a simulation-based flexible platform for the design and evaluation of rail service infrastructures

Simulation Modelling Practice and Theory 27 (2012) 31–46

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Simulation Modelling Practice and Theory

journal homepage: www.elsevier .com/ locate/s impat

A simulation-based flexible platform for the design and evaluationof rail service infrastructures q

Alicia García ⇑, Isabel GarcíaDepartamento de Ingeniería Mecánica, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, 28911 Leganés, Madrid, Spain

a r t i c l e i n f o

Article history:Received 24 July 2011Received in revised form 9 March 2012Accepted 3 May 2012Available online 7 June 2012

Keywords:Intermodal transportRail–road terminalTerminal simulationFlexible simulation

1569-190X/$ - see front matter � 2012 Elsevier B.Vhttp://dx.doi.org/10.1016/j.simpat.2012.05.003

q This work stems from the participation of the au⇑ Corresponding author.

E-mail addresses: [email protected] (A. García

a b s t r a c t

We present a simulation-based flexible platform developed to support strategic and tacti-cal decision making related to terminal design and redesign. The platform may be used toimplement a wide range of rail–road terminal models, in a rather detailed manner. More-over, the platform is very easy to use, as no programming skills are needed to construct acomplete terminal model and run simulations with it. The platform is composed of twobasic elements: a terminal simulation model implemented in Witness� (a commercial sim-ulator) and an interface, implemented in MS Excel�, which enables the user to define theterminal in terms of resources, infrastructures, layout and demand patterns for trains andtrucks. The platform provides indicators of service level, productivity and resource use.After presenting the platform, we illustrate its use through a case study where we imple-ment the model of a specific terminal and study its performance under a variety of workingconditions.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction

The changes experienced by the economy and the production systems in the last decades have led to a constant increasein the transportation sector for European freight, and therefore, transportation has become, more than ever, a critical activityfor companies [12]. The transportation modes have attracted these new flows very un-equally, with road transportationturning out to be the most suitable transportation mode for most companies. As a result, according to the most recent Euro-stat statistics, roads are the main mode for inland freight transportation, insofar as they attract approximately 78% of theflows. This leaves only 16.5% for rail transportation and 5.5% for inland waterways. This spectacular growth of road trans-portation has occurred despite its major associated drawbacks in terms of automobile accidents, bottlenecks and environ-mental and health damage [13].

The European Commission is promoting, through specific actions (e.g. the Marco Polo Program), the transfer of flows fromroads to alternative modes like rail, maritime and fluvial transportation, thereby aiming to develop a new sustainable trans-portation system in the long term, from economic, social and environmental perspectives [32]. In regard to rail transporta-tion, where the most efficient use is associated with medium and long distance, the actions are oriented towards developinga fluid bimodal relationship with road transportation in order to guarantee the first and the final links of door-to-doorservice.

Despite the well-known benefits that could arise from the use of intermodal transportation, the choice of a transportmode relies on a guarantee of high-quality service. Thus, to overcome the low attraction of combined transportation, it is

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thors in a research project funded by the Spanish National Research Plan, reference: DPI2008-04872.

), [email protected] (I. García).

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32 A. García, I. García / Simulation Modelling Practice and Theory 27 (2012) 31–46

critical to analyze the transport chain from a global perspective in order to identify the bottlenecks and other barriers thatmay be preventing its development [11].

Among the factors involved in the resulting efficiency of a combined transport network, we have focused on the opera-tions performed in the transshipment terminals. The varied and complex operations involved in these terminals, combinedwith the added restrictions on resource planning that come from the fact of being a part of a whole transportation network,justify the number of research works found in the literature (see the literatures reviews [8,19]).

The description of our work is structured as follows. Section 2 describes the background and the research objectives. Sec-tion 3 presents the methodology and the resources used to design the simulation-based flexible platform. In Section 4, theresulting conceptual model as well as its software implementation is described. In Section 5, the tools used to validate thesimulation-based flexible platform are explained. This is followed, in Section 6, by an application of the simulation-basedflexible platform. Finally, Section 7 summarizes the conclusions and future work.

2. Background and research objectives

We have established the objectives of this research in the context of a 10-year research line on the application of simulationto the study and improvement of combined transport. Some of the previous works are focused on transportation networks,whereas some others are focused on single terminals, seeking a more detailed representation of the processes performed.

As a result of the initial research work, Garcia and Gutierrez [16] developed a simulation model representing the com-bined Spanish rail transport network. The network model, conceived for strategic planning, included 51 terminals, whereevery terminal was unique in terms of the types and number of cranes used, length and number of tracks. This work revealedthe importance of modularization for the implementation of network models. In a network model with a high number ofnodes, terminals should be considered as modules, which may be easily added or removed. These modules must be ableto adapt to the singularities of every terminal represented. In order to achieve valid results, terminal operations in the net-work model in [16] were rather simplified. Therefore, the following projects concentrated on achieving an accurate detaillevel on the simulation of singular terminals, see for example García Sánchez et al. [17] and Marín Martínez et al. [33].

We have used the experience gained from these previous projects to formulate the research objectives of the work re-ported in this paper. We have concentrated on rail–road container terminals, which are very numerous in many countries,although they have received less attention in the literature than others, namely the maritime container terminals. The abun-dance of research related to maritime terminals may be explained because they are usually bigger, in terms of container vol-ume; and they also have some specific complex problems to be solved, e.g. vessel loading/unloading operations. Conclusionsderived from research on maritime terminals may not be directly transposed to rail–road terminals, as these two types ofterminals differ substantially in resources, infrastructures, operations and layout.

Restricted to the framework of rail–road terminals, the first basic element of this research work is flexibility. Therefore, weaimed to design and implement a flexible platform for rail–road terminal simulation, where most of the terminal infrastruc-ture and resource characteristics (e.g. track number, storage capacity, crane number, etc.) may be easily configured withoutany extra coding effort. As we mentioned before, flexibility is essential for a cost-effective implementation of a network mod-el. Nevertheless, this is not the only interest of flexibility. Even when the terminals are studied in an isolated way, using a flex-ible platform for simulation is very useful. First, it allows for a rapid implementation of each singular terminal in case there isan interest on studying a set of terminals. Second, when examining a single terminal, the easiness for creating new scenarios isessential for the typical ‘‘what if. . .’’ analysis used in simulation. The next basic aspect that must be defined is the decisionlevel. The simulation-based flexible platform presented here aims to assist decision-making at strategic and tactical levels.As intermodal terminals usually work against a train timetable, the flexible platform uses that input to provide results relatedto terminal performance and resource use, given a specific terminal configuration (which may be very easily modified due tothe platform flexible design). For instance, the simulation-based flexible platform may be used to evaluate alternative designsfor new terminals, or to solve bottlenecks associated with the terminal infrastructure and equipment.

