=Paper=
{{Paper
|id=Vol-547/paper-56
|storemode=property
|title=A Multi-agent Framework for a Web-based Decision Support System Applied to Manufacturing System
|pdfUrl=https://ceur-ws.org/Vol-547/86.pdf
|volume=Vol-547
|dblpUrl=https://dblp.org/rec/conf/ciia/BessedikT09
}}
==A Multi-agent Framework for a Web-based Decision Support System Applied to Manufacturing System==
https://ceur-ws.org/Vol-547/86.pdf
A Multi-agent Framework for a Web-based Decision
Support System Applied to Manufacturing System
Bessedik Imène1 and Taghezout Noria2
1, 2
Department of Computer Science, University of Oran,
1
imeneinf2006@yahoo.fr
2
taghezoutnour@yahoo.fr
Abstract. The Combination between Web services and software agents provides a promising
computing paradigm for efficient service selection and integration of inter-organizational
business processes. This paper proposes an agent-based Web DSS; the main contribution of our
study is to provide an efficient tool that helps users find information resources available as an
online service within Intranet. The decision-making is not only guided by the information
provided by DSS but rather than the Web technology, the process is entirely based on
communication between ISP Agents and Web agent. While negotiating compromises for
conflict solving to share common resources, decision centres use Web service to conduct
various complementary tasks. To illustrate the idea, a simple case study is given.
Keywords: Decision Support System (DSS), Integrated Station of Production (ISP), Software
agents, Web-based DSS.
1 Introduction
Computer technology progress has led to widespread use of computerized support in
various activities. Particularly, traditional decision support systems (DSS) focus on
computerized support for making decision with respect to managerial problems. There
is an emerging and fast growing interest in computerized support systems in many
other domains such as information retrieval support systems, research support
systems, teaching and learning support systems, computerized medical support
systems, knowledge management support systems, and many more. The recent
development of the Web generates further momentum to the design and
implementation of support systems.
Obviously enough, there is a strong trend for studying computerized support
systems especially on Web platforms. Research on information retrieval support
systems, research support systems, teaching and learning support systems, decision
support systems, computerized medical support systems, and knowledge management
support systems are just some of their representatives.
This paper will focus on one of the important research topics of Web- based DSS
and provides an efficient tool that helps users find information resources available as
an online service within an Intranet. The organization of this paper is as follows. We
introduce the concept of Decision Support System and Web-based support systems in
the next section. Section 3 discusses issues of recent research in Web-based decision
support. In section 4 we propose our contribution. In section 5 we explain our
proposed model. Section 6 shows a sample application for the Web-based DSS.
Finally, a conclusion and future work are given in Section 7.
2 Decision Support System and Web Based Decision Support
System
2.1 Decision Support System
Before we start with detailed aspects of the issue, it is important to tackle the
definition of decision support systems.
Decision Support Systems can be defined as computer technology solutions that
can be used to support complex decision making and problem solving (see Shim et al.
[17]). To account decision problems complexity and uncertainty, we understand the
DSS as a set of computer-based tools that provide decision maker with interactive
capabilities. It aims to enhance his understanding and information basis about
considered decision problem through usage of models and data processing. The latter,
in turn, allows reaching decisions by combining personal judgment with information
provided by these tools. The classic DSS tool design is comprised of the components
for:
• Database management capabilities with access to internal and external data,
information and knowledge;
• Powerful modelling functions accessed by a model management system; and
• User interfaces that enable interactive communication between the user and
system.
Decision Support Systems (DSSs) are interactive computer-based systems intended to
help decision makers utilize data and models to identify and solve problems and make
decisions. The "system must aid a decision maker in solving unprogrammed,
unstructured (or 'semistructured') problems...the system must possess an interactive
query facility, with a query language that ...is ...easy to learn and use" [2]. DSSs help
managers/decision makers use and manipulate data, apply checklists and heuristics,
and build and use mathematical models.
According to Turban [21], a DSS has four major characteristics: it incorporates
both data and models; it is designed to assist managers in their decision processes in
semistructured (or unstructured) tasks; it supports, rather than replaces, managerial
judgment; and its objective is to improve the effectiveness of decisions, not the
efficiency with which decisions are being made.
According to [8], decision support systems fall into five categories:
¾ Communications-Driven DSS – uses network and communications
technologies to facilitate collaboration and communication;
¾ Data-Driven DSS – emphasizes access to and manipulation of a time-series
of internal company data and sometimes external data;
¾ Document-Driven DSS – integrates a variety of storage and processing
technologies to provide complete document retrieval and analysis;
¾ Knowledge-Driven - intended to suggest or recommend actions to managers.
These DSSs are personal computer systems with specialized problem-
solving expertise;
¾ Model-Driven DSS or Model-oriented DSS – emphasizes access to and
manipulation of a model, e.g. statistical, financial, optimization and/or
simulation. Simple statistical and analytical tools provide the most
elementary level of functionality.
