[Doi 10.1057_jos.2014.38] T. Van Vianen; J. Ottjes; G. Lodewijks -- Belt Conveyor Network Design...

8/16/2019 [Doi 10.1057_jos.2014.38] T. Van Vianen; J. Ottjes; G. Lodewijks -- Belt Conveyor Network Design Using Simulation

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Belt conveyor network design using simulationT van Vianen*, J Ottjes and G Lodewijks

Delft University of Technology, Delft, The Netherlands

In this paper simulation is applied to design belt conveyor networks at dry bulk terminals. Stochastic variations in ship interarrival

times, shiploads and equipment availabilities enforce the use of simulation. Parameters that affect belt conveyor network designs

like the network connectivity, storage policy and stochastic distributions are evaluated in this paper. One of the main ndings is that

installing the maximum number of connections does not necessarily lead to better performances. Another nding is that redundancy

of piles (a pile is in reach of two stockyard machines) is more ef cient than increasing the number of connections. In a case study,

designs for belt conveyor networks were formulated and assessed using the simulation model developed.

Journal of Simulation advance online publication, 13 February 2015; doi:10.1057/jos.2014.38

Keywords: transport; queuing; simulation; stochastic processes; allocation and scheduling

1. Introduction

Dry bulk terminals are essential nodes in the supply chains for

coal and iron ore. These bulk materials are used for the world-

wide production of energy and steel. To facilitate the expected

growing cargo ows, new dry bulk terminals will be built or

existing ones will be expanded. In the supply chains for these

materials, bulk ships and cargo trains are generally used for

transport. The terminal operation is complex when both ships and

trains have to be served at the same time to meet predened

agreements (Robinson, 2007). This research focuses on the

transport network at the terminals. Such networks have to

facilitate all required transportation needs linking several sourcesand destinations and consist of belt conveyors and transfer points.

In a transfer point, the material ow is transferred between

different belt conveyors.

In this paper simulation is applied to determine the parameters

that affect the design of belt conveyor networks and to assess

such designs. In section 2, a literature review is presented about

dry bulk terminal design and in particular the network design.

The simulation model developed is introduced in Section 3.

In Section 4, the impact of terminal parameters on the network

design (like the network connectivity, the storage policy and the

redundancy of stockyard machines) is investigated. Section 5

demonstrates the integration of simulation for a belt conveyor

network design. Finally, conclusions are presented in Section 6.

2. Literature review

In Section 2.1, a literature review is presented for the design of

dry bulk terminals and in particular for belt conveyor networks.

Due to the lack of a comprehensive design method, references

published about pipeline networks were investigated to verify if

models presented can be applied for belt conveyor networks.

In Section 2.2, these papers are reviewed. Section 2.3 presents an

evaluation of this review and the selection for the modelling

approach.

2.1. Dry bulk terminal design

Papers that discuss belt conveyor network designs were hardly

found; possibly due to the protection of its substantial commercial

value by industrial practitioners or consultation companies. Even in

the most comprehensive design method for terminals, already

introduced by the United Nations Conference on Trade and

Development in 1985 (UNCTAD, 1985), there was no information

found how belt conveyor networks should be designed.

Many authors used simulation for the design of (parts of ) dry

bulk terminals. In Table 1, an overview is listed. Most of these

references applied simulation for a specic case; these models

cannot easily be applied for the design of belt conveyor networks.

Two references discussed the network design in particular.

Lodewijks et al (2009) proposed several belt conveyor congura-

tions for an export terminal. In this paper the following parameters

were investigated that affect network design; direct transshipment

of materials, type of belt conveyor and using multiple shared or dedicated transport routes. Boschert and Hellmuth (2010) applied

the ‘Simulation Tool for Conveying Systems’, developed by

Siemens AG, to design conveying systems. Although the case

studies presented look promising, the commercial programme is

required to perform comparable studies.

