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Conceptual Agent Based Model Simulation for the Port Nautical Services
Large Scale Complex Systems: Theory andon Applications Proceedings of the 15th IFAC Symposium Proceedings of the 15thMay IFAC Symposium Delft, The Netherlands, 26-28, 2019andon Large Scale Complex Systems: Theory Applications Available online at www.sciencedirect.com Large Scale Complex Systems: Theory and Applications Proceedings of the 15thMay IFAC Symposium Delft, The Netherlands, Netherlands, May 26-28, 2019 on Delft, The 26-28, 2019 Large Scale Complex Systems: Theory and Applications Delft, The Netherlands, May 26-28, 2019
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IFAC PapersOnLine 52-3Based (2019) 19–24 Conceptual Agent Model Simulation Conceptual Agent Based Model Simulation Conceptual Agent Model Simulation for the Port Based Nautical Services for the Port Nautical Services Davydenko, R.W.Model Fransen* Conceptual Agent Simulation for I.Y. the Port Based Nautical Services I.Y. Davydenko, Davydenko, R.W. R.W. Fransen* Fransen* I.Y. for the Port Nautical Services
R.W. TNO Sustainable TransportI.Y. and Davydenko, Logistics, Anna vanFransen* Buerenplein 1, 2595 DA, the Hague The Netherlands TNO Sustainable Transport and Logistics, Anna van TNO Sustainable Transport and Logistics, Anna van Buerenplein Buerenplein 1, 1, 2595 2595 DA, DA, the the Hague Hague *Corresponding author, e-mail: [email protected] The Netherlands The Netherlands TNO Sustainable *Corresponding Transport and Logistics, Anna van Buerenplein 1, 2595 DA, the Hague author, e-mail: e-mail: [email protected] *Corresponding author, [email protected] The Netherlands *Corresponding author, e-mail: [email protected] Abstract: Deep sea international ports are competitive businesses serving the transshipment needs of shipping linking seaborne freight connections. levelthe of service is one ofneeds the imAbstract: Deep sea ports are competitive businesses serving transshipment of Abstract:lines Deep sea international international portswith are hinterland competitive businessesThe serving the transshipment needs of portant decision factors for the shipping lines’ decision on whether to call at a port. Therefore, port pershipping lines linking seaborne freight with hinterland connections. The level of service is one of the imshipping lines linking seaborne freight with hinterland connections. The level of service is one of the imAbstract: seacompetitive international ports are businesseswhile serving the transshipment needs of formance isDeep a vital component ofcompetitive the port functioning, theatnautical service chain atperthe portant decision factors for lines’ decision on to Therefore, port portant decision factors for the the shipping shipping lines’ decisionconnections. on whether whether The to call call at aaofport. port. Therefore, port pershipping lines linking seaborne freight with hinterland level service is one of the import can be considered as a complex system. Continuous improvement in port performance is anat imformance is competitive component of port while the service the formance is aa vital vital competitive component of the the port functioning, functioning, while the nautical nautical service chain chain atperthe portant decision factors for the shipping lines’ decision on whether to call a port port. Therefore, port portant goal of the organizations working at the port. Due to the fact thatinat the system consists of a port can be considered as a complex system. Continuous improvement port performance is an import can be considered as a complex system. Continuous improvement in port performance is an imformance aofvital competitive component of the the port. portgoals, functioning, while thethe nautical serviceconsists chain atofthea number ofisorganizations with their own independent other than normative approaches are needed. portant goal the organizations working at Due to the fact that port system portant goal of the organizations working at theContinuous port. Due to the fact thatinthe port system consists of port can beorganizations considered as a complex system. improvement port performance is needed. an im-a This paper presents a conceptual descriptive Agent Based Model Simulation (ABMS) for theare port nautinumber of with their own independent goals, other than normative approaches number of organizations with their own independent goals, other than normative approaches are needed. portant goalpresents of describing the organizations working at Agent thesystem, port. Due to the factinto thataccount the(ABMS) port system consists of a cal by the portdescriptive as a complex which takes operational complexity Thisservices, paper conceptual descriptive Based Model Simulation for the the port nautinautiThis paper presents aa conceptual Agent Based Model Simulation (ABMS) for port number of organizations with their own independent goals, other than normative approaches are needed. and interdependency between the parties working on the handling of deep sea ships. cal services, by describing the port as a complex system, which takes into account operational complexity cal services, by describing the portdescriptive as a complex system, which takes into account operational This paper presents abetween conceptual Agent Based Model Simulation (ABMS) for thecomplexity port nautiand interdependency the parties working on the handling of deep sea ships. and interdependency between the parties working on the handling of deep sea ships. © 2019, IFAC Federation Automatic Control) Hosting by Elsevier Ltd.operational All rights reserved. Keywords: port performance, port mapping, agentwhich based simulation model cal services, by(International describing the portprocess as aofcomplex system, takes into account complexity and interdependency between port the parties onagent the handling of deep sea ships. Keywords: performance, port processworking mapping, agent based simulation simulation model process mapping, based model Keywords: port port performance, service chain. The authors Keywords:1.port performance, port process mapping, agent based simulation model explicitly include aspects of serINTRODUCTION vices collaboration and argue that a cooperative port service service chain. The authors explicitly include aspects aspects of serserservice chain. The authors explicitly include of 1. INTRODUCTION chain is more effective. Talley and Ng (2016) provide evi1. INTRODUCTION vices collaboration collaboration and and argue argue that that aa cooperative cooperative port port service service Deep sea international ports are sophisticated systems serving vices service chain. The authors explicitly include aspects of serdence the individual service performance impact on evithe chain is ison more effective. Talley and Ng (2016) provide evithe needs of international shipping lines. Many stakeholders 1. INTRODUCTION Deep more effective. Talley and Ng (2016) provide Deep sea sea international international ports ports are are sophisticated sophisticated systems systems serving serving chain vices collaboration and argue that a cooperative port service performance of the port: congestion at one of the services can dence on the individual service performance impact on the operate in of these ports to ensure a smooth and safe entry and dence on the individual service performance impact on the the needs shipping lines. stakeholders the needs of international international shipping lines. Many Many stakeholders chain is more effective. Talley and Ng (2016) provide eviDeep sea international ports are sophisticated systems serving propagate to other services and impact the whole port perperformance of the port: congestion at one of the services can departurein for theports shippingensure lines. aAsmooth complex systementry of inter- performance of the port: congestion at one of the services can operate these and operate in of these ports to to ensure a smooth and safe safe entry and and dence on the individual service performance impact on perthe the needs international shipping lines. Many stakeholders formance. In context of the of Rotterdam, inport general propagate to the other services andport impact the whole whole action, cooperation and alignment ofcomplex operations in a of dynamdeparture for the shipping lines. A system interpropagate to other services and impact the port perdeparture for the shipping lines. A complex system of interperformance of the port: congestion at one of the services can operate in these ports toalignment ensure smooth safe entry and aformance. deep seaIn vessel can proceed theofterminal location if the the context context of the the to port Rotterdam, in general general ical setting occurs toand service all adeep sea and vessels the port action, cooperation of operations in dynamformance. In the of port of Rotterdam, in action, cooperation and alignment operations ininaa of dynampropagate tovessel other services and impact thetowhole portvessel departure foroccurs thechain. shipping lines. Aofcomplex system interpilot is sea onboard, the tugboats are available meet the aa deep deep can proceed to the terminal location ifperthe nautical service ical setting to service all deep sea vessels in the port seaIn vessel can proceed to theofterminal location if the ical setting occurs toand service all deep sea vesselsinina the port formance. the context of the port Rotterdam, in general action, cooperation alignment of operations dynamat a designated place, the terminal mooring location is availapilot is is onboard, onboard, the the tugboats tugboats are are available available to to meet meet the the vessel vessel nautical service chain. pilot nautical chain. sea vessel canthe proceed toThere the terminal location if the The setting deepservice sea ports areservice highlyall competitive organizations. For able ical occurs to deep sea vessels in the port reserved for vessel. are some other at deep designated place, the terminal mooring location is factors availaat aa and designated place, the terminal mooring location is availapilot is onboard, the tugboats areThere available to meet the vessel instance, the large West European ports oforganizations. Antwerp, Rotternautical service chain. The deep sea ports are highly competitive For that determine whether the vessel can call at the port, such as ble and reserved for the vessel. are some other factors reservedplace, for the vessel. There are some other factors The deep sea ports are highly competitive organizations. For ble at a and designated the terminal mooring location isconcenavailadam and the Hamburg service overlapping hinterland regions, instance, large West European ports of Antwerp, Rotterclearance by the port authority, however, this paper that determine whether the vessel can call at the port, such as instance, the large European ports oforganizations. Antwerp, Rotterdetermine whether the vesselThere can call at the other port, such as The sea ports West are highly competitive For that ble and reserved for the vessel. areservices some factors such deep as Germany, Austria, Belgium andhinterland the Netherlands, dam and Hamburg service overlapping regions, trates on the pilotage services, tugboat and space clearance by the port authority, however, this paper concendam and the Hamburg service overlapping hinterland regions, by the port authority, however, thisthepaper conceninstance, large West European of Antwerp, Rotter- clearance that whether the vessel canfactors call services at port, as whichas exchange cargo with