent.

Intelface

Figure 2. System architecture of multi-agent

simulation system in Jade

B. Simulation Case

The simulation system need test to refine, so it has

took a production case to run in the system. The case is

shown in Figure 3, it includes five manufacturing

agents(A l-A5), twelve buffer agents(B I-B 12), and ten

transport agents(Tl-T1 0). Dotted-arrow shows that the

transports need get help from transport agents; solid-arrow

shows that the transport needn't transport agents or time.

The original materials are stored in the buffers(B 1, B2,

B3), the final production in the buffer(BI2). The

scheduling rule for transport agents is the FIFO rule, the

production tasks are classified into three priorities, and the

tasks in the same priority are also scheduled according to

the FIFO rule. The case has been run for many times, it

showed that the system run in good. Via the view-agent

you could observe any data in any an agent that you want,

an example is shown in Figure 4, where you can see the

stock of materials(Q, F, Y) in buffer B7. Of course, you

could select how to display the data. Please see last page

of this document for Figure 3 and 4.

V. CONCLUSIONS AND REMARKS

This research analyzed the basic characteristics of

production scheduling system and all kind of production

factors. Then, the corresponding Agent model of many

production factors was established, and a

multi-agent-system-based Architecture of production

scheduling simulation system was brought up. At last, a

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2010 International Conference on Computer Application and System Modeling (ICCASM 2010)

distributed simulation platform based on all these study

was developed, and a case was run in the simulation

platform, the results show that the system is run in good

and easy to use. The research would provide

decision-making supports for the production scheduling.

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control "[1]. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 32(5), 560-573.

Figure 3. Production case model

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