=Paper=
{{Paper
|id=Vol-2732/20200854
|storemode=property
|title=The Procedures of Logistic Transport Systems Simulation in the
Petri Nets Environment
|pdfUrl=https://ceur-ws.org/Vol-2732/20200854.pdf
|volume=Vol-2732
|authors=Olexiy Pavlenko,Denys Velykodnyi,Olena Lavrentieva,Sergiy Filatov
|dblpUrl=https://dblp.org/rec/conf/icteri/PavlenkoVLF20
}}
==The Procedures of Logistic Transport Systems Simulation in the
Petri Nets Environment==
https://ceur-ws.org/Vol-2732/20200854.pdf
The Procedures of Logistic Transport Systems
Simulation in the Petri Nets Environment
Olexiy Pavlenko1[0000-0003-4237-4310], Denys Velykodnyi2[0000-0001-6375-5775],
Olena Lavrentieva2[0000-0002-0609-5894] and Sergiy Filatov2[0000-0002-1771-4734]
1 Kharkiv National Automobile and Highway University,
25 Yaroslava Mudrogo Str., Kharkiv, 61002, Ukraine
2 Kryvyi Rih State Pedagogical University, 54 Gagarin Ave., Kryvyi Rih, 50086, Ukraine
helav68@gmail.com
Abstract. The article presents the methodology of the logistic system simulation
into the Petri Nets environment on the example of agro-products delivery from
Ukraine to the Netherlands, as well as a set of case-studies that can be used as
didactic means for course “The modeling of transport processes” to teach the
future transport industry specialists. The content, features and capabilities of the
Petri Nets in the logistics systems simulation have been analyzed. A structural
logistic system model of agro-products delivery, which takes into account the
operation sequence, the combination peculiarities and the use resources in each
subsystem, has been constructed into the Petri Nets. A time criterion has been
proposed to determine the effective option for agro-products delivery. According
to the results of the logistics system simulation into the Petri Nets environment,
a statistical analysis of the parameters has been carried out. It has been made a
choice of a suitable option of the delivery scheme allowing for the time spent and
the magnitude of the possible costs associated with the use of certain transport
modes. It has been made conclusion about feasibility of creating the case-study
set, which is based on real facts and being solved into the Petri Nets environment,
in order to organize the future transport industry specialists’ study and research
activities.
Keywords: simulation, logistic system of cargo delivery, Petri Nets, vocational
training of future specialists for transport industry.
1 Introduction
Today, in fact, Ukraine is one of the world’s leading exporters of agricultural products.
The agro-industrial complex is the most priority sector of the Ukrainian economy. In
the near future the strategy of its entry into the European Union market is essential for
the development of Ukraine. In line with this viewpoint, along with the economic and
technical aspects, it becomes significant content and directions of the transport industry
specialists’ vocational training. Such specialists should know the general patterns of
logistic systems development, the features and trends of management and material
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
flows optimization; they need orient and apply modern computer-based methods of
logistic systems simulation.
We would like be show the most indicative data describing the facts of increasing
the volume of agricultural products transportation. In the period January-September
2019, agricultural products were exported to the EU at 4.76 billion USD, which is
106.9% more than in the previous year, and it constitutes 30% of Ukraine’s total exports
[20]. According to IMF group Ukraine estimates, the total potential agricultural
demand for rail transportation by 2022 may increase from 52.4 (2018) to 70 or 95
million tons of annual freight. For its part, a rise in grain production causes a
corresponding growth in the consumption of chemical and mineral fertilizers. By 2022,
this figure can increase by 22.8% [29].