allBelgium theseports three ports. The shipping such Germany, Austria, and the Netherlands, availability the terminal as the determining the tratesdetermine on the theat pilotage pilotage services, tugboat andsuch space such as Germany, Austria, Belgium and the Netherlands, trates on services, tugboat services and space dam and Hamburg service overlapping hinterland regions, clearance by the port authority, however, this paper concenline’s choice for the port of call in the so-called Hamburg–Le which exchange cargo with these ports. The shipping of theatport availability thenautical terminalservices. as the the factors factors determining determining the the which exchange cargo with all allBelgium these three three The shipping quality the terminal as such as Germany, Austria, andports. the Hamburg–Le Netherlands, trates on theatport pilotage services, tugboat services and space Havre range of ports is determined by the capacity considera- availability line’s choice for the port of call in the so-called quality of the nautical services. line’s choice for the port of call in the so-called Hamburg–Le of the which exchange cargo with three capacity ports. Theconsiderashipping The ports putatport effort in the services. improvement of performance availability thenautical terminal as the factors determiningwith the tions, and byof the quality of all thethese processes, including costs of quality Havre range ports is by Havre range of ports is determined determined by the the capacity consideraline’s choice for the port of call in the so-called Hamburg–Le respect to ship handling, which might be complicated duewith to a quality of the nautical services. The ports ports put port effort in the the improvement of performance performance with handling and hinterland transport to the end destination, as The tions, and by the quality of the processes, including costs of put effort in improvement of tions, and byofthe quality of the processes, including costs of number Havre ports isthe determined by the the consideraparties involved in might the handling process due and to respect to toofship ship handling, which might be complicated complicated due toin-aa well asrange importantly quality to of the capacity nautical services. handling and hinterland transport end destination, as handling, which be handling and hinterland transport to the end destination, as respect The ports put effort in the improvement of performance with tions, and by the quality of the processes, including costs of trinsic complexity of the handling processes, information number of parties involved in the handling process and inWiegmans et al (2008)the underscore of the services. strategic number of parties involved in the handling process and inwell as importantly quality importance of the the nautical well as importantly thetransport quality of nautical services. respect torequirements, ship handling, which might be complicated due toofa handling and hinterland to the end destination, as sharing the need to synchronize activities trinsic complexity of the handling processes, information considerations at company level in port selection process. Wiegmans et underscore of strategic complexity of the handling processes, information Wiegmans et al al (2008) (2008)the underscore importance importance of the the services. strategic trinsic number of partiesproviders. involved in the process inwell as importantly of port the nautical different service Ascencio et al (2014) lookand at the sharing requirements, requirements, the need need to handling synchronize activities of Tavasszy et al (2011) havequality shown that the selection port transhipment considerations at company level in process. sharing the to synchronize activities of considerations at company level in port selection process. trinsic complexity of the handling processes, information Wiegmans et al (2008) underscore importance of the strategic port processes from the supply chain management point of different service providers. Ascencio et al (2014) look at the volumes are directly influenced by the generalized costs of Tavasszy et al (2011) have shown that the port transhipment different service providers. Ascencio et al (2014) look at the Tavasszy et al (2011) have shown that the port transhipment sharing requirements, thesupply need totosynchronize of considerations at operations. company level in the portgeneralized view, providing a reference modelchain link demandactivities andpoint supply port processes processes from the management of related toare the directly port Theby ofselection the port process. nautical volumes influenced costs of from the supply chainetmanagement point of volumes are directly influenced byquality thethe generalized costs of port different service providers. Ascencio al (2014) look at the Tavasszy et al (2011) have shown that port transhipment the providing services. The fact thatmodel a number of demand organizations work view, reference to link link and supply supply services the portport callnautical costs, of related to the operations. The quality of view, providingfrom aa reference modelchain to demand and related todirectly the port portinfluences operations. Thegeneralized of the the port nautical processes theport supply management point of volumes are byquality the generalized costs of port on handling of aThe single call make the processes less visiof the services. fact that a number of organizations work therefore directly the directly quality ofinfluenced the port nautical services can becosts, conservices influences the generalized port call the providing services. The fact thatmodel a number of demand organizations work services directly influences the generalized portport callnautical costs, of view, a reference to link and supply related to the port operations. The quality of the ble; performance measures are harder to design and estimate handling of of a single single port port call call make make the the processes processes less less visivisisidered asthe one of theofmain factors determining decisions of on handling therefore nautical services can be contherefore the quality quality of the the port port services can becosts, con- on of services. aThe fact thatare a number ofdesign organizations work services influences the nautical generalized port call their effectiveness ex-ante. This operational and ble;the performance measures harder to andorganizaestimate shippingasdirectly lines toofcall atmain the port. Considering these facts, the sidered one the factors determining decisions of ble; performance measures are harder to design and estimate sidered asthe one of theofmain factors determining decisions of on handling of a single make the processes less visitherefore quality the port nautical services can be contional complexity ofex-ante. theport portcall nautical processes requires retheir effectiveness This operational and organizaprimary subject ofcall thisatpaper is on understanding and modelshipping lines to the port. Considering these facts, the their effectiveness ex-ante.are This operational and organizashipping lines to call at the port. Considering these facts, the ble; performance measures harder to design and estimate sidered as one of the main factors determining decisions of search on the tools that help ports understand performance of tional complexity of the port nautical processes requires reling of the port nautical services. primary subject of this is understanding and modeltional complexity ofex-ante. the portThis nautical processes requires reprimary subject ofcall thisatpaper paper is on onConsidering understanding and modeltheir effectiveness operational and organizashipping lines to the port. these facts, the the services, assess improvement measures and help in buildsearch on the tools that help ports understand performance of ling of port nautical services. search on the toolsofthat help ports understand performance of ling of the the port(2014) nautical services. tional complexity the port nautical processes requires reTalley etsubject al methodology for and evaluating primary of thisprovide paper isa on understanding model- the ing services, consensus among port stakeholders. The tools can supassess improvement measures and help in buildthe services, assess improvement measures and help in buildsearch on the tools thatport help ports understand of performance of the port’s services using the concept of a port ing ling of the port nautical services. Talley et (2014) provide aa methodology for consensus among The performance tools can supTalley et al al (2014) provide methodology for evaluating evaluating ing consensusassess among port stakeholders. stakeholders. supthe services, improvement measuresThe andtools help can in buildperformance of the port’s services using the concept of a port performance of the port’s services using the concept of a port Talley et ©al2019 (2014) Copyright IFACprovide a methodology for evaluating 19 ing consensus among port stakeholders. The tools can sup2405-8963 © 2019, IFAC (International Federation of Automatic Hosting by Elsevier Ltd. All rights reserved. performance of the port’s services using the concept of a Control) port 19 Copyright Copyright © © 2019 2019 IFAC IFAC 19 Copyright 2019responsibility IFAC 19 Control. Peer review©under of International Federation of Automatic 10.1016/j.ifacol.2019.06.004 Copyright © 2019 IFAC 19
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port in building consensus if they show feasibility and usefulness of real life rollout of improvement measures. The complexity of the processes becomes a paramount issue in case of disruptions, where a domino effect may complicate getting the processes back to a normal state even more complex as argued by Loh and Thai (2014, 2016).
Requirement (1) necessitates sufficiently good representation of physical processes, quantitative properties of the system together with a certain degree of quantitative parameter manipulation to get a realistic representation of the system as it is and a way of assessment of the modelling quality, in other words a way to compare model output to the real observed state(s) of the port system.
Ports are meso-level structures, as opposed to micro (company level) structures and macro (regional level) structures. Friedrich (2010) provides an example of a simulation model for the meso-level structures, Lidtke (2009) provides a discussion on the use of micro models for understanding of the behaviour of meso-level systems. Ports represent a collaborative system, where a limited number of core partners determine how the port functions and influence its performance. In the case of the port of Rotterdam, the core partners are the port authority, harbour master, pilot organization, tugboat services and boatmen services, the provider of the port IT system, PortBase. The port IT systems, also known as Port Community Systems (PCS), provide a way to communicate information between the parties working at port and with the port (de Putter et al., 2016). These systems are potentially capable of port performance improvement, as a meso system (Aydogdu and Aksoy, 2015). Micro-level optimization at the port level is hardly possible, as it is not possible to run these organizations centrally, they all have their own goals and targets. On the other hand, the port cannot be approached from the macro system point of view due to the small number of organizations and their distinct properties. The meso-level provides opportunities to get in grip with the complexity of port operations.
