Comparison and Evaluation of Two Automated Guided Vehicle ...

Comparison and Evaluation of Two AutomatedGuided Vehicle Systems in the Transshipment ofContainers at a Container TerminalDr. Lawrence Henesey, Prof. Dr. Paul Davidsson, Dr. Jan A. PerssonDepartment of Systems and Software Engineering, Blekinge Institute of TechnologyKarlshamn 374 24, Sweden and Ronneby 372 25, Swedenlhe@bth.se, pdv@bth.se and jps@bth.seAbstract. Due to globalization and the growth of international trade, many container terminalsare trying to improve performance in order to keep up with demand. One technologythat has been proposed is the use of Automated Guided Vehicles (AGVs) in the handling ofcontainers within terminals. Recently, a new generation of AGVs, called C-AGVs, has beendeveloped which makes use of cassettes that can be detached from the AGV. We havedeveloped an agent-based simulator for evaluating the cassette-based system and comparingit to a traditional AGV system. In addition, a number of different scenarios of containerterminal equipment, e.g., number of AGVs and cassettes, have been studied in orderto find the most efficient configuration. The simulation results suggest that there are configurationswhich the cassette-based system is more cost efficient than a traditional AGVsystem.1. IntroductionThe transport of containers is continuouslygrowing and many container terminals(CTs) are coping with congestion andcapacity problems. For instance, thenumber of Twenty-foot Equivalent Unit(TEUs) containers shipped world-widehas increased from 39 million in 1980 to356 million in 2004 and growth is projectedto continue at an annual rate of 10per cent till 2020 [3]. Enormous pressureis on the management of ports and CTsto find more efficient ways of handlingcontainers and increase CT capacity.Thus, management in ports and CTs aresearching for solutions to increase theefficiency and capacity, including the useof automation, e.g., Automatic StackingCranes (ASCs) and Automated GuidedVehicles (AGVs). Automation of the terminalequipment is argued by Ioannou etal. [8] to be a suitable solution for increasingefficiency and reducing operationalcosts for CTs. CTs use differenttypes of transport equipment for movingcontainers between the marine side ofthe terminal (the quay) to the stackswhich are located in the yard of the CTThe use of AGVs is not a recent development.The first AGV system was introducedin 1955 for horizontal transport ofmaterials and AGVs were first used fortransporting containers in 1993 at theDelta/Sea-Land terminal located in Rotterdam.There has been much researchconducted in various areas incorporatingAGVs and CTs (c.f. Vis [15], for a comprehensiveliterature review on AGVs).Following two European Union sponsoredprojects; IPSI (Improved Port ShipInterface) and INTEGRATION (Integrationof Sea Land Technologies), a systemfor handling containers using cassettesand AGVs has been developed but todate has not been used in a CT [12]. Thecassettes are steel platforms detachablefrom the AGV and on which containerscan be set. A possible advantage or meritof using cassettes is their ability to act asbuffer, since containers can be placed onit without an AGV being present. To evaluatethis new development, we will comparea Cassette-AGV system (C-AGV) toa “conventional” AGV system, which willbe referred to as AGV. We perform acomparative analysis of the transport ofcontainers between ship-side operationsto the operations in the stacks located inthe yard of the CT.Because of complexity and capital / constructioncosts, simulation models areused intensively for understanding beha-