As we explain in the literature review, other authors have worked in this framework, and proposed simulation-based flex-ible platforms with similar objectives to the ones aforementioned. Nevertheless, in this work we intend to advance in thedegree of detail of the simulation performed, as well as on the generality of the flexible platform. The higher detail onthe operations performed improves model results on infrastructure and resource use, which is essential for an accuratecapacity assessment. As for the generality of the flexible platform, we intend to achieve a flexible platform design applicableto represent a wider range of rail–road terminals. In this sense, the possibility to simulate terminals with the double functionhub-entry/exit terminal is particularly relevant. These terminals usually have a classification yard, which is used in coordi-nation with the train loading/unloading area.

3. Conceptual design of the simulation-based flexible platform

3.1. Methodology

Here we explain the methodology of work we have used to accomplish the objectives stated in Section 2. However, wefirst describe the scope of this work more precisely.

A. García, I. García / Simulation Modelling Practice and Theory 27 (2012) 31–46 33

This research work concentrates on interior terminals which interchange containers between rail and road transport.Additionally, these terminals may have a classification yard which may have various functions. It may be used: to exchangerailcars between trains; to brake long trains that cannot be processed in the loading/unloading tracks of the terminal; or as awaiting area for incoming and outgoing trains.

We must point out that there is another type of interior terminal different from rail–road terminals: fluvial terminal.These terminals have an important role in some countries like, for instance, in Germany. Nevertheless from the point of viewof the simulation-based flexible platform design they are closer to maritime terminals than to rail–road terminals, and thuswe have excluded them from the present work.

As we have stated in Section 2, we intended to design a simulation-based flexible platform that may be used to representa wide range of rail–road terminals. At the same time, the terminal representation performed must be accurate; otherwise,the results would not be reliable. These two characteristics: accuracy and generality may be achieved thanks to the platformflexibility. Nevertheless, the flexible design must be accompanied by the appropriate working methodology.

The design methodology we have used combines three resources: literature review; existing rail–road terminal studiesand expert interviews; and investigation through on-line sources.

Literature review provides a first approach to the system definition, with the main processes, layout and resources. Sincethe review includes case studies and models from all over the world, the literature review is also a useful tool for general-ization. For example, the literature review has revealed that intermodal rail–road terminals are rather homogeneous in manyaspects across Europe and Australia, whereas in North America they present some specific characteristics. Obviously, wehave also used the literature review to verify the originality and interest of the approach proposed.

We have used existing rail–road terminal studies and expert interviews to advance in a detailed ‘‘system’’ definition. Gi-ven that the ‘‘system’’ to be represented is not a particular one, but a set of them, we have worked on two complementaryareas: achieving a detailed definition of the typical terminal layout, resources and processes; and establish what is commonand what is variable when comparing among terminals. The case studies and expert interviews have concentrated on theSpanish network.

Finally, investigation through on-line sources has mainly contributed to determine to what extent the simulation-basedflexible platform is a valid tool for representation of other terminals additional to the existing rail–road terminals studied.Thus, it has been a source for generalization, expanding and correcting the conclusions obtained from the literature reviewand the study of existing rail–road terminals.

3.2. Literature review

This literature review has a twofold objective: to validate the basic aspects of the simulation-based flexible platform con-ceptual design; and to verify the originality and interest of the research work itself. Therefore, the literature review we pres-ent derives conclusions regarding the two aspects.

A general review on intermodal terminal research leads to the conclusion that most of it is related to maritime terminals,rather than to interior terminals. Steenken et al. [43] and Vis and Koster [46] provide a literature review of the decision chal-lenges associated with maritime terminals and the quantitative solutions, models and algorithms provided to resolve them.An analogous review of interior terminals is provided by Caris et al. [8] and Macharis and Bontekoning [31].

As regarding to the nature of the analysis tool used, we have found that analytical models are most frequently applied tooptimize the performance of a single area of the terminal. For example, many authors have studied train loading/unloadingoperations [1,2,7,18] or the problem of container manipulation and storage [23,25,41,52]. Optimization models that attemptto optimize the terminal as a whole may also be found, although they are less numerous (see for example: [6,15,24,50]).

Simulation models have been frequently applied in combination with analytical models (to validate the analytical modelresults), and also as the basic analysis tool, giving information about decision problems related to intermodal terminals at allplanning levels: operational, tactical and strategic (see more details in [8,31,43,46]). The model, we present, could be used tosupport decisions both at strategic and tactical levels. Therefore we shall focus on the literature review regarding thatframework.

The other most determinant characteristic of the model we present is flexibility. Regarding the objectives of the presentwork, it is useful to divide simulation models of rail–road terminals into two categories: non-flexible models, aiming to rep-resent a particular terminal; and flexible platforms, which are intended to represent any terminal within a more or less widerange. We assume that models falling into this second category provide a user interface to configure most of the terminalinfrastructure and resource characteristics. The simulation-based flexible platform we present would be included in this sec-ond category.

Most of the models proposed in the literature to study rail terminals would fall into the first of these non-flexible modelcategories. For instance, Sarosky and Wilcox [40] developed a simulation model to study the consolidation of two rail–roadterminals. In that model, train load may be containers or trailers (a container on a trailer, which is loaded together onto therailcar). Weigel [49] describes a simulation model to study terminal capacity, providing results regarding equipment andinfrastructure usage and train schedule adherence. Ferreira and Sigut compare three alternative scenarios for a terminalworking with the RoadRailer system [14]. Lee et al. [29] simulate various alternative configurations for two new containerterminals. Kavicka et al. [22], Marinov and Viegas [34] and Marinov and Viegas [35] study the capacity of a classification yardusing simulation. The Linz Vbf yard (Austria) is studied in [22], the Gaia yard (Portugal) in [34] and the Entrocamento yard


(Portugal) in [35]. Similarly, other non-flexible models are used to support decisions at strategic or tactical levels at inlandports or maritime terminals (see for example: [3,10,30,42,47]).

We have only found four research works regarding flexible platforms for rail–road terminals. The first work we havefound in this field is Kulick and Sawyer’s flexible platform, described in two related conference papers presented at the Win-ter Simulation Conference in 1999 [26], and 2001 [27]. The first flexible platform version was developed for the BurlingtonNorthern Santa Fe Railroad operator (USA). The user would specify a train schedule and the main terminal characteristics(layout, equipment and operation times) by means of a database. In this model, each container is loaded from a skeletal trai-ler onto the corresponding train by means of a gantry crane. Similarly, a container arriving by train is unloaded to a skeletaltrailer, which may then leave the terminal or be moved to a storage area using a yard vehicle. The second flexible platformversion differs from the first one mainly on the user interface, which in this case is a spreadsheet. Authors specify that thisnew flexible platform version is appropriate to represent any of the West Coast intermodal facilities. We have not been ableto find journal papers of these authors explaining with more detail this flexible platform. In 2002, Rizzoli et al. [39] presentedanother flexible platform to simulate container rail–road terminals developed in the framework of the Platform project,funded by the Directorate General VII of the European Community. In this case the flexible platform description is more de-tailed. The flexible platform is implemented in MODSIM III, an object-oriented simulation software. Terminal components(e.g. road gate, rail gate, etc.) are objects, which are introduced into the model as building blocks. Afterwards, the corre-sponding parameters (e.g. number of gates) are read from a database to obtain a simulation scenario. Containers are handledby means of gantry cranes and front lifters. Two areas are used for storage: stacking lines and a stacking yard. The flexibleplatform provides similar performance indicators to Kulick and Sawyer’s flexible platform. They include an example of usage,where they study the impact of the processing time at the road gate on the average waiting time for trucks queuing at thisgate. More recently, in the 2008 Winter Simulation Conference, Benna and Gronalt [5] presented a simulation-based flexibleplatform for container rail–road terminals in Austrian. The flexible platform objectives are similar to the ones aforemen-tioned, although some new input parameters are included related to spatial restrictions (e.g. truck parking area capacityand relevant distances for equipment and trucks).

