Applying Multi-Agent System Modelling to the Scheduling Problem

in a Ceramic Tile Factory

Authors: Adriana Giret, Estefanía Argente, Soledad Valero, Pedro Gómez, Vicente Julian

Abstract

The actual ceramic tile sector needs dynamic production processes to offer its clients on-line

programming. Thus, companies can manage real-time response about their services and

delivery times of required products, tackling a Mass Customization process in which design

and sales activities are done before the production stage. The customer service must cover all

activities that can improve the client satisfaction (offers, orders, after-sales services, etc.).

Moreover, production tasks scheduling in a ceramic tile factory is a complex problem which

requires robust and flexible software applications. Recent advances in Multi-Agent Systems

applied to general scheduling problems and industrial applications have demonstrated the

advantages of the agent technology in complex distributed problems. In this work we present

a Multi-Agent System modelling for a scheduling problem in a ceramic tile factory. Our

approach tries to improve the production performance, increase the schedule reliability and

keep updated schedules. We propose a multi-agent system in which the constituent agents

cooperate to find a feasible schedule taking into account on-line orders, factory layout and

capacity, time constraints, anticipated demands and constraints imposed by the master plan.

The value of our approach is two fold. On the one hand it is useful for defining the production

tasks schedule, while on the other hand it can be suitable for simulating purposes, for example

to find out whether a customer order is feasible or to figure out different schedules for a

specific production lot.

Keywords: Process Design, Production Planning, Multi-Agent Systems

Address: Departamento de Sistemas Informáticos y Computación,

Polytechnic University of Valencia,

C/ Camino de Vera s/n, 46022 Valencia (Spain)

agiret@dsic.upv.es

Biographical notes

Adriana Giret is originally from Villarrica (Paraguay) and receives the BS and E.C.S. degree

from the Catholic University of Asuncion in 1998 and 1999, respectively. She is a lecturer

and a PhD student at the Computer Science Department of the Polytechnic University of

Valencia. Her research interests are multi-agent systems, holonic manufacturing systems, and

agent-oriented methodologies.

Estefania Argente is originally from Valencia (Spain) and receives the BS and MS degrees in

Computing Engineering from the Polytechnic University of Valencia in 2000 and 2003,

respectively. She is a lecturer and is doing her PhD at Computer Science Department of

Polytechnic University of Valencia. Her current research interests are multi-agent systems,

agent design, methodologies and software engineering for multi-agent systems, neural

networks and genetic algorithms.

Soledad Valero is originally from Valencia (Spain) and receives the BS and MS degrees in

Computing Engineering from the Polytechnic University of Valencia in 2000 and 2003,

respectively. She is PhD student at Computer Science Department of Polytechnic University

of Valencia. Her research interests are multi-agent systems, e-commerce and soft-computing

techniques.

Pedro Gómez is originally from Bilbao (Spain) and receives the MS degree in Computing

Engineering from the Polytechnic University of Valencia in 1992. He is a researcher and is

doing his PhD at Research Centre on Production Management and Engineering of Polytechnic

University of Valencia. His current research interests are scheduling algorithms and methods

and multi-agent systems.

Vicente Julian is originally from Valencia (Spain) and receives the BS and MS degrees in

Computing Engineering from the Polytechnic University of Valencia in 1992 and 1995,

respectively. He is a lecturer and obtains his PhD at Computer Science Department at

Polytechnic University of Valencia in 2002. His current research interests are in multi-agent

systems, agent design, information retrieval and real-time systems.

1 Introduction The ceramic tile sector is probably one of the major strengths in Spain, and fundamentally in

the Valencian Community. Probably due to its high concentration, it has been capable of

generating a dynamic of competition that has ended in a continuous improvement of the

sector. This progress is finally reflected in an increase of the service, the variety of products

and in a production cost decrease. One of the main problems of this kind of companies is

production programming. Typically, this problem has been modelled trying to simplify to

maximum the environment conditions. Nevertheless the related environment is in fact very

dynamic and it reflects the dynamic conditions and constant changes of the Ceramic Tile

Sector, such as new client requirements, dynamical work entrance, the availability of

machines due to breakdown, etc.

