International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 3

Number 2

June 2009

189

1 INTRODUCTION

Today the transportation organization systems, espe-

cially see transportation, are much complex and

must be more safety and robustness for any internal

and external disturbances resistance for any errors

and human mistakes. Criticality factors, which have

influence on each dedicated activity, are including:

safety, technology, exploitation (operation and main-

tenance), revenues, availability, reliability, maintain-

ability, and costs.

The market globalization place new challenges in

management of peculiar man activities in particular

in transportation activities. It is growing requirement

on so-called intelligent transportation technology

and transport service ITS type (Intelligent Transport

Services), as well as dynamical type management of

transportation devices DTM type (Dynamic Traffic

Management), both on large distances and at inte-

grated automated transportation worldwide industry

(manufacture). Transport industry today is a large-

scale distributed system.

Market globalization and an increase in customer

demands have forced companies to produce more

complex and individualized products in a shorter

lead-time [Le Duigou et al, 2009]. The increasing

needs for flexibility, reactivity and efficiency result

in a growing complexity of any systems including

transport industry and other manufacturing, and a

necessity of integration of their control based on

numerous and highly versatile dynamic data [Blanc

et al, 2008]. To solve this paradox (refocus on the

primary business and need of multiple specific

skills), companies have adapted by regrouping in or-

der to pool their mutual skills. When this is done

over a short period and on a specific project, it is

called virtual enterprise, and extended enterprise

when it is done over a longer period. Companies is

not structured enough to enable efficient coopera-

tion.

Knowledge is now a major driving force for or-

ganizational change and wealth creation, and effec-

tive knowledge management is an increasingly im-

portant source of competitive advantage and a key to

the success of modern organizations [Irma & Rajiv,

2001; Malhotra, 2002; Savvas & Bassiliades, 2009].

The core technological areas for the success of next

generation manufacturing related to information and

communication technologies have been expressed in

paper [Nof et al, 2008]. As a result, companies are

now implementing knowledge management proc-

esses and its supporting technologies. Knowledge

management systems (KMS) are a class of intelli-

gent systems (IS) developed to support and enhance

the organizational processes of knowledge creation,

storage/ retrieval, transfer and application [Alavi, &

Leidner, 2001; Chang et al, 2005]. Recent advances

in information and communication technologies

have allowed both transportation and manufacturing

systems to move from highly data-driven environ-

ments to a more cooperative information/

knowledge-driven environment [Panetto & Molina,

2008]. For many years, software has been developed

to pool all this information. From the EDM (Elec-

tronic Document Management) in the 1980s to the

PDM (Product Data Management) and the PLM

(Product Life Cycle Management) in the late 1990s,

the companies and particularly the contractors un-

derstand the benefit of such software. Today to gen-

erate a common language for communication be-

tween people [Studer et al, 1998] or interoperability

between systems [Uschold, 1996], more and more

researcher are looking for ontologies types ranging

in their formality, structure and intended use. The

Transportation System Architecture for

Intelligent Management

J. Szpytko

AGH University of Science and Technology, Krakow, Poland

ABSTRACT: The paper is focusing on transportation system architecture for intelligent management, espe-

cially in sea transport and transportation technology. Moreover control models of large-scale distributed en-

terprises systems and transport active knowledge base management model have been presented.

190

term ontology comes from philosophy and signifies

a systematic account of existence [Gruber, 1993]

and defines a common vocabulary for researchers

who need to share information in a domain [Noy &

McGuinness, 2001]. Building Information Modeling

(BIM) [Penttila, 2006; Succar, 2009] of any today

transportation and manufacturing systems is a set of

interacting policies, processes and technologies gen-

erating a methodology to manage the essential build-

ing design and project data in digital format

throughout the system life-cycle phases.

Due to the geographical and institutional separa-

tion between the different systems involved in the

product (system) lifecycle, it is today difficult to

query, to exchange and to maintain consistency of

product information inside the extended enterprise.