Maritime terminals, in general, use different container handling equipment than rail–road interior terminals. This is obvi-ous in the case of vessel loading/unloading process, but it is also the case of the storage area, where for instance, gantrycranes and staddle carriers are frequently used to handle containers. Additionally the degree of automation is much higher.Nevertheless, we have reviewed related literature to determine whether simulation-based flexible platforms are used forthis type of terminals, given that research in this area is much more abundant. In 1996 Ballis and Abacouskin [4] presenteda flexible model to study the truck service level for the Piraeus (Greece) maritime terminal. Although the model representsthe terminal as a whole, the detailed representation and flexible approach mainly concern the yard subsystem. The modelpresented in 2008 by Huang et al. [20] corresponds more accurately to the class of flexible platforms we are studying. A flex-ible simulation-based flexible platform is implemented and used to analyze three container terminals in the Singapore re-gion. The user must parameterize resources and infrastructure, although it is not clear that this also involves the vessel areaspecifically (number, length and layout of berths).

Regarding the first of the objectives of the literature review, the ‘‘system’’ definition, we have derived conclusions relatedto: basic rail–road terminal sub-systems; basic processes; types of load units and railcars; and alternatives for load-handlingequipment.

The most relevant differences among terminals come from the type of load unit transported by the train and stored at theterminal. This determines the type of railcars; the handling equipment for train loading/unloading; and the configuration ofthe storage areas. The load unit most commonly transported is the container. Once the container has arrived at the terminalby train, there are two main options for storage: the use of skeletal trailers and ground storage. Ferreira and Sigut [14] andKozan [24] coincide that these options present a regional pattern; in particular, the first option prevails in North America,whereas ground storage is more used in Europe, Asia and Australia. The handling equipment used to load/unload containersis mainly composed by gantry cranes and side lift cranes (as front lifters or reach stackers). Less frequently, the load unit is atrailer or a complete truck [24]. A different alternative is RoadRailer technology, using bi-modal trailers with the capability ofbeing hauled on road as well as on rail. Ferreira and Sigut [14] report that in 1991 there were 14 RoadRailer terminals in theUSA; while at the same time in Australia, this system was only being introduced.

In the case of the classification yard subsystem, three main types may be distinguished (see more details in [34,35]). Inthe first one, tracks are built with a hump over which incoming railcars are pushed by a diesel engine. Next, railcars roll bygravity in the destination track. In the second type, the whole yard is set up on a continuous falling gradient to reduce the useof the diesel engine. In the third type, tracks are built in a flat yard and a diesel engine is used to sort incoming railcars intodestination tracks.

Regarding the interest and novelty of the work proposed, the conclusion of this literature review, is that in the last decadea few research groups have worked on the same lines of this paper, with very similar general objectives. Compared to theseprevious works, the simulation-based flexible platform we propose advances further in some aspects. First, as we describe inSection 4, we have developed a more detailed simulation of the train loading/unloading area than the one reported in pre-vious works. For instance, the limited capacity for truck loading/unloading next to the train is taken into account and must beparameterized by the user. Similarly, the storage area near the tracks can be parameterized in the number and length of stak-ing lines, and it may even be eliminated (as, for instance, would be the case for a terminal using storage in skeletal trailers).Secondly, we have extended the flexible platform functionality to represent the classification yard associated to the train


loading/unloading area. This area is not explicitly represented in other flexible platforms, but actually in some terminals,with the double function hub-entry/exit terminal, the classification yard is used in a coordinated way with the train load-ing/unloading area, and thus, it may be interesting to include it in the model. Finally, other authors do not report a system-atic investigation of terminal alternative configurations, following a diversified methodology, as the one we perform here.

3.3. Existing rail–road terminal studies and expert interviews

We have collected on-site information about four Spanish rail–road intermodal terminals. Since we searched for a generalconceptualization of a container terminal, we selected this set of terminals so as to maximize the differences among them, interms of volume, location, infrastructure and resources. Additionally, we interviewed ADIF (the administrator of SpanishRailway Infrastructures) managers, as well as RENFE Operadora (the main Spanish railway operator) managers. The resultsof this empirical investigation have complemented the conclusions from the literature review to define the conceptual mod-el for the simulation-based flexible platform.

Regarding the general characteristics of Spanish rail–road terminals, the results of this field research are consistent withthe conclusions derived from the literature review. The load unit in the Spanish intermodal network is the container (withsome minor additional flows of swap-bodies). Terminals work with different sizes of containers. The most common are 20-foot containers and 40-foot containers. Trains arrive and depart from terminals according to a fixed schedule. The two typesof cranes used are gantry cranes and reach stackers. Containers are stored on the ground; and most terminals use both, stak-ing lines and a stacking yard.

Additionally, many rail–road terminals have a classification yard associated, which is used: for railcars storage, to sortrailcars and to brake long trains. The rail–road subsystem and the classification subsystem share diesel engines to move rail-cars between the electrified and non-electrified tracks of the terminal.

Case studies have been essential to define operating policies, as the literature review had only led to a general definitionof the main processes: truck arrival/departure, truck loading/unloading, train arrival/departure, train loading/unloading andcontainer storage. These operating policies comprise, for instance: crane functions and coordination, storage policies, prior-ities between train and truck loading/unloading, etc. Many of the operating characteristics and parameters may be easily leftin a parametric form. This is the case of: operation times, work shifts or train timetables. Nevertheless, some operating rulesand policies may be complicated to configure if a flexible design is not applied. This would be the case, for instance, of thestorage policy: in some visited terminals, both, stacking lines and a stacking yard are used to store containers, whereas inother terminals only one of these two types of storage areas is used.

More details in relation to operational policies of the terminals observed are explained in the simulation-based flexibleplatform conceptual model (see Section 4).

3.4. Investigation through on-line sources

We have carried out an investigation through on-line sources to expand and correct the conclusions obtained from theliterature review and the case studies. We have found two main sources of information about rail–road terminals, whichare the official web sites of two organizations: the Intermodal Association of North America and Terminal Interest GroupAGORA. These web sites provide information about the main infrastructure and resource characteristics of a vast numberof rail–road terminals in North America and Europe. This information includes the main layout characteristics (types ofcranes and number for each type, storage capacity, etc.); terminal opening times; and type of load unit transported (contain-ers on railcars, trailers on railcars, etc.). Additionally, we have found some official web sites for Australia, which we mentionbelow. We have not found equivalent web sites for other regions like South America and Asia, therefore, the scope of thisinvestigation is restricted to North America, Europe and Australia.

