Proposal for a flexible discrete event simulation model for assessing the daily operation decisions in a Ro–Ro terminal

Simulation Modelling Practice and Theory 61 (2016) 28–46

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Simulation Modelling Practice and Theory journal homepage: www.elsevier.com/locate/simpat

Proposal for a flexible discrete event simulation model for assessing the daily operation decisions in a Ro–Ro terminal Raffaele Iannone, Salvatore Miranda, Leandro Prisco, Stefano Riemma, Debora Sarno ⇑ Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy

a r t i c l e

i n f o

Article history: Received 25 June 2015 Received in revised form 27 October 2015 Accepted 28 November 2015 Available online 18 December 2015 Keywords: Ro–Ro terminal operations Discrete events simulation Terminal planning and scheduling

a b s t r a c t Roll-on/roll-off (Ro–Ro) terminals are important logistic hubs dedicated to vessels and transporters and their loading, unloading and temporary storage. This study aims at assessing the impact of managers’ decisions on planning and operational efficiency with a particular focus on vehicle handling. To this end, the terminal operations are described, the operation decisions are analyzed and the costs involved in terminal processes are formalized. The high complexity of the managerial problem, mostly caused by the stochastic nature of the related variables, is resolved with a discrete event simulation model (developed in Arena Rockwell). It supports day-by-day terminal managers’ decisions, enabling the evaluation of the economic impact of different operational alternatives on logistic costs and pollutant emissions. The verification and validation of the model were carried out in a real environment. Finally, an illustrative example of the tool usage is presented in a case study. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction The volume of maritime transportation in the European Union accounted for more than 8 billion goods in 2012 [23] and is expected to continue growing in the near future. In particular, vehicle logistics has risen impressively during the last decade (average rate of 4% per year; [21], leading to the emergence of a worldwide hub-and-spoke network [10]. Maritime inter-modality of the automotive supply chain takes place in special port terminals called Ro–Ro terminals, where roll-on/roll-off handling of vehicles is performed on/off special vessels called car carriers [19]. These vessels are ‘floating parking lots’ [25] provided with ramps and large doors, usually distinguished as short-sea carriers, which can transport 1000 vehicles, and deep-sea carriers, which have a capacity of up to 6000 vehicles [8]. Such a vessel is a very convenient means of transport because it has shorter dwell time than lift-on/lift-off vessels and it has been considered as one of the best mechanisms for reducing the turnover time in short-sea shipping routes, recently promoted by European and US governments as an alternative to polluting and road-congesting freight distribution systems [20]. Because of its central role in the development of logistic operations, an emergent paradigm in the automotive supply chain considers the Ro–Ro port terminal as able to provide economies of scope if it can allow buffering, warehousing with pre-delivery inspections and postponement customization, becoming a new decoupling point between the supply chain forecast-driven and the demand-driven sides [22]. Usually, this logistic platform is in charge of vessels loading/unloading

⇑ Corresponding author. E-mail addresses: [email protected] (R. Iannone), [email protected] (S. Miranda), [email protected] (L. Prisco), [email protected] (S. Riemma), [email protected] (D. Sarno). http://dx.doi.org/10.1016/j.simpat.2015.11.005 1569-190X/Ó 2015 Elsevier B.V. All rights reserved.

R. Iannone et al. / Simulation Modelling Practice and Theory 61 (2016) 28–46

Nomenclature Indexes and sets a yard parking Area, with a 2 A A set of yard parking Areas b Berthing position, with b 2 B B set of b Berthing positions h buffer area for transshipment, belonging to O k transporter parking area, belonging to O o Other terminal area (not used for vehicle parking), with o 2 O ¼ fB; h; kg O set of Other terminal areas (not used for parking) s incoming Ship during the simulation, with s 2 S S set of incoming Ships during the simulation t incoming Transporter during the simulation, with t 2 T T set of incoming Transporters during the simulation v Vehicle moving around the terminal during the simulation, with v 2 V V set of Vehicles moving around the terminal during the simulation Parameters and variables (All accented symbols refer to a variable that can be measured during the simulation) Allowed turnaround Time of s, assigned by the ship-owner AT s gs eFfective Turnaround Time for unloading/loading activities of s FTT gt Transporter’s Operations Time of transporter t in the port area TOP gv Vehicles’ Handling Time of a vehicle v VHT gs WBT Waiting in Bay Time of s gt Waiting in Yard Time of t WYT BP s Berthing Position chosen for s, with BP s 2 B 8s Berthing Time of s BT s CT s entranCe Time of s into the harbour gs DNO Driver’s Number hourly employed in the Overtime for s DNs Driver’s Number for s, the number of drivers assigned to the Ship s (each driver works for one shift/day) g DSH Distances covered by SHuttles Di;j Distance from area i to area j (with areas taken from set A or O) ET s or ET t Expected arrival Time of s or t locKing/unlocking Time of v into the ship, the time needed by a driver to lock/unlock a vehicle KT v LP s or LPt Lateness Probability of s or t LT s or LT t Lateness Time of s or t LV s or LV t total number of Loading Vehicles belonging to the loading list of s or t RC parking Row Capacity RNa parking Rows Number in a yard area a RT v Relocation Time of v , the time needed by a driver to relocate a vehicle parked in a row in order to withdraw another vehicle parked in the same row ST v Starting Time of v , the time needed by a driver to enter a vehicle and start it TT v loading/unloading Transporter Time of v , the time needed by a transporter driver for locking/unlocking a vehicle, starting it, unloading/loading it from a transporter UV s or UV t total number of Unloading Vehicles from s or t g VNDF Vehicle Number subjected to Driver Failure because not loaded/unloaded from ships g VNOP Vehicle Number parked in an Outside Park (exceeding the port storage capacity) g VNPF Vehicle Number subjected to Planning Failure and not loaded on ship loading list Variation Number of s: number of vehicles that can be added to the initial loading list VNs VPs loading list Variation Probability of s XT s eXit Time of ship s out of the harbour Costs cD cDF cDO C ENV cFU CH cOP

unit Driver cost per shift, defined by the driver’s company cost of Driver’s Failure in vehicle loading/unloading, because of oversight unit Driver cost per hour in case of Overtime environmental cost shuttle FUel cost per unit of distance handling cost cost of daily storage of a vehicle in an Outside Park