By results of 2019, the seaports of Ukraine had raised the volume of cargo
transshipment by 18.4% and reached record high performances in the whole history of
their existence. For the first time, the volume of transshipment of 13 Ukrainian active
ports exceeded 160 million tons [10]. The maximum indicators of transshipment of
grain cargoes and ore were recorded. Container transportation overcame a 10-year high
of 1 million TEU. In total for 2019 the Ukrainian seaports handled 11,850 vessels,
which as likely 196 ones (1.7%) as last year. Grain loads and oil accounted for more
than a third (37.7%) of the total cargo flow. Four ports of Ukraine became the leaders
in terms of transshipment in 2019. They were the Port Yuzhny – 53.9 million tons
(+ 26.1% to the last year), Mykolayiv – 33,4 million tons (+ 14.5%), Chernomorsk –
26.2 million tons (+ 21.4%), and Odessa – 25.3 million tons (+ 16.8%).
Along with the increasing volume of transportation, there are difficulties associated
with the efficient management and construction of a supply logistics “rigid schedule”
from the production location to the consumer. These difficulties are due to the fact that
the logistic system of agricultural products delivery has many links, including
informative ones, and involves a large number of elements [33]. Yet another problem
is the unjustified additional costs for the agricultural products delivery. In Ukraine grain
freight, e.g., costs an additional 8 US$ per ton compared to France and Germany.
Transportation value includes the cost of pure transportation, ware-housing, storage,
cleaning, drying of goods, handling operations, processing documents, packaging,
security, as well as the payment of any commissions, tariffs and fees because of the
grain export. On account to increased logistics costs, Ukrainian agricultural producers
receive less profit in contrast to world market prices. They have to bear the costs caused
by the inefficiency of the logistic system [31].
The active ICTs development in the previous years has allowed to accelerate the
process of presentation, processing and use of information in the logistic system for the
delivery of various cargo types [22]. But here too, there are troubles and problems in
creating effective information systems in enterprises. One of the first things is deficit
of prompt collection of reliable information in companies. Mostly companies suffer
losses as a result of untimely or incorrect data on the operational situation at each stage
of the logistics supply chain [5]. Therefore, today attention is increasingly drawn to the
information flow through which the movement of material resources is planned,
managed and controlled [34]. There is a need to model and study the behavior of the
logistic transport system in order to obtain information and forecast its most important
characteristics. The systems of parallel processing information and parallel objects
operating allow to solve the problem with effective interaction of individual elements
of this delivery frame. The best of these are models based on the Petri Nets Theory.
In light of these facts, the implementation in the content of the future transport
industry specialists vocational training the case-studies based on simulation into the
Petri Nets environment will be able to prepare them for work in real conditions of the
transport process organizing.
The purpose of the paper is to present the methodology of the logistic system
simulation into the Petri Nets environment on the example of agro-products delivery,
as well as a set of case-studies that can be used as didactic means for course “The
modeling of transport processes” to teach the future transport industry specialists.
2 Materials and methods
Nowadays, freight delivery management in Ukraine during the execution of
applications uses techniques based on heuristic methods; on own managerial experience
and on degree of responsibility. In practice, this means the occurrence of real losses
associated with the increase in the cargos delivery time, downtime of wagons at the
approaches to ports, at border crossings and at warehouses of enterprises (elevators), as
well as it caused by arrhythmia in the operation of transport subsystems in general. A
selective analysis of the cargo delivery time, conducted by Hanna I. Kyrychenko, shows
that the violation of the term occurs in transportation for all types of dispatch at 5%,
container – 32%, rail wagon – 17% and group shipments – 8% [14]. Therefore, the
logistics delivery system, e.g. of the agro-products, should cover the consideration of
the transportation organizing both by motor-cars and railroad wagon, and with the use
of containers [32].
It’s generally recognized the effective supply tracking is critical to managing global
trade and logistics [21]. To cope with the growing complexity of customer
requirements, supply chain organization and management systems are constantly
moving towards the area of collaboration, intellectuality, and service orientation. This
fact has been emphasized by Riccardo Giusti, Daniele Manerba, Giorgio Brunoa and
Roberto Tadei [7], Olexiy Pavlenko and Denis Kopytkov [16], Shenle Pan, Ray Y.