The multi-agent system model seems to be a suitable framework for dealing with the design

and development of an application which is flexible, adaptable to the environment, versatile

and robust enough for the efficient management of a production process. The employment of

the agent/multi-agent system paradigm has currently increased as an important field of

research within the Artificial Intelligence area. Recently, the application of these techniques

seems appropriate for solving complex problems which require intelligence. In particular, the

manufacturing industry is one of the domains where the multi-agent system technology

provides a natural way to solve problems that are inherently distributed. Moreover, problems

of this kind are much related within a holonic perspective. Holons and agents are very similar

concepts (Giret, 2004). The Holonic Manufacturing approach models the manufacturing

system as a composition of whole/part entities called holons (HMS, 1994). A holon is an

autonomous and cooperative building block of a manufacturing system for transforming,

transporting, storing and/or validating information and physical objects. The holon consists of

an information processing part and often a physical processing part. A holon can be part of

another holon and it is an autonomous and cooperative entity. In this work we use agent and

holon as similar modelling notions.

Manufacturing requirements impose important properties on modelling manufacturing

systems (HMS, 1994). These properties define functional attributes and specific requirements

for the system structure and the system development process which must be considered by the

methodology. Bearing these requirements in mind we have studied software engineering

methodologies which are best suited for problems of this kind. We conducted a study on

Object-Oriented Methodologies, Enterprise Modelling Language and Methodologies and

Agent-Oriented Methodologies. This study has demonstrated that MAS methodologies are

good candidates to work with (see section 3).

In this work we present a modelling experience using INGENIAS methodology (Pavon &

Gomez, 2003) to develop an agent-oriented solution to the scheduling problem in a ceramic

tile factory. INGENIAS is a complete MAS methodology that has good performance in the

development of complex systems.

The rest of the paper is structured as follows: a complete description of the problem to be

handled is done in section 2. Then, the multi-agent system approach is described in section 3.

Finally, related works and conclusions are detailed.

2 Problem description

Currently, the ceramic tile sector needs dynamic production processes that allow the selling

process to realize an on-line programming (ASCER, 2003). Therefore, companies can offer

their clients a real-time response about their services and delivery times of the required

products. Thus, a Mass Customization process is tackled as design and sales activities must be

taken before the production stage. Most existing Extended Enterprises have initially focused

on reinforcing the links and flows between companies that are involved in the same value

chain (Macbeth, 1998). This proposal considers the service time needed to satisfy an order in

a supply chain as a key factor, because it has been observed that this time has a relevant

weight in decision taking processes. Moreover, process automation is very important to

improve several aspects in each enterprise of the supply chain (Linthicum, 1999).

The production management process improvement required to face the on-line order

configuration automation problem in a ceramic tile sector is presented in this paper. The

production system in ceramic tile enterprises is weakly flexible itself (Andrés, 2001).

Nowadays ceramic tile enterprises do not have a production management system capable of

taking advantages of its own characteristics. Therefore an agile and reactive production

planning and scheduling system is considered a major issue. This paper proposes a framework

based on a scheduler that can take advantages of the distribution, singularity and dynamicity

in several processes to reach an agile order management.

The ceramic tile productive sub-system is represented as a three stage hybrid flow shop with

sequence dependency in which three stages can be identified: press and glass lines (first

stage), kiln (second stage), and classification and packed lines (third stage) (Andrés, 2001).

The problem of scheduling hybrid flow shops in absence of setup times has been considered

by many authors, see (Vignier, Billaut & Proust, 1999) for a survey. Nevertheless, there are

few papers on machine setup times (Allahverdi, Gupta & Aldowaisan, 1999; Yang & Liao,

1999), perhaps because the usual application area are flexible manufacturing systems, where

setup times are negligible. However, in many real world cases, setup times and events in

general should be considered to improve the scheduling process. Taking these events into

account, different approaches based on the multiagent paradigm can be found (Shen, Norrie,

1999).

Figure 1. Dynamic Production Planning/Scheduling Platform

In the ceramic tile industry the master plan is normally used as the major input data to

generate the production scheduling. Nowadays, this generation process is characterized by: (i)

Weak automatization, where a large number of schedules are static and each of them is a

simple manual conversion from the master plan; (ii) Not reactive, where scheduling systems

do not face the set of several events happening in a period (break down, supplier fault,

environmental impacts such as humidity, temperature, etc.); (iii) Not taking advantage of

singularities, so schedules are based on global models that do not consider neither the own

peculiarity of each stage of the production process nor the differentiated states of each stage;

(iv) Not Distributed/No distribution, as schedules are executed on a single, centralized

computer; (v) Myopic, as models are based on one simple objective function.

In Figure 1, we show a common architecture of a ceramic tile manufacturing system. Each

module represents specific functionalities which all together implement the entire

manufacturing system. In this paper we focus in the production programming module

presenting a multi agent system modelling of its internal structure.