Requirement (2) necessitates such a modelling design that allows scenario-wise assessment of measures without changing model structure. Policy-related measures are often associated with varying certain costs components, which is relatively easy to do in a parameterized model. On the other hand, changes in a way how parties work and / or collaborate with each other require alterations in the underlying logic and workflows of entities or algorithms representing those parties. Therefore, the model should be capable of assessing impacts of changes in costs and other quantifiable parameters, as well as being capable to reflect changes in procedures and collaborative links (protocols) that are in practical use to coordinate activities of the handling and overseeing parties at the port. 2.2 Agent Based Model Given the requirements above, normative optimization models will not work well at the top modelling level, as there is no practical way to mandate specific changes to internal operational procedures of the actors. Furthermore, there is no party with authority to prescribe how individual actors should work, even though the outcome can be the best for the port and benefit all parties involved. Macro descriptive models are not suited well as the number of actors involved is not significantly large for an aggregation. This fact makes application of traditional discrete choice techniques for modelling of the port processes unpractical.
This paper describes the development of a simulation model for the port nautical service chain. The modelling requirements are described in section 2, the process is described in section 3 and the model-implementation for this complexsystem in section 4.
Given the fact that behaviour of individual parties has to be well represented in the model, the simulation models, especially agent-based models, present and opportunity for an implementation. Bonabeau (2002) established that from complexity science point of view the agent-based modelling is a powerful simulation modelling technique for applications to real-world business problems. Sanchez and Lukas (2002) argue that agent based models are well suited for models involving sequential time-stamp states, however can also be relatively computationally inefficient (O(n2)) to run such models, particularly if the model logic requires every agent to compare their location and/or communicate with every other agent at each time-step, such as it is the case in the port model, where there are a number of movable agents (e.g. ships, pilots, tugboats). Still, the models can be run in polynomial time, meaning that computation time is just a matter of computing resources and the model will not hit the wall of computational complexity, such as it the case for NP-Complete models.
2. MODELLING CHOICES 2.1 Goal and requirements The goal of the model for port nautical chain services is to provide a quantitative tool for assessment of the port performance and for ex-ante assessment of improvement measures. The model simulates the ports system behaviour, generates system KPI’s and provides insights on what factors lead to a deterioration or improvement of the port performance. Subsequently, the improvement measures may concern operations of individual parties at the port, and improvement measures crossing organizational boundaries, such as, for example, sharing real-time information and coordination between the actors in the chain. The basic requirements for the model of port nautical services is that (1) it represents “current situation” well to be acceptable by the stakeholders and (2) it is capable of strategic decision support through scenario-wise evaluation of potentially beneficial improvement measures.
The software agent can be made to represent operational logic of each class of the actors involved in the nautical port processes. It is even possible to make specific agents representing specific organizations or their departments. The 20
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Agent Based Model Simulation (ABMS) presents a promising modelling technique that has potentially a right abstraction level and modelling capabilities for the purpose of portrelated DSS. For instance, Borshchev and Filipov (2004) present a case for using agent based models as a DSS with a goal of policy development.
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abstraction on how the processes can be viewed for the modelling point of view: randomly arriving jobs to be handled by a set of handling agents (machines). This abstraction allows following the jobs from its appearance within the boundaries of the system until they leave it. There are two essential components in modelling of deep sea vessel handling by the port. The first is the spatial component; the second component is time. The spatial component determines locations of the agents and the course of their actions, as for instance a ship arriving at the port is heading in a certain direction and triggers certain actions. The time component sets the dynamics of the processes, as well as determines the benchmarks of the port performance.
The ports find it difficult to predict ship turnaround time, especially so in case of disruptions. Uncertainty of the conditions at which the port operate make this even more difficult and require the ability to adjust the planning “on the fly”, based on specific operational conditions. Therefore, the ability of agent based models to incorporate responsive behaviour of actors and enabling system flexibility is a big advantage, e.g. dynamically rescheduling capacity planning. Rodriguez et al. (2010) present a case on the use of agents for an implementation of dynamic and adaptive planning capabilities to distribute tasks among the agent as effectively as possible. Boukhtouta et al. (2010) developed a mathematical model for distributed, semi-cooperative planning involving multiple agents. Using this model the authors showed that the system is capable of reaching a solution quality that approaches that of the ideal situation where the entire problem is controlled by a single agent (Boukhtouta et al. (2010)). These and other research efforts (e.g. Keogh and Sonenberg (2012) and George et al. (2010)) suggest that agent based modelling approach is not only suitable for representing the system as it is, but also funding ways to improve its performance, while not resorting to the system-wide normative optimization modelling.
For the conceptual port ABMS we limited the number of agent classes representing organizations and their production logic to a minimum required to schematically represent the handling of deep sea ships (Kotachi et al. (2013) provide an example of a port model from a production, discrete event simulation point of view). For the simplicity, we leave out some agent classes from consideration in this conceptual model, such as boatmen services and ship agents. The agent classes included into the conceptual model are the following: 1. 2. 3. 4. 5.
The ABMS are a challenging type of models with respect to calibration, as they often show a highly chaotic behaviour (Terna, 1998). Darvishi and Ahmadi (2014) note that a key challenge about ABMS is difficulty in their validation and verification and provide a discussion of validation techniques for ABMS with spatial components. To overcome this challenge, the validation criteria should be on the one hand practical and acceptable for the users of the model, on the other present a meaningful way of changing system behaviour. For instance, such aggregate performance indicators measured over a period (e.g. one year), are considered to be a good candidate: the average ship turnaround time, the average chance of a ship delay, the upper boundary of ship handling time (e.g. 95% of the ships handled in less or equal amount of time). Klügl (2008) provides further methodological suggestions for validating agent-based simulation models that combines face validation, sensitivity analysis, calibration and statistical validation. The nature of ABMS models is very well suited for the requirement 2, as changes in operating procedures of an agent class are incapsulated to that agent class and would in generally not require a change in model structure.
Deep sea ships Pilots Tugboats Terminals The port: port authority and harbour master
3.1 Deep sea ship arrival process The nautical service chain services ships for arriving, departing or shifting berth at the port. All three process require actions from these five agents, which are integrated into the port model. Only the arrival process is described in this paper, the main flow of the other process is similar, but differs in details.
3. MAPPING OF STAKEHOLDERS AND PROCESSES For the port model we consider deep sea vessels as jobs to be handled by the port. The port has a number of handling procedures, some of them executed consequentially and some of them requiring coordinated procedures carried out by a few organizations together. This set up provides a useful mental
Fig. 1. Overview main arrival process and key agents 21
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Fig 1. gives an overview of the key operations taking place in the arrival process and also which key agent is responsible for this operation. A more detailed mapping of the arrival process can be found in Fig. 2. These mappings define agent behaviour that is implemented in port simulation model. Ship Birth
Ship is reported by the Ship Agent (SA)
6 hours before arrival at Maas CEnter (MC), Ship Captain (SC) contacts / informs Piloting Company (PC)
At this moment Pilot is planned / ordered Ship establishes VHF contact with Port Authority (PA) some 2,5 hours before arrival at MC
Berth place has to be free 2 hours before arrival
Terminal has allocated a vacant Berth Place (BP)
Pilot can be cancelled for free up to 1 hour before reaching MC
Yes
While at AA, stay in queue for the specific terminal
Proceed to terminal
If Pilot has not boarded, wait In exceptional cases, the ship can be turned around and sent to anchor area Trafic control (VTS) ensures separation between the ships, taking into account pilot availability
Fig. 3. Screenshot of NetLogo port simulation model
Go to the Anchor Area (AA) and cancel Pilot service
No
3. Not FIFO: the terminal decides according to its optimization
No
Pilot boards Ship at MC, if available and weather permits,
Terminal has allocated a vacant Berth Place (BP)
Yes Tugboats are ordered, which includes the number of tugs, meeting place and time
Ship is ordered to proceed to terminal
Tugboats are ordered, which includes the number of tugs, meeting place and time
het Lage Licht for ships destined to Maasvlakte, Vlaardingen for ships destined upstream locations
Arrived at meeting place with the tugs
No Tugboats available?
Wait: slow down; pilot controls / ensures a proper meeting with tugs
This set of port KPIs is essential for the assessment of the basic port performance in relation to the quality of the ship handling. The set of KPIs can be indeed extended with other KPIs, for instance, taking into account variability of the KPIs above within a certain period, or measuring the number of disruptions occurred within a period.
Yes All in place, do the mooring
Average utilization rates of the providers of the nautical services. a. Utilization rates for the pilots, measured as percentage of the time that available pilots are at work on a ship. This utilization rate is further split between departure and arrival activities. b. Utilization rate for the terminal, measured as the percentage of the mooring space being occupied by the ships. c. Utilization rate for the tugboats, measured as the percentage of the time that available tugboats are working on ship arrival or departure.
