Magnetic Suspension Railroad Systems

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MMagnetic Suspension Railroad SystemsHigh-speed and urban magnetic levitation (maglev)vehicles were developed as alternatives to conventionalrailroad systems. For distances up to 500 km, high-speed maglev trains have been proven t o be supe rior toairplanes with regard to energy consumption and tra-velling time. Mechanical and electrical modularizationand decentralized hierarchical control architectures ofthe levitation and guidance systems (LGS) are out-standing characteristics of robust operational vehicles.Further key technologies are linear induction motors(LIM) for propulsion and linear induction generators(LIG ) for on-board power generation.

Between 1960 and 1970, the necessity of trackedhigh-speed ground transportation systems for pas-

senger travel as well as transportation of goods wasrecognized worldwide (Hochleistungs-SchnellbahnStudiengesellschaft 1971). Th e challenge w as to find aground-based system that could better com pete with airtransportation and which, in order to be accepted bythe public, had to be efficient and economic on the o nehand (see Fig. 1) and fast and attractive on the other(Rogg 1988). Different levitation concepts were investi-gated, but only two technologies have been pursued:electrodynamic suspension (ED S) and electromagneticsuspension (EM S).

1 . Reasons for Using MaglevTh e dynamic load o n guideway and undercarriage fora given guideway increases quadratically with the

a i r p l a n e (STOL)7600 -

l o n g d i s t a n c e )

I - u s

I I I

0 200 400 6 800 loo0

Speed k m h-')

Figure I

Comparison of primary energy consumption indifferent transportation systems

velocity of the vehicle. For wheel-on-rail systems,the necessary effort with respect to accuracy and

bedding of rails and guideway increases rapidly withvelocity, resulting in costly consequences for guidewayconstruction.

Equally, the op erating expenses for maintenance andrepair du e to wear and sagging increase.

In ad dition , the noise level produced by a high-speedwheel-on-rail system is hardly acceptable in terms ofenvironmental considerations (Gottzein 1984).

For magnetic guidance and levitation systems,contrary to wheel-on-rail vehicles, loads are evenlydistributed and, therefore, extension gaps within theguideway are admissible (s ee Fig. 2).

For contactless guidance and levitation systems, con-tactless and friction-independent propulsion and brakesystems will be used, as well as contactless powertransfer.

Thus, new economically and ecologically advan-tageous solutions to guideway construction may befoun d. A n elevated concrete-or steel-beam guide-way represents a low-cost solution and the stiffguidance and levitation system allows steep banks,allowing small curve radii without any reduction of ridecomfort. T he possibility of combining vertical a nd hori-zontal radii and to realize steeper gradients results in amuch higher flexibility in tracing. With better adap-tation of the guideway to the terrain, it is possible toreduce the number of costly tunnels and bridges

required for a given route.

2. Maglev Techniques

In the EMS technique, attractive forces are generatedby controlled electromagnets on board the vehicleagainst ferromag netic rails on th e guideway.

EDS uses superconductive helium-cooled coil mag-nets which generate extremely high magnetic field

f r ic t ion- independentgu idanc e and lev i t a t i on forces

a b)

Figure 2Characteristics of (a) wheel-on-rail and (b) maglevsystems

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Magnetic Suspension Railroad Systems

Table1Maglev vehicles developed for high-speed transportation between 1971 and 1989

Central NumberCountry Year or of Short- or

Company/ of of Speed LGS decentral persons long-statorVehicle institution origin origin (km h-') principle LGS carried LIM Remarks

Prinzipfahrzeug MBB Germany 1971TR 02 KM Germany 1971ROMAG TM Rohr Ind. USA 1971/1973

ML 100TR 04

Test vehicle

EET 01HMB 2

KOMET

HSST 01KOMET M

ML 500HSST 02TR 05

ML 500 R

JNR JapanKM GermanyUniversity UK

Seimens GermanyTH Germany

MBB Germany

of Sussex

JAL JapanMBB Germany

JNR JapanJAL JapanTransrapid GermanyEMS

JNR JaDan

197219731974

19741974

1975

19751976

197719781979

1979

90164

6 0 b

253.2

23036

401.3

307.8341

512l00b75

201

EMSEMSEMS

EDSaEMSEMS

EDSEMS

EMS

EMSEMS

EDSEMSEMS

EDS

centralcentralcentral

centralcentralcentral

centralcentral

central

centraldecentrala

centralcentraldecentral

central

148

12/6

4204

24

7 ( + a68

shortshortshort

shortshortshort

shortlong

short

longshortlong

lone

integrated levitationguidance andpropulsion

iron-coredlong-stator LIM

propelled byhot-waterrockets

propelled by

hot-waterrockets

first operationalmaglev transportsystem, inserviceat the IVA,Hamburg

MLU 001-01/02/03 JNR Japan 1980/1981/ 400 EDS 32 long 400 km h-' in1982 1 section

operationTR 06 Transrapid Germany 1983 412.6 EMS decentral 192 ( 4) long full-scale

ve hideEMS high-speed

HSST 03 JAL Japan 1984 30b EMS 45 shortTR 07 TH Germany 1988 EMS decentral long under test

Source: Rossberg (1983)

a Proof of concept b Nominal speed

forces. Th e coils induce curren ts in supp ort rails, caus-ing repulsive support or guidance forces, lifting thevehicle when t he take-off speed is exceeded.

