AGENT-BASED ARCHITECHTURE FOR FLEXIBLE LEAN CELL DESIGN, ANALYSIS AND
EVALUATION

T.E. Potok, N.D. Ivezic and N.F. Samatova

# Collaborative Technologies Research Center

*Computer Science and Mathematics Division,*

# Oak Ridge National Laboratory, Oak Ridge,TN, USA

*E-mail: <potokte@ornl.gov>*

**Keywords** supply chain information infrastructure, lean cell
formation, agent-based systems

Abstract We present an agent architecture in support of lean operations
across manufacturing supply chains. Our architecture addresses the issue
of heterogeneous information management, the need for analyzing mixed
manufacturing approaches, and efficiency and accuracy tradeoffs of
analysis methods. We propose five distinct layers to address these
requirements: an XML/RDF/DAML Information Access Layer, a Resource
Vector Representation Layer, an Analysis Layer, a Coordination Layer,
and a Visualization Layer. Our successful results in developing agent
systems in support of lean manufacturing cell and supply chain
management validate our layered approach.

1 INTRODUCTION

In today's manufacturing environment, the concept of producing the right
product, at the right time, for the right price is a driving goal. To
achieve this goal, a manufacturer must have great insight into the
supply chain that feeds the manufacturing process. Many would advocate
the use of MRP or ERP software systems to achieve this objective. The
drawbacks with this approach range from an enormous initial investment
to regimenting dated practices. Once such a system is put into place, it
can stifle the move to new manufacturing technologies. We believe that
the flexibility of agent-based systems provides a great benefit over
existing manufacturing control software.

We propose a flexible agent framework that is based on rich
representation of manufacturing resources, and advanced analysis
techniques. With this framework, we can address two key problems within
the manufacturing supply chain. The first being the formation of lean or
efficient manufacturing cells, and the second is the analysis of
material and part flows through the supply chain. The first problem
looks at optimal ways of organizing a manufacturing environment, or
multiple environments. The second looks at how to understand and analyze
the flow of activities that are required to build products.

We have recently developed and deployed two agent-based systems for
optimizing manufacturing processes and analyzing supply chain logistics
\[1-3\] that provide great insight into these problems. From our
experience with building agent-based systems for supply chain analysis,
we believe that agent technology is ideally suited to be the foundation
for a supply chain information infrastructure. In this paper we present
an adaptive framework and some preliminary findings for such systems.

2 BACKGROUND

The idea of software agents interacting with a manufacturing environment
is not a new one. The typical approach reported in the literature is to
have agents represent various components of manufacturing, such as
parts, machines, cells, and people \[4\]. Through negotiations among
these agents, work can be efficiently scheduled based on a variety of
possible constraints. However, the scheduling of work is not performed
from a systematic engineering approach, but from efficiencies gained
from advanced negotiations techniques \[5\]. These negotiation-based
techniques have proven very effective. However, rather than generalizing
these concepts over the supply chain, we believe there may be a more
effective approach. Rather than letting agents simulate real-world human
interactions, we propose that agents operate based on manufacturing
efficiency principles. By doing so, these agents can help build,
analyze, optimize manufacturing cells and supply chains from engineering
principles. We develop our framework based on two aspects of supply
chain analysis, 1) the formation of lean or efficient manufacturing
cells, and 2) the analysis of material and part flows through the supply
chain.

3 AGENT FRAMEWORK

Several unique challenges arise when building agent-based systems.

1.  The agent framework must be able to access and understand
    heterogeneous and distributed information within the supply chain
    system. Typically, each supplier will represent and store
    information in different ways than other suppliers. This information
    may include metrics on part inventories, machine utilizations, or
    production goals.

2.  The agent framework must be flexible enough to be able to adapt to
    different manufacturing methods. One supplier may produce products
    using traditional batch production methods, with a focus on machine
    utilization, while another supplier may use agile production
    processes, where a part value stream analysis is important.

3.  The agents must be able to adapt to different analysis methods.
    There are times when a quick, ballpark estimate may be required, and
    other times when an in-depth analysis is necessary. The system must
    be capable of producing both types of analysis.