Another relevant aspect of this on-line sources investigation is that, apart from terminal opening hours and the type ofload unit transported, no other operational characteristic is reported on these web sites. Therefore, these sources enable onlya partial study of the terminals, which excludes almost all operational aspects.

First, we have consulted the web site of the Intermodal Association of North America in order to get information aboutNorth American carriers and rail–road terminals [21]. This web site provides information about 12 carriers and more than200 interior terminals located in Canada, USA and Mexico. We have restricted the scope of our study to 6 carriers which usemore than 10 terminals each, which covers 85% of the North American terminals (Burlington Northern Santa Fe, CanadianNational, Canadian Pacific, CSX Intermodal, Norfolk Southern and Union Pacific). Next, we have classified the terminals usedby each carrier according to their storage capacity, their cranes and the type of load units transported. This study reveals thatin most of the terminals a large parking area, to store skeletal trailers or trailers, is used. Containers and trailers are loaded/unloaded by means of gantry cranes and side lift cranes (forklift or reach stackers). Less common are RoadRailer or bi-modalterminals, used by carriers like Triple Crown Services or Wabash National [45,48].

European and Australian rail–road terminals store mostly containers, not trailers; and use gantry cranes and reach stack-ers to load/unload containers onto/from railcars [37,38,44].

The web site of the Terminal Interest Group AGORA provides information about more than 120 interior terminals locatedin Austria, Belgium, Bulgaria, Czech Republic, France, Germany, Italy, Netherlands, Poland, Romania, Spain, Sweden and Swit-zerland [44]. We have systematically reviewed the web site information to classify these 120 terminals according to their


main design characteristics (type and number of cranes, number and length of tracks, storage capacity, etc.) and the type ofload unit transported. This study reveals a significant similarity between the Spanish terminals of Section 3.3 and other Euro-pean terminals. In all the European countries listed above, there are rail–road container terminals. Reach stackers and gantrycranes are the handling resources used in all of these countries, except in Belgium, where staddle carriers are used instead ofreach stackers. More than 50% of the terminals reviewed, have a classification area next to the loading/unloading tracks or,sometimes, a few kilometers away. Fluvial terminals are also important interior terminals in some countries, like Germany orAustria, but not all the countries have adequate rivers for transportation. Usually the load unit transported is the container,although some Austrian, German and Swiss terminals may load/unload complete trucks [36,44].

4. Simulation-based flexible platform: conceptual model and implementation

Based on the information and conclusions of the investigation summarized in Section 3, we have established the concep-tual model of the simulation-based flexible platform.

Regarding infrastructure and resources, the simulation-based flexible platform we present, may be used to represent con-tainer terminals which store containers on the ground; using gantry cranes and reach stackers for container handling; and,eventually, performing classification operations. According to the information we have reviewed, this is the case of the vastmajority of rail–road terminals in Europe and Australia. Most North American rail–road terminals also move containers;although ground storage is less frequently used than skeletal trailer storage.

Regarding terminal operating policies and parameters, we have mainly based the flexible platform design on the existingrail–road terminal studies, as the other two sources (literature review and on-line sources) provide very scarce informationabout terminal operations. The design achieved is flexible in many aspects of the operation, as we detail in the next subsec-tion. Nevertheless, expanding platform flexibility on operation is one of the interesting areas for further research.

4.1. Conceptual model

In accordance with the processes usually considered for rail–road terminals in the literature (see for example: [31,39]),we have divided the activity of the terminals into the following main processes: train arrival, departure and classification;container storage; train loading/unloading; truck arrival/departure; and truck loading/unloading.

Fig. 1 shows a schema of the resources and processes involved in these terminals.We describe below the modelization of these resources and the operating policies we have assumed. We have arranged

this description according to the five aforementioned processes, although in some cases resources are shared by two or moreof these main processes. This is, for instance, the case of cranes, which are described under the second title, ‘‘container stor-age’’, although they are shared with the train and truck loading and unloading processes.

At the end of the processes description, we explain the model output variables.

4.1.1. Train arrival, departure and classificationTrains arrive and depart from the terminal according to a fixed schedule, which remains rather stable and has a 1-week

periodicity. Thus, the incoming and the outgoing trains are balanced in this period. Nevertheless, if trains do not follow theschedule strictly, a stochastic pattern of delay or anticipation, for incoming and outgoing trains, may be introduced.

Trains enter the terminal using a single entry track and leave it using a different exit track. Once a train enters the ter-minal, it is carried to a reception/expedition track where the electric engine is replaced by a diesel engine. Next, the train iscarried to an available loading/unloading track and the electric engine is stored. Outgoing trains undergo through the inversesequence of operations.

In many cases the terminal has a classification area, whose main function is to carry out classification operations for sometrains. This could be, for instance, the case of an incoming train which carries some railcars that are to be reassembled to adifferent outgoing train. In this case, the train composition would be broken down in the classification area, and some of therailcars would be carried to the loading/unloading area, while the rest of the railcars would remain in the classification areauntil the corresponding outgoing train is composed. The classification tracks may also be used as reception/expeditiontracks; or to brake long trains that cannot be processed in the loading/unloading tracks.

4.1.2. Container storageAlthough many times the container transshipment is instantaneous, in many other cases, the incoming and the outgoing

containers need to be stored. Two types of stacking areas may be distinguished:

– Stacking yard. It is a high capacity area for container storage, not necessarily located next to the loading/unloading tracks.Reach stackers are used to move containers in and out of the stacking yard. Based on terminal observation, we haveassumed that every terminal has got a single stacking yard. Its capacity is one of the parameters set at the beginningof the simulation.

Fig. 1. Terminal resources and processes schema.


– Stacking lines. Containers are stored in lines on one or both sides of the loading/unloading tracks (see Fig. 2), and bothreach stackers and gantry cranes may be used to move containers from/to these lines. In concurrence with on-site obser-vations, we have assumed that incoming and outgoing containers are stored in separate lines.

The flexible platform may represent terminals which have one type of these stacking areas (stacking yard or stackinglines) or both.

As a result of literature review [39] and direct observation of the terminals analyzed, we have established an operationalpolicy where containers are stored in the stacking lines as a first choice. This policy generally reduces container handling. Thestacking yard is used when the stacking lines used for train loading are full. If the lines used for train unloading reach a cer-tain percentage of their maximum capacity (specified by the user), containers start being moved to the stacking yard. Thisoperation is necessary to avoid gantry crane blocking.

The storage areas, as well as the train and truck loading/unloading, are managed by cranes. These resources are thereforeshared among three of the main terminal processes. Usually there are two types of cranes in interior terminals with a certaincontainer volume:

Fig. 2. Work area of the gantry cranes.


– Gantry cranes move along loading/unloading tracks. These cranes are mostly used for train loading and unloading,although they can also be used to load or unload trucks. Thus, they have access to the loading/unloading tracks, the stack-ing lines and the truck loading/unloading positions next to the tracks (see Fig. 2).