Terminal operation in progress
Fig. 2. Simplified ship arrival process at port. 4. MODEL IMPLEMENTATION
This conceptual model is capable for scenario-wise assessment of the individual company performance measures, such as service capacity considerations, as well as assessment of improvement measures that concern collaboration of a number of organizations. The first type of measures related to capacity considerations can be stylized as S-shaped function that links the number of servers in the system (e.g. pilots, tugboats, terminal berth places, depending on the context) with the share of service requests satisfied on time, see Fig. 4. If there are too few servers available (segment 1), only few requests will be satisfied on time. A certain number of servers, each additional pilot will increase the share of ships handled on time substantially (segment 2). Finally, each additional server will not improve the service substantially (segment 3), representing the zone of diminishing returns. Moreover, the simulation shows that solving one service “bottleneck”, shifts the bottleneck to another place in the system. The second type of improvement measures is related to the measures that influence operation of a number of actors at the same time. For instance, the pilotage company and the tugboat company start extensively sharing operational data and plan their services together. The simulation model will show the impact of these measures on the port and individual service KPIs, and show possible pitfalls of such measures to take into account at the implementation stage. For instance, it can happen that pilots are allocated to arriving ships and tugboats to the departing ships, thus creating artificially a grid-
The conceptual agent based simulation model for the port nautical services is implemented in the NetLogo multi-agent programmable modelling environment. The model implements all five classes of the agents and realizes interaction between them according to the mapped process flow, of which Fig. 1 shows the arrival process. The model allows varying ship departure and arrival rates, number of pilots and tugboats in the system, adjusting ship sailing speeds and other parameters “on the fly”, i.e. without resetting and restarting the simulation environment, see Fig. 3 for a screenshot of the NetLogo model environment. The core port performance KPIs that the model evaluate are the following: 1. Average time (hours) of ship turnaround at the port. 2. Average waiting time for the entrance to the port. This waiting time is decomposed to the parties unavailability of whose services causes the ship to wait. a. Average waiting time related to unavailability of a berth place at the terminal. b. Average waiting time related to unavailability of the pilotage services. c. Average waiting ti.me related to unavailability of the tugboat services. Note that the ships are not sent to the anchor area to wait for the tugboats, the waiting occurs at the entrance to the port.
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lock in the system. The NetLogo model is capable of evaluation of these measures, however, require re-programming of the operational logic of the agents, as opposed to capacity decisions, which can be assessed by changing constants in the simulation environment.
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for a different number of pilots in the system. All other simulation parameters were fixed, most importantly with 20 tugboats in the system and a ship arrival rate of 35 vessels per day. Although all input parameters were fixed, there are some stochastic variables in the model such as the ship arrival and ship departure events are generated stochastically to represent realistically incoming and departing flow of ships. The desired berthing terminal of incoming vessels are also assigned with a random component.
Fig. 4. Stylized relationship between number of servers and service level. 5.
NUMERICAL RESULTS
Fig. 5. Average waiting time for pilot service for ship arrival and ship departures with different number of pilots in the system.
At this moment the presented model is still a conceptual one, as it represents a subset of real world agents and agent operations and no calibration of the model on empirical data has been done so far. Calibrating elements such as sailing speeds of vessels or berthing time at the different terminals is required to create an empirically valid simulation. Nonetheless, the simulation of a part of the nautical processes gives a decent overview and insight into the system behaviour. The presented model can still be used to get some insight in the system behaviour and impact of certain parameters on the system performance. As described in section 4 the system can be seen as a number of servers handling jobs. Simulating with a different number of servers, in this case a varying number of pilots, visualizes the effect of the number of pilots on the system behaviour.
Fig. 6. Service level recorded during simulations indicating share of total number of ships having a waiting time less than 2 hours.
Several simulation runs have been done to verify that the system behaves as it should. Runs with different number of pilots in the system, where pilots are so-called servers in the system, should have a logical response on the average time of ship arrivals or departures, in line with the theoretical predictions. The more servers the less waiting time ships have before arrival or departure. This response, in line with expectations, can be seen in Fig. 5.
6.
CONCLUSIONS
The presented model is a meso-level descriptive approach for the modelling of port nautical services with the aim of adequate modelling of the basis state of the port and capabilities for assessment of future scenarios. The model is based on a simplified mapping of stakeholders and nautical processes at the port of Rotterdam, which provides an adequate basis for a conceptual representation of nautical service chain. The real world processes are indeed much more complex and contain more detail and an industrial implementation of such a model will consider including more detail, however, for the purpose of the basic process representation the presented level of abstraction is sufficient. The paper identified the most important classes of agents and organizations, and provide insights on ship handling sequence and interaction between the agents.
In addition to the total waiting time at arrival and departure, a service level parameters was recorded. For this purpose we use a definition of “a good service if a ship’s waiting time for a pilot is less than 2 hours and “a bad service” when this time is longer than 2 hours. The more pilots in the system the higher the service level should be and with unlimited pilots service level should near 100%. The result can be seen in Fig. 6 and shows clear similarities to the theoretical relationship in Fig. 4. For the results presented below a simulation of 100 ships entering the port, berthing and departing has been repeated
A NetLogo agent based model simulation (ABMS) has been developed based on the stakeholder and process mapping. 23
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The model proves feasibility of the approach in the form of functionality for the assessment of improvement measures. The model is capable of computing basic port performance KPIs and provide a platform for the experiments related to ex-ante assessment of candidate port performance improvement measures. The model is useful for an assessment of capacity-related decisions, namely establishing a link between a desired service level and the number of servers (i.e. tugboats) present in the system. The model is also capable for scenario assessment related to a change in the operational logic of the actors, as for instance, a step from individual organization planning to a more collaborative approaches to planning, and measures supporting data exchange and operational visibility.
Friedrich, H. (2010), Simulation of logistics in food retailing for freight transportation analysis, PhD Dissertation, Karlsruhe Institute of Technology Keogh K. and Sonenberg L.(2012). Adaptive coordination in distributed and dynamic agent organizations. In S. Cranefield et al. (Eds.), COIN 2011, LNCS 7254, pp. 3857, 2012. Klügl F (2008) A validation methodology for agent-based simulations, In: Proceedings of Applied Computing 2008, Fortaleza, Brazil Kotachi M., Rabadi G., Obeid M.F. (2013), Simulation Modeling and Analysis of Complex Port Operations with Multimodal Transportation, Procedia Computer Science, Volume 20, 2013, Pages 229-234, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2013.09.266. Loh, H. S., & Thai, V. V. (2016). Managing port-related supply chain disruptions (PSCDs): a management model and empirical evidence. Maritime Policy & Management, 43(4), 436-455. Loh, H. S., & Van Thai, V. (2014). Managing port-related supply chain disruptions: A conceptual paper. The Asian Journal of Shipping and Logistics, 30(1), 97-116. Rodriguez S., Perez-Lancho B., Bajo J., Zato C. and Corchado J.M. (2010). Self-adaptive coordination for organizations of agents in information fusion environments. In E.S. Corchado Rodriguez et al. (Eds.), HAIS 2010, Part II, LNAI 6077, pp. 444-451, 2010. Putter J.G. de, Davydenko I.Y., Cappuyns B., Ruiz A. (2016), Port Systems: feasibility and business behind port community integration, e-Compliance project Deliverable D5.1 Sanchez, S. M., T. W. Lucas. 2002. Exploring the world of agent-based simulations: Simple models, complex analyses. E. Yücesan, C.-H. Chen, J. L. Snowdon, J. Charnes, eds. Proc. 2002 Winter Simulation Conf. Institute of Electrical and Electronics Engineers, Piscataway, NJ, 116–126 Talley, W. K., & Ng, M. (2016). Port multi-service congestion. Transportation Research Part E: Logistics and Transportation Review, 94, 66-70. Talley, W. K., Ng, M., & Marsillac, E. (2014). Port service chains and port performance evaluation. Transportation Research Part E: Logistics and Transportation Review, 69, 236-247. Terna, P. (1998). 'Simulation tools for social scientists: Building agent based models with SWARM'. Journal of Artificial Societies and Social Simulation 1(2). http://www.soc.surrey.ac.uk.JASSS/1/2/4.html Tavasszy L., Minderhoud M., Perrin J., Notteboom T. (2011), A strategic network choice model for global container flows: specification, estimation and application, Journal of Transport Geography, Volume 19, Issue 6, 2011, Pages 1163-1172, ISSN 0966-6923, https://doi.org/10.1016/j.jtrangeo.2011.05.005. Vangen, Siv, and Huxham Chris. 2003a. Enacting leadership for collaborative advantage: Dilemmas of ideology and pragmatism in the activities of partnership managers. British Journal of Management 14:S61–76. Wiegmans, B., van Der Hoest A. and Notteboom T. (2008) ‘Port and terminal selection by deep-sea container operators’, Maritime Policy and Management, 35 (6), 517–34.