3 EMS Vehicle Development and History

The idea of magnetic levitation dates back to thebeginning of th e th entie th century. Proof of feasibilitywas given by Kemper (1935). Roughly from 1965onwards, development of maglev for vehicles began inmany industrialized nations (Gottzein and Rogg 1984).Test vehicles were developed in Germany, USA,

USSR, Japan, the UK and Romania, but onlyGermany, Japan and the UK continued developmentof operational transportation systems, as shown inTables 1 and 2.

In Germany, electromagnetic suspension systemsbased on controlled electromagnets have been underdevelopment since 1970. Th e feasibility of magneticallylevitated vehicles was proved with the Prinzipfahrzeug

in 1971. This ma de use of a centralized control concept(degree of freedom control) which proved an a dequ atesolution for heave, sway, pitch, yaw and roll in theshort, compact test vehicle. The linear induction motorchosen was a short-stator asynchronous motor, thestator on board the vehicle and an aluminium part ofthe guideway representing the squirrel cage. Problemsof power transmission at high speed and the loss ofpayload, due t o heavy stators and power conditioningunits on board led to long-stator solutions with linearinduction motors for propulsion and linear inductiongenerators for power transmission.

236




In this version, th e stator winding is mounted on th eguideway. The levitation magnets on board the vehiclerepresent the “rotor.” Proof of the feasibility of thisconcept was established by the vehicle HMB 2. Toprove the high-speed capability of EMS a linear high-speed test stand was built to operate the test vehicle

KOMET. On the 1 200 m track in Manching nearMunich, the KO M ET , propelled by hot-water rockets,reached 401.3 km h-’. T he limitating factor w as thelength of the guideway, not the magnetic levitation.

For reasons of mechanical tolerances, mass andpower budget, guideway disturbances and redundancyand safety aspects, a decentralized hierarchical controlconcept has been developed for the KO M ET M (modu-lar), and subsequently, modular levitation andguidance systems have been chosen for all full-scalevehicles (see Fig. 3). This allows for decentralizationinto autonomous control functions and electrical andmechanical separation of hardware modules. By elasticsuspension of individual magnets, the masses to beaccelerated are made smaller, the influence of themechanical tolerances of vehicle and guideway on themagnet gap is nearly eliminated, excitation of structuralmodes by the single-magnet controllers is nearlyimpossible and malfunction of a single magnet has noinfluence on vehicle operation.

4. Basic Subsystems of EMS Vehicles

The basic subsystems of EMS vehicles are describedher e based on th e full-scale vehicle Transrapid 06 (TR06),which corresponds to an operational vehicle for

high-speed transportation between cities and airports.T h e T R 06 consists of two sections (see Fig. 4). Ea chcabin is carried by four magnet frames. The secondarysuspension between cabin and magnet frames consists

Table 2

Maglev vehicles dev eloped for local traffic between 1971 and 1989

of pairs of pneumatic springs. The magnet framesthemselves are interconnected by joints.

Each of the four magnet frames of one section carriesfour levitation and th ree o r four guidance magnets oneach side.

The autonomous subsystems consisting of a single

magnet with individual suspension and single-magnetcontroller is called a “m agnetic wheel” (G ottze in 1984).The vehicle is propelled by a synchronous long-statormotor, which also performs the braking.

For safety, the TR 06 is equipped with an auton-omous m echanical em ergency braking system and elas-tically suspended emergency skids for levitation andguidance.

The guideway is a continuously elevated single-ordouble-beam design, partly steel , partly con crete witha clearance height of at least 4.5 m.

The magnetic wheels are the core of the modularlevitation and guidance system of t h e T R 06. Eachsection is equipped with 60 autonomous magneticwheels. Th e components of a magnetic wheel are:

(a) an electromagnet with individual magnet suspen-sion consisting of springs and d amp ers,

(b) the magnet current drivers,

(c) a gap sensor and accelerometer,

(d) a single-magnet controller, and

(e) command and telemetry interfaces and monitoringequipment.

For the T R 06, an overall availability of 99.5% duringan 18h service period is achieved by functional redun-dancy of magnetic w heels which allows for switching offmagnets without interruption t o service.