vior and testing strategies in CTs, e.g.,see [1], [6] and [7]. Also, the simulationapproach provides a method of evaluatinga concept that has not been used inthe real world [10]. We have developed amulti-agent based simulator (MABS) forcomparing the performance of the twoAGV systems according to a number ofcriteria, e.g., service time for a ship, utilizationrate for the CT equipment, andoperating cost.The remainder of the paper is organizedas follows; in Section 1 a description ofthe problem is provided. In Section 2, themethodology and model is presented.Section 3 provides a description of thesimulation experiments. Results are presentedand discussed in Section 4. Conclusionsare presented in Section 5 withpointers for future work.2. Problem Description and ModelAssumptionsA CT is a place where ships will beberthed so that they can be unloadedand/or loaded with containers by QuayCranes (QCs). The CT are often viewedas an intermodal interface for transport ofcontainers between modes of transportlinking the landside with the marine-side,e.g., containers are arriving or departingby, ships, trains or trucks and while in theCT they are temporarily stored in stacks[7]. In addition, we view CTs as an interfacewithin modes of transport, e.g. thetransshipping of containers from one shipto another ship in which the containermay be temporarily stored at the CT.Ship owners often demand a fast turnaroundtime for their ships. By minimizingthe time a ship is berthed at a CT, theship may have more time for sailing andthus opportunity for extra voyages, whichimplies that more revenue is generatedby the ship. With the advent of “Just-In-Time” philosophy and the importance ofspeed in global supply chains, customersdemand that their containerized cargo istransported fast and on time. Therefore,from a logistics perspective, improvingthe transport within the CT may help indecreasing the total transport time andcost of transporting cargo in containers.CT managers often seek to optimize theuse of their terminal resources. The mostexpensive piece of terminal equipment isoften the quay crane (QC), for which thecapital costs can be € 7 million or more[11]. Other CT resources are the transportersand in the case of C-AGVs, thecassettes. Many CT managers view theinterface between the QCs and the yardas the most critical planning problem [4].An objective that many CT managersshare is to keep the assigned QCs frombeing idle so as to quickly serve (minimizethe turn-around time) a ship.In the problem studied, a ship arrives at aCT with a number of containers to beunloaded and another set of containersare loaded onto the ship before it departs.The unloaded containers are to betransported from the QC area to stacks inthe yard and the containers to be loadedare picked-up from stacks and transportedto the QC area. This transportationis carried out by AGVs (either “conventional”AGVs or C-AGVs with theiraccompanying cassettes) and we call thetime it takes to perform a transport move(including the return without container(s)as well as the load and unloading operations)the AGV cycle time. This conceptof cycle time is similar to that used in astudy comparing a Straddle Carrier systemwith an Automated Stacking Cranesystem by Vis [16]. The stacks are locatedin different areas of the yard andtherefore have varying distances to theQC, implying different transport times.We model this by letting the AGV cycletime have random component for eachtransport. We also consider the time forthe unloading and loading of containersfrom and to a ship by a QC, called thecontainer handling time.The technical specifications of the twoAGV systems is provided courtesy ofTTS AB and presented in Table 1.Table 1: Specifications of AGV systemsC-AGV AGVSpeed in km /h 20 15Max. TransportCapacity in TEUs4 2Lifting time 15 sec. n.a.Capital cost inMillions of Skr.3,5 2,7Capital cost percassette in Skr.80,000 n.a.

3. Methodology and Simulation ModelAs Isoda [9], points out, mapping realworld entities into programming languageshas been one of the greatest desiresof software developers. When buildingsimulation models the ability to performsuch mapping is of particular importance.Object-oriented modeling supports themapping the behavior of objects andagent-oriented modeling extends this bysupporting also the modeling of proactiveentities as well as the interactionbetween such entities.3.1 Simulation ModelThe entities of the real world that wemodel are; QCs, Ships, Buffer, AGVs (C-AGV and AGV), cassettes and containers.These entities have to coordinatewith each other to complete the maintask, which is the unloading/loading aship. Therefore we model the real worldentities as agents and in the simulationsoftware we implemented them as objectswith their own execution thread.During the simulation these objects worklike processes in the operating systemand perform their task in parallel. Thisbehavior is close to replication of the realworld scenario where the entities of thesystem continuously perform their taskand they sometimes have to coordinatewith each other to finish the main task.The simulation model was implementedusing DESMO-J, an open source libraryfor the JAVA computer programming languagethat is available for download fromthe University of Hamburg, Germany,U.H. [13]. DESMO-J provides a runtimeprocess based simulation engine thatcan be used to map port entities to softwareentities and to simulate the coordinationof these process. In the computersimulation, the entities use the ContractNet protocol to coordinate tasks. Thisprotocol is used because of its ability todistribute tasks and self-organize a groupof agents [2]. The protocol is suitablesince our model describes tasks that canbe characterized as hierarchical in natureand are well-defined. The Contract Netprotocol implies that one agent will takethe role of a “manager”, which initiates ajob to be performed by one or more otheragents. The job may require that a numberof participating agents respond with aproposal and the manager will accept aproposal and confirm it to a selectedagent and reject the other proposals.This protocol seems to closely reflect theoperational decisions that are made bythe actual workers in the CT, especiallywhen a foreman will communicate viaradio with drivers and QC operators.The system that we have modeled for anautomated CT using C-AGVs is a singleship that is docked along a quay withcontainer stacks but with two differenttypes of AGV systems. We have followeda general simulation process as describedby Law and Kelton [10] andtherefore we are testing a prototype withreal data. We have focused on modelingthe operations that involves the QCs andthe AGVs that transfer containers betweenthe quay and stacks.3.2 Entities in the ModelThe entities that are modeled are thefollowing:• Ship: contains the containers.• Quay cranes: used to unloadand load the containers.• AGVs (C-AGVs or AGVs): usedto transport container, from/toQC from/to a container stack.• Buffer: Pick-up and drop-offarea behind the QC that temporarilystores containers on cassettesor AGVs.• Cassettes: A set of cassettesbound to a QC.• ContainersThe CT equipment in the simulation arepartly modeled as agents, e.g., QCs, Bufferand the AGVs (C-AGVs, and AGVs).Ships and cassettes are modeled as objectsin the simulator. The agents, whichare considered to be reactive, make theirdecisions, based the state of the entity itcorresponds to and on the information inthe messages they receive from otheragents. The agents' goals are only implicitlyrepresented by the rules describingtheir behavior. The major advantage inusing reactive agents, according toWooldridge [17]; “is that overall behavioremerges from the interactions of thecomponent behaviors when the agent is3