By analogy with the definition of interoperability as

the ability of two or more systems to exchange in-

formation and have the meaning of that information

accurately and automatically interpreted by the re-

ceiving system [Wegner, 1996; Panetto, 2007], the

product oriented interoperability as the ability of

different enterprise systems to manage, exchange

and share product information in a complete trans-

parency to the user and utilize essential human la-

bour only has been introduced [Baina et al, 2009].

Transport modes (road, rail, water, air, manufac-

ture) integration and interoperability in transporta-

tion systems is a key concept to face the challenges

of new transportation environment. The integration

and interoperability concepts need undertake under

the consideration the following problems: miscella-

neous transport enterprise integration and interop-

erability, transport system as distributed locally and

globally organization that can be readily reconfig-

ured, methodology for system synthesis and simula-

tion for all transportation operations, possible trans-

portation activities and devices monitor and control

with use proper model-based methodology, possible

heterogeneous environments of transportation activi-

ties, open and dynamic structure of transport system,

internal and external cooperation between transport

modes and devices, technologies that can convert in-

formation into knowledge for effective decision

making, enhanced human - machine interfaces based

on integration of humans with software and hard-

ware involved in transportation activities with use

in-build intelligence, continuous educational and

training methodology that would enable the rapid as-

similation of existing and future knowledge and

practice, transportation system safety and availabil-

ity keeping with use preventive maintenance meth-

odology, processes that minimize energy consump-

tion resulting new innovative-based solutions in

transportation system design and exploitation.

The paper is focusing on transportation system

architecture for intelligent management, especially

in sea transport and transportation technology.

2 CONTROL MODELS OF LARGE-SCALE

DISTRIBUTED SYSTEMS

The main target of any complex system is a transport

execution system (TES). The TES aim is controlling

the transport system: what and when to replace, how

and when to use the available resources, which and

when to launch orders. The proposal of execution

system based on manufacturing execution system

MES using the holonic manufacturing system

(HMS) concepts is presented in publication [Blanc et

al, 2008]. An HMS is a highly decentralized manu-

facturing system concept, consisting of autonomous

and cooperating agents called holons (proposed by

Koestler in 1969) that respects some flexible control

rules forming a holarchy. Holonic architectures are

based on a typology of manufacturing elements,

where each one corresponding to a type of holons:

− products, product holons own reference models of

products, for manufacturing execution and quality

control,

− resources, resources holons are components used

as bricks with local intelligent decision-making

system embedded and based on characteristics of

the tasks they perform a specialization of resource

holons; resource holons corresponds to the physi-

cal devices of the manufacturing system (ma-

chine, workforce, transport device, etc.); they al-

locate, organize and control the production re-

sources; each physical device of the manufactur-

ing system is a part of a resource holon,

− orders, order holons are related to product de-

mand in time, manufacturing task and product

item; order holons correspond to a task in the

manufacturing system; they control the logistics

aspects of the production as much as the negotia-

tions with other order holons or with resource ho-

lons in order for the task to which it corresponds

to be performed correctly and on time.

In paper [Zachman, 1987] author propose two-

dimensional classification complex system model

based around the six basic communication interroga-

tives: what (based on data), how (based on function),

where (based on locations), who (based on people

and devices), when (expressed via time), and why

(expressed on motivation base), intersecting six dis-

tinct model types which relate to stakeholder groups:

visionary, owner, designer, builder, implementer and

worker, to give a holistic view of the enterprise. The

proposed view of the enterprise can be extended on

product - driven control concept.

191

Product-driven control is a way to exchange the

hierarchical integrated vision of plant-wide control

for a more interoperable/ intelligent one [Morel et al,

2007; Pannequin et al, 2009] by dealing with prod-

ucts whose information content is permanently

bound to their material content and which are able to

influence decisions made about them [McFarlane et

al, 2003]. This approach is applicable at the supply

chain decision systems, such as MRP2 (Manufactur-

ing Resource Planning II) [Vollmann et al, 1997]

with newer distributed control approaches. Product-

driven control may enable manufacturing companies

to meet business demands more quickly and effec-

tively. But a key point in making this concept ac-

ceptable by industry is to provide benchmarking en-

vironments in order to compare and analyze their

efficiency on emulated large-scale industry-led case

studies with regard to current technologies and ap-

proaches.