A next stage in further model development is to make a step from the conceptual model to a model calibrated on real world data, accurately capturing the port processes as they are. This step will have two overcome two challenges: finding a suitable operational data set and finding right model calibration parameters and techniques to calibrate the model. 7.
ACKNOWLEDGEMENT
The work presented in this paper has been done in the framework of SwarmPort NWO project. The authors are grateful to Raymond Seignette of the Port of Rotterdam, Gert van der Lee of Intertransis, Geert Jongeling of TOS - Energy & Maritime Crew for their insights on the processes at the Port of Rotterdam and their inputs to the mapping. The authors are grateful to SwarmPort project colleagues for their input on the modelling requirements and techniques. REFERENCES Ascencio, L. M., González-Ramírez, R. G., Bearzotti, L. A., Smith, N. R., & Camacho-Vallejo, J. F. (2014). A collaborative supply chain management system for a maritime port logistics chain. Journal of applied research and technology, 12(3), 444-458. Aydogdu, Y. V., & Aksoy, S. (2015). A study on quantitative benefits of port community system. Maritime Policy & Management, 42(1), 1-10. Bonabeau, Eric. "Agent-based modeling: Methods and techniques for simulating human systems." Proceedings of the National Academy of Sciences 99.suppl 3 (2002): 7280-7287. Borshchev A., Filippov A. (2004), From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. Accessed from http://www.systemdynamics.org/conferences/2004/sds_2 004/papers/381borsh.pdf Boukhtouta A., Berger J., Powell W.B. and George A.(2011). An adaptive-learning framework for semi-cooperative multi-agent coordination. IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, pp. 324-331, 2011. Darvishi M., Ahmadi G. (2014), Validation Techniques of Agent Based Modelling for Geospatial Simulations, XL (2014), pp. 15-17, November, https://doi.org/10.5194/isprsarchives-XL-2-W3-91-2014 24
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IFAC PapersOnLine 52-3Based (2019) 19–24 Conceptual Agent Model Simulation Conceptual Agent Based Model Simulation Conceptual Agent Model Simulation for the Port Based Nautical Services for the Port Nautical Services Davydenko, R.W.Model Fransen* Conceptual Agent Simulation for I.Y. the Port Based Nautical Services I.Y. Davydenko, Davydenko, R.W. R.W. Fransen* Fransen* I.Y. for the Port Nautical Services
R.W. TNO Sustainable TransportI.Y. and Davydenko, Logistics, Anna vanFransen* Buerenplein 1, 2595 DA, the Hague The Netherlands TNO Sustainable Transport and Logistics, Anna van TNO Sustainable Transport and Logistics, Anna van Buerenplein Buerenplein 1, 1, 2595 2595 DA, DA, the the Hague Hague *Corresponding author, e-mail: [email protected] The Netherlands The Netherlands TNO Sustainable *Corresponding Transport and Logistics, Anna van Buerenplein 1, 2595 DA, the Hague author, e-mail: e-mail: [email protected] *Corresponding author, [email protected] The Netherlands *Corresponding author, e-mail: [email protected] Abstract: Deep sea international ports are competitive businesses serving the transshipment needs of shipping linking seaborne freight connections. levelthe of service is one ofneeds the imAbstract: Deep sea ports are competitive businesses serving transshipment of Abstract:lines Deep sea international international portswith are hinterland competitive businessesThe serving the transshipment needs of portant decision factors for the shipping lines’ decision on whether to call at a port. Therefore, port pershipping lines linking seaborne freight with hinterland connections. The level of service is one of the imshipping lines linking seaborne freight with hinterland connections. The level of service is one of the imAbstract: seacompetitive international ports are businesseswhile serving the transshipment needs of formance isDeep a vital component ofcompetitive the port functioning, theatnautical service chain atperthe portant decision factors for lines’ decision on to Therefore, port portant decision factors for the the shipping shipping lines’ decisionconnections. on whether whether The to call call at aaofport. port. Therefore, port pershipping lines linking seaborne freight with hinterland level service is one of the import can be considered as a complex system. Continuous improvement in port performance is anat imformance is competitive component of port while the service the formance is aa vital vital competitive component of the the port functioning, functioning, while the nautical nautical service chain chain atperthe portant decision factors for the shipping lines’ decision on whether to call a port port. Therefore, port portant goal of the organizations working at the port. Due to the fact thatinat the system consists of a port can be considered as a complex system. Continuous improvement port performance is an import can be considered as a complex system. Continuous improvement in port performance is an imformance aofvital competitive component of the the port. portgoals, functioning, while thethe nautical serviceconsists chain atofthea number ofisorganizations with their own independent other than normative approaches are needed. portant goal the organizations working at Due to the fact that port system portant goal of the organizations working at theContinuous port. Due to the fact thatinthe port system consists of port can beorganizations considered as a complex system. improvement port performance is needed. an im-a This paper presents a conceptual descriptive Agent Based Model Simulation (ABMS) for theare port nautinumber of with their own independent goals, other than normative approaches number of organizations with their own independent goals, other than normative approaches are needed. portant goalpresents of describing the organizations working at Agent thesystem, port. Due to the factinto thataccount the(ABMS) port system consists of a cal by the portdescriptive as a complex which takes operational complexity Thisservices, paper conceptual descriptive Based Model Simulation for the the port nautinautiThis paper presents aa conceptual Agent Based Model Simulation (ABMS) for port number of organizations with their own independent goals, other than normative approaches are needed. and interdependency between the parties working on the handling of deep sea ships. cal services, by describing the port as a complex system, which takes into account operational complexity cal services, by describing the portdescriptive as a complex system, which takes into account operational This paper presents abetween conceptual Agent Based Model Simulation (ABMS) for thecomplexity port nautiand interdependency the parties working on the handling of deep sea ships. and interdependency between the parties working on the handling of deep sea ships. © 2019, IFAC Federation Automatic Control) Hosting by Elsevier Ltd.operational All rights reserved. Keywords: port performance, port mapping, agentwhich based simulation model cal services, by(International describing the portprocess as aofcomplex system, takes into account complexity and interdependency between port the parties onagent the handling of deep sea ships. Keywords: performance, port processworking mapping, agent based simulation simulation model process mapping, based model Keywords: port port performance, service chain. The authors Keywords:1.port performance, port process mapping, agent based simulation model explicitly include aspects of serINTRODUCTION vices collaboration and argue that a cooperative port service service chain. The authors explicitly include aspects aspects of serserservice chain. The authors explicitly include of 1. INTRODUCTION chain is more effective. Talley and Ng (2016) provide evi1. INTRODUCTION vices collaboration collaboration and and argue argue that that aa cooperative cooperative port port service service Deep sea international ports are sophisticated systems serving vices service chain. The authors explicitly include aspects of serdence the individual service performance impact on evithe chain is ison more effective. Talley and Ng (2016) provide evithe needs of international shipping lines. Many stakeholders 1. INTRODUCTION Deep more effective. Talley and Ng (2016) provide Deep sea sea international international ports ports are are sophisticated sophisticated systems systems serving serving chain vices collaboration and argue that a cooperative port service performance of the port: congestion at one of the services can dence on the individual service performance impact on the operate in of these ports to ensure a smooth and safe entry and dence on the individual service performance impact on the the needs shipping lines. stakeholders the needs of international international shipping lines. Many Many stakeholders chain is more effective. Talley and Ng (2016) provide eviDeep sea international ports are sophisticated systems serving propagate to other services and impact the whole port perperformance of the port: congestion at one of the services can departurein for theports shippingensure lines. aAsmooth complex systementry of inter- performance of the port: congestion at one of the services can operate these and operate in of these ports to to ensure a smooth and safe safe entry and and dence on the individual service performance impact on perthe the needs international shipping lines. Many stakeholders formance. In context of the of Rotterdam, inport general propagate to the other services andport impact the whole whole action, cooperation and alignment ofcomplex operations in a of dynamdeparture for the shipping lines. A system interpropagate to other services and impact the port perdeparture for the shipping lines. A complex system of interperformance of the port: congestion at one of the services can operate in these ports toalignment ensure smooth safe entry and aformance. deep seaIn vessel can proceed theofterminal location if the the context context of the the to port Rotterdam, in general general ical setting occurs toand service all adeep sea and vessels the port action, cooperation of operations in dynamformance. In the of port of Rotterdam, in action, cooperation and alignment operations ininaa of dynampropagate tovessel other services and impact thetowhole portvessel departure foroccurs thechain. shipping lines. Aofcomplex system interpilot is sea onboard, the tugboats are available meet the aa deep deep can proceed to the terminal location ifperthe nautical service ical setting to service all deep sea vessels in the port seaIn vessel can proceed to theofterminal location if the ical setting occurs toand service all deep sea vesselsinina the port formance. the context of the port Rotterdam, in general action, cooperation alignment of operations dynamat a designated place, the terminal mooring location is availapilot is is onboard, onboard, the the tugboats tugboats are are available available to to meet meet the the vessel vessel nautical service chain. pilot nautical chain. sea vessel canthe proceed toThere the terminal location if the The setting deepservice sea ports areservice highlyall competitive organizations. For able ical occurs to deep sea vessels in the port reserved for vessel. are some other at deep designated place, the terminal mooring location is