2.2. Pipeline network design

On the design of pipeline networks for the transport of water,

natural gas or hydrogen, a signicant amount of research has

*Correspondence: T van Vianen, Delft University o f Technology, Department

of Marine and Transport Technology, Mekelweg 2, 2628 CD, Delft,The Netherlands.

E-mail: [email protected]

Journal of Simu lation (2015), 1 – 9 © 2015 Operational Research Society Ltd. All rights reserved. 1747-7778/15

www.palgrave-journals.com/jos/

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been performed; see for an extensive review André et al (2013).

Similar to the dry bulk industry products are transported in a

continuous mode. A belt conveyor can be compared with a pipe

and stockpiles with tanks. Some of the rst authors that discusspipeline networks were Mah and Shacham (1978). These authors

formulated the optimal network design as a constrained mini-

mization problem based on the number of pipe sections, the

length and diameter of the pipe sections, and cost coef cients that

are directly related to investment costs. This problem corresponds

with the determination of the required transport capacity of belt

conveyors but gave no suggestions for network layouts. Further-

more, a difference between dry bulk and tank terminals is that

generally at tank terminals, product dedicated or customer

dedicated pipelines are used between specic sources and

destinations.

André et al (2013) presented a methodology for the simulta-

neous determination of the topology and the diameters of

hydrogen transport networks. These authors stated that with the

choice made for a quadratic cost function and with head losses,

losses of energy due to the internal wall friction of pipes, optimal

networks are trees. This suggestion is a relevant one and can be

translated to belt conveyor networks where the yard conveyors

(that feed the stockyard machines) form the trunk and conveyors

that connect the loading and unloading machines to these yard

conveyors are branches. A difference between pipeline networks

and a belt conveyor network is that the rst networks are

generally equipped with multi-sources (ie many hydrogen pro-

duction plants) and the network ’s objective is to realize an

optimal facility location/allocation problem. At dry bulk term-inals there are multiple transports required at the same time from

specic sources and to specic destinations using a limited

number of belt conveyors.

2.3. Evaluation and selection of modelling approach

Design methods for belt conveyor networks were not found in

literature, although several authors used simulation to assist

during the design process of dry bulk terminals. Several batches

of materials must be transported simultaneously and on time

while taking the stochastic arrival processes, equipment break-

down behaviour and material ows into account. Furthermore,

dedicated transports have to be performed at the same time using

a limited number of belt conveyors. A simulation model will bedeveloped to consider the stochastic processes mentioned.

By varying characteristics in belt conveyor networks and by

registering the corresponding performances, relevant insight will

be acquired to design such networks.

3. Simulation model

This section introduces the simulation model that was developed

for the design of belt conveyor networks. The advantage of this

model is that not only the stochastic processes are considered but

also specic terminal operational procedures like the storage

policy and particular network characteristics are taken intoaccount. The approach followed is mentioned in Section 3.1.

Specic details of the simulation model are presented in Sections

3.2 and 3.3; the verication of this model is discussed.

3.1. The simulation-based approach

For the development of the simulation model the process-

interaction method introduced by Zeigler et al (2000) and

Fishmann (2001) was followed. The terminal was virtually

broken down into relevant element classes each with their typical

attributes resulting in an object-oriented data structure of the

system. For all active element classes process descriptions, whichdescribe the functioning of each element as a function of time,

were dened. In the simulation model all active elements act

parallel in time, synchronized by the sequencing mechanism of

the simulation software, in this case Delphi®, using the simula-

tion application TOMAS (Veeke and Ottjes, 1999).