Over the last decade, agent technology has shown

great potential for solving problems in large-scale

distributed systems. By definition, in multi-agent

systems, several agents work together and share

their knowledge for achieving certain manufacturing

objectives. One of the important features of these

systems is that they facilitate integration and auto-

mation and provide benefits with several advantages,

especially to the distributed manufacturing systems

[Oztemel & Tekez, 2009]. However, the integration

and coordination, as well as communication of these

agents still need more attention and research.

The reason for the growing success of multi-agent

technology in this area is that the inherent distribu-

tion allows for a natural decomposition of the sys-

tem into multiple agents that interact with each other

to achieve a desired global goal [Hernandez et al.,

2002]. The multi-agent technology can significantly

enhance the design and analysis of problem domains

under following three conditions [Adler & Blue,

2002]: the problem domain is geographically dis-

tributed, the sub-systems exist in a dynamic envi-

ronment, sub-systems need to interact with each oth-

er more flexibly.

A dynamic and demanding environment charac-

terizes the modern society. Intelligent products nor-

mally need to provide services that require decision-

making and goal-oriented behavior. This human as

an intelligent being mirrors its product’s, reflects

corresponding reality while delegating all decision

making to the intelligent agent Intelligent systems

(IS) can be defined as systems which process input

signals to actuate an output action, the form of

which will depend on rules based on previous expe-

riences where the system learned which actions best

let it reach its objectives [Barton & Thomas, 2009].

Artificially intelligent systems (AIS) incorporate ad-

ditional functionality, often through intermediary

agents, to simulate, decide and control the output

signal or action. AIS must be interoperable with oth-

er components, such as common sense knowledge

bases, in order to create larger, broader and more ca-

pable AI systems. New technologies such as RFID

(Radio Frequency Identification), Auto-ID (Identifi-

cation), UPnP (Universal Plug - and - Play) enable

identification and information embedding on the

product itself. Moreover, technologies related to

multi-agent systems make it possible to involve the

product in decision making protocols at the shop

floor level.

The concept of dynamic hierarchical control sys-

tem architecture is presented in paper [Brennan et

al., 1997]. This concept organizes multiple agents

dynamically based on task decomposition of the sys-

tem. To achieve dynamic organization, a number of

heterogeneous agents are dynamically grouped into

virtual clusters as needed.

Increasing flexibility and the ability of the trans-

portation systems deal with the uncertainty in a dy-

namic environment. A stationary type agent executes

only on the system where it begins execution, and

the code of stationary agents, including control algo-

rithms and provided services, cannot be changed

during execution. The above inconvenience can be

replaced by the introducing to the transportation sys-

tem mobile type agents. Mobile type agent has the

unique ability to replace itself from one system in a

network to another and to move to a system that

contains an object with which the agent wants to in-

teract and then to take advantage of being in the

same host or network as the object. Since mobile

agents can be generated dynamically during the exe-

cution, new software components (control algo-

rithms or operations) can be deployed as mobile

agents and be executed on any sub-systems in a net-

work [Hernandez et al, 2002]. The strength of mo-

bile agents has great value for the application in traf-

fic management systems. A traffic information

system is usually distributed and the integration of

data from distributed detection stations takes a long

time. If a mobile agent can migrate to detection sta-

tions near incident scene and process data locally, it

will significantly reduce the delay of incident re-

sponse. Mobile type agent technology has been dis-

cussed by several researches [for example: Lange

and Oshima, 1999; Gray et al., 2002; Szpytko, 2004;

Szpytko & Kocerba, 2008]. The mobile type agents

for example have strong influence on work in heter-

ogeneous environments and disconnected operation

supporting, network load reducing and network la-

tency overcoming, as well as are able to deploy new

decision making algorithms dynamically.