CentralCountry Year or Number of Short- or

Company1 of of Spee d LGS &central persons long-statorVehicle institution origin origin (km h-’) principle LGS carried LIM Remarks

Transurban 03 KM Germany 1974 73 EMS central 12 short

EML 50 JNR Japan 1975 40 EM S central shortBirmingham Peop le UK 1984 54 EMS central 8 short only maglev

Maglev Mover transport systemGroup in p ublic service

(Birminghamairport)a

GmbH 70-110 magnet1M40-1lM70-2 Magnetbahn Germany 197611980 47 40-53 long combined permanent

wheel-levitation andguidance system

Source: Rossberg (1983)a Proof of concept

237




Table 3Specificationsof the TR 06

C )

I Sect ion

.12m

I Sec t ion

24 m

Figure 3Maglev vehicles with modular levitation and guidancesystem, drawn to scale: (a) Kom et M , 1976; (b)TR 05,1979; and (c) TR 06, 1983/1984

5. StQte of the Art and Prospects

Development of maglev transportation systems is stillbeing continued by Japan, the UK and Germany.

In Ja pan , competitive development of E MS a n d E D Sis performed by Japanese airlines (JAL ) and Japanes enational railroads (JNR), respectively. The unmannedtest vehicle ML 500 holds the world record for EDSvehicles with a speed of 512 km h-' . Th e concurrentEM S development is a three-section vehicle ML U 001,designed for travel at 22 0km h-' and 32 persons(Masada 1988).

Birmingham Maglev is the only maglev transpor-

tation system in public service. It connects BirminghamInternational airport and the railroad station.Design and development of t he T R 06 in Germany

was based on ten years of experience in maglev and, inparticular, o n test experience with eight vehicles, eachdesigned to be a milestone towards an o perational high-spee d train for public service.

The main data of t he T R 06 are summarized in Table3. Tests of t he T R 06 are performed on the TransrapidVersuchanlage Emsland (TVE) (see Fig. 5). T h e TVE

guideway has all the featu res of a n opera tiona l maglev

Figure 4TR 06 on the TVE

238

Length (m) 54.2

Height (m) 4.2

Empty weight (t) 102.4

Total weight (t) 122.4

Number of seats 192

Width (m) 3.7

Payload (t)20.0

guideway, such as high-speed straights, reversing loopsand switches.

Based on the TR 06 experience, the status of high-speed maglev train development in Germany has beensumm arized by Rogg 1988) as follows:

(a) functioning of contact-free levitation on all guide-way elements over the full speed range of up to500 km h-' with full-scale ope ration al vehicles;

(b) high travelling comfort;

(c) excellent acceleration and braking ability as aresult of acceleration sections of the long-statormo tor an d low vehicle weight (e.g., 0.5 t per seat);

(d) reasonable investment cost as a result of low guide-way loads and flexible routing parameters (curverad ii of 4OOO m at 400 km h-I; gradient up to 10 );

Neubargen

wippingen

Renken berge

N

Lw Site plan

z

Meppen 0 1 2 3 4 5 6

km

Figure 5Site plan of the TVE (SW is a high-speed switch, LWa low-sp eed switch and V Z the test center)



Maintenance and Reliability of Traffic Control Systems

favorable operating costs as a result of levitation/guidance/propulsion and braking systems free

from wear;

low-energy consumption (e.g., 60W h per seatkilometer a t 400 km h-' from a substation);

high safety stand ards du e to derail-safe vehicles;

relatively low noise level, especially when e nterin gdensely populated urban areas at reduced speed s(e.g. , maximum noise level at 200 km h- ' mea-sured at 25 m distance is 77 dB (A)) and no mech-anical vibrations as a result of contact-free ridetechnique;

minimal land requirem ents (2000 m2 km-I); and

totally negligible stray electromagnetic fields.

From 1983 onward, the TR 06 has been operated on theTVE. U p t o mid-1985, it carried more than 20000peo ple over a distance of mo re than 40 000 km. It holds

the present world record for manned maglev trains of412.6 km h-'. From present results there is no reason tobelieve that the limitations of the magnetic LGS havebeen reached (Menden et al. 1989).

See also: High-speed Railroad: M odelling and Simulation

BibliographyGottzein E 1984 Das Magnetische R a d als autonome

Functionseinheit modularer Trag- und Fiihrsysteme fiirMagnetbahnen. Fortschr. Ber., VDI Z. Reihe 8 8

Gottzein E, Rogg D 1984 Status of high speed M AG LEVtrain development in the FRG. Report No. C 407/84.Institute of Mechanical Engineers, London

Hochleistungs-Schnellbahn Studiengesellschaft 1971 Bun -desministerium fiir Verkehr, Studie iiber ein Hoch-leistungsschnellverkehrssystem. HSB Studiengesell-schaft, Mu nich, Germany

Kemper H 1935 Trassierung mit raderlosen Fahrzeugen.German Patent No. 644,303

Masada E 1988 EMS-technique and state of applicationsinJapan. 10th Int. Conf. Magnetically Levitated Systems.TUV Rheinland, Cologne, Germany

Menden W, Mayer W J , Rogg D 1989 State of develop-ment and future prospects of the MAGLEV-systemsTransrapid, M-Bahn and Starlim. 11th Int. Conf.

