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МОДЕЛЮВАННЯ ЗАДАЧ ТРАНСПОРТУ ТА ЕКОНОМ1КИ
UDC 656.2.078/.09
А. M. OKOROKOV
1*
1 Dep. «Management of Operational Work», Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan, Lazaryan St., 2, Dnipropetrovsk, Ukraine, 49010, tel. +38 (056) 373 15 70, e-mail Andrew_okorokoff@mail.ru, ORCID 0000-0002-3111-5519
STRATEGIC MANAGEMENT OF TRANSPORT CARGO COMPLEX
Purpose. Making the qualitative administrative decisions defining strategy and tactics of transport cargo complexes development, and also its subsystems, is possible only in the presence of flexible optimization model. This model has to consider multiparametricity and multicriteriality of the given task, uncertainty and vagueness of input information, and also to provide process automation of searching the best parameters of the given production facility. The purpose of the research is to develop procedures for the strategic management of complex with view of the most important factors and their stochastic nature, which will execute the improvement of technical equipment of TCC. Methodology. The problem of strategic management is based on solving the complex of issues of the optimal number of shunting locomotives, optimal processing capability of handling the front and rational capacity of warehouses. The problem is solved on the basis of the proposed optimality criterion - the specific set of profit per unit of capital assets of freight industry. The listed problems are solved using simulation modeling of the freight industry. Findings. The use of developed procedure allows one to improve the technical equipment of the freight stations and complexes. Originality. For the first time it was developed the procedure of strategic management of development. This procedure allows taking into account the probabilistic nature of demand for services of transport freight complexes and technological processes of client services on the complex stations. The proposed procedure can be applied during when planning the investments in the creation of transport freight complexes. Practical value. Use as a basic tool of simulation models of complex cargo operation allows estimating the effectiveness of the capital investments, the level of operating costs, as well as the quality of meeting the demands of potential customers in transportations at the stage of transport cargo complex.
Keywords: management; transport junction; shunting locomotive; processing ability; profit; functional dependence; optimization
Introduction
In this regard during the design, planning and management of the TCC a set of interrelated optimization problems should be considered. Their solution is a multistage iterative process consisting of two mandatory interacting phases. They are planning and regulation. Planning is realizing at the level of strategic management and regulation - at the level of tactical (operational) one [6, 11]. Optimal development strategy of TCC is determined by the parameters reflecting the most important of their relationships, as well as the connections with other
Making the qualitative administrative decisions defining strategy and tactics of transport cargo complexes (TCC) development, and also its subsystems is possible only in the presence of flexible optimization model. This model should take into account the multiparametricity and multicriteriality of the given task, uncertainty and vagueness of the input information, as well as provide the process automation of searching the best parameters of the given production facility [1, 2, 5].
Наука та прогрес транспорту. Вкник Дншропетровського нацюнального ушверситету залiзничного транспорту, 2014, № 4 (52)
subsystems (number of loading and unloading machines, feed to the cargo fronts, the working hours of cargo front during the 24, etc.) [7, 8, 9].
At the present time the researchers developed a number of models designed for managing the different objects of railway transport, which are based on different methods [3, 4, 12, 13], but the issue of planning and management of TCC was not considered at all or was covered in fragments.
Purpose
The purpose of the article is to develop the procedure of strategic management of cargo complexes taking into account the most important factors and their stochastic nature that will improve the technical equipment of DCC.
Methodology
Strategic management of cargo complex provides the formation of the development plan of technological system, first of all, the development of fixed assets of the enterprises that are the part of TCC. Fixed assets, which are used directly during servicing the car traffic volume, include switch powers, means of freight mass processing -freight-handling mechanisms, as well as the corresponding storage equipment. Thus, the main problems of optimal management of the cargo complex at the strategic level include the following ones:
- substantiation of the optimal number of switch powers that will be used for servicing freight trains coming to the transportation junction;
- determination of the optimal processing capacity of handling fronts, namely, determining the necessary number of mechanisms of the certain type providing the appropriate significance of the processing capacity of the front;
- calculation of the storage capacity of transportation junction, sufficient for storage processing of the incoming cargo.
