The 14 International Conference “RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION – 2014”

Proceedings of the 14th International Conference “Reliability and Statistics in Transportation and Communication”(RelStat’14), 15–18 October 2014, Riga, Latvia, p. 58-59. ISBN 978-9984-818-70-2Transport and Telecommunication Institute, Lomonosova 1, LV-1019, Riga, Latvia

MODELING OF CONTAINER TRAINS IN THE STRUCTURE OF A DRY PORT WITH THE USE OF TECHNOLOGY “BLOCK TRAIN”

Mikhail Malykhin1, Aleksandr Kirichenko2

1. Admiral Makarov State University of Maritime and Inland Shipping, Saint Petersburg, Russian Federation, mmalykhin@stanlgs.ru

2. Doctor of Technical Sciences, Professor, Admiral Makarov State University of Maritime and Inland Shipping, Saint Petersburg, Russian Federation, a.v.kirichenko@mail.ru

Keywords: simulation modeling, dry port, “Block Train” technology

Summary

The purpose of this research is to develop a simulation model of transport communication between the sea and inland container terminals for determination the mode of this unit and the necessary volume of railway rolling stock.

Introduction

At this moment, the most of Russian seaports that possess container terminals, have nearly completely exhausted their capacities.

The main restriction is the city location of terminals, which creates a natural bound for the expansion of the port infrastructure.

As the obvious disadvantage of city location, is the negative impact at the environment due to the fact that the input and output of cargo to the port are, usually carried by trucks.

In addition to geographical factors, the capacity of terminals is reduced by long-term storage of import and export cargoes at sea terminals due to customs’ clearance.

Moreover, the experience shows that the possibilities’ lack to expand the areas of sea terminal, stevedores, to maintain existing capacities, are forced to reject customers’ requests for such services as the unstuffing of LCL containers, or, vice versa, the consolidation of parties.

The development of substitution port facilities at the cities’ outside is the expensive and difficult project not only by technology reasons, but also by social ones, since the location of new terminals outside the cities should be accompanied with their stable supply with workforce.

Dry port as a way to solve the problems of Russian sea port

One of the ways to solve the mentioned problems is the commissioning of inland terminals or, as they are called, “dry ports” which are located around of the

Session 5. Transport and Logistics System Modelling

city limits. First, these decisions have been applied in Western Europe in the middle of the last century to diversion the cargo flows from the destroyed by Second World War port facilities. At this moment, the effectiveness of dry ports has already been proved both in the worldwide practice, and in Russia (Kirichenko, Kuznetsov, 2014; Kirichenko, Kuznetsov, Izotov, 2013; Kuznetsov, Eglit, Kirichenko, 2013; Scherbakova-Slusarenko, 2014). As the example the terminal in Shushary, that is used as the dry port for First Container Terminal.

The implementation of the "dry port" technology allows marine terminals to perform only transit or transshipment functions. The storage of cargo during the customs’ clearance as well as the consolidation operations is provided by the inland terminal. Thus, it can function as a distribution center.

Problems of dry ports and existing research to address them

The technological problems include docking modes of transport in a dry port and uninterrupted implementation of transport between the sea and inland terminals. Appearance the additional link at the cargo supply chain, and the emergence of extra cargo operations, creates added cost of transporting goods, which is reflected in the cost of services and end-user operator (Scherbakova-Slusarenko, 2014).

Two possible ways of reducing the value added are cutting costs of terminal’s operator to optimize the using of expensive coastal areas (no need long-term storage site containers at the Marine Terminal) and the costs of transport communication between the sea and inland terminals. There is a need to develop scientific methods specified optimization.

Currently in Russia there was carried out a number of applied research related to the operation of "dry ports". The simulation modeling is a more often applicable method in these studies.

The first group of studies related to problems of seaport’s design.So, papers by of A. L. Kuznetsov, M. N. Goryntsev and others, a seaport

regarded as macroobject, it capacity is based on the superposition of states of its elements (Goryntcev, 2014; Kirichenko, Kuznetsov, 2014; Kuznetsov, 2009). Hence, the derived models do not allow to take operational decisions in the chain "sea terminal – inland terminal".

Local models developed by simulation modeling apply to newly constructed port of Taman (in particular, the work simulated the approach channel), where the problem of "dry port" is not relevant.

The second group of studies is related to railway and directly concerns the problem of "dry ports", as in the work of J. N. Panova (Panova, 2012). In this paper a probabilistic description of technological areas of container terminals by discrete-event simulation software environment “AnyLogic” was developed. However, the main attention was paid to cooperation with the automobile transport (as the limiting level of the container terminal was selected checkpoint).

The application of the "dry port" concept poses several requirements to the cargo terminal:

- A single operator possessing the sea terminal;- The mandatory presence of the railway communication between two terminals;

The 14 International Conference “RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION – 2014”

- The simplification of customs procedures for moving containers between the sea and inland complex;

- The harmonized system of cargo flow management for both the sea and the inland terminal.

However, this solution has a major drawback: the usage of inland terminal forms additional transport link for delivering of cargo from the sea to inland terminals. Consequently, there arise additional costs that are reimbursed by the operator of logistic complex.

Optimization of dry port’s operation by using of simulation modeling

The study considered the possibility of optimizing the number of railway rolling stock, which includes shunting locomotives at sea and inland terminals and flatcars to carry containers. Rolling stocks mentioned above are owned by the operator of infrastructure. In addition, one should determine the required amount of mainline locomotives, which carry out transportation of platforms between gateways to which front and rear terminals are assigned. Locomotives, in accordance with the legislation of the Russian Federation, are the property of JSC "Russian Railways" and are the subject of the commercial work on the railways.