factors availaat aa and designated place, the terminal mooring location is availapilot is onboard, the tugboats areThere available to meet the vessel instance, the large West European ports oforganizations. Antwerp, Rotternautical service chain. The deep sea ports are highly competitive For that determine whether the vessel can call at the port, such as ble and reserved for the vessel. are some other factors reservedplace, for the vessel. There are some other factors The deep sea ports are highly competitive organizations. For ble at a and designated the terminal mooring location isconcenavailadam and the Hamburg service overlapping hinterland regions, instance, large West European ports of Antwerp, Rotterclearance by the port authority, however, this paper that determine whether the vessel can call at the port, such as instance, the large European ports oforganizations. Antwerp, Rotterdetermine whether the vesselThere can call at the other port, such as The sea ports West are highly competitive For that ble and reserved for the vessel. areservices some factors such deep as Germany, Austria, Belgium andhinterland the Netherlands, dam and Hamburg service overlapping regions, trates on the pilotage services, tugboat and space clearance by the port authority, however, this paper concendam and the Hamburg service overlapping hinterland regions, by the port authority, however, thisthepaper conceninstance, large West European of Antwerp, Rotter- clearance that whether the vessel canfactors call services at port, as whichas exchange cargo with allBelgium theseports three ports. The shipping such Germany, Austria, and the Netherlands, availability the terminal as the determining the tratesdetermine on the theat pilotage pilotage services, tugboat andsuch space such as Germany, Austria, Belgium and the Netherlands, trates on services, tugboat services and space dam and Hamburg service overlapping hinterland regions, clearance by the port authority, however, this paper concenline’s choice for the port of call in the so-called Hamburg–Le which exchange cargo with these ports. The shipping of theatport availability thenautical terminalservices. as the the factors factors determining determining the the which exchange cargo with all allBelgium these three three The shipping quality the terminal as such as Germany, Austria, andports. the Hamburg–Le Netherlands, trates on theatport pilotage services, tugboat services and space Havre range of ports is determined by the capacity considera- availability line’s choice for the port of call in the so-called quality of the nautical services. line’s choice for the port of call in the so-called Hamburg–Le of the which exchange cargo with three capacity ports. Theconsiderashipping The ports putatport effort in the services. improvement of performance availability thenautical terminal as the factors determiningwith the tions, and byof the quality of all thethese processes, including costs of quality Havre range ports is by Havre range of ports is determined determined by the the capacity consideraline’s choice for the port of call in the so-called Hamburg–Le respect to ship handling, which might be complicated duewith to a quality of the nautical services. The ports ports put port effort in the the improvement of performance performance with handling and hinterland transport to the end destination, as The tions, and by the quality of the processes, including costs of put effort in improvement of tions, and byofthe quality of the processes, including costs of number Havre ports isthe determined by the the consideraparties involved in might the handling process due and to respect to toofship ship handling, which might be complicated complicated due toin-aa well asrange importantly quality to of the capacity nautical services. handling and hinterland transport end destination, as handling, which be handling and hinterland transport to the end destination, as respect The ports put effort in the improvement of performance with tions, and by the quality of the processes, including costs of trinsic complexity of the handling processes, information number of parties involved in the handling process and inWiegmans et al (2008)the underscore of the services. strategic number of parties involved in the handling process and inwell as importantly quality importance of the the nautical well as importantly thetransport quality of nautical services. respect torequirements, ship handling, which might be complicated due toofa handling and hinterland to the end destination, as sharing the need to synchronize activities trinsic complexity of the handling processes, information considerations at company level in port selection process. Wiegmans et underscore of strategic complexity of the handling processes, information Wiegmans et al al (2008) (2008)the underscore importance importance of the the services. strategic trinsic number of partiesproviders. involved in the process inwell as importantly of port the nautical different service Ascencio et al (2014) lookand at the sharing requirements, requirements, the need need to handling synchronize activities of Tavasszy et al (2011) havequality shown that the selection port transhipment considerations at company level in process. sharing the to synchronize activities of considerations at company level in port selection process. trinsic complexity of the handling processes, information Wiegmans et al (2008) underscore importance of the strategic port processes from the supply chain management point of different service providers. Ascencio et al (2014) look at the volumes are directly influenced by the generalized costs of Tavasszy et al (2011) have shown that the port transhipment different service providers. Ascencio et al (2014) look at the Tavasszy et al (2011) have shown that the port transhipment sharing requirements, thesupply need totosynchronize of considerations at operations. company level in the portgeneralized view, providing a reference modelchain link demandactivities andpoint supply port processes processes from the management of related toare the directly port Theby ofselection the port process. nautical volumes influenced costs of from the supply chainetmanagement point of volumes are directly influenced byquality thethe generalized costs of port different service providers. Ascencio al (2014) look at the Tavasszy et al (2011) have shown that port transhipment the providing services. The fact thatmodel a number of demand organizations work view, reference to link link and supply supply services the portport callnautical costs, of related to the operations. The quality of view, providingfrom aa reference modelchain to demand and related todirectly the port portinfluences operations. Thegeneralized of the the port nautical processes theport supply management point of volumes are byquality the generalized costs of port on handling of aThe single call make the processes less visiof the services. fact that a number of organizations work therefore directly the directly quality ofinfluenced the port nautical services can becosts, conservices influences the generalized port call the providing services. The fact thatmodel a number of demand organizations work services directly influences the generalized portport callnautical costs, of view, a reference to link and supply related to the port operations. The quality of the ble; performance measures are harder to design and estimate handling of of a single single port port call call make make the the processes processes less less visivisisidered asthe one of theofmain factors determining decisions of on handling therefore nautical services can be contherefore the quality quality of the the port port services can becosts, con- on of services. aThe fact thatare a number ofdesign organizations work services influences the nautical generalized port call their effectiveness ex-ante. This operational and ble;the performance measures harder to andorganizaestimate shippingasdirectly lines toofcall atmain the port. Considering these facts, the sidered one the factors determining decisions of ble; performance measures are harder to design and estimate sidered asthe one of theofmain factors determining decisions of on handling of a single make the processes less visitherefore quality the port nautical services can be contional complexity ofex-ante. theport portcall nautical processes requires retheir effectiveness This operational and organizaprimary subject ofcall thisatpaper is on understanding and modelshipping lines to the port. Considering these facts, the their effectiveness ex-ante.are This operational and organizashipping lines to call at the port. Considering these facts, the ble; performance measures harder to design and estimate sidered as one of the main factors determining decisions of search on the tools that help ports understand performance of tional complexity of the port nautical processes requires reling of the port nautical services. primary subject of this is understanding and modeltional complexity ofex-ante. the portThis nautical processes requires reprimary subject ofcall thisatpaper paper is on onConsidering understanding and modeltheir effectiveness operational and organizashipping lines to the port. these facts, the the services, assess improvement measures and help in buildsearch on the tools that help ports understand performance of ling of port nautical services. search on the toolsofthat help ports understand performance of ling of the the port(2014) nautical services. tional complexity the port nautical processes requires reTalley etsubject al methodology for and evaluating primary of thisprovide paper isa on understanding model- the ing services, consensus among port stakeholders. The tools can supassess improvement measures and help in buildthe services, assess improvement measures and help in buildsearch on the tools thatport help ports understand of performance of the port’s services using the concept of a port ing ling of the port nautical services. Talley et (2014) provide aa methodology for consensus among The performance tools can supTalley et al al (2014) provide methodology for evaluating evaluating ing consensusassess among port stakeholders. stakeholders. supthe services, improvement measuresThe andtools help can in buildperformance of the port’s services using the concept of a port performance of the port’s services using the concept of a port Talley et ©al2019 (2014) Copyright IFACprovide a methodology for evaluating 19 ing consensus among port stakeholders. The tools can sup2405-8963 © 2019, IFAC (International Federation of Automatic Hosting by Elsevier Ltd. All rights reserved. performance of the port’s services using the concept of a Control) port 19 Copyright Copyright © © 2019 2019 IFAC IFAC 19 Copyright 2019responsibility IFAC 19 Control. Peer review©under of International Federation of Automatic 10.1016/j.ifacol.2019.06.004 Copyright © 2019 IFAC 19
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port in building consensus if they show feasibility and usefulness of real life rollout of improvement measures. The complexity of the processes becomes a paramount issue in case of disruptions, where a domino effect may complicate getting the processes back to a normal state even more complex as argued by Loh and Thai (2014, 2016).