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cPF cSE C SS cSWB cSWH cTE cTWY cVE

cost of Planning Failure in vehicle loading, because of unavailability (vehicles parked outside the port and not recalled on time by the management) average hourly CO2 Emissions of a Ship switched on in the port area (in bay and at the berth) seaside cost Ship’s Waiting in Bay cost (hourly cost) Ship’s Waiting in Harbour cost (hourly cost) average hourly CO2 Emissions of a transporter moving in the port area Transporter Waiting Cost (hourly cost) average hourly CO2 Emissions of a Vehicle handled in the port area

and their temporary storage before they travel to their final destination. Its efficiency in planning and performing terminal operations is of great importance because it influences both the queuing time of vessels to access/exit the port or berth (with the consequent high cost), and the congestion of the landside road network caused by queues of logistics operators (transporters) and cars. Moreover, the effect of the duration of the started engines of waiting vessels and vehicles on the degree of environmental pollution and the safety of the actors involved cannot be ignored. For the above-mentioned reasons, researchers highlight the need for more efficient terminal operations in order to transport more vehicles while reducing logistics costs [12]. Such efficiency can be achieved through the introduction of operations management techniques [4]. Many studies have addressed the optimization of operations management in container terminals (see, for example, [3,15,16]), but only a few have considered car terminals [13]. Unfortunately, terminal container models cannot be translated to car terminal models for a number of reasons. Containers can be stacked to increase storage space, may be relocated several times during their stay in a hub, and require several means of transport (cranes, forklifts, reach stackers, etc.). Conversely, vehicles cannot be stacked, are usually not relocated in order to reduce danger or damage to the vehicles (in transshipment, damage levels between 0.5% and 1.0% are considered acceptable according to [10], and are handled by drivers [18]. In terms of car terminal process resources, one specific characteristic of drivers is that they are considered to have infinite capacity, because they can generally be hired flexibly from a port-wide workforce pool [18]. The significant thing is the time the ship is willing to stand in the port. On its basis, the terminal defines the number of drivers needed for each shift. Nevertheless, some studies try to balance the allocation of manpower in order to avoid short-term hiring of inexperienced drivers, which would increase damage rates [18,11]. Finally, the main limited resource is the yard: since vehicles are frequently not directly delivered to clients, they have to be stored in the terminal. The storage capacity, organized in ‘rows’, has to be managed according to the uncertainty about the departure time of the incoming and leaving of seaside and landside vehicles [11]. In particular, because of the minimization of relocation, vehicles supposed to leave the port together are usually stored in interconnected parts of parking areas [11] and are generally defined as a group on the basis of the vessel of origin, the model and the brand [8]. This study proposes a tool for assessing the impact of Ro–Ro terminal managers’ decisions on the terminal efficiency of planning and operations, using as performance indicator the total cost related to the traffic generated by transit (both handling and waiting) of vessels and vehicles (mainly, cars and transporters) and the related pollution. To this end, the terminal operations are described, the operation decisions are analyzed and the costs involved in terminal processes are formalized. The high complexity of the managerial problem, mostly caused by the stochastic nature of the related variables, is dealt with a simulation model. This research project has been co-financed by the Italian government under the measure for Smart Cities and Communities of the National Operational Programme for ‘‘Research and Competitiveness” 2007–2013 (http://goo. gl/0Fgfbc). The rest of the paper is structured as follows: in Section 2 the literature review is presented; Section 3 deals with the description of the terminal operation framework; in Section 4 the performance cost function is proposed; Section 5 deals with the simulation model logic and structure; Section 6 provides the verification and validation; Section 7 describes the case study; Section 8 is about the design and analysis of experiments; Conclusions and Future Studies follow. 2. Literature review In this section, a brief review on Ro–Ro terminal operations is reported, followed by some container terminal researches carried out by means of discrete event simulation. Finally, the main novelties of the present paper are illustrated. As already stated, few studies have focused on Ro–Ro terminal operations. Keceli et al. [13] have developed a Discrete Event Simulation (DES) model to identify possible bottlenecks within the area of a Ro–Ro port terminal that serves trucks and trailers. They simulated the four import/export processes and evaluated them in terms of utilization rates (which can detect over-investments) and the maximum number of vehicles in the queue (to measure congestion). Mattfeld and Kopfer [18] coped with vehicle transshipment with the objective of balancing the allocation of manpower among shifts, since car location assignment in the terminal yard and manpower usage are interdependent on the duration of loading/unloading tasks. They stated the stochasticity of the input data and the integration of planning and scheduling tasks

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but, because of the resulting combinatorial complexity, they used a static framework for hierarchical problem separation, and solved it with a heuristic procedure, developing an iterative decision support system. Fischer and Gehring [11] matched manpower management planning with parking area constraints with the objective of minimizing required drivers’ time and balancing the manpower usage over shifts. First of all, they described the car terminal planning processes in a case study. Then, taking advantage of the approach described by Mattfeld and Kopfer [18], they divided the problem into three tasks: quay management (considered as an independent sub-problem), storage allocation and deployment scheduling, together with an estimation of the vehicle departure time. The first three sub-problems were assigned to three different agent types in a Multi-Agent System (MAS). A further agent was in charge of coordinating the activities in order to reach the minimization objectives. Longo et al. [16] evaluated the impact of factors such as inter-arrival time between vessels, loading/unloading time and the total number of cars and trucks to be handled in the turn-around time, chosen as the main port performance indicator. By means of DES, they simulated the following terminal macro-activities, considering both containers and vehicles to be handled: ships’ arrival; possible wait in roadstead because of berth unavailability; mooring operations and loading/unloading operations. An interesting contribution has been made by Cordeau et al. [8], who focused their attention on the scheduling aspect of the assignment of cars to parking rows in the transshipment mode. Under the assumption of deterministic arrival and departure time of car groups and on the basis of berth-yard distances, they developed an integer linear programming formulation to assign car groups to adjacent parking areas, minimizing the total handling time. Moreover, they presented some extensions of the model, with the objective of simultaneously minimizing the car group fragmentation risk and balancing the manpower usage. Because of the computational complexity of the problem, demonstrated to be an NP-Hard, a metaheuristic algorithm was proposed. DES models are widely used as a descriptive tool for both prediction and exploration of the behaviour of a terminal under different operation decisions, as underlined by many researchers such as Boschian et al. [5] and Bierwirth and Meisel [3]. Some interesting papers related to container terminal DES are presented in the following. Cartenì and de Luca [7] provided four DES models of container terminal operations by using the Witness software. They estimated the activity time for different handling equipment and different container types in simple models characterized by sample mean variables and random variables, identifying a suitable family of distribution functions to simulate handling equipment and a method to choose the best one. A DES carried out for investment planning purposes with Arena software was developed by Lin et al. [14]. The loading/ unloading activities of container ships by means of mechanical cranes were simulated by considering flexible berth allocation and dynamic crane scheduling in order to find the best cranes in terms of size and economy to cope with the demand of the next years. Another simulation study was carried out with Arena to assess the ability of inland port facilities to cope with an increase in logistic volumes, in which different types of cargo and different logistic activities were simulated [9]. Almaz and Altiok [1] developed another Arena model to study the impact of deepening on the navigational efficiency of a river using some port performance measures. A new simulation of a port terminal was presented by Bruzzone and Longo [6] and Longo et al. [17], who developed a federation of simulators for marine port operators’ training in a three-dimensional virtual environment. As can be seen from the literature review, no research, except for the one here presented, focuses on minimizing both the logistics costs and the environmental costs, managing simultaneously all the daily operation decisions instead of dividing the problem into sub-problems for separate analysis. Moreover, many literature contributions recognize the stochasticity of the port environment and propose a DES model based on a case study to analyse specific aspects; in terms of the state of the art, however, no study provides a customizable simulation tool to assess operation decisions. In more detail, the main novelties of the present paper are:  detailed description of all process and operation decisions of a Ro–Ro terminal mainly focused on vehicle handling;  definition of a cost function to assess the daily operation decisions based on logistic and environmental costs;  presentation of the implementation choices to develop a flexible DES model (adaptable to different port areas) to assess the daily operation decisions in a stochastic environment and optimize the vehicle allocation in parking areas;  provision of detailed operation data of a real case study. 3. Ro–Ro terminal operation framework The Ro–Ro terminal environment can be described by presenting the main physical and information flows (Section 3.1) and the operation decisions over the planning horizon (Section 3.2). Finally, a cost function and a set of performance indicators enable performance evaluation of the operation decisions (Section 3.3). Operation and decision flows are summarized in Fig. 1 with a Business Process Model and Notation (BPMN), a graphical flowchart language that is able to represent a process in an intuitive visual form (www.bpmn.org). The process flow is contained within a ‘pool’ composed of four ‘swim lanes’, which represent the main actors involved in the process. In this way, tasks are attributed to single stakeholders and the interaction among them is modelled by means of connecting arrows. Task colours indicate the modelling choice made with the simulation tool proposed in the following sections.