Magnetically Levitated Systems. IEE , TokyoRogg D 1988 Th e deve lopm ent of high-speed magnetic

levitation systems in the Federal Republic of Germany,

objectives and present state, 10th Int. Con Mag-netically Levitated Systems. TUV Rheinland, Cologne,Germany

Rogg D, Schulz H 1978 Systementscheidung bei derMagnetschwebe technik. ETR Eisenbahntech. Rundsch.

Rossberg R R 1983 Radlos in die Zukunft. Orell-Fiissli,

E. Gottze in[MBB Deutsche Aerospace , Munich , Germany]

W. C r a m e r[Fachhochschule , Rosenhe im, Germany ]

11, 721-8

Zurich

Maintenance and Reliability of TrafficControl SystemsIn th e traffic control sect or, notwithstanding th e safetyproblems, the reliability of the systems is seldomtackled in a rigorous way. Taking as an example the

areaof

traffic light control, few products are ch aracter-ized by precise data on reliability; that is, by theaverage rates of various kinds of failures.

To highlight this failure, it is sufficient to make acomparison betw een th e designing and maintaining ofautomation and control systems in manufacturingplants and in traffic control systems. No private enter-prise would accept systems satisfying the m aintenanceand reliability requirements of most control systemsinstalled on road intersections.

This failure is obviously less pronou nced in t he indus-trialized coun tries, but th e gap betwee n manufacturingautomation and automation in traffic management islarge everywhere. Consequently, higher costs burden

both communities and transport authorities. In fact, thelow reliability of the components used, their missingharmonization, the lack of systems for diagnosing an dautomatically signalling the failures and the weaknessof the design with respect t o maintenan ce problems a reall leading to higher failure rates and more difficultmaintenance with longer repair times. The presence of

faults increases the danger and causes delays to thedetr iment of the community. Excessive failure ratesand poo r maintainability of the system bring about anincrease of maintenance costs which are borne by thecompanies o r authorities involved.

Th ere ar e, however, signs pointing towards a change

of direction. T he first large traffic contr ol system which,according to the literature, has been designed to con-form to modern reliability criteria is in Tokyo (hose1976). Other systems have followed, among them anexperimental project in Turin (D onati et al. 1984) an d,especially worthy of mention , work in Londo n, where afault control center (FCC) h as been implemented and isnow in operation (Oastler 1985, Oastler and Palmer1989).

Th e urban traffic light contro l system is considered asa representative example and reference case for thisarticle. Design concepts and the technology of thesubsystems evolved rapidly during t he 1980s; neverthe-less, the recommended guidelines and the conclusionsreached should be applied both to existing and operat-ing systems and to projects still in the developmentstage.

1 Types of Failures

In a traffic contro l system all kinds of failures can occur;an attem pt t o classify such failures on the basis of whatcan break down would have no significance here.Indeed, this would be a classification peculiar to aspecific comm ercial prod uct. Changing th e product a nd

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Maintenance and Reliability of TrafJic Control Systems

consequently the hardware structure of the systemmeans th at the possible failures will also change.

This article examines the failures from the point ofview of their effects rather tha n with reference to whatbreaks do wn ; that is, the problem is tackled as the usersees it, following an approach that aims to formulate

the gen eral specifications for a supply orde r.In ord er to establish a hierarchy for appreciating thelevel of operation of the system a nd also for determin-ing the m aintenance r equi rem ents , the following classi-fication into four types of failures is given.

I Dangerous Failures

The class of failures that cause dangers to the usersincludes

(a) opposing greens (a conflict situation existing whengreen lights are given simultaneously to two direc-tions of traffic flow so that collisions can occur),

(b) missing red (the red light is missing for on e trafficstream so that the user moving in that directionmight think tha t the traffic light is blacked o ut),

(c) missing am ber (this situation is particularly dan-gerous whenever the “removal phase” does notenvisage an “all-red’’ interval of time),

(d) cycle sto p on specific colors, and

(e) when one green time lasts less than the givenminimum or extends for more than the (given)maximum period.

The presence of failed lamps (either red or amber)

should not be considered as dangerous failure if theuser ha s the possibility of seeing at least an othe r trafficlight, clearly visible, belonging to the same group ofsignals.

1.2 Apparent Failures

T he failures that cause evident malfunctioning are on esthat, being associated with evidence of faults, havenegative influence on t he traffic and increase the wait-ing times without originating an immediate danger forthe users. They are, therefo re, failures of all kinds inone of the following situations:

(a) signals blacked out o n all approaches, and

(b) flashing am ber.

These faulty states can be caused by a wide variety offailures which can hit th e system at different points.

For example, a protection device is normally usedagainst potentially da nge rous faulty states which, whenrecognizing the faulty s tate , sets the system in flashingamber or in all blacked out. As a consequence, thefaulty state considered here is frequently due to afailure that potentially belongs to the category des-cribed in Sect. 1.1 (e.g., the burning out of the lamps).

It can also happen that the protection device isreleased due t o disturbances. In this case, the device forputting the system into operation need only be reset.

Further examples include o ther failures of the elec-tronics of the controller, insulation failures of theelectric supply of controller and lamps, and short cir-

cuits in the lamp holders of the traffic lights.