2.2 Web-based Decision Support System
Web used technologies are employed to improve the capacity of decision support
systems through decision models, On-line Analysis Processing (OLAP) and data
mining tools that allow "standardized" publishing and sharing of decision resources
on the Internet. In a web-based decision support system, all decision support related
operations are performed on a network server n order to benefit from platform
independence, shorter learning curves for already familiar users with the Web tools
and web navigation, lower software distribution costs, ease of performing system
updates and “reusability” of decision modules and information on the Internet through
standardized protocols and formats [8].
According to [5], the importance of using Web-based DSS originates from the
growing amount of available information that should be identified, controlled and
accessed remotely using web based tools to support reusability of integrated decision
modules. Using such systems, an enterprise can create survey software, Web based
forms, build document-driven DSS for requests and approvals. They help global
enterprises manage and improve decision processes through improved efficiency,
better process control, improved customer service, more flexible re-design, and
streamlining and simplification of business processes.
Using Web-based DSS, decision-makers can share open decision modules on the
Internet using standardized protocols such as HTTP, and a standardized format like
XML or DAML.
According to [16], Web-based systems are regarded as «platforms of choice” for
delivering decision support while taking into account many technical, economic and
social considerations. The migration towards web based DSS denotes a shift from
DSS generators (that allow users to develop specific applications characterized by
limited deployment, inflexibility) to integrated cross application orientations that
emphasize the reuse of applications and components. By deploying Web capabilities,
multiple knowledge bases and knowledge processing techniques can be used. The
design of decision support systems has been affected by the availability of a wide
range of web based tools, techniques and technologies. The use of web tools are
reshaping the description of relations between information components and decision
modules in a way that affects both the physical and logical design of the DSS, model
visualization, sharability of decision modules and the development life cycle of DSS.
As a result, the underlying architecture for Web-based DSS has moved from
mainframes, to client–server systems, to Web and network technology based
distributed systems that enable the integration of large amounts of data and decisions
support tools originating from heterogeneous multidisciplinary sources for the
provision of value-added information using knowledge discovery and data mining
tools.
3 Recent research in Web-based Decision Support System
This section reviews and summarizes the state of Web-based DSS research in two
areas: (a) architectures and technologies and (b) applications and implementations. A
number of articles have reviewed more specific topics related to Web-based DSS. For
example, Kuljis and Paul [12] reviewed Web-based simulation and Kersten and
Noronba [10] reviewed Webbased negotiation support.
3.1 Architectures and technologies
A number of articles discuss architectural issues, frameworks, usability, and other
technology topics that are generally applicable to Web-based DSS.
Gregg et al. [6] developed a DSS metadata model for distributing decision support
systems on the Web. Bharati and Chaudhury [1] conducted an empirical study to
investigate customers’ satisfaction with a Web-based decision support system. Iyer et
al. [9] studied model management for decision support in a computing environment
where enterprise data and models are distributed. Guntzer et al. [7] proposed
Structured Service Models that use a variant of structured modeling. This proposed
approach can help users find information resources available as an online service
withinin Intranet. Zhang and Goddard [22] applied Software Architectures to the
design of Web-based DSS. Mitra and Valente [13] provided an overview of Web-
based optimization for model-driven decision support, discussed two paradigms (ASP
and e-Services), and articulated technology issues for an e-Services model.
3.2 Applications and implementations
Many researchers and vendors have reported Web based DSS case studies and the
development of prototype applications. Kohli et al. [11] reported a case study of a
Web-based DSS for hospital management called Physician Profiling System (PPS).
Ngai and Wat [14] developed and implemented a Web-based DSS that used a
model based on fuzzy set theory to perform risk analysis for e-commerce
development. Dong et al. [4] developed a Web-based DSS framework for portfolio
selection. Sundarraj [19] identified key issues in managing service contracts and
developed a prototype that can support a manager’s planning process. Ray [15]
reported a case study that demonstrates the implementation of Web-based decision
support technologies. Delen et al. [3] developed a Web-based DSS, called Movie
Forecast Guru, to help decision makers in the movie industry.
There are many additional case studies related to deploying Web-based decision
support systems. For example, Sugumaran and Meyer [18] report the development of
a Spatial DSS prototype for the City of Columbia, Missouri.
4 Contribution
Given the multidisciplinary data sources and related decision support tools, the
design, specification, and implementation of a Web-based DSS often in a distributed
environment is still an open research problem [22]. Firstly, a Web-based DSS often
consists of the data and related tools, which come from multidisciplinary areas. Those
data and related tools originally are not designed to work together. Traditional DSS
design methods lack the ability to help organize them in a hierarchical view and
specify the software architectures of a Web-based DSS in a formal way. Secondly,
with the assistance of Web and network technology, the data and decision support
tools from multidisciplinary areas can be located on computers distributed over a
network. In such a distributed environment, a Web-based DSS needs a distributed
framework to manage and integrate the data and tools in a seamless way.