There is a need to determine the optimum amount of rolling stock to provide additional transport link and the subsequent optimization of costs for the purchase or lease of rolling stock` and its subsequent operation.

At this research the solution of this task are proposed by using of simulation modeling.

The research model was developed on the base of the “AnyLogic” software.This product allows the user to create models with the help of the set of active

elements that simulate real-world objects and experiments that define the configuration settings of the model.

This software is based on the JAVA computer language and supports three well-known methods of simulation modeling:

- System dynamics;- Discrete event simulation;- Agent based modeling.The operational activity of any transport system may be represented as a

chronological sequence of events. Modeling such kind of system relates to discrete modeling.

Description of the source data, formation of the model, the experiment

For modeling purposes, following factors were defined:- determination the transport unit operations mode;- determination the rolling stock required number;- improving the “Block train” transportation technologies.As a background information for the simulation modeling the available statistic

data on import cargo flows between the “First Container Terminal” and a terminal in

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Shushary was selected, as well as the data on traffic conditions in the area “Avtovo railway station – Shushary railway station” of the October Railway.

As a random variables were specified next parameters: probability of railroad trucks’ occupied, probability of availability of free transport units, probability of transport units fault, probability of cargo’s lack. For definition of these parameters first of all were studied a statistics information concerning these events at railway stations of the October Railway, serving the “First Container Terminal” and a terminal in Shushary. Then, applying statistical law sought quantities were obtained.

The model considers the application of the “Block-Train” rail transportation technology, which implies the presence of dedicated timetable of train moving and reducing the time of inspection at the intermediate railway stations.

Current algorithm of the model is shaped by standardized units available and configured settings or by means of the JAVA language.

Input data for this model is taken from the of statistical information, but may be modified manually before the start of the experiment. Each container in that model is named “entity” and loaded to it by the “Source” object.

Events are considered as models of discrete events, which occurring at a present time. Agent based modeling is not suitable at that case, since entities are not independent objects.. General view of the block diagram model is presented in Figure 1.

Figure 1. General view of the block diagram model

Application of Technology "Block Train", which follows a dedicated timetable is regarded as the highest priority on several parameters:

- removes the dependency on the availability of the involved rolling stock;- reduces the inspection at the junction;- provides simplified coordination of train schedule.

The first object <Source> – simulates the receipt of entity in the system that is input stream of containers. In this model, they are operating according to the exponential distribution.

The 14 International Conference “RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION – 2014”

Next comes the launch of an experiment to determine the necessary number of rolling stocks. The experiment, 30 days for a given container cargo traffic 274 cont./day. Figure 3 shows the simulation results in graphical form. The simulation period is limited to one minute. The accelerated mode of the model allows to realize for a few minutes the experiment for the period, corresponding to one calendar month.

Figure 2. Simulation results

By running the model in an accelerated mode, one can simulate the movement of trains on the route during the calendar month. The model visualization is occurring in 2D and 3D modes, and allows the user to demonstrate the operation of this transport link. The simulation results are presented in numerical and graphical form.

As Figure 2 shows, subject to specified parameters, such as the length composition, its speed, while commercial inspection, delays in loading and unloading, as well as probabilistic parameters, the bandwidth of this stretch of track is 134 40-foot containers per day. Number of trains per day is 2. Given the number of containers in circulation at the site path must be 289 rail flatcars. Thus, this model allows us to determine the capacity of the path, as well as the required amount of rolling stock to ensure it.

The adequacy of this model should be discussed. The adequacy of a model is its correspondence to an object, which is being studies. It is well known, that a model can replace the physical object only if they are quite similar. If the model is not adequate to the object, the results obtained during the simulation cannot be taken as a truth.

The adequacy of this model was checked by the following method:- model behavior is enough concurrency to object’s behavior;- purpose of the model corresponds to target of the study;- the results, obtained during simulation modeling, are checked on the accuracy.

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Conclusion

This example shows that with the help of the abstraction model, the method allows one to determine accurately the number of rolling stock, taking into account the inherent potential delays in the model, with different causes and factors.

References

1. Goryntcev M. N. (2014) Simulation modeling Taman port. 2nd Interuniversity scientific and practical conference "System analysis and logistics of transport": Abstracts Admiral Makarov State University of Maritime and Inland Shipping, Saint Petersburg.

2. Kirichenko A. V., Kuznetsov A. L. (2014) Interrelations between cities and ports: evolution and perspectives. Транспорт РФ. № 1 (50). С. 12–15.

3. Kirichenko A. V., Kuznetsov A. L., Izotov O. A. (2013) Methodology decisions in transport logistics. Final Report on the scientific work. Admiral Makarov State University of Maritime and Inland Shipping, Saint Petersburg. № reg. 01201172251.

4. Kuznetsov A. L., Eglit Ya. Ya., Kirichenko A. V. (2013) On the issue of organising the operation of a transport hub. Транспорт РФ. № 1 (44). С. 30–33.

5. Kuznetsov A. L. (2009) The Methodology of modern container terminal’s technological design. Academy of Transport of the Russian Federation, Saint Petersburg.

6. Panova J. N. (2012) Justification development phasing rear container terminals. Dissertation for the degree of candidate of technical sciences. Saint Petersburg Sate Transport University.

7. Scherbakova-Slusarenko V. N. (2014) The concept of dry ports in the world and in the Russian Federation. 2nd Interuniversity scientific and practical conference "System analysis and logistics of transport": Abstracts. Admiral Makarov State University of Maritime and Inland Shipping, Saint Petersburg.

8. Sterman John (2000) Business Dynamics: Systems thinking and modeling for a complex world. McGraw Hill.

9. Grigoriev Ilya (2012) AnyLogic 6 in Three days: A quick course in Simulation Modeling