I .3 Degradation

Some failures do not originate conditions dangerous tothe users o r evidence of faults, but they can still cause adegradation of the control system performance an d anincrease of waiting times at the intersections for allvehicles (or for so me categories of them). These fail-ures are typical of modern computerized controlsystems which, eq uipp ed with self-diagnosis, recognizethe faulty state and actuate a consistent strategy ofdegraded control.

For example, this category includes faults with sen-

sors detecting both private and public vehicles that thecontrol system is able to diagnose and, therefore, to

actuate a new control strategy that excludes the use ofthe sensors that ar e faulty. S hould, however, the self-diagnosis not be provided or be inefficient, then thefailure of one sensor for a traffic light that is trafficactuated or has the task of assigning priority to publicvehicles is a dang erou s failure (see S ect. 1.1).

Another example is any fault concerning the centralcontrol system or the communications system betweencenter and intersections. Obviously, these kinds of

failures are recognized by the peripheral system, whichautomatically sets itself into a local degrade d operatin g

condition.

1.4 Failure with no Effect

For some com ponents, active or standby redunda ncieshave been provided so that th e user neither perceivesthe fault nor bears any consequences.

This class of faults regards only the maintenanceservice which must a ctuat e special diagnosis me thod s inorder to recognize the failure and act as soon aspossible so as to restore t he redundancy conditions.

2. Indices of OperationConsidering the smallest functional unit of the controlsystem, which typically corresponds t o an intersection,the possible states of a traffic light control system aresubdivided into fo ur classes:

(a) in operation (state 0),

(b) dangerous failure (state l ,

(c) app aren t failure (state 2), and

(d) degradation failure (state 3).

240



Maintenance and Reliability of Tr afj c Control Systems

Sta te s 1 , 2 and 3 correspond to the existence of failuresconnected with the categories given in Sects. 1.1, 1.2and 1.3, respectively. The state of the whole controlsystem is then described by the combination of thestates of its elementary units (i.e., of the individualintersections).

Th e probability th at eac h unit of th e system is in anyone of the four s tates describes the operational level of

the system itself.Given an observation period, each probability corre-

sponds to the expected value of the ratio of the timeduring which the system remains in th e state to th e totaltime considered.

Den oting the probabilities of states 0, 1, 2 and 3 byPo, P , , P2 and P 3, respectively, then

Po P , P 2 + P 3= 1 (1)

Po is the index of the operational level; PI,P2 and P3 areindices of unavailability.

These probabilities represent indices summarizingthe characteristics of the plant reliability, of the main-tainability and of the efficiency of the maintenanceservice.

The reliability is expressed by the failure rate or,

mo re usually, by its inverse value-the mean timebetween failures (MTB F). W ith reference to the classi-fication of failures introduced and to the functionalunit, three different values MTBF1, MTBF? andMTBF, are employed, corresponding to the averagetime interval between occurrences of two differentfailures that bring th e system into the faulty state s 1, 2

and 3, respectively.The maintainability, like the reliability, is a charac-

teristic dependent on the plant design. It is evaluatedthrough the mean time to repair (MTTR); that is, thetotal corrective maintenance repair time required tocomplete a number of corrective maintenance oper-ations, divided by the number of operations. To beprecise, a specific MTTR corresponds to each specificfailure. It is convenient to introduce three averagevalues MTTR1, MTT R2 and M TTR,, defined as theaverage time required to repair the “average” faultsthat bring the system into the faulty s tates 1, 2 and 3,

respectively.

Both the fault-locating time and the true repair timecontr ibute to the M TTR. To reduce the M’ITR values,it is impo rtant to adop t automatic techniques of failurediagnosis, as well as to use modularization of thesubsystems: the fault re pair phase “in field” will then b ereduced to the replacement of the module located bythe self-diagnosis device.

The efficiency of the maintenance service can beexpressed through two indices: the m ean time t o signal(MTTS) and the mean time to intervene (MTTI). TheMTT S can b e significantly reduced by using automaticfault diagnosis and signalling systems connected withthe FCC . How ever, in the ext rem e case of a fault signal

due to user complaints, the value of the M’ITScan become very large. The MTTI depends essen-tially on the size of the maintenance service (as thenumber of maintenance crews is relative to the plantsize), on th e num ber of hours per day of operation ofthis service, on w hether o r not the service is operating

during holidays and, not of least importance, on thecommunications system between crews and the F CC .The sum of M’ITR, M TTS and MTT I gives the mea n

down time (MDT); that is, the average time the plantremains in a faulty state after the failure has occurred.Obviously, with regard to the three types of faultspossible, three values can be defined: MDT1, MDT2and M DT3.

With small acceptable approximations, the followingrelations hold:

MDTlP --

I MTBFl

MDT2P2=-

MTBFi

MDT3P ---MTBF3

(3)

4)

Th e indices Po, P , , P 2 and P 3 are the most appropri-ate ones for expressing the requirements of thecommunity. It is the duty of the designer and ofthe operator to adapt the various items that affect thesystem operation in orde r to minimize the costs under

the same final performance.