– Reach stackers are wheeled vehicles that may circulate throughout the terminal. They are used to manage the stack-ing yard, to move containers between the stacking yard and the stacking lines and to load and unload trucks. Reachstackers move around the terminal along paths that link the various functional areas of the terminal, thus we havetaken into account the time consumption involving these movements as well as the potential interference amongthem.

Both types of cranes may have deterministic or stochastic values (defined by an empirical distribution) for the cycle time.Stochastic failure patterns may also be defined.

4.1.3. Train loading and unloadingThe resources and infrastructures involved in train loading and unloading are: railcars, gantry cranes and tracks. As we

have mentioned previously, the number of incoming trains equals the number of outgoing trains each week. Thereforethe railcars used for loading containers are provided by the incoming trains.

Trucks may bring the containers to be loaded some days in advance, or the same day when the train is departing. Depend-ing on the arrival time, the container will be temporarily stored in the stacking yard, in the stacking lines, or may be directlyloaded onto the train. The train loading process starts as soon as the necessary railcars are available and finishes 2 h beforetrain departure. This implies that a container arriving when the loading process is closed would not be loaded onto the trainand, thus, the real train load would differ from the scheduled. The train unloading process starts as soon as the railcars arelocated on the corresponding loading/unloading track. Containers are unloaded to a truck, to the stacking lines, or to theground (to be transported by a reach stacker to the stacking yard).

4.1.4. Truck arrival and departureContainers cover the first and the last segment of their door-to-door transportation by means of trucks. We have assumed

that the flexible-platform user has information about the truck arrival pattern, which he must, therefore, introduce as a sto-chastic arrival process. Time between truck arrivals may be defined using the exponential distribution function. This distri-bution is typically used for independent arrival processes. For instance, [9,39,51] use it to generate truck inter-arrival timesand [3,10,42] use it to generate ship inter-arrival times in a maritime terminal. However, if the user has better data comingfrom empirical observation, truck inter-arrival time may also be defined as an empirical distribution.

Trucks use the following resources linked to this process: terminal entry and exit gates, paths for circulation and parkingareas.

A truck arriving at the terminal may bring a container for delivery or may come to pick up a container. In both cases, itregisters at the entry gate, where the corresponding container is identified. The truck uses the paths to arrive at the re-quired loading/unloading position. If all the possible loading/unloading positions are occupied, it waits in the parkingarea.

When the truck finishes the loading/unloading operation, it drives to the exit. If the exit gate is occupied, the truck waitsin the parking area.

4.1.5. Truck loading and unloadingTrucks have a certain number of loading/unloading positions: one set is located next to the stacking yard and the second

one is located next to the stacking lines under the gantry cranes.A truck that transports a container to be loaded onto a train will preferably transfer it directly to the train (by means of

the gantry crane, without any intermediate storage). Therefore, firstly, trucks look for a free loading/unloading position nextto the stacking lines. If none is available, they occupy a free loading/unloading position next to the stacking yard and wait fora reach stacker to come by and unload the container and take it to the stacking yard. In the case there are no free loading/unloading positions, the truck waits in the parking area.

Trucks which pick up containers perform a rather symmetrical process to the unloading process described below. Thetruck will drive to the area where the container is located at that moment. If the container is still loaded onto the train whenthe truck arrives, gantry cranes may directly transfer it to the truck. Otherwise, the container is temporarily stored in thestacking lines or even in the stacking yard.

4.1.6. Simulation output variablesAs a result of the terminal processes simulation, the model produces the output variables, which may be grouped into

three sets: service level, productivity and resource use. Service level results arising from the model are: number of trainsper week which fail to be loaded/sorted on time; average delay in train departures; average time spent on train loading/unloading/sorting; average servicing time for trucks; and the number of trucks that wait outside the terminal and in otherwaiting areas, as well as the average waiting times associated. Terminal productivity is measured, by: the number of loaded/


unloaded/sorted trains per day; number of containers loaded and unloaded to/from trains; number of railcars sorted; num-ber of served trucks per day; and number of direct transshipment operations between trains and trucks. Finally, the modelquantifies the use of the terminal resources and infrastructures: gantry cranes, reach stackers, diesel engines, stacking areas,loading/unloading tracks, exit/entry tracks, classification tracks, exit/entry gates, parking area and truck loading/unloadingareas.

4.2. Implementation

We have implemented the conceptual model described in the previous subsection using a combination of two softwaretools: Witness� (a commercial simulator) for the implementation of the simulation flexible model; and MS Excel� for theimplementation of the user interface.

The implementation of the simulation model has required a careful analysis in order to establish the detailed model spec-ifications and the range of available values that should be provided regarding each design parameter.

Fig. 3 is a screen shot of the Witness model. The areas shown in this figure are: train entry and exit; stacking lines; truckloading and unloading locations next to the stacking lines; and train loading and unloading area.

The user interface, implemented through a MS Excel file, is structured according to the areas defined in the Witness mod-el. The data necessary to define the infrastructures, resources, truck arrival patterns and train schedules is introduced by theuser through this MS Excel, without any need for extra programming work in Witness. Once all the required data is filled in,the user can simply open the Witness file and run the simulation.

To illustrate how the Witness model and the MS Excel file are interrelated, Fig. 4, shows two superimposed screen shots,from the MS Excel interface and from the Witness model. The MS Excel worksheet provides a brief explanation of the param-eters that must be entered. On the page containing the examples, the first parameter to be input is the number of reachstackers, which may vary from one to four (see Fig. 4). Over this line, the Witness screen shot shows the code that readsthe number of reach stackers specified in the MS Excel file. As may be observed in Fig. 4, the next data to be input is related

Fig. 3. Screen shot of the Witness simulation model.


to the reach stacker work shifts. This data is inserted in a table, which initially shows the default values. The right lower cor-ner of Fig. 4 shows the Witness code that reads the work shift details for the first reach stacker from the MS Excel file.

The user interface has been provided with many utilities that prevent it from making mistakes in the data input process:explanations about the data required; error messages that are displayed when the input introduced is incorrect; links be-tween worksheets, which help follow the right order; and protecting instruments so that the worksheet structure cannotbe modified.

5. Simulation-based flexible platform validation

The validation of a simulation-based flexible platform is less straight forward than the case of a non-flexible model (amodel representing a single terminal), as the system to be compared with is not a single one, but one of a wide range. Inthis case model validation relies largely on the validation of the conceptual model, and therefore we have used a model de-sign methodology that could integrate all the useful sources of information about the systems for which the model is con-ceived. Following this design methodology (see Section 3) we have integrated conclusions from the literature review withthe information obtained through the interviews with terminal managers and direct observation of real terminals, and otherinformation obtained through on-line sources. We have used these conclusions in the conceptualization phase to establishthe typical terminal characteristics and operation.

Once the flexible platform was implemented, we have used it to represent simple terminals based on the information weobtained from the interviews and the visited terminals. Unfortunately, we have only been able to perform this type of val-idation for Spanish terminals, as these implementations require a great quantity of data, that may only be obtained throughdirect access to the terminal to be represented.