The typical way of addressing these requirements is to employ an MRP or
ERP system that integrates a vast amount of information, and makes this
information available to a broader range of participants. However,
unless the suppliers in the supply chain adopt the same system, the
information will be available only from within a single manufacturing
environment, and not the entire supply chain. Other approaches, such as
distributed databases, or distributed object brokers can provide a
solution, however, a great deal of specialty software must be written
and maintained.

A software agent approach provides a number of attractive features. One
is the flexibility of communication among agents. Given a common
framework, agents throughout the supply chain can communicate to
determine the best course of action. Another is the distributed nature
of agents, where the information can literally be anywhere.

Looking at the above requirements in terms of agent frameworks provides
a more complete justification for a supply chain information framework.
Much has been written about the first requirement. The challenge of this
requirement is quite pervasive, and unfortunately, current technology is
not able to provide much help. In order for an agent to be able to
gather and understand manufacturing information, the agent must have a
priori knowledge of the information, such as how the information is
organized, what it means, how it is formatted, and what units it is in.
Our proposal is to build an encapsulation layer from the current and
emerging standards: XML, RDF, and DAML. The Extensible Markup Language
(XML) is the universal format for structured documents and data on the
Web; The Resource Description Framework (RDF) is a foundation for
processing metadata; it provides interoperability between applications
that exchange machine-understandable information on the Web; DARPA Agent
Markup Language (DAML) is an emerging facility built on top of RDF to
enable creation of a Semantic Web and fully interoperable agents.

The second requirement is critical to the manufacturing environment. If
the representation of information is limited by the current
manufacturing technology, then changing to a new technology will require
a dramatic change to the existing information infrastructure as well.
The resources of the manufacturing environment must be represented in a
way so that improvement in the future will not require changes to the
resource representation. This representation must be flexible and
capture essential information about a resource, and critical
relationships among resources. We believe that a vector-based
representation for manufacturing resources would be ideal for the
resource description, cell analysis, and visualization.

The third requirement is to be able to direct the level of analysis
needed. The general impact of a change for forecasting purposes will
require much different analysis than that needed for short-term
production planning. This requirement calls for the user to be able to
choose between the duration of analysis versus the accuracy of the
analysis. Again, a key aspect of this challenge is to represent this
information in such a way that various levels and types of analysis can
be performed.

The key to our framework is how the supply chain is represented, and how
agents are used to analyze this information. We propose five layers as
shown in Figure 1: 1) an XML/RDF/DAML access layer that provides a means
of searching, accessing, and interpreting manufacturing information. 2)
a resource vector representation that relates resources, such as parts
and machines, so that each process step for a part is represented as a
dimension in space. 3) an analysis layer where this part/machine
information can be clustered or grouped to form cells, 4) a coordination
layer to enhance agent communication; and 5) a visualization layer where
the results of this analysis can be displayed.

![](media/image1.wmf){width="4.0in" height="2.1083333333333334in"}

*Figure 1 The five layer agent framework for supply chain information
analysis.*

3.1 XML/RDF/DAML Access Layer

This layer provides access and definition to the information available
from the manufacturing environment. The information can be gathered,
converted or transformed to feed into the resource representation layer.

We have used the extended markup language (XML) as a means of describing
and integrating distributed information. XML can provide an
encapsulation layer that provides the ability to translate and
manipulate representations of information. This XML representation can
form a basis for an ontology for a broader range of agents to access and
understand the information sources. Furthermore, this XML representation
can be used as a method to define uniform syntax of a language for
communication among agents.

To achieve a full ontological foundation of communicating agents,
additional components of the layer are needed. We have experimented with
RDF, a simple, but extensible and potentially powerful representational
layer delivered on top of XML. In addition, we have begun to investigate
the impact of DAML to provide much more expressive semantics on top of
the RDF layer using formal logic to express concepts, relationships,
rules, and functions. The XML/RDF/DAML progression of increasingly
expressive languages allows one to capture the meaning of manufacturing
concepts for their use by higher layers that perform
manufacturing-specific activities.

3.2 Resource Vector Representation Layer

The second layer calls for a solid representational structure for a
manufacturing environment. These systems are typically represented by
traditional means, such as a bill of material, or a part-machine matrix.
These are usually one or two-dimensional structures, and fairly simple
to manipulate. Unfortunately, they are not well suited for representing
other factors, such as the direction of the flow of a part through a
cell, or the similarity of resources. We believe that with a stronger
representational scheme, the agents will be able to provide better
information and improved analysis.