3.2. Simulation model

The simulation model is applicable for both import and export

terminals but in this section the model will be explained for

Table 1 Review of references that applied simulation-integrated design of dry bulk terminals

Author(s) Year Design Application

Baunach et al 1985 Compare alternative berth and equipment congurations Coal terminal in Indonesia El Sheikh et al 1987 Planning of future berth requirements Third-world port Park and Noh 1987 Simulate future port capacity required Port of Mobile (US)Kondratowicz 1990 Simulation methodology for intermodal freight transportation terminals General

King et al 1993 Planning and de-bottlenecking studies Power plant in China Weiss et al 1999 Optimize receiving, storage, blending and shiploading facilities GeneralDahal et al 2003 Design and operation, including equipment replacement and operational scheduling Iron ore terminal in UK Sanchez et al 2005 Determination number of berths Power plant MexicoOttjes et al 2007 Improving operational control GeneralLodewijks et al 2009 Network design layouts and belt conveyor types (bi-way or single-way) Iron ore terminal India Boschert and Hellmuth 2010 Design and optimization of belt conveyor systems Stockyard at a steel factoryCassettari et al 2011 Determination grab unloader and storage capacities Coal power plant

2 Journal of Simulation


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import terminals. Ships deliver bulk materials and trains pick upmaterial in small portions. Figure 1 shows an arbitrary terminal

layout with the main element classes; ships and trains with their

generators, piles of bulk materials at stockyard lanes (L1–L4),

belt conveyors and stockyard machines, in this case stacker-

reclaimers. Stacker-reclaimers combine the two functions of

stacking and reclaiming into a single unit. Consequently, only

one of the two functions can be fullled at a time.

Bulk handling activities are called jobs. A job can be a

trainload or a (part of the) shipload. The job material is stored

temporarily in a pile at the stockyard. Piles stored at the middle

lanes (L2 and L3) are in reach of two stacker-reclaimers. These

piles can be stacked and/or reclaimed together or separately at thesame time. In Figure 1 two generators are shown; one for ships

and one for trains. In the ship generator, the ship arrivals and

shiploads are determined using historical data or are sampled out

of analytical distributions. In the train generator the pile’s storage

time is determined using the storage time distribution and trains

are generated to pick up the pile’s material within its storage time.

To express the network connectivity, the indicator (τ ) [ − ] was

introduced. This indicator expresses the ratio between the number

of installed and the maximum number of connections. A connec-

tion is formed by a transfer point. For example, the routing

exibility (τ ) is 7 / 12 for the network shown in Figure 1 because

this network is equipped with seven transfer points while the

maximum number is 12.

3.3. Veri cation

Verication of the simulation model is required to check the

correct translation of the conceptual model into computer code

and to determine if the simulation model performs as intended.

Simulation results for a simplied network (as shown in

Figure 2a ) were compared with analytical results using queuing

theory. For the incoming (Qin) as well as the outgoing material

ow (Qout ) it was assumed that the interarrival times werenegative exponential distributed. Furthermore, it was assumed

that there was no variation in job size (called in queuing theory:

deterministic (D)) and both incoming and outgoing job sizes were

assumed as 100 kilotons [kt]. To each stacker-reclaimer three

grades were assigned. For example, at the lanes within the reach

of stacker-reclaimer 1 only material with grades A, B or C is

stored. When these conditions are considered, the terminal layout

of Figure 2a can be represented by two individual M/D/1-queuing

systems, as shown in Figure 2b.

The relation for the job waiting time as function of the service

time and the stockyard machines utilization was derived from an

M/G/1-queuing system. This relation was formulated by Tijms andKalvelagen (1994) and is expressed algebraically in Equation (1).

Wt ¼1

21 + c2 B

ρSR1 - ρSR

1

μ(1)

where Wt is the average job waiting time, expressed in the

inverse of the service rate [ μ], cB [ − ] is the variation coef cient

for the service times (for the M/D/1-queuing system this

coef cient is 0) and ρsr

[− ] is the average utilization for the

stacker-reclaimers.

For the simulation results the average ship and train port times

were determined at the end of each simulation run (displayed as a

single dot in the graphs that show the results) as function of the

machine utilization. To achieve an accuracy of ± 5% at least 8000ships have to be generated per simulation run.