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Fig. 1. Import/export processes and modelling choices in the proposed simulation model. BPMN developed with Bizagi Software.

3.1. Operations During the import, export or transshipment processes, each means of transport in transit in a Ro–Ro terminal takes part in specific activities described in the following. 3.1.1. Vessel booking and confirmation Usually one week in advance, each Ro–Ro vessel willing to berth in the harbour communicates its Expected arrival Time and the maximum time allowed for handling activities. Moreover, for each car to be loaded/unloaded, the identification number, characteristics, destination and departure time from the port are listed. The day before the vessel’s arrival, the Expected arrival Time is confirmed. 3.1.2. Vessel arrival and berthing According to the arrival plan defined by terminal management, the vessel has to wait in the bay until it is allowed to enter the harbour by the terminal staff. As soon as it reaches the assigned berthing position, loading/unloading operations begin. 3.1.3. Vehicle unloading from vessel The unloading of the vessel usually takes precedence in loading activities. It is carried out by drivers belonging to the terminal workforce. A shuttle takes the drivers from the parking areas onto the ship. There, the vehicles are parked and locked to the floor for safety reasons. Once the block is unlocked, drivers bring the vehicles into planned landside parking areas, where the shuttle waits for a new round of withdrawals. In this phase, damage to vehicles (bumps, scratches, etc.) and errors in the selection of the vehicles to unload can be made. 3.1.4. Vehicle parking The parking place of vehicles in the terminal yard depends on the allocation policy. They are assigned to a specific slot within an area and the parking takes place in rows, in order to facilitate the consequent withdrawal (FIFO logic). Here also allocation mistakes can happen. In case of space unavailability, an outside yard is used. Moreover, in the transshipment case, if the time between unloading from a ship and loading on another is short, or when the ship stand has to be short, drivers can park the vehicles into buffer areas located in the proximity of the vessel berthing position. In this last case, parked vehicles are finally taken out from the storage area and transported to another area to reorganize the yard. Moved cars are tracked in order to keep information systems updated. 3.1.5. Vehicle loading on vessel Once the unloading operations are over, a shuttle takes drivers to the cars to be loaded (according to the vessel’s booking schedule) and drives them onto the ship. In the ship’s hold, they lock the cars and, by shuttle, return to the yard areas for another round. Again, errors and damages can occur. 3.1.6. Transporter booking Finally, transporters are the feeders of vehicles for export operations and the collectors for import operations. A transporter’s access to the Ro–Ro terminal is orchestrated by logistic operators and, in the case of an integrated supply chain, contracted with the port stakeholders. A transporter can be unloaded and/or loaded.

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3.1.7. Transporter loading/unloading A transporter enters the terminal from the landside entrance gate. Then, it reaches a specific location indicated by terminal managers and is parked. In the case of unloading, vehicles are driven to the indicated storage area. In the case of loading, they are transported from the storage area to the carrier, loaded and secured. Then, the transporter leaves by the landside exit gate. 3.2. Operation decisions In order to describe the complexity and the interdependences among the decisions the Ro–Ro terminal managers have to make, in the following the problem is split into six sub-problems classified on the basis of the planning horizon to which the solution refers (Fig. 2). 3.2.1. Yard partition While a few studies propose optimal vehicle parking oriented to minimum travel length and based on deterministic arrival and departure time, terminal managers define a virtual labelling of the parking areas according to the access frequency in the most part of the cases and starting from the evidence of the stochastic nature of vehicles handling. In the long planning horizon, decisions about the rough-cut capacity planning of the terminal yard are made according to:  terminal layout (yard points of access/exit, distances with respect to berth and landside gates);  yard storing capacity;  parking demand data (quantity, length of stay and destination of vehicles stored in the yard for a period of time). The mentioned data are used to define:  capacity of yard sub-areas allocated to each main destination;  position of these areas according to the access frequency and average distance from the point of origin of demand (seaside/landside). The output of this phase is the ‘static vehicles’ destination labelling’ of the yard (which ‘structurally’ reduces the handling costs).

TIME HORIZON year/month

week/day

day/shift

day/shift

shift/hour

shift/hour

PLANNING TASK Yard Partitition

Access Planning

Berth Planning

Driver Planning

Parking Allocation

Transporter Scheduling

DECISION Segmentation of parking areas according to vehicle’s destination

Port access schedule for each incoming vessel

Mooring position assignment to each incoming vessel

Number of drivers to be hired for each shift

Parking position in the yard areas for each vehicle

Schedule of transporters’ access to the port

Fig. 2. Terminal management planning tasks, with indication of time horizon.

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3.2.2. Vessel access planning Taking as input vessels’ estimated time of arrival, waiting cost and maximum time allowed for handling operations, mean capacity of workers and yard, the terminal managers perform demand aggregation (on a daily/weekly basis) and decide the order in which vessels can access the port, managing the possible arrival of two or more vessels at the same time. Note that ports, because of their morphology and structure, allow the simultaneous entrance/exit of a limited number of vessels (also one vessel at a time) and exit has priority over entry [16]). 3.2.3. Berth planning Once the vessel arrival time is confirmed, terminal managers formulate a berth plan in the attempt to minimize the seaside costs, on the basis of:  yard partition;  yard-berth distances;  manpower capacity. This assignment is generally determined during daily meetings with the harbourmaster. 3.2.4. Driver planning Based on the above mentioned plans (which give the time windows and the distances to be covered) and considering the number of cars to be handled, terminal managers decide on the most convenient number of drivers for each shift. 3.2.5. Parking allocation In daily handling operations dynamic yard management takes place: car clusters are defined in order to allocate homogeneous groups of vehicles to available parking zones inside the defined partitions by taking into account quantity, type, destination and withdrawal time in order to avoid or minimize relocation. In most cases, only transshipment vehicles are covered by this information. As regards the others, Fischer and Gehring [11], for example, estimated departure time by means of a learning classifier system. 3.2.6. Transporter scheduling Finally, taking into account the transporters’ request to access the port (if available), terminal staff can agree with them an access plan to minimize possible waiting and congestion. 3.3. Technical and economic performance evaluation The relevant costs incurred by the actors involved in the daily operations can be used to measure the terminal plan performances:  C SS : seaside cost;  C H : handling cost;  C ENV : environmental cost. They have been proposed as a draft in a previous paper by the authors [24]. Moreover, some other performance indicators can be provided to the terminal management, together with the ones proposed by Morales-Fusco and Saurí for Ro–Ro terminals [20]. 3.3.1. C SS : seaside cost This is a cost dependent on access plan and berth allocation decisions (vessel’s access/exit and mooring positions) and includes:  cSWB : Ship’s Waiting in Bay cost (hourly cost);  cSWH : Ship’s Waiting in Harbour cost (hourly cost). In access planning, priority has to be accorded to vessels that ask to enter or leave the bay within the same time bucket; in berth allocation, the vessel’s mooring position choice influences the time taken by terminal workers (drivers) to load/unload cars. In both cases, waiting times reflect on the service level offered by the terminal to the shipping company. Moreover, both activities are closely related to external constraints such as: the number of vessels crossing port access together, special disposals of the port authorities, and depth of seabed. Given that s is the index for a ship, the relevant times are:  AT s : Allowed turnaround Time of ship s, assigned by the ship-owner; g s : eFfective Turnaround Time for unloading/loading of s, measured during the process;  FTT