3 . Indicative Values of Indices

3.1 Dangerous Failures

The index P1expresses the probability that the systemis in a condition that is dangerous for road users. Itshould be mad e as small as the community requires andis willing to pay for .

Rodgers (1971) identified for each operation a risklevel of as the boundary betwee n acceptable andunacceptable, and asserted that, for risk levels belowlo-’, the US public tends to ignore the risk and doesnot approve of additional expenditure to reduce therisk further.

Th e op eration considered is the crossing of an inter-section when traffic signals and rules are observed.Th ere is the risk that t he signal given by the traffic lightcontrol system is wrong and there is the question ofwhat would originate the dang er of a serious accident.

Considering the minor increase in costs that wouldnowadays make it possible to reach a high level of

safety, the limits given by Ro dge rs and proposed againby Hulscher (1977) should be considered as a level of

risk no longer acceptable.

241



Maintenance and Reliability of TrafJic Control Systems

Th e average user in a m edium to large town for thehome-work-home journ ey crosses abo ut 30 intersec-tions equipped with traffic lights every day. A risk of

means that roughly one event per year will beexperienced. Even considering that crossing an inter-section when its control system is in a d ang erou s faulty

state does not necessarily mean involvement in anaccident (though it does mean taking a serious risk), itmust be concluded that one eve nt per year per user istoo high.

With a suggested figure of or in mind,estimation of a more reasonable limit will be con-sidered. P rotection against dan gero us failures isobtained through a hardware card which detects thefaulty state and sets the system into one of the notdangerous faulty states: flashing amber or all blackedout. This card must be designed with an adequateMT BF, typically higher than for the o ther parts of theequipment.

If as usual, no safeguarding of the protection is

operative, it must be assumed that a protection failureis not signalled and is therefore not repaired until thedangerous failure against which the protection isdesigned to act occurs. It is only the app eara nce of thisfailure that leads to th e discovery that th e protection isnot working. The MTBFl of the appearance of adangerous failure is therefore given by adding theMTBF of the failure occurrence to the MTBF of theprotection card.

Assuming that a dangerous failure should be quicklysignalled, at least at the first accident and that theintervention of a police officer should be sufficient forswitching the controller into the not dangerous, fault

state 2, the M DT l in urban areas can be estimated to beabout 1h. Thus

M T D

PIMTBFlz-

To be conservative, MTBFl can be replaced with theMTBF of the protection card. Assuming PI= itfollows that MTBF= 10000h. Given the simple logicstructure of the card , this can be o btained at very lowcost, without any kind of component sifting or carddebugging. An MTBF = 100 h, which reduces P1 o

can be obtained without t oo m uch difficulty, stillat low costs, by conveniently sifting the componentsand by breaking in the card in order to eliminatepossible failures caused when assembling it.

Without going deeper into the problem, it seemsreasonable to conclude that modern traffic controlsystems should have values of PInot greater thanwith as a goal for the not too distant futur e.

However, it must be emphasized that traffic lightcontrol systems (some of them built in the 1980s) arestill operating in several countries without any specialprotection against state 1 failures. In these conditions,

it is likely that they run at PI level between andloT4.The most recent realizations do not, however,have PIvalues larger than

3.2 Apparent Failures

The range of acceptable values for P 2 depend s essen-tially on t he location of the traffic control system and onthe importance of the traffic conditions that the trafficcontrol has t o manage.

It should be no ted tha t, in low-traffic conditions, th eblackout of the light controller does not cause trafficdelays but raises the probability of accidents at theintersection, whereas a traffic increase causes a delaythat grows quickly with traffic intensity, up t o com pleteblockage of the intersection for medium-high traffic(i.e., for a normal situation in a large town).

This failure, therefo re, causes an increase of costs forthe public administration, as they are compelled tokeep a group of signalmen in the service of the local

police, ready to interv ene as soon as a failure becomesknown, the number of signalmen required being pro-portional to the average num ber of failures.

Finally, if the failure rate goes above levels con-sidered re asonable, th e public image of the administ-ration will suffer.

To find a compromise value of the index P2 , thedam age due to the faulty state must be com pared withthe higher costs to be faced in reducing P2 .

A n accurate cost-benefit comparison valid for allcountries cannot be performed, since the economicevaluation of the dam age du e to a failure gives resultsthat are different from country to country, both

because of the different labo r costs and because of thedifferent ways of perceiving som e types of costs.

Nevertheless, at least for what concerns the mostindustrialized countries, th e trade-off point is app aren t.Given the present cost of electronics (low comparedwith the cost of labor), it is convenient to reduce P2towards the smallest possible value within the range ofapplication of normal commercial products. That is,without requiring special products which in small serieswould be too expensive, convenience points towardshigher-quality commercial products. At the same time,it is suitable to exert a continuous pressure on thecontrol system manufacturers to obtain increasinglybetter products com parable to the presen t developmentof electronics in ot her sectors.