Figure 3 shows the results for the verication study. From this

gure it can be concluded that the simulation results of the

simplied network correspond with the analytical results for the

M/D/1-queuing system. For more complicated networks and

more realistic input data (that takes the real-world ship interarrival

times and shiploads into account) the tracing function of TOMAS

was used to follow the unloading process of several ships. Results

of this analysis have shown suf cient reasons to consider that the

simulation model performs as intended.

Ship

generator

Interarrival time

distribution

Shipload

distribution

Bulk ship Cargo trainStacker-

reclaimer

Pile SR1

SR2

SR3

Train

generator

Storage time

distribution

Belt conveyor Transfer station Pile

Stockyard

lane

(un)loader Control signals

L1

L2

L3

L4

Figure 1 Schematic representation of the simulation model (description follows in text).

Teus van Vianen et al —Belt conveyor network design using simulation 3


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4. Assessment of design parameters

In this section the following parameters that affect the network

design will be investigated; the network connectivity, the storage

policy and the stochastic processes. The network connectivity (τ )

was already introduced in the previous section and expressed the

number of transfer points installed versus the maximum number.

The storage policy and stochastic processes will be further

explained in Section 4.1. Simulation experiments are shown in

Section 4.2

4.1. Storage strategy and stochastic processes

For the storage policy, two different strategies were introduced by

Leech (2010); the cargo assembly mode (CAM) and Identity

Preserved (ID). For CAM, materials are stored in piles based on

their grade and for the ID-storage policy segregated piles are

formed for individual clients. The CAM storage policy is

generally applied at export terminals, where materials from a

limited number of mines are stored, or at single-user import

terminals. When the ID-storage policy is applied several piles can

contain the same grade but the pile owners are different. The

ID-storage policy is generally applied at stevedoring import

terminals where customers’ materials have to be stored individu-

ally to prevent mixing and to realize tracking and tracing of material. The potential downside of the latter storage policy is

that it demands a greater level of network exibility and

operational planning.

For the stochastic processes, several distribution types were

proposed. For the ship interarrival time distribution UNCTAD

(1985) and Bugaric and Petrovic (2007) stated that the arrivals of

bulk ships are best approximated by a Poisson arrival process.

The ship interarrival times can then be represented by a negative

exponential distribution (NED). Altiok claimed that at specialized

dry bulk terminals (eg, single user terminals) the interarrival

times show less variation. An Erlang-2 distribution can better be

applied for these terminals (Altiok, 2000).

For the ship service time distribution two references proposed

an Erlang-2 distribution, UNCTAD (1985) and Jagerman and

Altiok (2003). However, these distribution types do not corre-

spond with measured data from three dry bulk terminals. This

analysis has shown that the shipload varies signicantly and a t

with analytical distributions cannot be made. Empirical shipload

data can better be used as input to comply with real-world

operation.

There was no reference found that discussed the stochastic

variations of material stored at stockyards. To get an impression,

real-world data that contain storage times of 8500 piles during

19 years of operation for a specic terminal was analysed. The

average pile’s storage time was 0.2 year. A χ2

method was usedto t the storage time distribution with analytical distributions.

Results of this t show a match with the NED. This distribution

was implemented in the simulation model but using empirical

data is also possible.

For the belt conveyor network, the conveyor breakdown

behaviour must be considered as well. A transportation route

consists of multiple belt conveyors in series, if one belt conveyor

fails the entire route fails. According to van Beek (2009), the

system reliability can be approximated by multiplying the

availability of the individual components. Historical operational

Qin+ Qout

SR1

M/D/1

SR2

M/D/1

A B C

D E FQout

(M/D) 100 [kt]

B

C

Qin

(M/D) 100 [kt]

A

E

F

D

a b

SR1

SR2Qin+ Qout

Figure 2 Verication of the simulation model for a simplied network layout (a), which can be represented by two individualM/D/1-queuing systems (b).

