BALTPORTS-IT - IST-2001-33030 ____________________________________________________ WP7 Marine...

BALTPORTS-IT - IST-2001-33030

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WP7 Marine information Systems KUT

The BALTPORTS-IT Project:Application of Simulation Models and IT solutions

in Maritime Sector of the Baltic States

Dr.Eberhard BLUMEL1, prof.,habil.dr. Leonid NOVITSKI2,

prof.,habil.dr. Henrikas PRANEVICIUS3, prof.,habil.dr. Yuri MERKURYEV2

1Frauhofer IFF, Germany2Riga Technical University, Latvia

3Kaunas University of Technology, Lithuania


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WP7 Marine information Systems KUT H.Pranevicius


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Objectives• Set-up of the Baltic sub-regional Competence Center for promoting and

supporting the distribution of research knowledge in the field of advanced IT-solutions and simulation with maritime applications, Riga (Latvia)

• Dissemination of research knowledge gained during the execution of the EC projects AMCAI, DAMAC-HP and SPHERE and regional project in the field of IT-solutions and simulation of harbor managing

• Industrial customisation and exploitation of the project results from AMCAI, DAMAC-HP, ITMK and SPHERE by involving user groups in the Baltic region

• Development of recommendations for the application of results and thus creating new market opportunities

• Creating opportunities for the training of specialists in maritime information systems design and port logistics by using web-based technologies and distance learning courses


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Expected Results

• the non-monetary evaluation of general characteristics for port operations

• the optimisation of logistic operations in container terminals

• the optimisation of logistic processes in oil terminals • a methodology of combining port simulation and

information systems.

The industrial customisation of simulation systems in collaboration with user groups from the Baltic region will provide new approaches for

A demonstrator for distributed and web-based simulation of port environments will be built.


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Piece-linear aggregate formalism for business process analysis


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Purpose

• To create the dynamical models of business processes in Klaipeda oil terminal, which could be used to evaluate logistic processes of oil transportation and in terminal operative information system


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Concepts of Business Process Modeling

• Goal;• Activity;• Time;• Change;• Chronicle;• Event.


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PIECE-LINEAR AGGREGATES (PLA) FORMALISM


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DEFINITION OF PIECE-LINEAR AGGREGATES (PLA)

• PLA belongs to the class of automata models and defined by

• The state of aggregate consists of two components:

where

:,,,, GHZYX

NxxxX ,,, 21

MyyyY ,,, 21

RttzZ ,

tzttz ,

,,,1 ttt n .,,1 tztztz k


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• In time intervals when there are no input signals

• The state of aggregate changes in discrete time instances – input signal arrives,– continuous component acquires zero value.

TRAJECTORY OF PLA

t constdz t

dt , 1

t t tm0 1, , , ,

ZXZH :ZEZH :

ZXZG :ZEZG :

Transition and output operators


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THEORETICAL BACKGROUND OF PLA

Piece-linear Markov processes (PLMP)( prof. I. Kovalenko )

Piece-linear Markov processes (PLMP)( prof. I. Kovalenko )

Piece-linear aggregates (PLA)PLA = Aggregates + PLMP( prof. N. Buslenko )

Piece-linear aggregates (PLA)PLA = Aggregates + PLMP( prof. N. Buslenko )

PLA + Controlling Sequences( prof. H. Pranevicius )

PLA + Controlling Sequences( prof. H. Pranevicius )

ModellingModelling

SimulationSimulation

Formalspecification, simulation & validation

Formalspecification, simulation & validationBehavior

analysis

Behavior analysis

Performance analysis

Performance analysis


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THE USE OF CONTROL SEQUENCES FOR FORMAL SPECIFICATION OF PLA

• Two kinds of events are introduced:

(injection).

• The set of operations is introduced:

– - duration of the i-th operation.

},,...,,{},,...,,{, ''''2

''1

''''2

'1

'fN eeeEeeeEEEE

X E

,,,, 21 fOOOO

,EO ,ijiji ee ,Eeij ;,1 ,,1 jfi

ji


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• The time instant when the j-th operation ends is defined by co-ordinate:

– – number of events which have occurred during time interval .

w e ts e t i

i mi m r e t

i

i m

,, ,

,, 1

if - th operation is active,

otherwise,

r e ti m, ei

t tm0 ,


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• The set of co-ordinates defines the continuous component of PLA:

• Remark. Co-ordinates , , can change their values only in discrete time instances


w e ti m, , i f1,

z t w e t w e t w e tm m m f m 1 2, , , , , ,

mi tew , i f1,t ii , , , 1 2


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TIME OF EVENT OCCURENCE• Next time instant when internal event

occurs is calculated:

• Recalculation of continuous co-ordinates:

,,min1

1 mifi

m tewt ;,minarg

1mi

fitewr

otherwice,

active, isoperation th -if

,

,,:, 1

rtewtew

rjmr

mr

rifitewtew mimi ,1,,:, 1


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Relation between concepts used in conceptual description of business process and PLA

Goal Final state: Z(tm)Activity Operation:

Time Time: tm

Change Transition operator: H(e)Event Events: E’, E’’

Chronicle

i

Z(t1), Z(t2), … Z(tm)


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Methodology used for creation dynamical models of harbor business

processes• Object-oriented analysis and design method

(OOA&D);

• Piece-linear aggregate formalism (PLA);

• The use UML for integration OOA&D and PLA.


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Main activities of OOA&D method

Problem- domain analysis

Application-domain analysis

Component design

Architectural design

ModelModel

Req.Req.