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g s : Waiting in Bay Time of s, measured during the process.  WBT The cost formulation is:

C ss ¼

X

g s ; cSWH  ð FTT g s  AT s Þg: max fcSWB  WBT

ð1Þ

s

Eq. (1) indicates that it is always expensive to have a ship waiting in bay, but the cost is greater if there is also a delay with respect to the turnaround time allowed by the shipowner. Some performance measures are:  mooring position usage, a frequency analysis of the preferred positions for possible re-contracting with the port authority,  vessel service level, dependent on the waiting times, indicator of the shipping company’s satisfaction. 3.3.2. C H : handling cost It takes into account the costs of:    

drivers who load/unload cars from vessels and relocate them in parking areas during the shifts and in overtime; shuttle costs; car delivery to outside park (because of unavailability of storage locations in the port yard); mistakes in vehicle load/unload (because of possible postponement of checks after the vessel’s departure to speed its processes);  transporter wait, in case the transporter plan is shared/coordinated by the terminal. The cost formulation is the following:

C H ¼ cD 

X X g s þ cOP  VNOP g þ cDF  VNDF g þ cPF  VNPF g þ cTWY  g t: DN s þ cDO  DNO WYT s

ð2Þ

t

where  cD : unit Driver cost per shift, defined by the driver’s company;  DN s : Driver Number for s, that is number of drivers assigned to the Ship s, with each driver working for one shift/day  cDO : unit Driver cost per hour in case of Overtime; g s : Driver’s Number hourly employed in Overtime;  DNO  cOP : cost of daily storage in an Outside Park of a vehicle; g Vehicle’s Number parked in an Outside Park (exceeding the port storage capacity);  VNOP:  cDF : cost of Driver Failure in vehicle loading/unloading, owed to oversight; g : Vehicle’s Number subjected to Driver Failure because not loaded/unloaded from ships;  VNDF  cPF : cost of Planning Failure in vehicle loading, because of unavailability (vehicles parked outside the port and not recalled on time by the management); g : Vehicle’s Number subjected to Planning Failure and not loaded on ship;  VNPF  cTWY : Transporter Waiting Cost (hourly cost);  t: index for a Transporter g t : Waiting in Yard Time of a transporter t.  WYT The main related performance measures are:     

yard congestion, number of operations in the unit of time; shuttle saturation, number of seats occupied divided by the available ones; park area saturation, a measure of the efficiency of the yard partition; driver saturation, a measure of the efficiency of the dynamic yard management and the driver plan; service level to transporters, depending on the waiting time, expressing the ability to keep to transporters’ schedule.

3.3.3. C ENV : environmental cost This takes into account the emission of pollutants released by ships, vehicles and transporters. This cost item represents the ‘green’ side of the model and it is based on the recent European ruling on environmental impact: reduction of pollutants will be rewarded by tax deductions. Cost is a function of the CO2 emissions from ships (depending on the different vessel states) transporters and vehicles, and it is proportional to the time the engines work.

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In particular:  cSE : average hourly CO2 Emissions of a Ship switched on in the port area (in bay and at the berth);  cVE : average hourly CO2 Emissions of a Vehicle handled in the port area;  v : index for a Vehicle; g v : Vehicle Handling Time of a vehicle v ;  VHT  cTE : average hourly CO2 Emissions of a transporter moving in the port area; g t : Transporter Operations Time of transporter t in the port area;  TOP The cost expression is the following:

C ENV ¼ cSE 

X s

g s þ cVE  FTT

X v

g v þ cTE  VHT

X

g t: TOP

ð3Þ

t

4. Simulation model 4.1. Approach In this very complex environment, mostly because of the stochastic nature of the related variables (such as vessel/transporter arrival times, loading lists and handling activity duration), we use simulation to support terminal managers in their day-by-day decisions by enabling the evaluation of the economic impact of different operation alternatives on both logistic costs and pollutant emissions. In particular, we present a configurable simulation model which can be parameterized to account for terminal layout, space and handling task duration of a Ro–Ro terminal and test the effect of operation choices (Section 3.2) on terminal performance, using as performance indicator the above-mentioned cost function (Section 3.3). The tool is able to test the impact of all the operation decisions reported in Section 3.2, from yard partition to transporter scheduling, taking as input in the case of long-term and medium-term planning horizons, the forecast of some of the variables involved (for example, vessel demand or parking saturation). In order to keep the emphasis on the daily planning horizon, in the description that follows and in the case study the simulation model assumes that the long-term and medium-term planning horizon decisions (Yard Partition and Vessel Access Planning) and the decisions made outside the port (Transporter Scheduling, see colours in Fig. 2) are frozen, and tries to minimize the overall costs, suggesting the best short-term planning decisions (daily Berth and Driver Planning). The Parking Allocation, instead, follows an optimal planning strategy. For each incoming Vessel and Transporter, arrival, unloading, loading and leaving activities are simulated with their inner stochasticity. Moreover, the drivers’ handling of single cars with specific final destinations and identification numbers in the terminal area is reproduced with stochastic driving speed, and shuttles are simulated for batched transfer of drivers. As disclosed in the first sections, the simulation method chosen is the Discrete Event Simulation (DES), a very common approach for modelling the operations of a system as a discrete sequence of events over time. DES’s main components are entities, queues, resources and events. Entities are dynamic objects which interact according to given rules and are characterized by attributes, which have fixed or variable values. Queues are the lines in which entities wait to perform any activity. Resources process or serve the entities in a queue. Events happen at a specific instant of time during the simulation and may change entity attributes, variables, etc. As a simple example, consider a car as an entity that has to be unloaded from a ship (served) by drivers (resources). It waits in a queue on the vessel with the other cars. As time passes and the unloading activities take place (events that change the state of the system), the car’s turn arrives and the work is done. In the end, its attributes may change: the parking position in the yard is updated, while the identification number stays unchanged. A stochastic DES model was developed in Arena Rockwell simulation software. The following details are provided: assumptions and stochasticity (Section 4.2), inputs (Section 4.3) and, finally, a description of the model (Section 4.4). 4.2. Assumptions and main variabilities involved The flexible simulation tool developed to evaluate daily operation decisions is based on the following assumptions, looking at the trade-off between (1) time needed by terminal managers to load input data for simulation and computational time and (2) usefulness of the incremental detail of results:  Ro–Ro goods modelled are vehicles (cars and trucks), because there is a very low incidence of other Ro–Ro goods (mainly containers handed by chassis) subjected to handling activities;  parking slots in yard areas have the same length, in order to simplify park allocation simulation;  vessels never enter the port earlier than planned, because they are usually late and, even if they are not, there is no possibility of rescheduling drivers and other personnel in a short time in order to anticipate the handling operations;