The optimal number of locomotives should ensure demand for TCC services (first technological phase), which is described by the incoming applications (car flow that are coming in groups - in freight trains).
Optimal processing capacity of the cargo front should ensure uninterrupted performing of the service operations of loading and unloading of cars in the transportation junctions (second technological phase). Processing capacity of the front is deter-
mined on the basis of the known number of locomotives that provide car supply to the front and their removal after the service operations.
Optimal capacity of the storage complex should ensure the need for storage of goods (third technological phase), that are coming to the TCC. It is determined on the basis of the known number of switch powers and processing capacity of cargo front.
At the first stage the minimal number of switch powers that will ensure the timely movement of cars, which have arrived to the transportation junction, to the handling fronts and their removal from the front after servicing is calculated. Thus, it is reasonable the assumption on the lack of idle waiting of cars at the handling front for service (this assumption provides sufficient processing capacity of the front, which is provided on the next stage of developing a strategic management plan of TCC):
Сг ^ 0,
(1)
where r0B0ar - is total downtime of cars at the handling front waiting for the start of servicing, hrs.
According to the accepted efficiency criterion of the TCC operation at the stage of strategic management [5] the following expression can be used as the target function:
Поф(N)=
Д t - Bt ( N ) оф, ( N )
->max,
(2)
where Nl - is the number of switch powers servicing the car traffic volume.
At the initial stages of the simulation, when the technical equipment of cargo complex is uncertain, the mathematical model of justification of optimal number of switch powers includes the assumption of independence of TCC income on the locomotive number:
Д, = const.
(3)
Among the costs of TCC enterprises it is reasonable to consider in the justification of optimal number of switch powers the operational component Et as well as in general form - the taxes H In this case the costs for debt capital payments and capital expenditures can be taken equal to zero:
с, = 0:
Kt = 0,
Ct * Кt.
(4)
The case when Kt is greater than zero, is a kind of task that involves the acquisition of new mate© А. М. Okorokov, 2014
Наука та прогрес транспорту. Вкник Дншропетровського нацюнального ушверситету залiзничного транспорту, 2014, № 4 (52)
riel with full payment of their value (in this case Ci = 0) or partially (in this case, Ci > 0).
In general terms, the tax amount of TCC enterprises can be determined on the basis of profits as follows:
Н = (Дt 5FAr - Et ) 5 ;
(5)
El = E™ + E^ = const.
(6)
It is assumed in the study that the total balance value of the storage space, handling fronts, approach lines, as well as administrative and household structures of TCC does not depend on the number of switch powers, involved in the servicing of the car traffic volume:
Б = БСк
;.нрф
■ Б™ = const. (7)
Based on the average balance value E™K for
one locomotive the value E™K is defined as follows:
Блок г лок лт t = Бод N1 .
(8)
Поф ( n ) =
(1 - 5vat 5p ) - (1 - 5p ) El ]
Б1 + БЛДк N
(1 - 5p ) E™ (N )
" Б1 + БЛДк N
->max.
(9)
mine the functional dependence E™ (N{). Using the calculation principle adopted in [5] the operational costs associated with the movement of car supply on the TCC territory during the period t can be represented as follows:
where 8VAT - is the rate of value added tax; Sp - is the rate of income tax.
Note that the costs Et in the expression (5) is a function of the number of TCC switch powers.