Zhong and Ting Quc [21] and others. Majority logistic companies in the world have
made cargo tracking a must in the delivery chain [15]. However, flow management is
still a challenge for supply participants. This fact confirms the feasibility of the idea of
a smart system for monitoring the promotion of cargos in containers in the multimodal
transportations context [32]. A number of papers (Victor Aulin, Oleg Lyashuk, Olexiy
Pavlenko, Denis Velykodnyi, Andrii Hrynkiv, Sergii Lysenko, Dmytro Holub, Yuriy
Vovk, Volodymyr Dzyura, Mariana Sokol, Dmitriy Muzylyov, Natalya Shramenko,
Vladyslav Shramenko) also reveal a complex multifactor system to track cargos
movement on the grounds of computer-based simulation [2; 19; 26].
Undoubtedly the technology of transport systems computer simulation is a very
sophisticated professional technology. It provides a system for information gathering
with use of data collection card, which is too much costly and not always an effective
process. In so far years, thanks to widespread implementation of ICTs, the simulation
technologies have been developing rapidly. Based on the model of activities of
organization, researchers (Haya Hasan, Esra AlHadhrami, Alia AlDhaheri, Khaled
Salah, Raja Jayaraman) reveal the content, concepts and methodology of simulation
technologies, methods of simulation modeling and simulation experiment, as well as
typical algorithms for simulation of various processes. In [8] the authors also proved
the relevance of simulation of events occurring in the pragmatist processes. General
issues about computer-aided students’ simulation training have been covered by Roman
M. Horbatiuk, Olena O. Lavrentieva, Serhiy O. Semerikov, Vladimir N. Soloviev, Illia
O. Teplytskyi and others [9; 17; 28].
It has been found that the most promising for describing the process of cargos
delivered by transport technological schemes is the use of simulation modeling.
Unfortunately, there are some complexities while designing and creating new models.
They are cased not so much by number of projected system elements, but by the
complication of causation in the complex of components which are interacting,
interconnected and interdependent. To solve the problems of optimizing the functioning
of logistic systems separately and as a whole, Yi-Min Deng, Aibing Yu, Weihua Li and
Di Zheng [11], Natalya Shramenko, Dmitriy Muzylyov and Vladyslav Shramenko [27]
developed the neural networks. At the same time, it is the Petri Nets that can be more
effectively used to simulate the systems with parallel processing of information and
objects.
The Petri Nets, as it has been pointed out by Wei Zhu Zhong, Xiao Qing Fu and Ya
Ping Wang [36], Natalya Shramenko, Olexiy Pavlenko and Dmitriy Muzylyov [25],
Guan Xiang Zhang, Shan He and Zhi Yong Zhang [35], Mouhaned Gaied, Anis
M’halla, Dimitri Lefebvre, Kamel Ben Othmen [6] and others, are much more
commonly exploited to investigate an object behavior in real time and to evaluate the
performance of the transport system. The Petri Net Theory is a mathematical apparatus
designed to work with parallel and asynchronous systems. It first described in 1962 by
the German mathematician Carl Adam Petri. Now the theory is being considerable used
virtually in every field of research [3]. In the following we consider the potential of the
Petri Nets environment in transportation system simulation, as well as its benefits for
organizing vocational training for future specialists for transport industry.
3 Theoretical background
First of all, to simulate the transportation system an object-oriented model should be
constructed. This type of simulation is a simple and concise method of modeling and
competent mathematical analysis for all spheres of activity, including logistic systems.
An object-oriented model describes the structure of system objects, their attributes,
operations, links with other objects [12]. Such simulation reflects those concepts and
objects of the reality that are principal for the system development. It is object-oriented
technology that is combined with Petri Nets for studying logistic processes [18].