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 physical distances between pairs of port areas (berthing, parking and buffer areas) are measured between their centres of mass, in order to easily calculate travel time for moving vehicles on the basis of matrices reporting the distance from the origin to the destination;  no variation in the berth and driver planning is considered during the simulation. The proposed model enables terminal managers to simulate the stochasticity of the main activities involved in the examined processes, providing a probabilistic assessment of the operation choices. In particular, the following main variabilities are taken into account in terms of the related time series:  time of vessel arrival in comparison with the access plan and duration of vessel entrance/exit, which can be dependent on weather conditions and can influence the start/finish of operations and determine unplanned contemporary service requests and waits;  time of transporter arrival in comparison with the schedule. Lateness can be owed to landside congestions and can compromise vessel loading and determine port congestion and waits;  variations in vessel loading list. Because of loading issues in a vessel’s previous stops, the vessel can present a residual transport capacity in comparison with the communicated loading list. This influences the vessel loading time and driver workload;  handling duration. Stochasticity in carrying out the handling tasks (vehicle/vessel/drivers moving in the port area) is taken into account;  handling errors. Because of human error, some handling activities can be mismanaged or not happen at all, influencing the service level offered to clients. 4.3. Inputs Simulation inputs can be distinguished between (1) static, which are needed to configure the port environment, and (2) dynamic, which are recorded to simulate the daily operations within that environment. All data can be easily uploaded into the simulation model by means of Microsoft Excel spreadsheets, given that:     

a: yard parking Area, with a 2 A; b: Berthing position, with b 2 B; s: Ship, with s 2 S; v : Vehicle moving around the terminal, with t: Transporter, with t 2 T.

v 2 V;

4.3.1. Static inputs  Terminal layout and characteristics: – number of berths (jBj) and parking areas (jAj); – row numbers within parking areas (RNa ) and capacities (RC); – Di;j : physical distances between areas, with areas taken from set of parking areas (A) and Other areas (o 2 O ¼ fB; k  1; kg), that is Berthing positions (B), buffer area for transshipment (k  1) and transporter parking area (k); – seaside and landside gate capacity (number of vessels/transporters that can cross the gates at the same time); – speed of vehicles, shuttles, transporters and people in port; – work shift duration and schedule; – KT v : locKing/unlocking Time of v into the ship, the time needed by a driver to lock/unlock a vehicle; – TT v : loading/unloading Transporter Time of v , the time needed by a transporter driver to lock/unlock a vehicle, start it, unload/load it from a transporter; – ST v : Starting Time of v , the time needed by a driver to enter a vehicle and start it; – RT v : Relocation Time of v , the time needed by a driver to relocate a vehicle parked in a row in order to withdraw another vehicle parked in the same row; – CT s ; BT s and XT s : entranCe, Berthing and eXit Times of a ship s, respectively.  Terminal frozen plan (mentioned in Section 3.2): – yard partition.  Cost parameters, mentioned in Section 3.3. 4.3.2. Dynamic Inputs  Ships booking and confirmation. The data communicated by incoming ships (s 2 S), that is: – AT s : Allowed turnaround Time of s; – vehicle loading/unloading list with model of each vehicle, identification number, car destination and expected withdrawal time.

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 Vessel and transporter statistics. Vessel statistics can be employed to model the stochasticity of the events related to vessels: – LPs or LP t : Lateness Probability of s or t; – LT s or LT t : Lateness Time of s or t; – VPs : loading list Variation Probability of s; – VNs : loading list Variation Number of s, number of vehicles added to the loading list.  Transporter booking: – car loading/unloading list with model of each car, identification number and car destination and expected withdrawal time;  Terminal frozen plans (mentioned in Section 3.2): – vessel access planning, that is, the Expected arrival Time of each vessel (ET s 8s); – transporter scheduling, that is, the Expected arrival Time of each transporter (ET t 8t).  Parking area conditions (places and characteristics of parked vehicles).  Terminal tested plans (mentioned in Section 3.2): – Berth planning (BP s 8s); – Driver planning (DNs 8s). 4.4. Model description Based on the simulation approach of Section 4.1, four highly interdependent processes were implemented:    

terminal setting; seaside logistics; terminal logistics; landside logistics.

4.4.1. Terminal setting This allows configuration of the terminal model in the simulation, uploading of static and dynamic inputs and downloading of simulation results on Microsoft Excel spreadsheets by means of ‘Read-write’ blocks. 4.4.2. Seaside logistics The main entity of this process is ‘vessel’. The activities of vessel entrance, berthing (at ‘Station’ blocks), loading list variation, drivers’ allocations/release (by means of a ‘Halt’ block) and leaving the port are simulated and the waiting times caused by operation decisions are measured. 4.4.3. Terminal logistics The unloading activities take place before the loading ones. The drivers, modelled as resources’ sets, transport the cars from the berth to the parking area by means of ‘Request’ and ‘Transport’ blocks, with a time proportional to the covered distance. Different sets of drivers express their different experience level in inverse proportion to the probability of damages or errors which can be added as a simulation input by managers. As Mattfeld and Kopfer observed [18], the port management wants to leverage the manpower usage in order always to hire skilled drivers. Finally, when a vessel leaves the port, the idle workforce is assigned to the remaining moored ships. Rows of cars in a yard partition are represented by queues within ‘sub models’. The simulator is provided with a smart algorithm to allocate cars in parking areas according to the characteristics of the incoming vehicles and the others already stored in the area. In particular, in order to minimize re-locations:  the area is selected on the basis of the destination of incoming cars, according to the yard partition;  the row in the area is chosen in the attempt to park new cars near vehicles of the same model and departure date, trying not to divide lots and use the available spaces in partially filled rows;  if no attribute matches, an empty row is chosen;  if no empty row is available, a row with enough space is chosen in which the parked cars are the same model and are going to leave before the incoming ones;  if no row is able to receive all incoming vehicles, the lot has to be fragmented in the parking area;  if no space is available in the area for the lot, the nearest areas are examined following the same logic in order to park the lot;  finally, if no space is available in the yard, vehicles are delivered to an outside park, and the relative cost is measured. This feature is particularly interesting for terminal managers, giving exact indications of how to allocate vehicles on yard areas’ rows as an output of the simulation tool. Finally, for short stops in the transshipment case, a buffer parking area is provided.