Among the selected in [5] operating cost components the costs associated with the movement of car supply depend functionally on the number of locomotives N. Thus, the total operating costs related to the execution of storage operations and the maintenance costs for the operation of handling fronts are constant ones relating to the number of locomotives:
E™ =1
i=1
с лок пост 7 лок . t04i ^ слок 7 лок . РУх i
3Mi
N 1V wi +1 j=1 ( сван пост j г ван tоч j + сван 3M j
споР ч пост j 7 пор оч j + с пор зм kj
Гп°Р
(10)
where Nlwi - is a number of cars, serviced by i locomotive during the period t; c™CT i - is the fixed costs concerning the operation of the i switch power, hrn/hr; t™ - is the total downtime of the i
locomotive waiting for the arrival of freight train or the end car servicing at the front of freight operations, hr; c™* - is unit costs for operation of the i
locomotive during its movement, hrn/hr; tp™ - is
total time of idling of the locomotive when servicing the car traffic volume during the period t, hr;
- is unit costs for downtime of the j car in the
пост j
loaded condition, hrn/hr; ¿0Bj - is time of idling of the j car in the loaded condition waiting for the delivery to the freight operation front, hr; c3B™j - is
unit costs for displacement of the j car by the locomotive to the freight operation front, hrn/hr;
- is the time of displacement of the j car to the
Taking into account the above mentioned assumptions the objective function for the task of determining the optimal number of locomotives can be written as:
To solve the task of finding the extremum of function (9) regarding N it is necessary to deter-
nep i
front of freight operation, hr; c™pT j - is unit costs
for down time of the j car in the unloaded condition,
hrn/hr; t"0™p - is down time of the j car in the
unloaded condition when waiting for removal of the
freight operation front, hr; c^Op- - is unit costs for
removal of the j car by the k locomotive from the
freight operation front (k = 1...N), hrn/hr; f^O1- - is
the time of displacement of the j car from the front of freight operation, hr.
Unit costs for displacement of the loaded cars to the handling front and for removal of empty cars from the front are determined on the basis of costs for idling of cars in loaded or unloaded state respectively, unit costs of the locomotive to displace, as well as the number of cars in the supply:
Наука та прогрес транспорту. Вкник Дншропетровського нацюнального ушверситету залiзничного транспорту, 2014, № 4 (52)
ван ван
С = с
зму пост ]
Спор = Спор зм к] пост j
вп]
вп/
(11)
(12)
N > 1, N е Z ,
(13)
where Z - is a set of integers;
2) the total number of cars that were delivered as a part of freight trains within the period of time t, is equal to the total number of cars that were delivered to the front of freight operation by all the switch powers of the station (i.e., it is expected no-failure operation of the serving system - all the demand for transport services is provided):
- • j
I
... »г ван
Nl N4i
N„=11 n],
]=1 k=1
(14)
3) the total number of cars that were delivered to the front of handling operations is equal to the total number of cars that were removed from the front by switch motors (i.e. all the cars that were delivered to the handling front should be serviced):
where nBnj. - is the number of cars in the supply,
which include the j car.
Time technological indices that make up the expression (10) and the number of cars in the supply nBnj are random variables whose values depend
on the number of switch powers used when servicing the car traffic volumes. Therefore, the values of
operating costs E™ are calculated using the simulation modeling of service process and estimation of the dependency E™ (N{) is possible on the basis of regression analysis of the results of simulation experiment conducted for the given parameters of TCC.
Solving the problem of maximizing the objective function (9) by choosing the optimal value of the parameter Nl is possible taking into account the following restrictions:
1) the value of the parameter Nl is an integer greater than 1:
... ван
Ni Nп/
Цпгн
]=1 k=1
N, К,
вп/k
]=1 k=1
вп/k
(15)
where N™p - is the number of supplies of the serviced cars that the j locomotive removes from the handling front; nBjk - is the number of cars serviced in the k supply. They being removed from the handling front by the j locomotive.
Processing capacity of the handling front is determined on the basis of performance of handling mechanisms (HM) used at the front for servicing the cars. In general terms, the processing capacity of the front W^ is the following sum:
^вф =I
W-
(16)
i=1
where Ng - is the number of the HM, included to the handling front; wt - is the performance of the i HP, tn/hr.
In case when for calculating of the processing capacity the average value of the performance mechanism wg is used the expression (16) can be written as follows:
^вф = NsWg
(17)
When the wg value is known, the estimation task of the optimal processing capacity of the handling front is transformed into the task of justification of the optimal number of mechanisms Ng. Similar to the expression (2) the objective function to solve this problem is the maximum value of the profit per unit for cost unit of the capital assets:
where NT - is a number of freight trains, that were delivered to the TCC during the investigated period of time, trains/period; N™ - is the number of car supplies that are serviced by the j locomotive when delivering cars to the handling front; nBjk -
is the number of cars in the k supply that are serviced by the j locomotive when delivering the cars to the freight operation front;
Поф( Ng ) =
Дt - В, (Ng ) ОФ, ( Ng )
->max .