Furthermore, the work reported in [20] concerns a novel approach for decision
making. In her paper, she addressed an agent architecture-based model in order to
present a multicriteria DSS which can be applied to solve some uncertainty problems
in dynamic production system scheduling. The established negotiation contract thus
deals with certain exceptions; it is based on the agent approach. The major advantage
with this modeling paradigm consists in facilitating access to the executed tasks
carried out by Integrated Stations of Production (ISP) agents.
This paper proposes an agent-based Web DSS, its principle is to help users solve
the problems of failure of their resources in an industrial estate on a web service using
online within an intranet. In addition to the Web Service in our contribution we will
use negotiation between ISP Agent and other agents on the web (Fig.1). The ISP
Agent will check the resources of a workshop at the resource failure, it will contact
the agents based Web that will help for decision making to resolve the failure. To
illustrate the feasibility of the idea, an AUML diagram is given in Fig.2.
Entreprise 1 Entreprise 2 Entreprise n
Web Site 1 Web Site 2 Web Site n
Web Service 1 Web Service 1 Web Service 1
Web-based DSS
Resource Agent
Proposal Agent
Analyzer Agent
Resource 1 Tâche 1
Resource 2 Tâche 1
Workshop
Fig.1. General architecture
5 Scenario for Proposed Model
An Analyzer Agent A Resource Agent A Proposal Agent
Analyze and filter Seek the reference of the
The breakdown resource broken down
Found reference Research
X
Not found reference
Found reference
Seek the solution of the breakdown
Found solution
X
Not found solution
Found solution
Repair
the breakdown Not found solution
Order the resource
Accept the order
Resource ordered
Repair
the breakdown Not found reference
Order the resource
Accept the order
Resource ordered
Repair
The breakdown
Fig. 2. Communication between Agents (Diagram of Sequence)
5.1 The Analyzer Agent Structure
The analyzer agent includes several types of functional modules such as: analysis
module, proposal generator module, a database, a knowledge base, rules base, a filter
and an interface. The analysis module is the core of this architecture; its role is to use
the data input which is stored in database, knowledge which is stored in knowledge
base and the rules which are stored in rules base. This is to analyze and filter the
breakdowns, and produce like exit a generator of proposal of analyzed and filtered
breakdowns. The interface module manages the information exchanges between the
agent analyzer and the other agents.
During the problem resolution, the analyzer agent will require the resource agent to
seek the resource reference on the Web.
Database
Interface Module
Knowledge
Base
Analysis Module
Rules Base
Filter
Proposals Generator
Module
Fig. 3. The analyzer agent structure
5.2 The proposal agent structure
After having to find the reference of the resource broken down on the Web, the
agent analyzer will require the proposal agent (see Fig. 4) to launch an advanced
research on the Web concerning the breakdown envisaged for this resource.
The proposal agent includes several types of functional modules such as: research
module, solutions generator module, a data base, a knowledge base, rules base, web
data base and an interface. The research module is the core of this architecture; its
role is launching an advanced research on the Web (in the Web Database) as for the
breakdown of the resource. At this time there, it will generate several solutions. The
interface module manages the information exchanges between the agent proposal and
the other agents.
Knowledge
Base Database
Interface Module
Web data
Research Module
Base
Rules Base Solutions
Generator Module
Fig.4. The proposal agent structure
6 Sample Application for the Web-DSS
We use the resource allocation problem to demonstrate how the agents solve
problems by interactions among agents. A company wishes to produce a special
computer installation with its own hardware and software for a customer. The
following print screens given in both (Fig.5) and (Fig.6) show a practical situation
when decision-making is necessary.
Fig.5. Simulation Function
Fig.6. Simulation of a Breakdown in the Resource
7 Conclusion
A Web-based DSS uses the Web as a portal to the underlying DSS. It lets
interested users access and make use of the underlying DSS through the Web.
Moreover, we believe a distributed implementation of the underlying DSS is also
important for a Web-based DSS presents a challenge, which needs the combination of
a DSS with distributed computing technology. Our proposed multi-agent approach
provides a practical way to implement a Web-based DSS.
More precisely, we have integrated agents into Web-based DSS for the purpose of
automating more tasks for the decision maker, enabling more indirect management,
and requiring less direct manipulation of the DSS. In particular, agents were used to
collect information and generate alternatives that would allow the user to focus on
solutions found to be significant. Based on this, and considering that communication
capabilities play an essential role in Web-based DSS to enable ‘any-time, any-place”
operation mode of the system. Further work based on coordination protocols between
agents needs to be done. Particularly, the context information domain included in the
software tool will be extended in order to improve the support for decision making
and the coordination activities.