Traffic light controllers with a M TB F of 15 h areon the market. It is conceivable that values of about25000 h can be reached without an increase in costs.

There are highly reliable traffic lights protectedagainst water and dust (IP 55). The reliability of theelectric subsystem depends o n the care taken during therealization rather than on th e cost of the materials. Theweak point is in the lamps which, besides having anonnegligible infancy failure ra te, h ave an average lifeof the order of 6000 h.

The problem of lam p failures is often tackled thr oug h

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Maintenance and Reliability of Traffic Control Systems

a planned replacement at a specific time during th eirlife. This approach, without being a perfect solution,offers considerable advan tages bu t is rather expensive.

The way to a radical solution seems to be activeredundancy of the lights seen by each driver, togetherwith local automatic monitoring of burnt-out lamps,

repeated at the FCC.The maintenance services are not efficient every-where: for large towns an MDT of 24 h can be ac-cepted. In London, a central FCC operates through anactive maintenance service continuously. The mosturgent faults are repaired within 5 h (Blase 1979,Oastler 1985, Oastler and Palmer 1989).

It is thought, however, that a threshold of con-venience for the costs exists: the M DT should not bereduced below certain limits, especially in medium andsmall towns.

With reference to modern systems installed in largetowns, typical values of P2 are in the range 10-2-10-3.In fact, in a modern design (all causes considered anddamage through accidents included), it is possible toremain at 1-2 failures per year (Donati et al. 1986). A nM D T of 24h gives P2 within the acceptable range.Values close to lo-’ are considered to be very good.

3.3 Degradation

Very few existing systems use sophisticated controlstrategies that, as a consequence of failures, can oper-ate in a deg raded way. In most cases, degraded strat-egies ar e not provided; the refor e, the failure of even anonessential comp one nt puts the system in failure state1 or 2.

Wh ere degraded opera ting conditions are admitted,then failure state 3 exists. Since this state does notapp ear externally, it is perceived with difficulty by th euser. E ven when noticing a slowing down of the traffic,the user thinks of a traffic increase rather than adegradation of t he strategies. T he first visual effects ofa degradation of the control strategies are an increasein car density and extension of queues, the samephenomena that appear whenever a traffic increaseoccurs.

This is one of the reasons why, in modern controlsystems of hierarchical type, the failure rate of thefailure state 3 category is always fairly high. Th e oth er

reason is the need for keeping a check on expend itures.Th e failure rate of the compu ters mostly used for thesepurposes (minicomputers or microcomputers) is fairlyhigh. Obviously, the rate changes, depending on thesystem configuration, but M TB F values a roun d 1000 hare normal. T o these must be ad ded stops for ordinarymaintenance, stops due to bugs in the software, stopscaused by disturbances on the telecomm unications linesand stops because of faulty operation by the FC C. Asthis is the beginning of the use of sophisticated systems(such as those operating in several European towns-each one representing an innovative solution-whichact uat e coordin ated control of public-private traffic,

with priority given to public vehicles), the conditions ofoperation are rather difficult since public adminis-trations a re no t willing to s pend too much for innova-tive systems.

In ad dition , in keeping down t he costs, the reliabilityof the c entra l system is given up, w hich would a t least

require a “hot” (active) redundancy of all elementssubject to frequent failures.However, for economic reasons, the MDT is never

too small because even if the failure is immediatelymade known, outside firms are entrusted with therepairing contracts and this does not always ensure aprompt intervention. Consequently, the index P3 is inthe range 5-10%.

As an example, the SIS system operating in Turinwill be considered (G entile and M auro 1988). This hasthe purpose of ensuring the regularity of the surfacepublic trans port system interacting with t he traffic lightcontrol Prog etto Torino. On average, it does not suc-ceed in keeping mo re than 90-95% of the assigned

vehicles under control, because of a wide number offailures of on-board equipment and because of distur-bances in telecomm unications.

It is to be expected tha t the value of P3 will decreaseconsiderably in the near future, as soon as the nextgeneration of control systems are in opera tion.

See also: Road Traffic Monitoring Equipment; Safety ofRoad Traffic

Bibliography

Blase J H 1979 Com puter aids to large-scale traffic signal

maintenance. Traffic Eng. Control 20 341-7Donati F, Margaria A, Piglione M C 1986 I1 sistemasemaforico sperimentale “Progetto Torino”. Rend.Convegno Nazionale AN IP LA .

Donati F, Mauro V, Roncolini G, Vallauri M 1984 Ahierarc hical-d ecentr alized traffic light contro l system.The first realization “Progetto T orino”. Prepr. IFAC 9thWorld Congr., Vol. 2. IFAC, Laxenburg, Austria, pp.

Gentile P, Mauro V 1988 Experience on S . I . S . , Torinopublic transport operation aid system. Conf. AutomaticVehicle Location.