placed in its environment”. In the nextsection we define the agents in the systemin more detail.3.3 Agents in the modelThis section defines the agents of thesystem along with their attributes, functionsand messages.3.3.1 Quay Crane (QC) AgentThe QC is responsible for the unloadingand loading the containers from and tothe ship. The number of QCs serving aship is specified by the user.AttributesEach QC has:• a unique name• a set of AGVs (C-AGVs orAGVs) assigned to it• a set of cassettes (for the C-AGVs)• a buffer area where its cassettesor AGVs can be placed for loading/unloadingcontainers• a container handling time for unloadinga container, this may bedifferent for each container.Moreover, the following are recorded foreach QC:• the number of containers unloadedfrom the ship and loadedto the ship• the time it has been working (notincluding the idle time).Functions• In a real CT the QC will have varyingcontainer handling times,which we simulate by a computergenerated random number froma uniform distribution in a rangespecified by the user.• When unloading, the QC will unloada container from the ship ifthere is a cassette (or AGV) withfree space in the buffer area. Ifnot, it waits until there is oneavailable.• When there are containers to beloaded available in the bufferarea, the QC will load one containerat a time to the ship.• The QC finishes working whenthere are no containers to be unloadedor loaded for the ship.3.3.2 Buffer AgentA buffer agent is assigned to a QCAttributes• Unique name.• Assigned to a QC.FunctionsA buffer is assigned to a specific QC andthe buffer is responsible for allocatingfree AGVs to either pick-up a containerand move it or move empty (to pick-upcontainer(s) at another location). Thebuffer is also responsible for the QC tostop unloading if there is no cassetteavailable or the cassette is full and tostop loading if there is no containersavailable on cassettes or on AGVs. TheBuffer agent will communicate with theAGVs and assign an AGV that is free topick up a cassette (for the C-AGV). Whenusing AGVs, the buffer agent tries to finda free AGV and assign a container to thatAGV. If no AGV is free then it waits untilan AGV is free at the buffer. Pseudocodedescribing the unloading and dispatchingstrategy of the buffer agent forthe cassette-based system is given below:WHILE still containers to unload DOIF cassette available has capacityfor more containers THENAsk QC to unload a container andplace it on cassetteAsk all AGVs for their statusWait for status reportsIF AGV idle THENAsk AGV to fetch loaded casetteELSEIF cassette is full THENAsk QC to stop unloadingREPEATAsk all AGVs for their statusWait for status reportsUNTIL at least one AGV is IdleAsk the idle AGV to fetch theloaded cassetteENDWHILE

3.3.3 AGV Agent (C-AGV and AGV)Each QC has a number of AGVs assignedto it. This value is specified by theuser before the start of the experiment.An AGV is responsible to transport containersbetween a QC Buffer and containerstacks.AttributesEach AGV has:• a unique name• a state (“free” or “busy”)• a cycle time for transportinga cassette/containerfrom thebuffer to the stack andreturn back to the buffer.Or vice versa during theloading phase. The cycletime can be different foreach move.Moreover, the following are recorded foreach AGV:• the number of containerstransported to/from thestack• the time it has beenworking (not includingthe idle time).Functions• An AGV is responsible for transportinga container/cassette thatis assigned to it by the QC.• In a real CT the AGV will havevarying transport times, which wesimulate by a computer generatedrandom number using a uniformmethod chosen in a rangespecified by the user.4. Experiment DescriptionThe input parameters are stored in a textfile from which the simulator reads theparameters. The output of the simulationis a set of files implemented from theDESMO-J library, which contains informationof all events taken place duringthe simulation. A trace file contains theoverall performance of each QC, AGVand cassette involved in the simulation.The performance criteria that are usedfor evaluating and comparing the CTtransport systems are:• Service Time: is the time it takesto complete the unload/load operationsfor a ship, also known inthe maritime industry as “turnaroundtime”.• Utilization Rate: Active time /Service Time (Active time + Idletime). Active time is the time apiece of CT equipment is busy,such as moving a container fromthe QC to a stack and Idle time isthe time that it is not working.The utilization rate for the followingCT equipment is recorded:QC, AGV and Cassette.• Throughput: Avg. number of containershandled per hour duringService time for: QC, AGV andCassette• Total Cost: Equipment cost forserving a ship is calculated in thefollowing ways (OPEX = operatingcost per hour for a unit of CTequipment):– QC cost: number of QCsx OPEX for QC x ServiceTime.– AGV cost: number ofAGVs x OPEX for AGV xService Time.– Cassette cost: number ofcassettes x OPEX forCassette x Service Time.– Total Cost: QC costs+AGV costs + Cassettecosts4.1 Scenario SettingsThe scenario settings were based upondata provided by industrial partners. Theresults from the simulations are based onaverage values, which necessitates thata number of simulation trials are made inorder to get a valid estimation. The cycletimes used in the simulation have beendetermined from prior analysis in whichthe stack distances and maximumspeeds of the AGVs were tested. Weused an approximation method to calculatethe minimum number of simulationruns required in order to obtain resultsfrom a simulator with small enough statisticalerrors. The approximation method ispresented in Law and Kelton [10] andapplied by Vis et al. [14] in vehicle allocationat a container terminal. In this me-5