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.3 0.4 0.5 0.6 0.7 0.8 0.9

W t [ 1 / µ ]

sr [-]

M/D/1

Simulation Results

Figure 3 Verication of the simulation model; comparison

between analytical results (M/D/1) and simulation results for thesimplied network layout as shown in Figure 2a .



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data of the utilization of belt conveyors at an export terminal

during 1 year of operation has shown a variation of the tech-

nical availability between 0.9 and 0.97. In this research, a

technical availability of 0.97 will be used for each belt conveyor.

In accordance to Tewari et al (1991) and van Beek (2009) the

(negative) exponential distribution is implemented in the simula-

tion model to describe the probability density function of a

failure. This distribution type corresponds with real-world opera-

tion; usually the failure time is relatively small (eg, fuse out due

to overloading) and a single time exceptional long (eg, the belt is

demolished).

In the simulation model, the active job’s handling time will be

extended with the repair time of the broken conveyor. This

corresponds with reality, changing a route is not regularly

performed due to the short downtime and relatively long start-up

times of another route.

4.2. Simulation experiments

A network layout as shown in Figure 1 is used to investigate thestorage policy on the network design. The simulation model was

applied to determine for several scenario’s the sum of the average

ship and train port times. The input parameters used are listed in

Table 2.

Two network congurations are shown in Figure 4 for the

CAM-storage policy. The allocation of grades to the stacker-

reclaimers is different for both scenarios. In Figure 4a each grade

is assigned to a single machine and in Figure 4b each grade is

assigned to two machines. This distribution of grades across

multiple machines corresponds to the stockpile duplication

proposed by Leech (2012) and introduces a stacker-reclaimer

redundancy.

Figure 5 shows the sum of the average ship and train port times

versus the annual throughput (Q) for both layouts as shown in

Figure 4. Another layout with a fully equipped network (τ =1)

was assessed. This layout is not displayed separately in this paper

but can easily be derived by combining the network conguration

from Figure 6b with the stockyard layout of Figure 4a . From

Figure 5 it can be concluded that stacker-reclaimer redundancy

realizes a larger reduction of the sum of the average ship and train

port times than a fully equipped network.

For the assessment of the ID-storage policy, the network

congurations as shown in Figure 1 and 6 are used. The network

connectivity varies from τ =7/12 (Figure 1) until τ =1 (Figure 6b).

0

30

60

90

120

150

10 12 14 16 18 20

W s h i p + W t r a i n

[ h ]

Q [Mt/y]

Fig. 4A, CAM, τ=7/12

CAM, τ=1

Fig. 4B, CAM, τ=7/12, SR-redundancy

Figure 5 The sum of the average ship and train port times versusthe annual throughput for the CAM-storage policy.

(CAM, τ=7/12)

SR1

SR2

SR3

A

BC

DE

F

(CAM, τ=7/12, stacker-reclaimer redundancy)

SR1

SR2

SR3

BA

C D

E F

A B

a b

Figure 4 CAM-storage policy with different grade allocation procedures; (a) each grade is assigned to a single stacker-reclaimer and

(b) each grade is assigned to two stacker-reclaimers.

Table 2 Input parameters for the simulation model

Parameter Value Parameter Value

Ship interarrival time distribution NED SR-capacities [kt/h] 2.5Shipload distribution Historical data* Average pile’s storage time [h] 500Storage time distribution NED Average shipload [kt] 101Belt conveyor technical availability [− ] 0.97 Trainload 4

*Based on data of 898 visited ships at a dry bulk terminal.



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Figure 7 shows the results determined. As expected, the average

port times decrease when the network connectivity increases.

However, the reduction of the port times is limited when the

network connectivity was increased from ¾ until the maximum

value of 1. For this case, a fully equipped network does not

bring that much improvement anymore compared to a less

extended belt conveyor network.