In this section, we present one of the examples used to validate the simulation-based flexible platform outputs, based on aSpanish terminal. Through a questionnaire of more than 40 questions, we have obtained information about the train plan(number of trains unloaded/loaded per week, number of containers and railcars per train, etc.), the truck arrival pattern (num-ber of trucks unloaded/loaded per week, time slots with more arrival volume, etc.), the infrastructure characteristics (numberand capacity of the stacking areas, etc.), the resource characteristics (number of cranes, crane handling times, equipment

Fig. 4. Data input for the number of reach stackers and reach stacker1 work shift.


speeds, etc.) and the operation rules used, for instance, to manage the train loading/unloading area or to assign work to humanresources.

The real-world terminal has the following infrastructures and resources: one entry/exit gate for trucks, one stacking yardwith a maximum capacity of 300 ITUs, one stacking line for incoming containers with a maximum capacity of 150 ITUs, onestacking line for outgoing containers with a maximum capacity of 150 ITUs, six 450 m long loading/unloading tracks, onediesel engine, one gantry crane and one reach stacker. This terminal unloads/loads five incoming trains, five outgoing trainsand around 300 trucks per week. In order to meet this demand, the entry/exit gate opens from Monday to Friday from 7:30 to19:30; the diesel engine is always available; two crane operators are available from Monday to Friday from 9:00 to 17:00(one to handle the gantry crane and another to handle the reach stacker); and only one operator is available to handle bothcranes from Monday to Friday from 7:30 to 9:00 and from 17:00 to 19:00.

We have simulated terminal operations for a week. As some simulation-based flexible platform input data are randomvariables, 10 replications have been performed and their individual outputs have been compared with the information pro-vided by the interviewed terminal managers. Based on availability of real-world data, two measures have been selected tocompare the real-world terminal and the simulation-based flexible platform: number of trains which depart with delay andmaximum number of trucks that wait simultaneously outside the terminal. The first measure shows that all the trains de-parted on time in the performed replications and in the real-world terminal, due to the availability of resources and infra-structures. In accordance with this second measure, the simulation-based flexible platform also shows a similar performanceto the one indicated by the terminal managers: the gate working peaks are focused between 11:00 and 13:00 (a periodwhere the flow of trucks is higher) and the maximum number of trucks that wait simultaneously outside the terminal neveris higher than 5.00 (2.00 or 3.00 are the achieved maximum values in the replications). As a result, we may conclude that themodel generated by means of the simulation-based flexible platform provides similar performance results to the ones re-ported by the managers of the real-world terminal.

6. Application of the simulation-based flexible platform

In this section, we present an application of the simulation-based flexible platform in order to study the capacity of a ter-minal under various conditions. In Section 6.1, we use the platform to construct a terminal model and study its performancegiven a certain demand. In Section 6.2, we assess the performance of the same terminal in an increased demand scenario. Inthis case, we compare two possible operation alternatives for handling this demand.

The case study is based on an existing Spanish rail–road terminal. It has the following resources and infrastructure:

– Classification area: eight, 700 m long, classification tracks. One diesel engine.– Stacking areas: one stacking yard, with 400 ITUs capacity. One stacking line for incoming containers and one stacking line

for outgoing containers, with a 100 ITUs capacity, each.– Five, 400 m long, loading/unloading tracks.– Four cranes: two gantry cranes and two reach stackers.– One entry/exit gate for trucks. One parking area with a maximum capacity of three trucks. Two areas for truck loading/

unloading: one next to the stacking yard and one next to the tracks. Both loading/unloading areas have a 10 truckcapacity.

All the processing times have been represented as random empirical distributions.

6.1. Terminal capacity study

6.1.1. Simulated scenarioThe terminal exchanges direct trains with five other terminals that we indicate with indexes 1–5. We have assumed that

the trains perform round trips between the studied terminal and any of the other five. Table 1 shows the 11 incoming trains(I) and the 11 outgoing trains (O) that arrive/depart weekly. This Table shows the scheduled time for the arrival/departure ofeach train and the index of the origin/destination terminal (T = i, i = 1, . . . ,5). The train schedule remains unchanged formonths. The effective arrival/departure time of the trains may vary randomly around its scheduled value, due to interferencewith other trains traveling along the railway network. Trains have the same length as the loading/unloading tracks (400 m)and the average number of containers per train is relatively high (22 containers), where 60% are 40-foot containers and 40%are 20-foot containers.

All trains arrive between 3:20 and 7:20 (see Table 1) and gate opening hours are Monday to Friday from 7:30 to 20:30,therefore most of the trucks that come to pick up containers arrive during the morning. Specifically, their arrival profile var-ies as follows: a moderate amount arrives between 7:30 and 9:00, the truck flow increases between 9:00 and 11:00 and itreturns to being moderate between 11:00 and 20:30.

The trucks that deliver containers to the terminal (to depart by train) start arriving at the terminal 1 day in advance of thetrain departure. During that period truck flow varies as follows: truck flow is moderate between 7:30 and 11:00 and after-wards it increases until the peak period between 17:00 and 19:00.


In order to meet this demand, the terminal uses a single reach stacker Monday to Friday from 7:00 to 21:30. The secondreach stacker only operates if the first one breaks down. The two gantry cranes work in parallel Monday to Friday from 7:00to 10:00. This is a high demand period, as most of the incoming trains arrive during the night and early morning. After thisperiod, a single gantry crane remains in operation until 21:30. To avoid gantry crane blocking, reach stackers move contain-ers from the stacking line used for train unloading to the stacking yard when this line reaches 80% of its maximum capacity.

6.1.2. ResultsWe have simulated the terminal operation for 4 weeks with a prior 2 week warm-up period. As various input data are

random variables, 10 replications have been performed. Using a personal computer Intel Core 2 Duo processor with 3 GbRAM, it took 35 min to execute each replication.

We have used three sets of performance indicators: service level, resources and infrastructures usage and bottlenecks. InTable 2, each output variable is represented by the average of its value in the 10 replications, together with the estimatedstandard deviation (SD) and a confidence interval for the average, with an individual confidence level of 99.5% (CIa = 0.005). In almost all the cases the standard deviation is small and the confidence interval is narrow, therefore these esti-mates may be considered representative of the corresponding output variable. Using the solution provided in [28] for themultiple-comparison problem, that arises when providing a set of performance measures, we determine that this individualconfidence level of 99.5% would lead to an overall confidence of more than 90% for the set of output variables analyzed.