The proposed architecture of the Web-based DSS is under development. One of
our perspectives is to completely implement it, test it in a manufacturing industry in
order to obtain feedback on the usability of the developed system.
References
1. Bharati, P., Chaudhury, A.: An empirical investigation of decision-making satisfaction in
Web-based decision support systems. Decision Support Systems 37 (2), 187—197, (2004)
2. Bonczek, R.H., Holsapple, C.W., Whinston, A.B.: Foundations of Decision Support
Systems. Academic Press, New York (1981)
3. Delen, D., Sharda, R., Kumar, P.: Movie forecast guru: a Webbased DSS for hollywood
managers. Decision Support Systems 43, 1151--1170 , doi:10.1016/ j.dss.2005.07.005,
(2007)
4. Dong, J., Du, H.S., Wang, S., Chen, K., Deng, X.: A framework of Web-based decision
support systems for portfolio selection with OLAP and PVM. Decision Support Systems 3,
367– 376, (2004)
5. Friedman, T. L.: The World Is Flat: A Brief History of the Twenty-First Century, Farrar,
Straus and Giroux, (2005)
6. Gregg, D.G., Goul, M., Philippakis, A.: Distributing decision support systems on the WWW:
the verification of a DSS metadata model. Decision Support Systems 32, 233--245, (2002)
7. Guntzer, U., Muller, R., Muller, S., Schimkat, R.D.: Retrieval for decision support resources
by structured models. Decision Support Systems 43, 1117--1132 ,
doi:10.1016/j.dss.2005.07.004. (2007)
8. Huaiqing, W., Stephen, L., Lejian, L.: Modeling constraint-based negotiating agents.
Decision Support Systems, 33(2), pp. 201--217, (2002)
9. Iyer, B., Shankaranarayanan, G., Lenard, M.L.: Model management decision environment: a
Web service prototype for spreadsheet models. Decision Support Systems 40 (2), 283– 304,
(August (2005))
10. Kersten, G.E., Noronba, S.J.: WWW-based negotiation support design, implementation,
and use. Decision Support Systems 25 (2) (March), 135--154, (1999)
11. Kohli, R., Piontek, F., Ellington, T., VanOsdol, T., Shepard, M., Brazel, G.: Managing
customer relationships through E-business decision support applications: a case of hospital–
physician collaboration. Decision Support Systems 32, 171--187, (2001)
12. Kuljis, J., Paul, R.J.: An appraisal of Web-based simulation: whether we wander?
Simulation Practice and Theory 9, 37--54, (2001)
13. Mitra, G., Valente, P.: The evolution of Web-based optimization from ASP to e-services.
Decision Support Systems 43, 1096--1116, doi:10.1016/j.dss.2005.07.003. (2007)
14. Ngai, E.W.T., Wat, F.K.T.: Fuzzy decision support system for risk analysis in e-commerce
development. Decision Support Systems 40 (2) 235--255, (2005 (August))
15. Ray, J.J.: A Web-based spatial Decision Support System optimizes routes for
oversize/overweight vehicles in Delaware. Decision Support Systems 43, 1171--1185 (this
issue), doi:10.1016/j.dss.2005.07.007, (2007)
16. Shaw, N.G., Mian, A., Yadav, S.B.: A comprehensive agent-based architecture for
intelligent information retrieval in a distributed heterogeneous environment. Decision
Support Systems 32 (4), pp. 34--42, (2002)
17. Shim J.P., Warkentin M., Courtney J.F., Power D.J., Shards R., Carlsson Ch.: Past, Present
and Future of Decision Support Technology. Decision Support Systems, Nr. 33, pp. 111--
126, (2002)
18. Sugumaran, R., Meyer, J.: Building a Web-Based Spatial Decision Support System
(WEBSDSS) for Environmental Planning and Management, URL DSSResources.COM,
(2003)
19. Sundarraj, R.P.: AWeb-based AHP approach to standardize the process of managing
service-contracts. Decision Support Systems 3, 343--365, (2004)
20. Taghezout, N., Zaraté, P.: Negotiation Process for Multi-Agent DSS for Manufacturing
System, in : Collaborative Decision Making: Perspectives and Challenges, P. Zaraté et al.
,ed., IOS Press, Vol. 176, Frontiers in Artificial Intelligence and Applications, pp. 49--60,
2008
21. Turban, E.: Decision Support and Expert Systems: Management Support Systems. 4th
Edition, Macmillan Publishing Company, New York (1995)
22. Zhang, S., Goddard, S.: A software architecture and framework for Web-based distributed
decision systems. Decision Support Systems 43, 1133—1150 doi:10.1016/
j.dss.2005.06.001, (2007)