The impact of the stochastic distributions on the network

design was investigated by evaluating the layout of Figure 1 with

different stochastic distributions. The rst series (Figure 1, ID,

τ = 7/12, M/G (Hist. data)) was already shown in Figure 7. For

the same layout, the Erlang-2 distribution was used to represent the ship interarrival times and shiploads were sampled uniformly

between 50 and 150 [kt]. The results are shown in Figure 8.

A reduced variability of the ship arrival processes enables

installing a less extended belt conveyor network still guarantee-

ing the predened performance.

5. Network design: a case study

To demonstrate the application of simulation during the design

process of belt conveyor networks a case study was formulated.

A terminal operator planned to redesign a part of its belt conveyor

network. Within this part, shown with the hatch lled rectangle in

Figure 9, many connections between different belt conveyors can

be made. The rst step was to implement Layout 2011, which is a

combination of Figures 9 and 10a , into the simulation model to

determine the initial performance. Besides, the possible connec-

tions for Layout 2011 were investigated. Two alternative designs

will be formulated and assessed in this case study. These designs

must comply with the requirement that at least the same

connections must be possible as it was in the original layout.

More details from the formulation of both alternative designs are

listed below.For the rst design (as shown in Figure 10b) an extra

requirement was formulated that even a comparable simultaneity

of the transport activities should be realized as in the current

layout. This means that the number of routes that can be used at

the same time may not be reduced. To reduce the number of

transfer points, dedicated belt conveyors are proposed between

the stacker-reclaimers (SR1: 210, SR2: 220 and SR3:230) and

loading machines. The consequence was that all quay conveyors

(numbers 10, 20 and 30 in Figure 10b) need to be connected

to the three stacker-reclaimers via the four cross-conveyors

SR1

SR2

SR3

(ID, τ=¾) ba (ID, τ=1)

SR1

SR2

SR3

Figure 6 ID-storage policy applied at two layouts each with different values for the network connectivity (τ ).

0

30

60

90

120

150

10 12 14 16 18 20


[ h ]

Q [Mt/y]

Fig.1, ID, τ=7/12

Fig.6A, ID, τ=¾

Fig.6B, ID, τ=1

Figure 7 The sum of the average ship and train port time versusthe annual throughput for different network layouts.

0

30

60

90

120

150

10 12 14 16 18 20


[ h ]

Q [Mt/y]

Fig.1, ID, τ=7/12, M/G (Hist.data)

Fig.1, ID, τ=7/12, E2/G (50-150 [kt])

Figure 8 ID-storage policy applied for network layout as shownin Figure 1 with different stochastic distributions.



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(100, 110, 120 and 130) while one or two of them are active with

reclaiming. Using dedicated belt conveyors reduced the total

number of belt conveyors in the terminal from 51 to 45. Two

extra transfer points are needed to realize all connections resulting

in an increase of the network connectivity (τ ) from 0.9 to 0.94.

Advantages of this design are the expected decrease of the

disturbance time, thanks to the reduction of belt conveyors, and

the decrease of the transportation power because the material

does not need to be fed up as frequent as in the existing layout.

For the formulation of the second design (as shown in

Figure 10c) it was allowed that some routes, which are hardly

used simultaneously based on historical operational data, cannot

be performed at the same time anymore. The transport of

materials to the second barge loader (belt conveyor 520 in

Figure 10c) cannot be performed at the same time anymore when

material is transported to the iron ore railcar loader (135) or to the

blending silo’s (114). This concession was justied by the fact that at the terminal three barge loaders are installed and loading of

three barges at the same time did hardly happen. Moreover, a

relatively small amount of material (11% of coal) is fed to the

blending silos so the probability is limited that this conicting

situation will occur. Design 2 applies further the fundamentals of

the rst design; all quay conveyors need to be connected to the

cross-conveyors and dedicated belt conveyors are proposed for

the transport to the loading machines. In Design 2 less transfer

points are then needed resulting in a decrease of the network

connectivity (τ ) to 0.83.