(18)
When solving task (18) the assumption (3) and (4) are also appropriate. It is reasonable to represent the operating costs for cargo complex operation for this task as:
Е = Eg
:нРф
(19)
N
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where Ef - is the constant of operating costs rela- the unit cost of the lost hour for / mechanism, tive to Nf, hrn:
Ef = ЕГ
ЕПл = const.
(20)
Fixed assets of the TCC enterprises when solving the task (18) is also reasonable to divide into the constant component relative to Ng and the component that depends on the number of HM:
ОФ = Б?
-;нрф
(21)
where Ef - the total balance value of the storage space, switch locomotives, access lines, as well as the administrative and household structures of the cargo complex, hrn:
Бf = БСк
- Б™ = const. (22)
The balance value of the handling front equipment can be determined on the basis of average balance value for one mechanism E"!x :
тгнрф _тгмех лт
Б - Бод Nf .
(23)
Поф( Nf ) =
[Д (1 - Sa Sр ) - (1 - Sp ) Ef ]
Бмех N од J f
(1 - Sp ) E^ (Nf )
■ Бg + БМДх Nf
->max.
(24)
Operating costs for the operation of handling fronts when serving customers over the period t
can be determined for known values of the HM
downtime waiting for the arrival of cars, downtime of cars during maintenance and downtime of cars waiting for the start of service at the front:
енрф=I
мех г мех постi очi
NW ( сван . (F4, . +t
I
j=1
пост J V оч j ' " обсл J ) мех
+С"М" t б
^ змг обсл j
, (25)
where Ni, - is the number of cars that were ser-
viced by the i HM during the period t; сВ
- is
hrn/hr; f0™x - is the total downtime of the i mechanism during the period t, hr.; ?0фj - is the downtime of the j car in loaded condition waiting for the start of service at the handling front, hr.; c™x - is
the unit cost of servicing the car by the i mechanism, hrn/hr; fo6cj j - is the service time of the j car
at the handling front, hr.
Determination of functional dependence Е^ (Ng) as the dependence Е™ (N{ )is based on
the processing of the simulation experiment results.
Maximization of the objective function (9) by choosing the optimal value of Ng is performed on the basis of the following restrictions:
1) number of HM is an integer larger than 1:
Nf > 1, Nf e Z ;
(26)
Thus, the objective function for the problem of determining the optimal number of HM, taking into account the adopted notations and assumptions, as well as the relationship (5) can be written as follows:
2) the total number of cars that came to cargo complex is equal to the total number of cars that were serviced at the front (all the cars coming to the TCC are served on the handling front):
NT Nf
I NN . = I Nf..
i .
j=1
(27)
i=1
The optimal capacity of storage is determined on the basis of data on incoming cargo traffic. The source of input material flow is the car supplies that are serviced at the handling front, as well as the means of other transport modes that interact with the railway transport in the junction.
Let us consider in detail the capacity optimization of storage facilities used for storing and processing of goods that are delivered by railway transport.
The average intensity of cargo receipt ABX from the handling front for the period t is determined as the ratio of received cargo to the period of time based on the known value of the cargo share, which is overloaded by direct option (from the railway cars to other transport modes):
Nt N w
^-х = II q.j Yij,
t
(28)
i=1 j=1
where nnp - is the cargo share that is overloaded using the direct option.
If the known intensity of shipment from the storage ABHX (to other transport modes for further deliv-
N
i=1
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ery), the cargo volume QC]ffl that is constantly kept in stock for the period t, can be determined as follows:
OL - (Хвх - Хвих ) t •
(29)
The value QC]ffl reflects value of the required storage capacity for servicing the material flow with set characteristics ABX and ABHX.
On the basis of QCra and the known value of the admissible loading oCKn for 1 m2 of the storage the required area of storage can be calculated using the formula:
^скл -
Q
t
скл
(30)
хв
1 Nv
- 7 У '
I V-1
(37)
It should be noted that with the known value of the average interval of transport mode coming to the TCC for removal of cargo, the mathematical expectation of the value Nv is defined using the formula:
) -
t
вих )
(32)
It should be noted that the value QCra does not reflect the total amount of certain constantly stored consignments. It is the accumulated value that indicates the average amount of cargo in stock at the end of period t. In this case, the calculation of cargo amount stored in the stock for the period t using the formula (29) is based on the assumption of uniformity of receipt and export of goods, when for the real object these processes are discrete in time and uneven.