thod, data from a limited number of replications(trial sample) is used to approximatethe required minimum number ofreplications in the actual experiment (denotedas i) such that the relative error issmaller than γ (0 < γ

• C-AGV: 60 Skr/hr• Cassette: 0,50 Skr/hrTable 3: Total terminal equipment operatingcosts in Swedish kroner (Skr)Total QC operating costs in (Skr)No. of CassettesNo.AGV0 + 1 + 2 + 3 + 41 27 241 27 376 26 743 25 657 25 0692 19 412 19 096 13 937 13 304 12 9873 12 625 12 444 11 720 11 494 11 4944 11 765 11 675 11 584 11 267 11 1325 11 403 11 403 11 358 11 222 11 132Total AGV operating costs1 1 294 1 815 1 773 1 701 1 6622 1 845 2 532 1 848 1 764 1 7223 1 800 2 475 2 331 2 286 2 2864 2 236 3 096 3 072 2 988 2 9525 2 709 3 780 3 765 3 720 3 690Total cassette operating costs1 n.a. 15 30 43 552 n.a. 21 31 44 573 n.a. 21 39 57 764 n.a. 26 51 75 985 n.a. 32 63 93 123The operating costs for both AGV systemsincrease as more AGVs are used,however, C-AGVs operating costs decreaseas more cassettes are deployedto work with each C-AGV. There exist atrade-off in that additional cassettes increasethe operating costs associated forcassettes, hence we need to considerthe total costs.Table 4. Total operating costs for servinga ship in Swedish kroner (Skr).No.AGVNo. of Cassettes0 + 1 + 2 + 3 + 41 28 535 29 206 28 545 27 400 26 7862 21 257 21 649 15 816 15 112 14 7663 14 424 14 939 14 090 13 837 13 8564 14 001 14 796 14 707 14 330 14 1825 14 112 15 215 15 186 15 035 14 945In comparing the total operating costs inTable 4, the addition of AGVs and cassettesleads to lower costs until three C-AGVs and three cassettes are employed.The total costs when adding furtherequipment increases, i.e., the timegained does not compensate for the extracosts. A possible choice for assigningCT equipment in the scenario studiedwould be the use of three C-AGVs withthree cassettes each.6 Discussion and Future WorkThe cassette-based system (C-AGV)posses some advantages in that it canact as a ‘floating’ buffer, meaning that itcan allow the QCs to keep unloading/loadingand not having to wait for anAGV to be available. Waiting time is lowerfor the QCs and thus they are obtainingbetter utilization rates. The initial resultsfrom the prototype AGV simulatorprovide some interesting observationsuseful for determining the number ofunits to allocate for serving a ship. Thesimulation experiments conducted arealso creating further questions that requiremore investigation. Naturally thereis a trade-off to be expected betweenservice time and the costs for purchasingand operating equipment.Compared to traditional simulation approaches,the agent-based modeling approachcan provide better granularity inmodeling the entities and having themcommunicate and coordinate amongstother entities. In Elder [5], he mentionsthe advantages of simulation methodsover queuing techniques and we alsofind it difficult with traditional queuingmodels to model problems such as theAGV dispatching strategies (in this studycarried out by the Buffer agent) that areused when applying cassette-based systems.The simulator has provided usmuch insight in the relationships amongvarious terminal equipment types thatcan be used in container terminal operations.For future work we would study othermodels for container handling time by theQC and the cycle time for AGVs (e.g.,including stoppages caused by malfunctioningequipment, etc. which affects theproductivity at a real CT). Another topicworth studying is different dispatchingstrategies for allocating containers toAGVs and cassettes. We plan to extendthe model in several directions, e.g., includingthe unloading/loading taking7

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