Sales

Negotiator

Master Plan

Generator

Production Programming

Monitor

Salesman

WarehouseSystem

ProductionManager

ProductionManager

Stage 1 Monitor

Stage 2 Monitor

Stage 3 Monitor

Stage Manager

Customers

Supplier

Dynamic ProductionPlanning/Scheduling

3 Multi-Agent Modelling Approach

Complex manufacturing systems consist of a number of related subsystems organized in a

hierarchical fashion. At any given level, subsystems work together to achieve the

functionality of their parent system. Each component can be thought as achieving one or more

objectives. Thus, entities should have their own thread of control (i.e., they should be active),

and they should have control over their own actions (i.e., they should be autonomous). Given

this fact, it is apparent that the natural way to modularize a complex system is in terms of

multiple autonomous components that act and interact in flexible ways to achieve their

objectives. Therefore, the agent-oriented approach is simply the best fit.

Regarding the ceramic tile manufacturing system, a multi-agent system (MAS) could be used

to achieve integrated optimization of the dynamic production scheduling in a ceramic tile

floor. Some advantages of MAS are: (i) it enables using different models and methods to

solve the scheduling problem in every stage of the manufacturing process; (ii) it may integrate

and optimise a range of scheduling objectives related to different processes and it can adapt to

changes in the environment while still achieving overall system goals; (iii) it provides a

foundation to create an architecture that helps reaching the complexity reduction, flexibility,

scalability and fault tolerance needed; (iv) it improves reactivity to events and enables

dynamic scheduling problem resolution; (v) it allows rapid response to new system

requirements through the addition of new modules or reconfiguration of existing ones; (vi) it

enables to dynamically integrate new agents, remove existing ones or upgrade agents; (vii)

agents operate asynchronously and concurrently, which results in computational efficiency.

In next sections we present the agent-oriented models and methodology we have used in the

development process of the scheduling problem of production tasks in a ceramic tile factory.

3.1 Description of the modelling process

Agents are a powerful abstraction tool for the design and construction of a complex system,

because they offer an appropriate way to consider systems with multiple distinct and

independent components. In this work we present a modelling experience using INGENIAS

methodology (Pavon & Gomez, 2003) to develop an agent-oriented solution to the scheduling

problem in a ceramic tile factory. INGENIAS methodology is based in MESSAGE (Caire,

Coulier, Garijo, Gomez, Pavon, Leal et al., 2002) and employs several meta-models and a

meta-model language for constructing models. A meta-model defines the primitives and

syntactic and semantic properties to be used in a model. All meta-models are based on

objects, attributes and relationships. INGENIAS methodology also integrates its meta-models

in the Rational Unified Process (RUP) for developing software systems and offers a graphical

development tool (Ingenias Development Kit, IDK).

During analysis and design phases, five different meta-models are used: (i) organization meta-

model, that defines how agents are grouped and which are the system functionality and the

existing constraints in agents behaviour; (ii) agent meta-model, which describes the particular

agents to be used and their internal mental states; (iii) interaction meta-model, that details

how agents are coordinated and interact between them; (iv) environment meta-model, that

defines what type of resources and applications are used by the system; and (v) tasks and

objectives meta-model, that relates the mental state of each agent with its tasks.

In this paper we focus in the analysis phase of the production programming (Figure 1) in

order to develop a multi-agent system for the production programming process of a ceramic

tile factory. In the following subsections we will show analysis diagrams of a distributed,

flexible and autonomous production programming system. This system could be easily

connected with the other subsystems of the ceramic tile factory in order to implement the

agile manufacturing enterprise.

3.1.1 Use Case Diagrams

A use-case diagram provides a snapshot model of a set of system behaviour that meets a user

goal. Thus, this description represents a functional requirement, showing what happens, but

not how it is achieved by the system. As mentioned above, our study is focused on the

scheduling system where four main use-cases can be identified (Figure 2). In the Schedule

Creation use-case, a feasible schedule to be carried out in the following weeks is created. This

schedule is developed based on the manufacture lots defined in the master plan. Regarding the

Schedule Modification use-case, previous schedules that have arisen problems during their

execution are modified. Therefore, those schedules are reconfigured in order to adjust them to

factory changes. Concerning the Schedule Execution Monitoring use-case, the current weekly

schedule in execution is supervised, informing about the arisen problems. Finally, in the

Master Plan Alteration Detection use-case, problems that might alter the master plan are

detected.

Figure 2. Uses Cases for the Schedule and Control of Production Tasks

3.1.2 Organization Model

The organization model is defined by the organizational goals and tasks; the workflows that

determine associations among tasks and general information about their execution; groups,

which may contain agents, roles, resources or applications; and social relationships.