The Petri Net is a clear and well-formalized simulator for the behavior of parallel
systems with asynchronous interactions. It reflects in a compact form the links structure
between the system elements and dynamics of changing its states under given initial
conditions [4]. The model abstraction level is very high. It corresponds to the
description of interactions in the system in terms of only two concepts – events and
conditions. The Petri Nets are integration of a graph and a discrete dynamic system.
Thus, they can serve as a static and dynamic object model, which in turn makes it
possible to solve a fairly wide class of tasks [4]. Petri Nets allow modeling the assembly
lines, simulation and analysis of technological processes and resource allocation of the
enterprise.
Graphically, the Petri Net is interpreted as a bipartite oriented graph with a given
initial state, called the initial marking (or token) – Mo [4]. The graph consists of nodes
(vertices) of two types – places (positions) P (i.e. conditions) and transitions T (i.e.
events that may occur); they are connected by arcs. The same type nodes can’t be tied
together. The marking can be put within the position and may move by network. At the
Petri Net diagram, positions are represented by circles, transitions – by bold bars, and
arcs are signified by arrows. The directed arcs describe which places are pre- and/or
postconditions for which transitions. The arcs are denoted by the weight factors
(integers). It’s accepted that the arcs with equal weights k are equivalent to parallel arcs.
Marking (state) attributes to each position regard as an integer non-negative number
M (P) equal to the number of chips in position P [1]. So, a formal presentation Petri
Net is following:
PN = (P, T, F, W, Mo), (1)
where Р = (р1, р2, ..., рm) – is a finite set of positions; Т = (t1, t2, ..., tn) is the finite set
of transitions; F – set of arcs (flow relationships)
F ( P T ) (T P ) , (2)
where W: F → (1, 2, 3, ...) is a weight function; М0: Р → (0, 1, 2, 3, ...,) is the initial
marking.
In modeling tasks, where the concepts of conditions (states) and events are applied,
positions comply conditions and transitions are events. Each transition (event) is
associated with a certain number of input and output positions-analog according to the
pre- and postconditions of this event. The presence of a chip in some position is
interpreted as the truth of the condition corresponding to that position, and also
indicates the availability in the position k of data elements or an appropriate amount of
resources [1].
Models based on Petri Nets allow to analyze two types of properties defined by the
original Mo marking and independent from it. The first-kind properties are called
behavioral ones. Reach, limited, and energy are main for them. The second kind
properties are called structural ones. They are characterized, as a rule, by the matrix of
incidents C and related equations and inequalities. These include structural limitation
and activity, safety, repeatability, and etc. The safety and repeatability properties enable
to respectively determine the positional and transient invariants into the Petri Nets
environment.
Known methods for analyzing the properties of Petri Nets are divided into three
groups: 1) methods based on building multiple reach; 2) ones using matrix equations
and network invariants; 3) ones allowing decomposition of the network to the subnet.
The main areas of applied the Petri Net Theory are synthesis, analysis and modeling.
The purpose of the Petri Nets simulation is to investigate the dynamics of studied
system functioning and its behavior for different initial conditions. However, the
popularity of Petri Nets is caused not only because of their high modeling possibility,
but also thanks to a well-developed formal apparatus for analyzing the properties of a
constructed model [24].
Next, we will consider the capability of the Petri Nets to solve the problems of
logistic systems simulation. By way of example we will analyze the agricultural
products delivery from Ukraine to the Netherlands. The choice of this country is caused
by the fact that the largest volume of Ukrainian agricultural products export accounts
for the Netherlands – 1.06 billion US$. Furthermore, of the 16 commodity items of
cargo shipment to this country, grain crops are of the highest importance; their part is
43.6% of the total volume of transportations [31]. To accomplish the construction task
of efficient logistics system, it is necessary to develop a model of agro-products
transportation from a manufacturer in Ukraine to a destination seaport in the
Netherlands, to perform simulations to determine the estimation parameter – the time,
as well as to clarify the conditions for the effective functioning of the entire delivery
process.