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At the end of this process, according to the loading list, vehicles are withdrawn from yard sub-area rows. The same submodel is used for transporter’s load. First of all, the vehicle is searched for in the outside parking area and eventually transported to the port as described in the landside logistics. A ‘Search’ block is instead used to find the cars in the port yard and buffer. If needed, relocations in parking areas are performed and finally, by means of ‘Remove’ Blocks, vehicles are withdrawn and loaded on vessels. Any possible handling failure in the search task is recorded. The time needed to carry out the loading/unloading activities is calculated, and affects all the involved costs. Drivers’ extra capacity, indeed, should be needed, causing additional workforce costs, lateness in vessel activity completion and pollutant emissions. 4.4.4. Landside logistics The last process copes with the transporter activities and is activated when transporters arrive at the entrance gate according their schedule (with eventual lateness) and are parked. Transporters can be both unloaded and/or loaded. Car handling is performed by transporter drivers who cannot interfere with terminal drivers’ activities (they have the priority in terms of handling cars in the parking area). Car recalls from the outside park are given as a further input and simulated here. 5. Verification and validation Verification, validation, data collection and analysis of experiments were performed thanks to the management of an Italian Ro–Ro terminal. The verification phase, the process of ensuring that the simulation model operates as intended, was carried out by means of interviews with 4 terminal managers, 3 terminal drivers and 3 transporter drivers. They checked the model with the authors in order to ensure that all the important components (activities, resources, queue, etc.) were included in the model in the right sequence. Then, the validation phase took place in order to ensure that the simulation model represents the reality at a given confidence interval. First of all, a table of results (in terms of berthing time, handling time, etc.) was discussed with the same panel of experts to assess the obtained order magnitude of variables based on their experience (face validity). Then, a statistical validity test was performed in order to make a quantitative comparison between the actual system and the simulated model. To this end, the ships’ Statements of Freight (SOF) for one month were examined and seven of them were randomly selected that: (i) exclusively dealt with the transport of cars and trucks and (ii) had a comparable total number of cars to load/unload. Each SOF was simulated by taking into account real operation decisions and timing. Moreover, the values of the important descriptive variables which occur in reality, such as port access, berthing and handling duration, were compared with the simulated ones with 10 replications. We applied in cascade the chi-square procedure for testing normality and the F-test to compare variances; the Smith-Satterwaithe test (which is used when variances are dissimilar) for each descriptive variable reported no statistically significant difference between the real data and the simulated ones at 0.05 level of significance, so the model is valid. 6. Case study The case study deals with one day’s operations in the aforementioned Italian Ro–Ro terminal, characterized by 3 quays, 8 berthing positions and 16 parking yard areas, with a total capacity of around 3600 vehicles, with a high vehicle turnover. On the day under analysis, three vessels (for load/unload) and seven transporters (four to load and three to unload) arrived at the port, where 2330 vehicles were parked. In detail, input data (plans, PDFs, yard state), as reported in Section 4.3, were collected together with terminal management operation decisions, which were compared with the solutions identified by means of the simulator, using the proposed simulation tool for cost savings. Static input data are shown in Appendix A. 6.1. Static inputs In particular, the yard has the following areas (a) labelled by vehicle destination, and each area is provided by a specific number of rows (RNa ) indicated in Table A1 in Appendix A. Each row can store 10 vehicles (RC). The distances between port areas (Di;j ) are reported in Table A2 in Appendix A. One ship at a time can enter from the seaside gate and one transporter at a time can enter from the landside gate. Each vehicle, transporter or driver task is associated with a time (Table A3 in Appendix A) or a speed (Table A4 in Appendix A). The work shift duration is six hours. Finally, the cost parameters have been estimated by means of questionnaires administered to terminal managers, profit and loss account analysis and collection of CO2 emission records from the Environmental Protection Agency (Table A5 in Appendix A). 6.2. Dynamic inputs Incoming ships (s 2 S) and transporters (t 2 T) are given by the data reported in Tables 1 and 2 respectively; loading and unloading lists are not shown for lack of space.

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Table 1 Ships’ data. Variables

s, ships

ET s , Expected arrival Time LPs , Lateness Probability LT s , Lateness Time (h) AT s Allowed turnaround Time of s, assigned by the ship-owner (h) VP s , loading list Variation Probability VN s , loading list Variation Number LV s , total number of Loading Vehicles belonging to the loading list UV s , total number of Unloading Vehicles

1

2

3

10:00 40% NORM (1.0; 0.4) 6.0 15% 10% of LV s 330 900

15:00 15% NORM (0.6; 0.15) 4.0 50% 10% of LV s 404 100

17:00 50% NORM (1.4; 0.5) 7.0 15% 10% LV s 470 550

Table 2 Transporters’ data. Variables

t, transporters 1

ET t , Expected arrival Time LPt , Lateness Probability LT t Lateness Time (h) LV t , total number of Loading Vehicles belonging to the loading list UV t , total number of Unloading Vehicles belonging to the loading list

2

16:00 15:00 70% NORM (3.0; 2.0) 0 0 6 6

3

4

5

6

7

15:00

15:00

20:00

22:00

22:00

0 6

0 6

6 0

6 0

6 0

The initial saturation of the yard, in terms of departure date, model and parking position of each vehicle parked in the yard at the beginning of the simulation, has not been reported for lack of space.

7. Design and analysis of experiments 7.1. Design of experiment The proposed simulation tool enables the management to economically assess their daily operation decisions with respect to berth planning and driver planning (see the legend of Fig. 2), allowing comparison of the performances of different alternatives. The Design Of Experiment (DOE) factors, that is, the variables which may have an effect on output performances, are berth and driver planning. In the following, for illustrative purposes, we describe the logic followed by managers to define the DOE. The levels of the first factor are four berth positions (the same for more than one vessel during the same day if the access plan allows it) selected by managers looking at the proximity of the berth to the parking area of the main vehicles’ destination. Eleven different drivers sets are assigned to each incoming vessel with two approaches: (i) statistical analysis of the pool of drivers previously allocated to similar handling operations and (ii) calculation of the number required depending on the mean capacity of a driver, the number of vehicles to be handled and the allowed turnaround Time assigned by the shipowner (AT s ) for each vessel. In a full factorial design, alternatives total 5720 (that is, 3-multicombination from 4 elements multiplied by 3-multicombination from 11 elements), but (i) some combinations of berth positions were clearly inconvenient if matched with the distances from target parking areas of the total group of vehicles to be handled, (ii) the total number of available drivers was fixed and (iii) given the number of vehicles to be handled, many size of driver sets were clearly useless. Taking into account these and other experts’ considerations, many combinations were discarded from the DOE, finally leaving the 15 alternatives reported below in addition to the solution preferred by terminal managers (number 0). Starting with the factor/ level table, the sets of configuration of levels of both factors have been chosen as shown in Table 3. Then, the table of alternatives (experiments) was generated (Table 4).

7.2. Analysis of experiments First of all, a replication analysis was performed in order to determine the number of replications required to statistically analyse the difference between the alternatives. In particular, a cost absolute error of 5.0% (that is, the percentage of dispersion exhibited by the total cost around its mean divided by the sample mean at a certain confidence level) has been set with 95% confidence level and it was found that 10 replications of each experiment were needed. The average costs resulting from the experiments are reported in Fig. 3. In particular, the average environmental costs and the sum of average seaside and handling costs (later defined as monetary cost) for each alternative on the replication sample are compared in the diagram. It can be seen that both variables vary over a wide range, and seaside and handling costs are

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R. Iannone et al. / Simulation Modelling Practice and Theory 61 (2016) 28–46 Table 3 Tested configurations for each factor. BP s , Berthing Position

DNs , Driver Number

ID config

BP 1 -BP 2 -BP 3

ID config

DN1 -DN 2 -DN3

ALPHA BETA GAMMA DELTA EPSILON

3-4-3 4-5-4 5-3-2 5-4-3 5-3-3

A B C D E F G H I L M N

16-15-17 14-10-15 16-16-18 17-18-17 18-22-17 18-24-17 18-26-17 18-28-17 18-29-17 18-29-16 18-29-15 18-29-14

Table 4 Table of tested alternatives. ID alternative

BP s -DN s

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

ALPHA-A ALPHA-B ALPHA-C ALPHA-D ALPHA-E ALPHA-F ALPHA-G ALPHA-H ALPHA-I ALPHA-L ALPHA-M ALPHA-N BETA-N GAMMA-N DELTA-N EPSILON-N

Fig. 3. Average costs (on the repetition sample) for each tested alternative.