Regarding the organization model for the Ceramic Tile Factory (Figure 3), the factory

organization has been decomposed in several groups focused in the different activities of the

company: Design, Commercial, Production and Purchases & Supplies. Each group contains

other groups or different roles. For example, the Production group encloses two groups:

Production Programming and Production Plant. Besides, the Production Plant contains four

roles: Plant Manager, Press Manager, Kiln Manager and Classification manager, where the

first one has authority over the rest. In addition, seven workflows are recognized: (i) Ceramic

Tile Design, where the product features to manufacture and market during the season are

specified; (ii) Sales, where commercial representatives sell factory products and manage

orders from customers; (iii) Analysis of Forecasted Sales, in which future demand predictions

are obtained from historical sales data, orders, etc.; (iv) Master Plan Definition, where

production orders are defined, sequencing the different product lots to be produced; (v)

Schedule and Control of Production Tasks, that includes activities such as determining start

time and resources allocation for a specific lot production; (vi) Ceramic Tile Production, that

covers all tasks related with the production of the different product lots; (vii) Ceramic Tile

Storage, where the final products are stored inside warehouses.

Figure 3. Ceramic Tile Factory Organization Model

Figure 4. Scheduling Process Organization Model

In the organization model for the scheduling process (Figure 4), several roles are

distinguished: (i) Manager, responsible for the agent organization, it maintains integrity

between all agents in charge of defining and controlling the schedule and regulates the

cooperation among the different roles; (ii) Production Plant Manager, that maintains

information about actual plant configuration and knows all restrictions and features of each

machine and plant element; (iii) Scheduler, that has the ability to schedule tasks and

resources; (iv) Schedule Execution Monitor, that supervises actual execution of a schedule in

a specific plant; (v) Master Plan Monitor, that controls possible changes in the Master Plan

(according to schedule execution, modification and creation errors) and informs the Manager

role when it identifies an alteration that must be propagated to the Master Plan Generator

Process; (vi) Schedule Modification Controller, that maintains information about changes

needed for adjusting the schedule because of failures in the manufacture process; (vii) Lot

Planner, that manages all information about the task sequence; (viii) Schedule Creation

Controller, that oversees the information about a new schedule order, more specifically about

resource assignment for a specific Master Plan Lot.

3.1.3 Interaction Model

Interaction model consists of identifying, for each use case, interaction goals, its members

(initiator and collaborators), nature and specification (by means of collaboration diagrams). In

Figure 5, only the interaction model for Schedule Creation use case is shown, because of lack

of space. Initially, the role Manager establishes an interaction process (called

ScheduleInitialization, Figure 6a) with the Schedule Creation Controller, asking for the

creation of a new schedule. Then, the Schedule Creation Controller informs the Master Plan

role that a new schedule is being created, by means of the ExecutingScheduleCreation

message. Next, it communicates with the Lot Planner (using the GetTasksSequence

interaction, Figure 6b) in order to obtain the sequence of tasks needed to produce the specific

lot to be scheduled. Then, the Schedule Creation Controller communicates the Scheduler the

deadlines and tasks to be scheduled (SequenceRequest interaction). The Scheduler has to

interact with the Production Plant Manager (Machine/ResourceQuery interaction) in order to

assign tasks to resources. Once all tasks have been assigned, the Scheduler sends a

NewSequence message to the Schedule Creation Controller, indicating that the new schedule

has been created. Finally, the Schedule Creation Controller provides the Manager with this

new schedule (Schedule message).

Figure 5. Schedule Creation Interaction model

Figure 6. a) ScheduleInitialization interaction; b) GetTasksSequence interaction

Figure 7. a) Schedule Creation Controller Agent Model; b) Lot Planner Agent Model

3.1.4 Agent Model

Regarding agent models, a specific agent has been assigned to each role identified in the

organization model. For each agent, its goals, tasks and mental states have to be associated.

Figures 7a and 7b show Schedule Creation Controller and Lot Planner agent models. The

Schedule Creation Controller agent is in charge of generating a new lot schedule, so it has to

initialize the schedule and then build a final proposal. The Lot Planner agent provides the task

sequence for a specific lot.

3.1.5 Tasks/Goals Model

This model attempts to answer the questions of why, who and how throughout the analysis

process: why refers to the goals that are defined for the system; who refers to the agents which

are responsible for the goal fulfilment; and how refers to the set of tasks which are defined to

achieve the goals. Regarding our analysis, the decomposition of the Schedule Creation

Workflow is shown in Figure 8 as an example of the tasks/goals model. The tasks associated

are the different steps of this workflow, and are done by concrete roles.