We propose a representational structure called a *Vector Space Model
(VSM).* This model helps us to capture all of the relevant features of a
resource in relationship to other resources. The basic premise in the
VSM is that manufacturing resources, such as machines and parts are
represented as elements of a multi-dimensional vector space.
Specifically, parts and machines are all vectors in the vector space.
The mathematical framework is composed of a k-dimensional vector space
and standard linear algebra operations on vectors. In general, the use
of a vector space model requires the specification of several aspects
such as the set of basis vectors, the dimensionality of the space, an
interpretation of the values for vector coordinates, and a similarity
measure or distance between two vectors. One of the main virtues of the
VSM is the ease with which individual vectors can be modified, making it
possible to adapt resource vectors to a dynamic environment without
reconsidering the entire data set. Moreover, the vector representational
structure provides the desirable flexibility to the Analysis and
Visualization Layers.

3.3 Analysis Layer

Once we have a strong representation of the manufacturing information,
we then are able to quickly analyze the information, and make
recommendations. There are two key ways of analyzing the VSM
representation. The first is by grouping vectors that are similar into
clusters. These clusters can be parts that are manufactured in a common
way, machine capabilities, or personnel skills. The other way to analyze
this information is by looking at the projection of the multidimensional
vectors onto two or three dimensions. This provides a meaningful way of
viewing information. The Vector Perturbation Approach \[6\] integrated
into the Analysis Layer is capable of directing the level of analysis
needed. Different aspects specific to a manufacturing environment can be
emphasized or obscured due to the variations of the vector perturbation
parameter. For instance, it can provide a desirable compromise between
the ease of resource assignment and the increase in production
throughput by taking advantage of interleaving various parts through a
common set of sequenced operations.

This method was applied to three large sets of military aircraft part
data. The results, though preliminary, are very promising. Our
conclusion is that the VSM representational model coupled with the
Vector Perturbation Approach are well suited to address the supply chain
information analysis problem, and can be used to enhance the efficiency
and utilization of most supply chains.

3.4 Coordination Layer

The coordination layer supports interactions within multi-agent systems.
We have experimented with skeleton-based approaches such as described in
\[7\]. This approach captures constraints on, and specifications for
agent behavior, specifically, within the context of an agent's
participation in dialogs with other agents. Such dialogs may arise
through coordination with other agents and collaborative problem
solving, to name a few.

The basis for an agent behavior description using skeletons is messages
sent or received by the agent. Each message has a corresponding event
specified by a skeleton. A skeleton is a directed, possibly cyclic,
graph that consists of states (nodes) and events (links). Each graph has
a unique start node and one or more end nodes. Each event (link)
represents a transition from its start state to its end state. Each
state and event has an associated name, action, and type. The actions
associated with states and events are executables. The types of states
and events are defined based on the semantics of the intended use of
skeletons. Temporal logics can be defined over the skeletons to capture
time-dependency among events and states in groups of agents.

3.5 Visualization Layer

The visualization layer must be very versatile to represent different
types of information in an appropriate way for a human user. We have
developed several components of this layer. First, we developed a
capability to visualize statistical properties of a distribution of
resources or their measurements over some manufacturing space. Second,
we developed a capability to render results of statistical clustering
methods that we used extensively within some part/resource grouping
algorithms. Finally, we developed animation component to visualize
interactions/communications among agents, movements of materials and
products, and the evolution of important manufacturing concepts such as
critical path analysis.

5 SUMMARY

An agent-based architecture has been proposed for supply chain
information analysis based on manufacturing efficiency principles. Such
an architecture facilitates heterogeneous and distributed information
processing, flexibility, and adaptability. Flexibility is facilitated
through a solid representational structure for a manufacturing
environment. Adaptation is supported through the layered structure of
the architecture and through multi-level analysis mechanisms. The
architecture is built on the information integration and agents
communication standards. The proposed architecture also addresses other
specific requirements for next generation manufacturing systems,
including scalability, interoperability, cooperation, and visualization.

The experimental results from applying the architecture to the formation
of lean manufacturing cells, and the analysis of material and part flows
through the supply chain have demonstrated the potential of the
agent-based approach for advanced supply chain analysis systems. The
architecture is generic and can be applied to heterogeneous and
distributed information analysis systems in other domains in addition to
that of supply chains.

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