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)
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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