Figure 8. Schedule Creation Workflow

Figure 9. Transitions considered in the Schedule Creation Workflow

Moreover, in Figure 9 the specification of the transitions considered in the above commented

workflow is shown. The process starts with the Identify lots task which creates the Generate

Lot Schedule goal. This goal will be satisfied at the end of the process with the execution of

the Build Final Schedule task. The rest of the tasks are: Initialize schedule which initiates the

control of the schedule creation; Get task sequence which allows to obtain the sequence

related to a specific lot; Get plant state in charge of obtaining the current state of the plant;

and Allocate tasks which provides a schedule proposal according to the retrieved information.

3.1.6 Environment Model

The environment model of the Production Programming organization is shown in Figure 10.

The ExecutedScheduleDB internal application is managed by the Production Programming

organization in order to store and update executed schedules created by the organization. The

ProductModelDB is an external application managed by the Design Group to store the

product definition specification in terms of production tasks. The LotPlanner agent uses

operations of the ProductModelDB to determine the task sequence needed to produce a given

product. The ProductDesignDB is an external application managed by the Design Group to

store the product definition specification in terms of materials and design patterns. The

Scheduler agent uses operations of the ProductDesignDB to query the bill of materials for a

lot of a given product. The SuppliesDB is an external application maintained by the

WareHouse Group to manage the warehouse of raw materials. The Scheduler agent uses

operations of the SuppliesDB to query the availability of raw materials needed to produce a

lot of a given product in a specific time. The MasterPlanDB is an external application

managed by the Production Manager. It is perceived by the Manager agent in order to figure

out when the ProductionProgramming Group has to initiate a new schedule creation process.

The PlantStateDB is an external application managed by the Production Plant Group to

maintain the plant status updated. The PlantManager agent uses operations from the

PlantStateDB in order to figure out whether a schedule modification is needed.

Figure 10. Environment model

4 Related Works

In the last ten years, an increasing amount of research has been devoted to holonic and agent

based manufacturing over a broad range of both theoretical issues and industrial applications.

We can divide these research efforts into two groups (McFarlane & Bussman, 2003): Control

Architectures and Control Algorithms. Some examples of control architectures are: PROSA

(Van Brussel, Wyns, Valckenaers, Bongaerts & Peeters, 1998), the agent based architecture

of Bussmann (Bussmann, 1998), agents and function blocks of Deen and Fletcher (Fletcher &

Deen, 2001), MetaMorph (Maturana & Norrie, 1997), INTERRAP based architecture

(Fischer, 1998). The developments about Control Algorithms range over (McFarlane &

Bussman, 2003): Planning and Scheduling (Gou, Hasegawa, Luh, Tamura & Oblak, 1994;

Biswas, Sugato & Saad, 1995), Execution and Shop Floor Control (Gayed, Jarvis & Jarvis,

1998; Heikkila, Jarviluoma, & Hasemann, 1995), and Machine and Device Control (Tanaya,

Detand & Kruth, 1997; Tanaya, Detand, Kruth and Valckenaers). In spite of the large number

of developments reported in these areas, there is very little work reported on modelling

manufacturing systems with a Software Engineering Methodology, although its benefits. To

date, many of the developments in the manufacturing field have been conducted in an almost

“empirical way”, without any design methodology.

5 Conclusions

This paper presents a methodological development, based on multi-agent technology, of an

application for the production programming problem in a ceramic tile industry. We have

explained that the actual ceramic tile sector needs dynamic production processes that allow

the selling process to realize an on-line programming. Our approach is based on a medium

time project. Its main goal consists of reaching an order management system in ceramic tile

industry able to satisfy each client requirement, adapting the service according to the real state

of the distribution/supply chain. This proposal is based on the system capability to offer the

most suitable product alternative even though it could involve scheduling changes, if the

global quality is improved.

In order to satisfy that goal, it is necessary to integrate the different distributed production

steps in a flexible, adaptable, versatile, robust and natural way. Agent/Multi-agent systems

(MAS) technology has been used in the resolution of this problem, since it provides the

required characteristics for manufacturing systems. Specifically, we have presented a

modelling experience using the IDK toolkit of the INGENIAS methodology, which has been

successfully employed in other domains.

Currently, we have centred our analysis in the programming problem due to its critical

importance in the whole process. Nevertheless, as future works we want to analyse the rest of

the identified subsystems in a ceramic tile industry.

Acknowledgments

This work has been partially funded by Polytechnic University of Valencia under grant PII-

UPV 5574.

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