The intensity of shipment is resulting indicator that reflects the interaction process in the cargo junction of several transport modes. If we consider the TCC system as the set of elements that interact in the process of moving cargo weight, the transport removing cargoes from transport junction is considered as the element of TCC system when performing operations within the storage complex only. When delivering the goods after their removal from the cargo complex the vehicles that perform these operations are considered as the elements of outside environment.
For this assumption the output intensity of cargo traffic volume XBHX is a random variable that is defined as follows:
where ^(iVv) - is mathematical expectation of the number of transport modes that were removing cargo from the TCC during the period t; Z BHX - is random variable of the arrival interval to the TCC of transport modes removing cargo, hr.; ^(Z BHX) -is mathematical expectation of the random variable
ZBHX , hr.
It should be noted that in determining the numerical values of cargo removal intensity from the storage it is necessary to take into account possible downtime (related to lack of cargo) of the vehicles arrived to TCC for loading. Therefore, the most appropriate instrument for selection of the value ABHX is the simulation experiment.
The system of storage facilities of cargo complex is not cumulative, i.e. the total amount of cargo arrived to the storage for a certain period of time T, which is considered in the process model, should be equal to the total cargo volume that was removed from the storage:
У X . t. -УX
/ у вхг г / у в
(33)
where Nv - is a random variable of the amount of transport modes that arrived to the cargo complex to remove the cargo during the period t; vt - is the amount of cargo that is removed by the i transport mode, tn.
where n - is the number of consecutive time periods; 11 - is the duration of the i period of time,
hr.; % = T.
i=1
Taking into account (33) the value QCKn for the period t = T will be equal to zero. That is there is no need for storage facilities, since in the operation of the system according to the conditions (33) a uniform accumulation of cargo in stock and its removal out of it will take place. For real objects, which are characterized by uneven supply and removal of cargo, the need for storage facilities arises in situations when the received cargo is not fully removed from the storage to the date of the next batch.
О
скл
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,потр скл
In this case, the need for storage facilities QC is determined by the maximum current cargo volume:
QCT = max QC
i=1...n
(34)
Ескл = r
^t - C]
Z Q
скл ti ,
(35)
i=1
Поф (qскл ) =
Дt - Bt
ОФt (Qскл )
->max.
(36)
The fixed assets of TCC enterprises can be represented as the sum of the constant component relative to the QCKJ and the component that depends on the storage capacity:
ОФ = БГ
(37)
where EW - is the total balance cost of the cargo front equipment, switch powers, access lines, as well as the administrative and household structures of the cargo complex, hrn:
Ew _"лнрф
t = Бt
-Б™ = const. (38)
The balance value of the storage facilities can be represented as the product of the storage area and the average balance value of equipment E for 1 m2 of the surface:
скл кв.м
= Б
скл кв.м
Qc
(39)
Taking into account the adopted notations the objective function for the task of justification of the storage optimal capacity on the basis of (36) can be written as follows:
Поф (Qc„ ) =-
Дt - Bt
where QCKJJ - is the current cargo volume, that is in stock during the period ti, tn.
Operating costs for the operation of storage facilities can be determined on the basis of the self cost of 1tn x h. for cargo storage:
Qc,
max. (40)
The limits to solve the task (40) should provide that the value of the storage capacity should meet the requirements for storage spaces:
QcM ^ Q
потр скл
(41)
where cCKJ - is unit cost of the storage of 1 tn. of cargo during 1 hr., hrn/tn hr.
As you can see, the component of operating cost EC™ does not depend on the capacity of storage space, so, taking into account the assumptions (3) and (4) one can state that the costs of TCC enterprises are constant relative to the capacity of storages. Then according to the adopted performance criterion the objective function to solve the task of optimal capacity justification of the storage facilities has the following form:
Primary analysis of the function noo (Qcra) suggests that the function takes maximum value at the minimum possible value QCKn. Taking into account (41) and (34) one can conclude that for the adopted performance criteria of TCC operation the optimal capacity of storage is equal to the maximum value of cargo volume that is in stock over some period of time. In the above mentioned formulation to solve this task is possible only on the basis of simulation modeling of the process of receiving and removal of cargo from the storage.