Table 2 shows a summary of the main average performance measures for this scenario. The first important result thatmay be observed is that the terminal is able to satisfy the train schedule, without generating any train delay. Trucks spendan average of 1.25 min queuing at the entry gate and an average of 5.54 min, inside the terminal, until a crane serves them.Trucks that deliver containers to the terminal spend an average of 13.82 min inside the terminal, whereas trucks that pick upcontainers from the terminal spend 19.54 min in average. This good service level is mainly due to the low workload com-pared to the available infrastructures and resources (see gantry crane and reach stacker utilization in Table 2). Nevertheless,despite the existing slack capacity in average values, a more detailed analysis may be performed, which reveals some specificmoments when certain infrastructures and resources are overloaded. For instance, the truck flow is so high between 9:00and 11:00 and between 17:00 and 19:00 that certain trucks must wait outside the terminal before gaining access to the en-try/exit gate, rising to a maximum value of 5.90 trucks. Between 7:00 and 10:00, the stacking line used to unload trainsreaches the 80% limit that triggers the movement of containers from the stacking line to the stacking yard in order to avoidgantry crane blocking. The stacking line used to load trains also reaches its maximum capacity in some specific moments andsome containers have to wait in the stacking yard until its departure by train. Likewise, the gantry crane presents randomworking peaks between 11:00 and 13:00 and between 17:00 and 19:00. These working peaks cause an increase in the num-ber of trucks that wait simultaneously in the truck loading/unloading area next to the tracks, thereby reaching a maximumvalue of 9.70 trucks.

In light of these results, the overloaded infrastructures and resources could be increased, for example, by putting a newworker at the entry/exit truck gate and making the two gantry cranes work in parallel for a longer period to cover the work-ing peaks. The decision-maker would only need to modify certain MS Excel interface cells in order to implement bothchanges.

6.2. Terminal capacity study in a scenario of increased demand

6.2.1. Scenario of increased demandIn this section, we consider a situation with a notably increased demand. This example illustrates how the simulation-

based flexible platform favors an iterative search process of promising configurations, which may improve system capacityto handle the new demand.

In this scenario, the terminal deals with 14 incoming trains (I) and 14 outgoing trains (O) weekly (6 more trains than inSection 6.1). All the trains have a length of 400 m, except the trains exchanged with the terminal origin/destination 3, which

Table 1Train timetable.

Monday Tuesday Wednesday Thursday Friday

3:20 I – 1 I – 14:25 I – 2 I – 2 I – 25:30 O – 1 O – 15:45 I – 3 I – 36:50 I – 47:20 I – 5 I – 5 I – 518:40 O – 419:15 O – 5 O – 5 O – 520:20 O – 3 O – 321:05 O – 2 O – 2 O – 2

Table 2Initial scenario average performance measures (time in minutes and utilization in percentage).

Average SD CI (a = 0.005)

Average delay time in outgoing trains 0.00 0.00 [0.00, 0.00]Average queuing time at the entry gate 1.25 0.21 [1.00, 1.50]Average truck waiting time for loading unloading ops. 5.54 0.82 [4.59, 6.49]Average time spent unloading trucks 13.82 1.52 [12.05, 15.59]Average time spent loading trucks 19.54 0.35 [19.13, 19.95]Classification tracks utilization 0.66 0.02 [0.64, 0.68]Loading unloading tracks utilization 28.53 0.01 [28.52, 28.54]Entry exit gate utilization 63.35 0.26 [63.05, 63.65]Stacking yard utilization 8.36 1.57 [6.53, 10.19]Stacking lines utilization 88.30 2.16 [85.78,90.82]Reach stacker utilization 46.17 1.13 [44.85, 47.49]Gantry crane 1 utilization 28.15 0.46 [27.62, 28.68]Gantry crane 2 utilization 20.32 0.74 [19.46, 21.18]Diesel machine utilization 2.97 0.01 [2.96, 2.98]


have a length of 600 m. These long trains need to be broken in the classification yard before being processed in the loading/unloading tracks. Table 3 highlights the new trains in bold.

6.2.2. Initial configuration results and alternative configuration designFirst, we have simulated the new train plan without modifying the terminal operating conditions used in Section 6.1. Sim-

ulation results evidence the terminal incapacity to face the new increased demand scenario: the cranes are incapable to loadtrains before their departure time and trucks wait to be serviced more than 30 min.

Results reveal that the poor service level is due to the overload of two resources: the cranes and the entry/exit truck gate.We have subsequently increased these resources. The two reach stackers and the two gantry cranes operate now Monday toFriday from 7:00 to 21:30 (in the previous scenario the second reach stacker was not used and the second gantry craneworked only a 3-h shift, see Section 6.1). The second change affects truck arrivals. In Section 6.1, the trucks that bring con-tainers to the terminal start arriving at the terminal 24 h prior to the departure of the train. In the new scenario, this antic-ipation is increased to 48 h (assuming that system managers have influenced their client behavior in order to increase thevalue of this parameter). When distributing the arrival of trucks in a wider time period, the work pressure at the entry/exitgate is reduced. Nevertheless, the time spent by containers at the terminal is increased. This may alter the equilibrium be-tween the stacking lines and the stacking yard as well as the use of the reach stackers (which move containers between thetwo stacking areas). The simulation enables the quantification of these effects to determine the net impact on terminalperformance.

In parallel, we have defined two alternative operating policies for the train loading/unloading area. If we observe the trainschedule (see Table 3), we may note that there are never more than four loading/unloading tracks in use at the same time. Asthere are five loading/unloading tracks under the gantry cranes (see the terminal description in Section 6), one of these trackscould be used as a stacking line (this is a practice that we have observed in terminals with a high workload). We summarizebellow the two resulting configurations:

– Configuration 1 (similar to the one presented in Section 6.1). There are five loading/unloading tracks and two stackinglines. One of these lines is used to store containers that will be loaded onto trains and the other one is used to store con-tainers that have been unloaded from trains.

– Configuration 2 (it includes changes on the initial infrastructures). Four tracks are used for train loading/unloading andthe fifth track is used to store containers that will be loaded on trains. Thus, two stacking lines are used to store containersthat will be loaded onto trains and the third line is used to store containers that have been unloaded from trains.

Table 3New train timetable.

Monday Tuesday Wednesday Thursday Friday

3:20 I – 1 I – 14:25 I – 2 I – 2 I – 2 I – 2 I – 25:30 O – 1 O – 15:45 I – 3 I – 36:50 I – 4 I – 47:20 I – 5 I – 5 I – 518:40 O – 4 O – 419:15 O – 5 O – 5 O – 520:20 O – 3 O – 321:05 O – 2 O – 2 O – 2 O – 2 O – 2

Table 4Configuration comparison for average performance measures (time in minutes and utilization in percentage).

Av. Xl Av. X2 Av. Z P-t Z (a = 0.005) Config. l – Config. 2

Average delay time in outgoing trains 2.50 0.00 2.50 [0.03, 4.97] YesAverage time spent loading trucks 20.15 18.99 1.15 [0.90, 1.40] YesAverage time spent unloading trucks 17.21 18.30 �1.09 [�2.89, 0.71] NoStacking yard utilization 48.16 27.45 20.71 [18.39, 23.03] YesReach stacker 1 utilization 69.08 51.23 17.85 [17.12, 18.58] YesReach stacker 2 utilization 69.28 51.59 17.69 [17.08, 18.30] YesGantry crane 1 utilization 17.70 26.46 �8.77 [�9.18, �8.36] YesGantry crane 2 utilization 18.52 27.03 �8.52 [�9.13, �7.91] Yes


6.2.3. Configuration comparisonWe have simulated the two prior configurations using the same initial conditions, warm-up time, simulation time and

number of replications as in Section 6.1.Below, we have used paired-t confidence intervals to compare the two configurations [28]. In Table 4, we show for each

variable: the average of the observed values in the 10 replications for the configurations 1 and 2 (Av. X1 and Av. X2); theaverage of the Z variable (Av. Z), which is the difference variable between X1 and X2; the paired-t confidence interval forZ, with an individual confidence level of 99.5% (P-t Z a = 0.005) and lastly, a column that indicates if the variable may beconsidered to have a different value for the two configurations (Config. 1 – Config. 2). If the paired-t confidence intervalfor Z misses zero, the variable may be considered to provide a different value in the two configurations and actually this con-fidence interval for Z may be used to estimate the magnitude of this difference (in the opposite case, when the confidenceinterval includes zero there is no statistically significant difference between the two configurations).