4 Technique for using Petri Nets to simulate the logistic
systems
4.1 General overview of the logistic system for agricultural products
delivery from Ukraine to the Netherlands
In general, the logistic system of agricultural products delivery in Ukraine has a
structure based on the interaction of such participants as: traders – organizers of
products export, manufacturers, storage systems, transport companies, as well as the
existing infrastructure of ports, railways and highways. The traders are a centralized
system of the required technological level, aimed at organizing the interaction between
participants, and besides, on receiving and analyzing input and output data, and to
ensure greater transparency of the delivery process [30]. The logistic system can be
interpreted as a holistic perspective, built in real time for all its participants allowing
traders to make appropriate short-term and long-term decisions directed at the process
development.
The system is built from the following subsystems: harvesting, transportation to the
departure port, shipping by sea. The macro-level structural model reflects it in a
comprehensive manner (see Fig. 1). To determine the effective delivery option, the
main investigated parameter we defined the delivery time of agricultural products.
Clearly, the time minimization process is affected by intensities of the receipt of orders
and technical means – ІV, the volume of the order – QV, and time of execution of the
main operations in the corresponding subsystem – tV. It means following:
T = {ІV; QV; tV} (3)
Each of the time minimization parameters consists of a number of components.
Presumably, duration of technological operations and time of work technological
intervals determine the variables that could be managed. The main system parameters
change under the influence of load on the simulation model and during its management.
These model parameters act as phase variables that can vary depending on the load on
the system.
Port
transportation Shipping
Harvesting subsystem subsystem subsystem
Transportation of agro-
– Reception of agro-
products by wagons
cultural products at the
Transportation agro-products by car toward
Reception and safekeeping agro-products at
port of Departure.
– Overload.
Harvesting of agro-products by the
– Safekeeping.
– Loading in a
seagoing vessel.
a storage point
a storage point
manufacturer
– Transportation from
a port in Ukraine
towards a port in the
Transportation of agro-
products by motor-cars
Netherlands.
– Unloading the vessel
at the port of
destination.
Fig. 1. The structural model of the agricultural products delivery system from Ukraine to the
Netherlands
To construct a transport system model, it is advisable to employ a stochastic estimation
of the Petri Nets, where the vertices of the places have an integer number of marks and
are used to quantify some parameters of the system functioning like reliability and
performance. In such networks each transition is attributed to a probability of its
activation over time [24].
Considering the functional connections between the elements, it’s possible to
construct an enlarged model into the Petri Nets environment (Fig. 2). The model will
be to describe the logistics system functioning – the process of promotion of agro-
products from the sender (Ukraine) to the destination seaport (the Netherlands).
The presented scheme depicts the process with the established elements. As for
transitions, these determine following ones: Т1, Т2 – intensity of applications receipt
from the trader and the farmer; Т3, Т5, Т3, Т10 –time of cargo moving respectively by
cars, wagons, sea vessels; Т4, Т7, Т8, Т9, Т11 – the corresponding distribution of cargo,
information, technical means. The positions are represented by: P1, P2 – the presence
of agro-products from the farmer and demands (technical means) from trader; P1–P13
are cargo position at each operation stage.
Fig. 2. The model of agro-products movement from the sender to the destination seaport
As can be seen, movements and positions are key points to the model presented.
Therefore, in the future in modeling process, it should be paid maximum attention to
these aspects.
4.2 General description of the Petri Nets application
As mentioned above the Petri Net modeling technique can be used for qualitative and
quantitative analysis of workflow and workflow system in general. Apps market
proposes a lot of offers that are founded on general modeling methodology into Petri
Net environment [23].
Petri Net Integrated Simulator is aimed at the research and development of complex
systems in various subject areas, including road transport. It provides following:
1) automation in construction of complex objects models; 2) simulation in step-by-step
modes for tuning models, and statistics collection; 3) the formal analysis of systems
represented by Petri Nets in terms of their structural and behavioral properties [23].