According to the proposed methodology of strategic management, the optimal number of locomotives is defined in two stages:
- determination of the optimal number of locomotives when servicing the cars providing the absence of downtime waiting for the start of unloading after arrival to the handling front. At this the condition (1) is fulfilled, and the total balance value of the handling front equipment is assumed to be equal to zero (since at this stage the number of HM is unknown);
- clarification of the number of switch motors taking into account the known value of the optimal number of HM at the cargo front. At this the number of locomotives is determined on the basis of the known balance value of the handling front equipment.
After clarification of the optimal number of switch motors it is carried out the re-modeling of the cargo complex operation for the known number of HM in order to determine the intensity of the input cargo traffic volumes to the storage complex. On the basis of the parameters of incoming and outgoing cargo, as well as the technical and economic performance of the storage operation its optimal capacity is determined.
G
скл
G
скл
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Findings
As a result of the studies it is determined the dependencies of technical parameters of the transport cargo complex operation on the technical parameters - the number of switch motors, handling machines and storage area, as well as the formed limitations for these dependencies.
The obtained dependencies make it possible to determine the costs for organization and technical equipment of TCC at the stage of the system design, and optimize the technical equipment to improve the operation of the existing facilities.
Originality and practical value
For the first time it was developed strategic management of the development, which allow taking into account the probabilistic nature of demand for TCC services and technological processes of customer servicing at the TCC stations. The proposed procedure can be applied when planning investments in TCC.
Conclusions
The proposed procedure of strategic management of TCC development requires the consistent justification of the optimal number of switch motors, determination of the optimal processing capacity of handling fronts, as well as calculating the storage capacity of transport junction. Use the simulation models of cargo complex operation as the main tool allow taking into account the probabilistic nature of demand for TCC services and technological processes of customer service at the cargo complex stations.
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Наука та прогрес транспорту. Вкник Дншропетровського нацюнального ушверситету залiзничного транспорту, 2014, № 4 (52)
МОДЕЛЮВАННЯ ЗАДАЧ ТРАНСПОРТУ ТА ЕКОНОМ1КИ
А. М. ОКОРОКОВ1*
1 Каф. «Управлшня експлуатацшною роботою», Дншропетровський нацюнальний утверситет зал1зничного транспорту 1мет академжа В. Лазаряна, вул. Лазаряна, 2, Дтпропетровськ, Укра!на, 49010, тел. +38 (056) 373 15 70, ел. пошта andrew_okorokoff@mail.ru, ОЯСГО 0000-0002-3111-5519
СТРАТЕГ1ЧНЕ УПРАВЛ1ННЯ ТРАНСПОРТНИМ ВАНТАЖНИМ КОМПЛЕКСОМ
Мета. Прийняття як1сних управлшських ршень, що визначають стратегш й тактику розвитку транспор-тно-вантажних комплекав, а також його пщсистем, можливо лише при наявносп гнучко! ошгашзацшно! модели Дана модель повинна враховувати багатопараметричшсть та багатокритер1альшсть поставлено! задач!, невизначешсть та нечггшсть вхщно! шформацп, а також забезпечувати автоматизацш процесу пошуку найкращих параметр1в даного виробничого об'екта. Метою дослщження е розробка процедури стратепчно-го управлшня вантажними комплексами з урахуванням найб1льш вагомих фактор1в та !х стохастично! при-роди, яка дозволить виконати удосконалення техн1чного оснащення транспортних вантажних комплекс1в (ТВК). Методика. Ршення задач1 стратег1чного управл1ння базуеться на розв'язанш комплексу питань 1з визначення оптимально! к1лькост1 маневрових локомотив1в, оптимально! переробно! спроможност! наванта-жувально-розвантажувальних фронт!в та рац!онально! м!сткост! складських прим!щень. Задач! вир!шуються на баз! запропонованого критер!ю оптимальност! - питомого прибутку комплексу на одиницю вартост! ос-новних фонд!в вантажного комплексу. Перел!чеш задач! вир!шуються за допомогою !м!тацшного моделю-вання роботи вантажного комплексу. Результати. Використання розроблено! процедури дозволяе виконати удосконалення техн!чного оснащення вантажних станцш та комплекс!в. Наукова новизна. Вперше розроблено процедуру стратепчного управлшня розвитком, який дозволяе врахувати ймов!рн!сну природу попиту на послуги транспортних вантажних комплекс!в ! технолог!чних процес!в обслуговування кл!ентури на ста-нц!ях транспортних вантажних комплекав. Запропонована процедура може бути застосована при плануван-н! !нвестиц!й у створення транспортних вантажних комплекс!в. Практична значимкть. Використання в якост! основного шструменту ¡м!тац!йних моделей функц!онування вантажного комплексу дозволяе виконати оцшку ефективносп кап!тальних вкладень, р!вня експлуатацшних витрат, а також якост! задоволення потреб потенц!йних кл!ент!в у перевезеннях ще на стад!! проектування транспортного вантажного комплексу.