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R. Iannone et al. / Simulation Modelling Practice and Theory 61 (2016) 28–46

almost doubled if alternatives 1 (ALPHA-B) and 14 (DELTA-N) are compared. Moreover, there is no alternative able to ensure minimization of both costs. Analysis of variance (ANOVA) was performed to ensure that the mean performance of one or more alternatives was statistically significantly different from the others at a given level of confidence. Alternatives were divided into two sets:  alternatives 0–10, in which ALPHA is the configuration used for the berthing positions while the number of drivers varies from A to N;  alternatives 11–15, in which N is the configuration used for the number of drivers, while the berthing positions vary from ALPHA to EPSILON. The one-way ANOVA was carried out on both sets for the three costs separately and then for the sum of the monetary costs (in summary, two sets of alternatives were subjected to four one-way ANOVA tests) with 95% confidence level. None of the means on the first set was statistically significantly different, that is, the berthing position variation had no significant effect on the final cost. This result was expected because, in the particular port of the case study (i) the port area is really small; (ii) the quay plan was defined by terminal managers based on experience and looking at the proximity to the main parking areas of the vehicles to be handled and (iii) terminal quays have a small number of close berthing positions (distances between them were short). The number of drivers, however, had a significant impact on the mean performance of alternatives in all four ANOVA tests. Although the costs mainly depend on DNs choices for each vessel, their lateness and waits, variations of loading list and distances from berths to parking area, it seems that there is a linear dependence between total number of hired drivers (number in square brackets reported on the x-axis of Fig. 4) and monetary costs (Fig. 4a), while an opposite trend can be seen for the environmental cost (Fig. 4b). Incidentally, Fig. 4 shows the results of the alternatives 0–11, in which ALPHA (the configuration used for the berthing positions) remains fixed while the number of drivers varies from A to N.

Fig. 4. Total monetary cost (a) and environmental cost (b) comparison for alternatives 0–11. Average value and dispersion around the mean.

Fig. 5. Average value of handling (C H ) and seaside (C ss ) costs. Comparison of alternatives 0–11.

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R. Iannone et al. / Simulation Modelling Practice and Theory 61 (2016) 28–46

(a)

(b)

(c)

(e)

(f)

Alternative 0 ALPHA-A [48]

(d) Alternative 8 ALPHA-I [64]

Fig. 6. Comparison between alternatives 0 (ALPHA-A) and 8 (ALPHA-I). Average value of cost elements: (a–d) seaside costs, (b–e) handling costs, and (c–f) environmental costs.

Table 5 Comparison between alternatives 0 and 3. Monetary and cost savings. Alternative

0 (10 replications) 3 (10 replications) 3 (100 replications)

Level of factors

Monetary cost

ID config BP s

ID config DNs

Avg.

ALPHA ALPHA ALPHA

A D D

€ € €

Savings (%) in choosing alternative 3

24%

Environmental cost St. dev.

8803 6662 6624

€ € € 49%

1105 562 830

Avg.

St. dev.

310,188 248,066 249,415

24,596 8937 8859

20%

64%

An evident exception is the alternative 0 (ALPHA-A). Then, the monetary costs were analyzed in terms of their two components (Fig. 5), showing an opposite trend: handling cost (C H ) clearly increases with the total number of drivers (because their wages have to be paid), while seaside cost (C ss ) decreases because the effective turnaround time is brought down thanks to the higher number of drivers working at the same time. This reasonable motivation is confirmed by Fig. 6, in which alternatives 0 (ALPHA-A) and 8 (ALPHA-I) are compared for each cost element that determines seaside, handling and environmental costs. Among others, it can be seen that in alternative 0 a significant part of the costs is owed to waiting in the bay by ship 3 (Fig. 6a), which means that the vessel access plan does not match well with the proposed berthing plan. This obviously increases the environmental emissions of the vessel, as shown in Fig. 6c. Terminal managers, who had chosen alternative 0 (ALPHA-A), can finally use these results to make wiser operation decisions or propose other solutions to be tested. The basic idea is to choose the alternative with the least total monetary cost, a little dispersion around the mean in order to reduce economic risk and a low environmental cost. Alternative 1 (ALPHA-B) does not meet the last condition (see Fig. 4), while alternative 3 (ALPHA-D) satisfies all requirements and should be pursued. The cost and emission savings with respect to alternative 0 are reported in Table 5. It is well worth noting that the impact of the standard deviation on the mean value of the results is due to the inner stochasticity of the process (see the value obtained for 100 replications in the third row of the table) and confirms the decision to use a simulation tool as a decision support system. The knowledge of the standard deviation of each solution can allow managers to make wiser decisions based on the risk the terminal is willing to undertake to save money and the service level it wants to guarantee its clients. 8. Conclusions The Ro–Ro terminal has become an important maritime logistic hub, whose efficiency significantly influences the performance of the whole automotive supply chain. Nevertheless, only a few studies deal with the optimization of terminal operation management and some of them make simplified deterministic assumptions. In this paper, the Ro–Ro terminal logistic

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Table A1 Row capacity (RNa ) for each yard area (a). a

RN a

a

RN a

a

RNa

a

RNa

1 2 3 4

23 16 26 15

5 6 7 8

24 15 29 24

9 10 11 12

22 22 26 15

13 14 15 16

17 24 30 33

Table A2 Matrix of the distances between port areas (Di;j , where i; j 2 A; O), (m). a

a

o

b

h k

h

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

9

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

0 41 41 4 49 95 51 14 123 74 24 145 89 17 99 6

41 0 82 37 8 136 92 27 164 115 17 186 130 24 140 47

41 82 0 45 90 54 10 55 82 33 65 104 48 58 58 35

4 37 45 0 45 99 55 10 127 78 20 149 93 13 103 10

49 8 90 45 0 144 100 35 172 123 25 194 138 32 148 55

95 136 54 99 144 0 44 109 28 21 119 50 6 112 4 89

51 92 10 55 100 44 0 65 72 23 75 94 38 68 48 45

14 27 55 10 35 109 65 0 137 88 10 159 103 3 113 20

123 164 82 127 172 28 72 137 0 49 147 22 34 140 24 117

74 115 33 78 123 21 23 88 49 0 98 71 15 91 25 68

24 17 65 20 25 119 75 10 147 98 0 169 113 7 123 30

145 186 104 149 194 50 94 159 22 71 169 0 56 162 46 139

89 130 48 93 138 6 38 103 34 15 113 56 0 106 10 83

17 24 58 13 32 112 68 3 140 91 7 162 106 0 116 23

99 140 58 103 148 4 48 113 24 25 123 46 10 116 0 93

6 47 35 10 55 89 45 20 117 68 30 139 83 23 93 0

477 518 436 481 526 382 426 491 354 403 501 332 388 494 378 471

1 2 3 4 5 6 7 8 9 10

527 396 182 144 58 65 301 421 477 91

568 437 223 185 99 106 342 462 518 132

486 355 142 106 65 93 331 451 436 52

531 400 187 151 110 138 376 496 481 97

576 445 232 196 155 183 421 541 526 142

432 301 88 55 97 135 377 497 382 20

476 345 132 99 141 179 421 541 426 64

541 410 197 164 206 244 486 606 491 129

404 273 65 52 142 179 420 540 354 64

453 322 114 101 191 228 469 589 403 129

551 420 212 199 289 326 567 687 501 162

382 251 52 70 182 223 463 583 332 100

438 307 108 126 238 279 519 639 388 156

544 413 214 232 344 385 625 745 494 206

428 297 107 135 251 290 530 650 378 171

521 390 200 228 344 383 623 743 471 264

50 50 50 50 50 50 50 50 0 50

Table A3 Task time (min). Task

Time (min)