192

3 TRANSPORT ACTIVE KNOWLEDGE BASE

MANAGEMENT MODEL

The transport system is mostly composed from three

categories of agents: device, man-operator (device,

service/ maintenance, general coordinator/ manage-

ment), surrounding. Between each agent exist speci-

fied relation/controls, for example between operator

and device attributes’ exists several correlations:

perception – information visualization, knowledge –

monitoring, skills – operation realization ability, de-

cision making ability – corrective auto-activity, reac-

tion on external stimulus – safety device and

strength.

Each agent is an object of supply and controls

(IN). Man-operators are equipment with modules of

knowledge and skills (with use of own in-build sen-

sors), which make possible auto-correction of done

controls as the results of undertaken activities

[Smalko & Szpytko, 2008]. Moreover the device,

depending on automation level, may be equipped in

auto-corrective module (self-acting). The output

products (OU) of activities undertaken by individual

agents are shaping for decision-making needs in

quality module.

The architecture of proposed integrated distrib-

uted agent-based transportation system has multiple

levels as shown in figure 1: real enterprise based on

nature type resources (RE), virtual enterprise (VE),

supervisor (SU).

Figure 1. Integrated distributed agent-based dynamic type

transportation management system (TMS)

Legend:

ACC - Agent Communication Channel

ADB - Agent Data Base

ADS - activities detection subsystems

ADS-N - activity detection subsystem, N-th type, (ST - station-

ary, MO - mobile type)

AES-M - activity execution subsystem M-th type, on particular

geographical scope (ST - stationary, MO - mobile type)

AME - Agent Management/ Execution Engine

AMM - Agent Maintenance Manager

ASM - Agent Security Manager

Ax - activity supported by the x type agent, x = {R - real, V -

virtual}

Axny - n-th activity of x-th type agent is composed from the

following possible basic activities: y = {S - storage, D -

displacement, P - processing, C - control}; to control one

at least activity of S or D or P type must occur

ID - identification agent cod ID = 1....n

ID n.m - identification activity code n.m means that the activity

is composed base on the two different activities with ID =

n and ID = m

INn - input, which is composed by the following suppliers:

ENIN-n - energy, KN-IN-n - knowledge and experience,

IFIN-n - information/ data, FI-IN-n - finance; IN =

{ENIN,KN-IN, IF-IN, FI-IN}

MR - agent real, types: ST - stationary, MO - mobile type

MRS - senor type agent of any operation parameters of real

world

MRx - agent x type, x = {M - devices, E - environment, H -

human}

MV - virtual type agent

OUn input, which is composed by the following suppliers:

ENOU-n - energy, KN-OU-n - knowledge and experi-

ence, IFn - information/ data, FI-OU-n- finance; OU =

{ENOU, KN-OU, IF-OU, FI-OU}

RE - resources (devices, environment, human, sensors/ detec-

tors, energy, knowledge and experience, information, fi-

nance)

TSS - transport supervisory subsystem (ST - stationary type),

system supervisor (SU)

VE - v

irtual enterprise subsystem (ST - stationary, MO - mo-

bile type)

The control architecture of transport management

system (TMS) has three layers: real devices operat-

ing in real enterprise type agent (lower layer level

A), e-devices operating in virtual enterprise type

agent (middle layer level B, electronic type plat-

form), supervisor agent (highest layer C, with human

support). Under certain scenarios, a number of vari-

ous agents on A-th level are dynamically grouped

and interact with each other to perform a given task.

The performed task is based on possible defined ac-

tivities: S -storage, D -displacement, P -processing,

C -control. The activity execution subsystem (AES)

agent coordinates agents operating on A-th level in a

193

sub-network. The transport supervisory subsystem

TSS type agent operation on C-th level can assign

tasks to either AES agents on B-th level or to the

lowest agents directly on A-th level. The communi-

cation between agents on all levels and inside each

level is based on agent communication language and

message exchange interaction protocols.