Results reveal that the two configurations present statistically significant differences in the average values for service le-vel. The first row of Table 4 shows that, in Configuration 1, some trains have a delay, which causes an average delay time inoutgoing trains of 2.50 min (total delay time/total number of departing trains), whereas in Configuration 2 all the outgoingtrains depart on time. The time spent at the terminal by trucks is rather similar for both configurations (with average valuesvarying from 17 to 20 min), specially in the case of time spent by unloading trucks, where there is no statistically significantdifference between the two configurations.

Both configurations also present statistically significant differences in the use of stacking areas and cranes. Table 4 showsa marked higher utilization of the stacking yard in Configuration 1 (48%) than in Configuration 2 (27%). Similarly reach stack-ers have a higher utilization in Configuration 1 (69%) than in Configuration 2 (51%). On the other hand, gantry cranes are lessutilized in Configuration 1 (18%) than in Configuration 2 (27%). These results are consistent with the fact that Configuration 1has less storage capacity in the stacking lines (two lines instead of three), which leads to a higher stacking yard utilization,with the consequent higher use of the reach stackers (which unload containers from trucks to the stacking yard and thentransfer them from the stacking yard to the train loading/unloading area). The rest of terminal infrastructures and resourcesdo not present statistically significant differences.

A more detailed study of the simulation dynamics helps understand the train delays observed in Configuration 1. In Con-figuration 1 reach stackers present random working peaks between 17:00 and 19:00 on Tuesday and Thursday (an intervalwhere the flow of trucks to be unloaded and the movement of containers from the stacking yard to the train loading/unload-ing area increase). These working peaks cause the observed deviations from the train schedule. In Configuration 2, there is anincrease in the use of the gantry cranes. This is due to the fact that a higher number of trucks may directly unload in thestacking lines.

In light of these results, Configuration 2 presents a better service level and a more balanced crane workload for this sce-nario of increased demand.

7. Conclusions and future work

We have presented a simulation-based flexible platform which may be used to create simulation models for a wide rangeof interior terminals, which interchange containers between rail and road transport and additionally, may have a classifica-tion yard. The platform is intended to support strategic and tactical decision making related to terminal design and redesign.

Usually the conceptual design of a simulation model is a non-trivial task, but in the case of a flexible platform it is evenmore complicated, as the flexible platform should be valid for the representation of a wide variety of terminals. To deal withthis problem, we have established and used a design methodology that entailed the use of various complementary sources ofknowledge: literature review; existing rail–road terminal studies and expert interviews; and investigation through on-linesources. The combined use of these elements has contributed, first to acquire the necessary details to build up a conceptualmodel valid for a wide range of terminals and second, to determine to what point the platform is general, and where thelimits of its application are. The use of this methodology has highlighted the great difference between the actual possibilitiesfor generalization at two levels of terminal characteristics: infrastructure and resources, and operational policies. We havebeen able to determine through literature review and on-line sources the basic infrastructure and resource terminal


characteristics for the great majority of North American and European rail–road terminals. In the case of the operational-pol-icy definition, the study of existing terminals and expert interviews have been essential, whereas the literature review hasonly had a marginal contribution. The on-line sources we have found have not been useful in this area. Another relevant con-clusion of this generalization study is that many rail–road terminals have an associated classification subsystem, additionalto the rail–road transshipment area.

As a result, the simulation-based flexible platform we have presented demonstrates that is applicable for a wide range ofterminals in Europe and Australia, in terms of infrastructure and resource characteristics. It is also applicable for many NorthAmerican rail–road terminals, which also move containers and use ground storage. The simulation-based flexible platformcould also be adapted to represent skeletal trailer storage, although with the present version this would not be possible.Operational-policy generalization is more complicated, as we have only been able to study this aspect through direct obser-vation of Spanish terminals. Nevertheless the way we have selected the set of terminals studied (so as to maximize the dif-ferences among them, in terms of volume, location, infrastructure and resources) provides some guaranties of including inthe analysis a wide variety of operational policies.

The simulation-based flexible platform implemented is composed of two elements: a simulation model and a user inter-face. The simulation model, which represents the rail–road terminal operations, has been implemented in the commercialsoftware Witness�. The user interface has been implemented in MS Excel�, and allows users to configure most of the termi-nal and demand characteristics, such as, number of tracks, stacking area capacity, layout, train plan, and truck arrival pattern.The advantage of this interface is that the user may implement and modify a model in the simulation-based flexible platformby just changing some input parameters of an Excel file. As a result of a model run, a set of performance indicators in terms ofservice level, productivity and the degree of utilization of the terminal infrastructure and resources are produced.

We illustrate the use of the simulation-based platform by simulating a specific terminal facing two demand scenarios. Themodels generated allow for an analysis of terminal capacity to provide service given a train schedule. We demonstrate thatperformance results provided by the model may be an important input for decision making, at two levels: to asses and com-pare various terminal configuration alternatives and to assist in the generation of new promising configurations.

Our future focus will be on increasing the range of terminal variants that may be implemented through the simulation-based flexible platform. For instance, new alternatives for the operation rules could be included; or new functions, like con-tainer transshipment between trains, could be added. Furthermore, the simulation-based flexible platform could be ex-tended to implement new types of terminals with other transportation modes involved, for example, maritime terminals.Additionally, if the necessary data was available, it would be interesting to validate the simulation-based flexible platformgenerality using it to represent other European or North American terminals.

Acknowledgements

The authors wish to acknowledge the managers of ADIF Logistic Services (administrator of the Spanish Railway Infra-structures) for the invaluable cooperation and advise they have provided for this research work.

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http://www.intermodal.org/skedz/index.php

http://ec.europa.eu/transport/marcopolo/

http://www.fomento.gob.es/NR/rdonlyres/17FBCF00-91E0-4761-A11C-88A16277D8A4/1550/01_lenguaje_transporte_intermodal.pdf

http://www.fomento.gob.es/NR/rdonlyres/17FBCF00-91E0-4761-A11C-88A16277D8A4/1550/01_lenguaje_transporte_intermodal.pdf

http://www.ntc.gov.au/

http://www.pota.com.au/services/intermodal-rail-terminals

http://www.intermodal-terminals.eu/content/e15/index_eng.html

http://www.triplecrownsvc.com/Bimodal.html

http://www.wabashnational.com/Intermodal.htm

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