The Petri Nets graphic editor is aimed at creation and edition of Petri Nets and their
modifications (F-networks, temporary networks, etc.) presented in graphical form. The
editor is based on the general ideology of the multi-window interface and is functionally
divided into two parts. The first part is the basic; it ensures the construction and edition
of the Petri network overall structure. The second part is parametric; it provides the
change of parameters of the network basic elements such as position (P) and transitions
(T). The parametric part, in turn, also consists of two parts where each one processes
either “position” or “transitions”. The work of parametric part is ensured by changing
the parameters of the network elements via directly interacting with the main objects of
the general data structure like “position” and “transition”.
The simulation process consists of two main parts. First one is the window user
interface (presented on Fig. 3) and second one is the Petri network operation procedure.
The simulation process in part of window-user interface is founded on the widespread
ideology of software complex organizing and uses the functions of the multi-window
interface created within it. By virtue of this fact, the process interacts with external
devices (and through them with the user) and messaging with other complex processes.
Fig. 3. Window fragment of the simulation process
The operation procedure applies for its work a special structure data created for storing,
modifying and processing the Petri Net description. The operation procedure is started
by the command of window-user interface and sequentially executes the simulation
algorithm until it is interrupted by the user or by the command window according to
the specified simulation mode. This mode sets in the Mode menu item.
An integrated system of modeling and formal analysis based on Petri Nets can be
applied in the future transport industry specialists vocational training process. In
particular, in order to create the content and educational tools of the course “The
modeling of transport processes” and for students’ study and research work.
4.3 A case-studies set for the analysis and statistical evaluation of
logistic models
In order to simulate the functioning of the logistic system of agro-products delivery, it
is necessary to carry out a statistical analysis of parameters that are used to set initial
values by transitions and positions in the Petri Net environment.
Case-study 1. You need, based on the data presented on the volume of cargo and
the number of wagons for Kernel Trade – a large vertically integrated agricultural
holding of Ukraine and information in [13], determine the time of grain delivery from
senders (farmers) to the port station Odessa-Port with overload on the Odessa port grain
terminal, and further – to the Netherlands. It should be considered that the cargo is
delivered from the Odessa port to the Netherlands ports by means of ships (Fig. 4). You
must find out the most appropriate type of transport for each operation stage.
Fig. 4. Scheme of agro-products transportation from the Odessa-seaport to the Netherlands-port
Some solution ways. For statistical estimation of the values of the used parameters given
in the models transitions – Ti, they are expediently divided into groups by characteristic
feature (Table 1). The initial values in the positions (Pi) can be determined by the
availability of statistical information of Ukrzaliznytsia, as well as by data of delivery
information services.
The complex system analysis based on the Petri Nets environment can be performed
with the use of simulation model. In line with this strategy, it is evidently needed to
specify the input flows of demands and determine the system response. The initial
parameters will be able to calculate processing the statistical material accumulated
during the simulation.
Apparently, if it has a good chance to use both a road and a rail transport, then there
are three variants of cargo flow distribution in the appropriate logistic systems of agro-
products delivery. They are:
Option 1 – 100% of cargo is transported by rail across the Ukraine.
Option 2 – cargo is transported equally for each mode of transport (rail and road).
Option 3 – 100% of the cargo is transported by road.
For all of those options, the models can be built in the Petri Nets environment. As a
modeling result, the data distributed over the duration of execution of basic
technological operations throughout the logistic system were obtained.
Case-study 2. You should analyze the behavior of the model depending on the
volume of order the agricultural products delivery (Fig. 5) and determine the best
variant of the grain cargo delivery chain from the sender toward the recipient
(destination port). The number of values is 54 units, it is determined by the level of
confidence probability, error, and number of observations. The value of transport
volume varies from 20 to 3240 tons [13].