For the terminal layout of Figure 9 combined with one of the

network layouts as shown in Figure 10, the average port times for

ships and landside jobs (trains, barges, coastal ships and exports

to the coal-red power plant) were determined using the simula-

tion model as function of the annual throughput. The input

parameters as listed in Table 2 were used and the results are

shown in Figure 11. From this gure, it can be concluded that the

average port times will be reduced when both designs will be

applied in comparison to the existing layout, which is

Layout 2011.

The minor difference in port times between design 1 and

design 2 is remarkable. Although the belt conveyor network of

design 2 is less redundant, the higher network connectivity does

not bring a signicant reduction of the average port times for

design 1. Apparently, the conicting situations when material

must be transported to the second barge loader, the iron ore rail

car loader and the blending silos at the same time are rare.In conclusion, for the redesign of the belt conveyor network

design 2 is proposed because a comparable reduction of the

average port times will be realized as for design 1 but the network

can be carried out simpler and cheaper.

6. Conclusions

Belt conveyor networks have to facilitate transport activities

to meet the contractual agreements for the terminal’s seaside

SR5

SR6

SR3

SR4

Figure 10

SR1

SR2

QCV1 QCV2 QCV3

L1

L7

L2

L3

L4

L5

L6

Figure 9 The investigated terminal layout with the redesign object (shown with the hatch lled rectangle).



8/9

and landside. Due to the lack of comprehensive models that

can support the belt conveyor network design and to take the

stochastic processes into account, a simulation model was

developed. To be in line with the terminal operation, empirical

data can be used as input. Nevertheless, analytical distri-

butions can also be selected to represent the stochastic

variations. The simulation model was applied to determine the

consequence of parameters like the network connectivity,

storage policies and stochastic processes on the design of belt

conveyor networks. The stacker-reclaimer redundancy, a pilestored at the stockyard is in reach of two stockyard machines,

results in a larger reduction of the average ship port time

(which is the objective for terminal operators) than an

increase of the number of connections. Another nding was that

installing the maximum number of connections does not

lead directly to better performances. The stochastic variations

should be considered as well during belt conveyor network

design. An increase of the variation requires more active

transport routes at the same time and more connections are

needed to realize the performance predened. In a case study,

Layout 2011 (τ: 0.9) Design 1 (τ: 0.94) Design 2 (τ: 0.83)

135

240 240

20

30

10

210

220

230

5 1 0

5 2 0

410

1 1 4

1 4 4

1 5 4

240

420

712

135

1 3 0

1 2 0

1 1 0

1 0 0

1 1 1

1001

1 3 1

1341000

340

330

210

220

230

135

20

30

10

1 3 0

1 2 0

1 1 0

1 0 0

210

220

230

1 1 1

5 1 0

5 2 0

410

1 1 4

1 1 1

1 2 1

1 3 1

1 4 4

1 5 4

134

340

420

712

330

Belt conveyor

1 1 4

10

20

connection

1 3 0

1 2 0

1 1 1

712

5 1 0

5 2 0

410

420

1 1 3

1 2 2

1 3 2

1 3 6

1 4 4

1 5 4

330

134

1 3 3

1 2 3

340

131121

Transfer point

30

a b c

Figure 10 Different network congurations for the redesign object; existing layout in 2011 (a) and two designs (b–c).

60

75

90

105

120

25 30 35 40

W s h i p +

W l a n d s i d e j o b [ h ]

Q [Mt/y]

Layout 2011 (τ=0.9)

Design 1 (τ=0.94)

Design 2 (τ=0.83)

Figure 11 The sum of the average port times for ships andlandside jobs (trains, barges, etc) for the existing layout and newdesigns.



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the simulation model was applied to formulate and to assess belt

conveyor networks for the replacement of a part of an existing

network.

Acknowledgements—The authors acknowledge the terminal operator whoprovided operational data of the ship arrival and storage processes and for giving valuable feedback during this research.

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Received 25 February 2014;

accepted 29 October 2014 after one revision


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