At the agent-level, the conformance includes

agent communication language (ACL), message ex-

change interaction protocols, communicative acts,

and content language representations. At the plat-

form-level, Mobile-C provides an agent manage-

ment system to manage the life cycle of the agents,

agent communication channel to allow agent com-

munication over the network, and directory facilita-

tor to serve as yellow page services.

The lowest level is composed of various activity

detection subsystems (ADS), which enclose various

MR type agents stationary and/ or mobile types (e.g.

transport devices) responding for particular activities

AR types (e.g.: S - storage, D - displacement, P -

processing, C - control types). Sensors (MRS) detect

real agents activity parameters that can be a subject

of monitoring for decision-making process. For ex-

ample the useful information for the operation man-

agement is travel time, transport device speed, inci-

dent verification, and traffic volume and for the

transport device technical state assessment - selected

operation parameters of agents. MR type agents can

dynamically group (taken under consideration over-

looked necessary activities type) into any cluster ac-

cording to the task assigned by the system supervi-

sor. Integrating stationary type agents with mobile

agent technology is leading to multi-agent subsys-

tems for distributed transport management system.

Mobile agents (operation base on dynamic adaptive

type algorithm) enhance the ability to deal with the

uncertainty in a dynamic environment and helps to

achieve the cooperation between distributed agents

response for various activities.

The second level so-called activity execution sub-

system (AES) agent, either stationary and/ or mobile

types, is a coordinator of lower level agents ADS

type in a sub-network. All of the lower level agents

agent. The AES type agent has the knowledge of ge-

ographical distribution of lower level agents and

their capabilities. The selected tasks of activity exe-

cution subsystem are: decompose tasks assigned by

the AES to sub-tasks, multi-operation with other

AES agents activities to solve inter-network prob-

lems (interoperability), serve as agent name server

and maintain the available services of agents in a

sub-network, dynamically group lower level agents

activities into a cluster according to the task as-

signed, coordinate agents activities to accomplish

the task resulting of planning, scheduling and track-

ing, integrate the information flow from lower level

agents and report to the supervisor SU agent.

The transport supervisory subsystem TSS type

agent (stationary) is designed to perform following

tasks: generate transportation tasks dynamically and

assign these tasks to lower level agents, analyze the

information from lower level agents and generate re-

ports or control proposals, create both stationary and

mobile type agents and dispatch them to various ac-

tivities undertaken via on purpose established com-

panies, interface the transport management system

(TMS) composed via both virtual and real enter-

prises to accept human commands. The structure of

transport supervisory subsystem TSS type agent is

composed on: Agent Communication Channel

(ACC, to route messages between local and remote

agents and realizes messages using an agent com-

munication language), Agent Security Manager

(ASM, to maintain security policies for e-platform

and whole transport infrastructure), Agent Mainte-

nance Manager (AMM, to provide preventive type

maintenance base on agents' condition monitoring),

Agent Management/ Execution Engine (AME, to

manage the life cycle of agents and to serve the exe-

cution environment for the mobile agents), Agent

Data Base (ADB, to store the data/ information and

knowledge in electronic format) and human operator

(H, to make the critical type decision). The same

counterparts we can find in the activity execution

subsystem (AES) agents.

4 FINAL REMARKS

The presented transport management system (TMS)

is dedicated not only to manage the defined trans-

portation target base on own distributed resources

based on dedicated agents, but also to manage the

life-cycle of the agents from operation and mainte-

nance point of view.

Using the described system is possible to conduct

the transportation system optimization taken under

consideration the safety, availability, reliability, fi-

nance, time and others important aspects.

Proposed transport active knowledge agent base

management model is possible to use to different

transport systems (e.g. see, air, road, rail, manufac-

ture) separately, but also in dedicated clusters.

ACKNOWLEDGEMENT

The research project is financed from the Polish Sci-

ence budget.

194

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