Table 1. The model parameters statistical estimation
Sample Limits of the parameter’s
Distribution
Parameter group name volume, mathematical expectation
law
units values
Intensity of receiving orders, units / h. 68 0,2-6 Exponential
Intensity of agro-products receipting from
83 10-42 Exponential
collection and storage sites, t / h.
Intensity of cars arrival, units / h. 49 1-5 Exponential
Intensity of wagons and locomotives
152 0,4-32 Exponential
arrival, units / h.
Arrivals and departures of trains, units / h. 91 0,1-2 Exponential
Intensity of arrival or departure of vessel,
47 0,1-1 Exponential
units / h.
Time for performance of harvesting
72 8-53 Exponential
technological operations, h.
Time for performance of technological
operations at storage in specialized places 59 0,5-28 Normal
– in the warehouses, elevators, h.
Time for performance of technological
63 0,2-34 Normal
operations on car, h.
Time for performance of technological
82 0,7-77 Normal
operations on railway transport, h.
Time of carrying out technological
operations on sea transport – into the 51 1,3-164 Normal
ports, h.
Time for performance of technological
operations during moving between ports, 37 320-384 Lognormal
h.
Some solution ways. From the histogram (Fig. 5) the best option of the delivery model
can be chosen. The choice should be made according to the criterion of minimum time
for agro-products delivery. Meanwhile, it is possible to clearly trace the trend
concerning parameter at position “30”, which corresponds to a cargo volume about
1470 tons. Under these terms, transportation by road is more advantageous. For larger
volumes, it is advisable to deliver cargo to the port by rail only. In this case the usage
of trucks is unacceptable alternative since this is due to the need to have a significant
number of trucks, which for Ukrainian transport operators is inefficient.
The presented above approach allows to decide not only the timing scheme, but also
to estimate directly the possible costs associated with determining the cost of delivery
in terms of existing resources.
These, and simpler, case-study that are created on fact data and solved into the Petri
Nets environment can be used as a basis for organizing future transport industry
specialists’ study and research activities. In next time students will be able to
independently look at the current important information about cargo delivery and use
it to estimate various logistical models with the help of simulation into the Petri Net
environment.
Time, hour
Option 1
Option 2
Option 3
Value number
Fig. 5. The logistic system modeling results for three options of agro-products delivery with
usage of Petri Nets app
5 Conclusions
It is established, in the context of increasing the intensity of transport flows and the
widespread of ICT, the approaches to the modeling of logistic transport systems need
to be qualitatively updated. From one part, globalization and information processes do
affect the content and methods of vocational training of future specialists for transport
industry.
It is found out that in Ukraine the logistic system of the agricultural products de-
livery has a structure based on the principles of alternative subsystem construction. The
operation of each subsystem individually, as well as in the interaction is most
effectively described with exploitation of the Petri Nets, which are used to investigate
the behavior of an object in real time.
The paper presents an analysis of the theoretical foundations of the Petri Nets and
the adequacy of their application for the transport processes simulation. It is shown that
the Petri Nets allow to study in depth the behavior of a simulated system and to obtain
information about its most important characteristics. It also takes into account that the
logistic systems for the agricultural products delivery should be considered as systems
for parallel processing of information and parallel operation of facilities. Modeling via
the Petri Nets enables to track and analyze the results, the same way as to predict and
control the forwarding cargo process (e.g. agricultural products) in various logistic
systems.
The advantages of the Petri Nets app are the low complexity of model development
and the possibility to simulate adequately, even with a small amount of source
information. During the simulation it was found that this tool allowed to analyze and to
forecast the work for all system participants, to calculate quickly the parameter values
for each specific control object in logistic system.
As an example, it’s provided the case-study set concerning simulation in three
options agro-products delivery from Ukraine to the Netherlands with the use of the Petri
Nets environment. The set can be applied in the system of students’ study and research
activities in transport area of expertise.
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