Requirement (1) necessitates sufficiently good representation of physical processes, quantitative properties of the system together with a certain degree of quantitative parameter manipulation to get a realistic representation of the system as it is and a way of assessment of the modelling quality, in other words a way to compare model output to the real observed state(s) of the port system.
Ports are meso-level structures, as opposed to micro (company level) structures and macro (regional level) structures. Friedrich (2010) provides an example of a simulation model for the meso-level structures, Lidtke (2009) provides a discussion on the use of micro models for understanding of the behaviour of meso-level systems. Ports represent a collaborative system, where a limited number of core partners determine how the port functions and influence its performance. In the case of the port of Rotterdam, the core partners are the port authority, harbour master, pilot organization, tugboat services and boatmen services, the provider of the port IT system, PortBase. The port IT systems, also known as Port Community Systems (PCS), provide a way to communicate information between the parties working at port and with the port (de Putter et al., 2016). These systems are potentially capable of port performance improvement, as a meso system (Aydogdu and Aksoy, 2015). Micro-level optimization at the port level is hardly possible, as it is not possible to run these organizations centrally, they all have their own goals and targets. On the other hand, the port cannot be approached from the macro system point of view due to the small number of organizations and their distinct properties. The meso-level provides opportunities to get in grip with the complexity of port operations.
Requirement (2) necessitates such a modelling design that allows scenario-wise assessment of measures without changing model structure. Policy-related measures are often associated with varying certain costs components, which is relatively easy to do in a parameterized model. On the other hand, changes in a way how parties work and / or collaborate with each other require alterations in the underlying logic and workflows of entities or algorithms representing those parties. Therefore, the model should be capable of assessing impacts of changes in costs and other quantifiable parameters, as well as being capable to reflect changes in procedures and collaborative links (protocols) that are in practical use to coordinate activities of the handling and overseeing parties at the port. 2.2 Agent Based Model Given the requirements above, normative optimization models will not work well at the top modelling level, as there is no practical way to mandate specific changes to internal operational procedures of the actors. Furthermore, there is no party with authority to prescribe how individual actors should work, even though the outcome can be the best for the port and benefit all parties involved. Macro descriptive models are not suited well as the number of actors involved is not significantly large for an aggregation. This fact makes application of traditional discrete choice techniques for modelling of the port processes unpractical.
This paper describes the development of a simulation model for the port nautical service chain. The modelling requirements are described in section 2, the process is described in section 3 and the model-implementation for this complexsystem in section 4.
Given the fact that behaviour of individual parties has to be well represented in the model, the simulation models, especially agent-based models, present and opportunity for an implementation. Bonabeau (2002) established that from complexity science point of view the agent-based modelling is a powerful simulation modelling technique for applications to real-world business problems. Sanchez and Lukas (2002) argue that agent based models are well suited for models involving sequential time-stamp states, however can also be relatively computationally inefficient (O(n2)) to run such models, particularly if the model logic requires every agent to compare their location and/or communicate with every other agent at each time-step, such as it is the case in the port model, where there are a number of movable agents (e.g. ships, pilots, tugboats). Still, the models can be run in polynomial time, meaning that computation time is just a matter of computing resources and the model will not hit the wall of computational complexity, such as it the case for NP-Complete models.
2. MODELLING CHOICES 2.1 Goal and requirements The goal of the model for port nautical chain services is to provide a quantitative tool for assessment of the port performance and for ex-ante assessment of improvement measures. The model simulates the ports system behaviour, generates system KPI’s and provides insights on what factors lead to a deterioration or improvement of the port performance. Subsequently, the improvement measures may concern operations of individual parties at the port, and improvement measures crossing organizational boundaries, such as, for example, sharing real-time information and coordination between the actors in the chain. The basic requirements for the model of port nautical services is that (1) it represents “current situation” well to be acceptable by the stakeholders and (2) it is capable of strategic decision support through scenario-wise evaluation of potentially beneficial improvement measures.
The software agent can be made to represent operational logic of each class of the actors involved in the nautical port processes. It is even possible to make specific agents representing specific organizations or their departments. The 20
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Agent Based Model Simulation (ABMS) presents a promising modelling technique that has potentially a right abstraction level and modelling capabilities for the purpose of portrelated DSS. For instance, Borshchev and Filipov (2004) present a case for using agent based models as a DSS with a goal of policy development.
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abstraction on how the processes can be viewed for the modelling point of view: randomly arriving jobs to be handled by a set of handling agents (machines). This abstraction allows following the jobs from its appearance within the boundaries of the system until they leave it. There are two essential components in modelling of deep sea vessel handling by the port. The first is the spatial component; the second component is time. The spatial component determines locations of the agents and the course of their actions, as for instance a ship arriving at the port is heading in a certain direction and triggers certain actions. The time component sets the dynamics of the processes, as well as determines the benchmarks of the port performance.
The ports find it difficult to predict ship turnaround time, especially so in case of disruptions. Uncertainty of the conditions at which the port operate make this even more difficult and require the ability to adjust the planning “on the fly”, based on specific operational conditions. Therefore, the ability of agent based models to incorporate responsive behaviour of actors and enabling system flexibility is a big advantage, e.g. dynamically rescheduling capacity planning. Rodriguez et al. (2010) present a case on the use of agents for an implementation of dynamic and adaptive planning capabilities to distribute tasks among the agent as effectively as possible. Boukhtouta et al. (2010) developed a mathematical model for distributed, semi-cooperative planning involving multiple agents. Using this model the authors showed that the system is capable of reaching a solution quality that approaches that of the ideal situation where the entire problem is controlled by a single agent (Boukhtouta et al. (2010)). These and other research efforts (e.g. Keogh and Sonenberg (2012) and George et al. (2010)) suggest that agent based modelling approach is not only suitable for representing the system as it is, but also funding ways to improve its performance, while not resorting to the system-wide normative optimization modelling.
For the conceptual port ABMS we limited the number of agent classes representing organizations and their production logic to a minimum required to schematically represent the handling of deep sea ships (Kotachi et al. (2013) provide an example of a port model from a production, discrete event simulation point of view). For the simplicity, we leave out some agent classes from consideration in this conceptual model, such as boatmen services and ship agents. The agent classes included into the conceptual model are the following: 1. 2. 3. 4. 5.
The ABMS are a challenging type of models with respect to calibration, as they often show a highly chaotic behaviour (Terna, 1998). Darvishi and Ahmadi (2014) note that a key challenge about ABMS is difficulty in their validation and verification and provide a discussion of validation techniques for ABMS with spatial components. To overcome this challenge, the validation criteria should be on the one hand practical and acceptable for the users of the model, on the other present a meaningful way of changing system behaviour. For instance, such aggregate performance indicators measured over a period (e.g. one year), are considered to be a good candidate: the average ship turnaround time, the average chance of a ship delay, the upper boundary of ship handling time (e.g. 95% of the ships handled in less or equal amount of time). Klügl (2008) provides further methodological suggestions for validating agent-based simulation models that combines face validation, sensitivity analysis, calibration and statistical validation. The nature of ABMS models is very well suited for the requirement 2, as changes in operating procedures of an agent class are incapsulated to that agent class and would in generally not require a change in model structure.
Deep sea ships Pilots Tugboats Terminals The port: port authority and harbour master
3.1 Deep sea ship arrival process The nautical service chain services ships for arriving, departing or shifting berth at the port. All three process require actions from these five agents, which are integrated into the port model. Only the arrival process is described in this paper, the main flow of the other process is similar, but differs in details.
3. MAPPING OF STAKEHOLDERS AND PROCESSES For the port model we consider deep sea vessels as jobs to be handled by the port. The port has a number of handling procedures, some of them executed consequentially and some of them requiring coordinated procedures carried out by a few organizations together. This set up provides a useful mental
Fig. 1. Overview main arrival process and key agents 21
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Fig 1. gives an overview of the key operations taking place in the arrival process and also which key agent is responsible for this operation. A more detailed mapping of the arrival process can be found in Fig. 2. These mappings define agent behaviour that is implemented in port simulation model. Ship Birth
Ship is reported by the Ship Agent (SA)
6 hours before arrival at Maas CEnter (MC), Ship Captain (SC) contacts / informs Piloting Company (PC)
At this moment Pilot is planned / ordered Ship establishes VHF contact with Port Authority (PA) some 2,5 hours before arrival at MC
Berth place has to be free 2 hours before arrival
Terminal has allocated a vacant Berth Place (BP)
Pilot can be cancelled for free up to 1 hour before reaching MC
Yes
While at AA, stay in queue for the specific terminal
Proceed to terminal
If Pilot has not boarded, wait In exceptional cases, the ship can be turned around and sent to anchor area Trafic control (VTS) ensures separation between the ships, taking into account pilot availability
Fig. 3. Screenshot of NetLogo port simulation model
Go to the Anchor Area (AA) and cancel Pilot service
No
3. Not FIFO: the terminal decides according to its optimization
No
Pilot boards Ship at MC, if available and weather permits,
Terminal has allocated a vacant Berth Place (BP)
Yes Tugboats are ordered, which includes the number of tugs, meeting place and time
Ship is ordered to proceed to terminal
Tugboats are ordered, which includes the number of tugs, meeting place and time
het Lage Licht for ships destined to Maasvlakte, Vlaardingen for ships destined upstream locations
Arrived at meeting place with the tugs
No Tugboats available?