RT v , Relocation Time into the parking yard KT v , locKing/unlocking Time into the ship ST v , Starting Time TT v , loading/unloading Transporter Time BT s , Berthing Time CT s , entranCe Time XT s , eXit Time

2.0 NORM NORM NORM NORM NORM NORM

(3.0; 1.0) (1.5; 0.3) (7.0; 3.0) (37.9; 37.8) (49.1; 13.6) (20.0; 8.0)

Table A4 Task speed (km/h). Task

Speed (km/h)

Vehicle moving in the port area Shuttle moving in the port area Transporter moving in the port area Person (Transporter’s driver) on foot

NORM NORM NORM NORM

(24; 5) (27; 6) (16; 3) (3; 0.8)

processes are described in terms of physical and information flows, operation decisions and cost measures. The paper proposes a flexible simulation tool for evaluating the stochastic performances of a Ro–Ro terminal in day-by-day decisions, providing a valuable tool for assessing the economic impact of different operation alternatives made on a daily basis in terms

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R. Iannone et al. / Simulation Modelling Practice and Theory 61 (2016) 28–46 Table A5 Cost parameters. Cost group

Cost parameter

Value

C SS , seaside

cSWB , Ship’s Waiting in Bay cost cSWH , Ship’s Waiting in Harbour cost

300 (€/h) 500 (€/h)

C H , handling

cD , unit Driver cost per shift cDO , unit Driver cost per hour in case of Overtime cOP , cost for daily storage in an Outside Park of a vehicle cDF , cost of Driver Failure in vehicle loading/unloading, because of oversight cPF , cost of Planning Failure in vehicle loading, because of unavailability cTWY , Transporter Waiting Cost

80 (€/shift) 100 (€/h) 100 (€/vehicle) 300 (€/vehicle) 300 (€/vehicle) 30 (€/h)

C ENV , environmental

cSE , average hourly CO2 Emissions of a Ship cVE , average hourly CO2 Emissions of a Vehicle cTE , average hourly CO2 Emissions of a transporter

10,000 (gCO2/h) 3000 (gCO2/h) 1500 (gCO2/h)

of both logistic costs and pollutant emissions. Moreover, the tool suggests the optimal allocation of vehicles in the yard (Parking Allocation) according to a defined algorithm. The discrete event simulation model (developed in Arena Rockwell software) has been verified and validated according to Italian terminal data. As an illustrative example of how to use the tool, an accurate cost parameter setting and an extensive data collection have been carried out in a small port area in which three vessels and seven transporters arrived in one day. The design of the experiment took as experimental factors the berthing position (berth plan) and the number of drivers (driver plan) – the daily decisions. The specific alternatives to be tested were selected by managers. The solution obtained by means of the tool allows an average saving of 24% of monetary cost and reduces 20% of CO2 emissions with respect to the initial decisions. The presented simulation model can be parameterized for the terminal layout, space and handling task duration of a Ro–Ro terminal, offering the possibility of testing the other operation decisions (from the yard partition to the transporter schedule) performing the forecast of some variables. In the implementation phase, the main efforts required of practitioners are the estimation of cost parameters and probability distribution functions of the variables involved. Moreover, some SQL queries are needed to automatically extract daily parking area conditions (places and characteristics of parked vehicles). In real applications, the range of alternatives to be tested increases with the number of vessels, berthing positions and drivers available and an automatic reduction procedure should be implemented for the sample of alternatives. Moreover, some tools such as OptQuest could be used to automatically search for optimal solutions within the Arena simulation model. Finally, the simulation tool could be integrated into an optimization process in order to evaluate the quality of the alternatives and identify optimal system configurations (simulation-based optimization) using, for example, the HeuristicLab framework [2]. Appendix. A. Case Study data set – static inputs See Tables A1–A5. References [1] O.A. Almaz, T. Altiok, Simulation modeling of the vessel traffic in Delaware River: impact of deepening on port performance, Simul. Model. Pract. Theory 22 (2012) 146–165, http://dx.doi.org/10.1016/j.simpat.2011.12.004. [2] A. Affenzeller, A. Beham, S. Vonolfen, E. Pitzer, S.M. Winkler, S. Hutterer, M. Kommenda, M. Kofler, G. Kronberger, S. Wagner, Simulation-based optimization with heuristiclab: practical guidelines and real-world applications, Appl. Simul. Optim. (2015) 3–38. [3] C. Bierwirth, F. Meisel, A survey of berth allocation and quay crane scheduling problems in container terminals, Eur. J. Oper. Res. 202 (3) (2010) 615– 627. [4] E. Battistoni, A. Bonacelli, A. Fronzetti Colladon, M.M. Schiraldi, An analysis of the effect of operations management practices on performance, Int. J. Eng. Bus. Manage. 5 (2013), http://dx.doi.org/10.5772/56919. [5] V. Boschian, M.P. Fanti, G. Iacobellis, G. Georgoulas, C. Stylios, W. Ukovich, A model based decision support system for logistics management, in: Proceedings of the European Modeling and Simulation Symposium, 2013, pp. 364–369. [6] A.G. Bruzzone, F. Longo, 3D simulation as training tool in container terminals: the TRAINPORTS simulator, J. Manuf. Syst. 32 (1) (2013) 85–98. [7] A. Cartenì, S. de Luca, Tactical and strategic planning for a container terminal: modelling issues within a discrete event simulation approach, Simul. Model. Pract. Theory 21 (1) (2012) 123–145. [8] J.-F. Cordeau, G. Laporte, L. Moccia, G. Sorrentino, Optimizing yard assignment in an automotive transshipment terminal, Eur. J. Oper. Res. 215 (1) (2011) 149–160. [9] P. Cortés, J. Muñuzuri, J. Nicolás Ibáñez, J. Guadix, Simulation of freight traffic in the Seville inland port, Simul. Model. Pract. Theory 15 (3) (2007) 256–271. [10] Drewry, Market Outlook for Car Carriers, Drewry Shipping Consultants Ltd., 1999. [11] T. Fischer, H. Gehring, Business process support in a seaport automobile terminal – a multi-agent based approach, Stud. Comput. Intell. 28 (2006) 373–394. [12] M.H. Kang, H.R. Choi, H.S. Kim, B.J. Park, Development of a maritime transportation planning support system for car carriers based on genetic algorithm, Appl. Intell. 36 (3) (2011) 585–604. [13] Y. Keceli, S. Aksoy, Y.V. Aydogdu, A simulation model for decision support in Ro–Ro terminal operations, Int. J. Logist. Syst. Manage. 15 (4) (2013) 338–358.

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