Ключовi слова: управлшня; транспортний вузол; маневровий локомотив; переробна спроможнють; при-буток; функц!ональна залежшсть; оптим!зац!я
А. М. ОКОРОКОВ1*
1 Каф. «Управление эксплуатационной работой», Днепропетровский национальный университет железнодорожного транспорта имени академика В. Лазаряна, ул. Лазаряна, 2, Днепропетровск, Украина, 49010, тел. +38 (056) 373 15 70, эл. почта аndrew_okorokoff@mail.ru, ОЯСГО 0000-0002-3111-5519
СТРАТЕГИЧЕСКОЕ УПРАВЛЕНИЕ ТРАНСПОРТНЫМ ГРУЗОВЫМ КОМПЛЕКСОМ
Цель. Принятие качественных управленческих решений, определяющих стратегию и тактику развития транспортных грузовых комплексов, а также его подсистем, возможно только при наличии гибкой оптимизационной модели. Данная модель должна учитывать многопараметричность и многокритериальность поставленной задачи, неопределенность и нечеткость входной информации, а также обеспечивать автоматизацию процесса поиска наилучших параметров данного производственного объекта. Целью исследования является разработка процедуры стратегического управления грузовыми комплексами с учетом наиболее значимых факторов и их стохастической природы, которая позволит выполнить усовершенствование технического оснащения транспортних грузовых комплексов (ТГК). Методика. Решение задачи стратегического управления базируется на решении комплекса вопросов по определению оптимального количества маневровых локомотивов, оптимальной перерабатывающей способности погрузочно-разгрузочных фронтов и рациональной вместимости складских помещений. Задачи решаются на базе предложенного критерия оптимальности - удельной прибыли комплекса на единицу стоимости основных фондов грузового комплекса. Перечисленные задачи решаются с помощью имитационного моделирования работы транспортных грузовых комплексов. Результаты. Использо-
Наука та прогрес транспорту. Вкник Дншропетровського нацюнального ушверситету з^зничного транспорту, 2014, № 4 (52)
вание разработанной процедуры позволяет выполнить усовершенствование технического оснащения грузовых станций и комплексов. Научная новизна. Впервые разработана процедура стратегического управления развитием, которая позволяет учесть вероятностную природу спроса на услуги транспортных грузовых комплексов и технологических процессов обслуживания клиентуры на станциях комплекса. Предложенная процедура может быть применена при планировании инвестиций в создание транспортных грузовых комплексов. Практическая значимость. Использование в качестве основного инструмента имитационных моделей функционирования грузового комплекса позволяет выполнить оценку эффективности капитальных вложений, уровня эксплуатационных расходов, а также качества удовлетворения потребностей потенциальных клиентов в перевозках еще на стадии проектирования транспортного грузового комплекса.
Ключевые слова: управление; транспортный узел; маневровый локомотив; перерабатывающая способность; прибыль; функциональная зависимость; оптимизация
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Prof. D. M. Kozachenko, D. Sc. (Tech.); Prof. Ye. S. Aloshynskyi, D. Sc. (Tech.) recommended this article to be published
Received: May 30, 2014
Accepted: Jul. 15, 2014