Wait: slow down; pilot controls / ensures a proper meeting with tugs
This set of port KPIs is essential for the assessment of the basic port performance in relation to the quality of the ship handling. The set of KPIs can be indeed extended with other KPIs, for instance, taking into account variability of the KPIs above within a certain period, or measuring the number of disruptions occurred within a period.
Yes All in place, do the mooring
Average utilization rates of the providers of the nautical services. a. Utilization rates for the pilots, measured as percentage of the time that available pilots are at work on a ship. This utilization rate is further split between departure and arrival activities. b. Utilization rate for the terminal, measured as the percentage of the mooring space being occupied by the ships. c. Utilization rate for the tugboats, measured as the percentage of the time that available tugboats are working on ship arrival or departure.
Terminal operation in progress
Fig. 2. Simplified ship arrival process at port. 4. MODEL IMPLEMENTATION
This conceptual model is capable for scenario-wise assessment of the individual company performance measures, such as service capacity considerations, as well as assessment of improvement measures that concern collaboration of a number of organizations. The first type of measures related to capacity considerations can be stylized as S-shaped function that links the number of servers in the system (e.g. pilots, tugboats, terminal berth places, depending on the context) with the share of service requests satisfied on time, see Fig. 4. If there are too few servers available (segment 1), only few requests will be satisfied on time. A certain number of servers, each additional pilot will increase the share of ships handled on time substantially (segment 2). Finally, each additional server will not improve the service substantially (segment 3), representing the zone of diminishing returns. Moreover, the simulation shows that solving one service “bottleneck”, shifts the bottleneck to another place in the system. The second type of improvement measures is related to the measures that influence operation of a number of actors at the same time. For instance, the pilotage company and the tugboat company start extensively sharing operational data and plan their services together. The simulation model will show the impact of these measures on the port and individual service KPIs, and show possible pitfalls of such measures to take into account at the implementation stage. For instance, it can happen that pilots are allocated to arriving ships and tugboats to the departing ships, thus creating artificially a grid-
The conceptual agent based simulation model for the port nautical services is implemented in the NetLogo multi-agent programmable modelling environment. The model implements all five classes of the agents and realizes interaction between them according to the mapped process flow, of which Fig. 1 shows the arrival process. The model allows varying ship departure and arrival rates, number of pilots and tugboats in the system, adjusting ship sailing speeds and other parameters “on the fly”, i.e. without resetting and restarting the simulation environment, see Fig. 3 for a screenshot of the NetLogo model environment. The core port performance KPIs that the model evaluate are the following: 1. Average time (hours) of ship turnaround at the port. 2. Average waiting time for the entrance to the port. This waiting time is decomposed to the parties unavailability of whose services causes the ship to wait. a. Average waiting time related to unavailability of a berth place at the terminal. b. Average waiting time related to unavailability of the pilotage services. c. Average waiting ti.me related to unavailability of the tugboat services. Note that the ships are not sent to the anchor area to wait for the tugboats, the waiting occurs at the entrance to the port.
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lock in the system. The NetLogo model is capable of evaluation of these measures, however, require re-programming of the operational logic of the agents, as opposed to capacity decisions, which can be assessed by changing constants in the simulation environment.
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for a different number of pilots in the system. All other simulation parameters were fixed, most importantly with 20 tugboats in the system and a ship arrival rate of 35 vessels per day. Although all input parameters were fixed, there are some stochastic variables in the model such as the ship arrival and ship departure events are generated stochastically to represent realistically incoming and departing flow of ships. The desired berthing terminal of incoming vessels are also assigned with a random component.
Fig. 4. Stylized relationship between number of servers and service level. 5.
NUMERICAL RESULTS
Fig. 5. Average waiting time for pilot service for ship arrival and ship departures with different number of pilots in the system.
At this moment the presented model is still a conceptual one, as it represents a subset of real world agents and agent operations and no calibration of the model on empirical data has been done so far. Calibrating elements such as sailing speeds of vessels or berthing time at the different terminals is required to create an empirically valid simulation. Nonetheless, the simulation of a part of the nautical processes gives a decent overview and insight into the system behaviour. The presented model can still be used to get some insight in the system behaviour and impact of certain parameters on the system performance. As described in section 4 the system can be seen as a number of servers handling jobs. Simulating with a different number of servers, in this case a varying number of pilots, visualizes the effect of the number of pilots on the system behaviour.
Fig. 6. Service level recorded during simulations indicating share of total number of ships having a waiting time less than 2 hours.
Several simulation runs have been done to verify that the system behaves as it should. Runs with different number of pilots in the system, where pilots are so-called servers in the system, should have a logical response on the average time of ship arrivals or departures, in line with the theoretical predictions. The more servers the less waiting time ships have before arrival or departure. This response, in line with expectations, can be seen in Fig. 5.
6.
CONCLUSIONS
The presented model is a meso-level descriptive approach for the modelling of port nautical services with the aim of adequate modelling of the basis state of the port and capabilities for assessment of future scenarios. The model is based on a simplified mapping of stakeholders and nautical processes at the port of Rotterdam, which provides an adequate basis for a conceptual representation of nautical service chain. The real world processes are indeed much more complex and contain more detail and an industrial implementation of such a model will consider including more detail, however, for the purpose of the basic process representation the presented level of abstraction is sufficient. The paper identified the most important classes of agents and organizations, and provide insights on ship handling sequence and interaction between the agents.
In addition to the total waiting time at arrival and departure, a service level parameters was recorded. For this purpose we use a definition of “a good service if a ship’s waiting time for a pilot is less than 2 hours and “a bad service” when this time is longer than 2 hours. The more pilots in the system the higher the service level should be and with unlimited pilots service level should near 100%. The result can be seen in Fig. 6 and shows clear similarities to the theoretical relationship in Fig. 4. For the results presented below a simulation of 100 ships entering the port, berthing and departing has been repeated
A NetLogo agent based model simulation (ABMS) has been developed based on the stakeholder and process mapping. 23
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The model proves feasibility of the approach in the form of functionality for the assessment of improvement measures. The model is capable of computing basic port performance KPIs and provide a platform for the experiments related to ex-ante assessment of candidate port performance improvement measures. The model is useful for an assessment of capacity-related decisions, namely establishing a link between a desired service level and the number of servers (i.e. tugboats) present in the system. The model is also capable for scenario assessment related to a change in the operational logic of the actors, as for instance, a step from individual organization planning to a more collaborative approaches to planning, and measures supporting data exchange and operational visibility.
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A next stage in further model development is to make a step from the conceptual model to a model calibrated on real world data, accurately capturing the port processes as they are. This step will have two overcome two challenges: finding a suitable operational data set and finding right model calibration parameters and techniques to calibrate the model. 7.
ACKNOWLEDGEMENT
The work presented in this paper has been done in the framework of SwarmPort NWO project. The authors are grateful to Raymond Seignette of the Port of Rotterdam, Gert van der Lee of Intertransis, Geert Jongeling of TOS - Energy & Maritime Crew for their insights on the processes at the Port of Rotterdam and their inputs to the mapping. The authors are grateful to SwarmPort project colleagues for their input on the modelling requirements and techniques. REFERENCES Ascencio, L. M., González-Ramírez, R. G., Bearzotti, L. A., Smith, N. R., & Camacho-Vallejo, J. F. (2014). A collaborative supply chain management system for a maritime port logistics chain. Journal of applied research and technology, 12(3), 444-458. Aydogdu, Y. V., & Aksoy, S. (2015). A study on quantitative benefits of port community system. Maritime Policy & Management, 42(1), 1-10. Bonabeau, Eric. "Agent-based modeling: Methods and techniques for simulating human systems." Proceedings of the National Academy of Sciences 99.suppl 3 (2002): 7280-7287. Borshchev A., Filippov A. (2004), From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. Accessed from http://www.systemdynamics.org/conferences/2004/sds_2 004/papers/381borsh.pdf Boukhtouta A., Berger J., Powell W.B. and George A.(2011). An adaptive-learning framework for semi-cooperative multi-agent coordination. IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, pp. 324-331, 2011. Darvishi M., Ahmadi G. (2014), Validation Techniques of Agent Based Modelling for Geospatial Simulations, XL (2014), pp. 15-17, November, https://doi.org/10.5194/isprsarchives-XL-2-W3-91-2014 24