Spec.Spec. Spec.Spec.


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Customization and exploitation of port simulation systems

• Simulation system for evaluation of logistics process of oil terminal


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The main components of logistic process of oil transportation through

Klaipeda

Oil Transportation

Company

Oil suppliers

Destination

Klaipeda Oil Terminal

Railway Company

Shipping Agency

Oil transportation


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Streams interaction scheme Stream of orders

transported of oil product

Stream of trains

Stream of tankers order


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The structure of model

QS S

1 Embankments

2

3

4

Platforms

Reservoir for light oil products

1

2

l Ql

Reservoir for dark oil products


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Streams in oil terminal

• Stream of orders transported oil products through terminal

• Stream of trains

• Stream of tankers


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Parameters of model (1)• Parameters of terminal input streams:

– Annual amount of transported oil.– Part of total amount of transported oil for each kind of oil.– Size of ordered oil.– Time interval during which oil products have to be

delivered by trains.– Time interval after the start of service, after which the

tanker have to arrive.– Number of wagons in train.– Capacity of wagon.


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Parameters of model (2)

• Parameters characterizing the structure of terminal:– Number of reservoirs and their capacities which are used

for each kind of oil.

– Number platforms.

– Subsets of platforms used to service of different kind of oil

– Number of wagons in each platform which can be served at the same time.

– Subsets of embankments used to service of different kind of oil.


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Parameters of model (3)

• Technological parameters of terminal:

– Time during which oil products are poured from wagons to reservoirs for each kind of oil

– Rate of pouring oil from reservoirs to tanker


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Parameters of model (4)• Parameters characterizing control of terminal:

– Order performance decision making algorithm which evaluates the number of wagons which are in railway station.

– Order performance decision making algorithm which does not evaluate the number of wagons which are in railway station.

– Algorithm carrying orders of tankers and evaluating only needed amount empty reservoir for realization of order.

– Algorithm carrying orders of tankers and evaluating needed amount empty reservoir for realization of order and number of wagons in railway station.


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Simulated characteristics (1)

• Characteristics characterizing terminal processes:– Number of wagons in terminal railway stations.

– Number of wagons in terminal railway station for each kind of oil product.

– Occupation coefficient of each platform.

– Average level of oil in reservoirs.

– Occupation coefficient of each embankments.


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Simulated characteristics (2)• Characteristics about performance of order for oil

transportation:– Wagons stay time in terminal, for different kinds of oil.– Tankers stay time in terminal, for different kinds of oil.– The performance time of order from time instant when the

order has been done till tanker leaves the terminal, for different kinds of oil products.

– The performance time of order from time instance when begins the transportation oil to terminal till tanker leaves the terminal, for different kinds of oil products.

– Average time of storing of oil products in terminal reservoirs for different kinds of oil.


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Average level of oil in reservoirs

0

10000

20000

30000

40000

50000

60000

3 4 5 6 7 8

Annual amount of transported oil (million tones)

Dark oil products Light oil products


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Average transportation time of oil

0

10

20

30

40

50

60

70

80

90

3 4 5 6 7 8


Dark oil produts Light oil produts


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Average service time of orders

5,5

5,6

5,7

5,8

5,9

6

6,1

6,2

6,3

3 4 5 6 7 8


Dark oil produts Light oil produts


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Maximum and average number wagon in railway station

0

100

200

300

400

500

600

3 4 5 6 7 8


Average Maximum


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Distribution function service time of orders

00,10,20,30,40,5

0,60,70,80,9

1

5 10 15 20 25 30

Service time, when annual amount is 7 million tones (days)

Dark oil products Light oil products

Annual amount of transported oil – 7 million


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Distribution function of wagons stay time in terminal

00,10,20,30,40,50,60,70,80,9

1

0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 1,8

Time (days)

Dark oil products

Annual amount of transported oil – 7 million tones


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Conclusions• The use of aggregate method permitted to formalize

logistic process of oil transportation.• Performed investigations with simulation model

showed main factors that influence transportation duration through Klaipeda terminal are annual amount of transported oil and used operative control algorithms.

• In order to fulfill user requirements to deliver oil to destination during specified time in some cases it is needed to restrict incoming stream of orders.


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Integration of Klaipeda Oil Terminal Simulation System

into IMS


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Operative control system of Klaipeda oil terminal

Service of wagons

Service of wagons

Service of tankers

Service of tankers

DB


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Service of wagons

• Registration of orders for load of oil products

• Creation of monthly schedule for loading wagons

• Receiving telegrams about arriving trains

• Unloading wagons


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Service of tankers

• Registration of request about possible arrival of tankers

• Confirmation that tanker will be served• Registration of information about arriving

tankers• Loading of tankers• Creation and adjustment of loading schedule

for tankers


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DB

Creation of loading schedule for tankers

SQL queries

Table views

Simulation model

Simulation model

Visualization

Visualization


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The use of simulation for forecasting

• Forecasting of business process can be evaluated by means of simulation;

• For realization forecasting it is needed to create simulation model which have to be integrated to information system.


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Creation of simulation models methodology

• Methodology use :– UML for problem domain analysis;– PLA for creating simulation model;– Microsoft .NET Framework for simulation model

implementation and integration to IS.


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DBDB

Use ADO.NET for integration

ADO.NET

ADO.NET

DataSetDataSet

ConnectionConnection

DataAdapterDataAdapter

Simulation model

Simulation model


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BALTPORTS-IT - IST-2001-33030 ____________________________________________________ WP7 Marine...

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