Hulscher F R 1977 Reliability aspects of road trafficcontrol signals. Traffic Eng. Control 16, 98-102

h o s e H 1976 Road-traffic control with p articula r referen ce

to the Tokyo traffic control and surveillance system.Proc. IEEE. 64, 028-39

Oastler K H S 1985 Maintenance of traffic signals inLondon. Traffic Eng. Control 26, 104-8

Oastler K H S , Palmer R F 1989 Revised arrangem ents forthe maintenance of traffic signals in London. TrafficEng. Control 30, 114-20

Rodgers W P 1971 Inlroduction to Safety SystemEngineering. Wiley, New York

1-6

F. D o n a t i a n d M . Vallauri[Poli tecnico di Torino ,

Turin, I ta ly]

243



Marine Fleet Planning and Scheduling


Th e increase in efficiency of transport system function-ing is closely connected to the problem of improvingand automating control processes in transportationsystems. Under these conditions, the task of efficientplanning when the influence of the environment is

essential plays an important role in these problems.The solution to these problems is very important fortransportation systems, which operate under complexnonstationary conditions.

The computer-aided system is designed for efficientplanning and scheduling of marine fleets allowing fortheir interaction with railroad networks. T he functionalbasis of the system is the task of short-range planningand scheduling of marine fleets, formulated as theproblem of obtaining optimal coordinated solutions forvarious periods of planning. The complex of modelswhich describe trans port ation processes an d controllingeffects with necessary detail h as been developed using

methods of nonlinear optimization, dynamic program-ming and dialog procedures.Th e system referred to in this article was designed for

the Vanino-Kholmsk ferry in the USSR. Ho wev er, themethods, models and program support may be used tosolve oth er practical problems that can be described bythese models (e.g., the short-range planning of otherkinds of marine fleets).

1 . Statement of Requirements

The objects to be controlled in the computer-aidedsystem for planning and scheduling of marine fleet

(CASPLAS) are ships as a specific part of the techno-logical system of combined freight transportationbetween ports.

In solving the task, it is necessary to have infor-mation a bout the ship dislocation o n line, as well as thestate of the ports (the quantity of freight), forecasts offreight flows to the ports and the hydrometeorologicalconditions. Irregularity of freight flow to ports andinaccuracy of transportation are also taken intoaccount.

The functional basis of the system is the task ofplanning and scheduling marine fleet (TPSMF), whichconsists of determining op timal plans and schedules forevery ship in a curren t plan period (mon th or quarter)and values of plan indexes describing the work of theferry being preassigned (pre set) for this schedule.

The effectiveness of a solution can be assessed byvarious criteria. The profit of the fleet for the currentplan period (CPP) has been taken as a main criterionfor the effectiveness of short-range planning, whilethe main resources may be considered as a constantquality.

The essential limitations of the TPSMF are thelimited capacity of by-port railroad terminals, tracksand port tracks, the limited carrying capacities ofmarine fleets, an d th e necessity for realization of CPP

indexes. A lso, the technological limitations ar e import-ant, such as the limited num ber of moorages in ports,the necessity for changing the working team schedule,the limited capacities of by-port railroad tracks andport tracks, t he limited capacities of ships, th e speciali-zation of ships to tra nspo rt specific kinds of freight, the

individual characters of ships and the account of cur-rent ships and state of the ports, the effect of hydro-meteorological conditions, the repair schedule of shipsand so on.

In general, t he TP SM F is determin ed as a problem ofprofit maximization from transportation of freight tak-ing into account CPP plan realization, forecasting thearrival of cars to the port, hydrometeorological con-ditions, the state of the ships and ports and so on.

2. Computer-Aided Planning and Control

Great experience has been accumulated in the use ofcom puters to control marine transport (A rdonin 1982,

Cashman, 1983, Container News 1983, Schonknecht1983). T he development of information systems takesplace stage by stage, as a rule, by increasing service andfunctional opportunities, permitting the performanceof information-and-advisory and information-and-controlling systems on this basis. These systems areusually developed for only one kind of transport. Theother kinds of interactive transports are users of thesesystems Container News 1983).

Th e control processes for combined transpo rtation of

freight in transportation and technological systems bythe scheme “from door to door” require the partici-pants of these systems to exchange information and

work out coordinated plans using computers,communication means and data transmission (Etsch-maier 1986, Polyantsev and Kiselev 1987).

Consider the use of com puters for controlling ferrytransportation, for instance between the USSR andBulgaria (Ilyichevsk-Varna), and between the USSRand German y (Kleipeda-Mukran). T o control ships atthe Ilyichevsk-Varna ferry , the united dispatchingcenter has been developed and equipped with thenecessary communication means for exchanging infor-mation between control points in Bulgaria, on theOdessa railway lines, on-board the ferries and also inthe coastal ferry complexes. Th e com puter enables theoptimum plans for the realization of a nnu al volumes of

freight transported to be calculated as well as solvingthe problems of ship scheduling and optimizing thetimetable (Pritulsky an d Sukolenov 1986a, b, Polyant-sev and K iselev 1987).

Accumulated experience has allowed the beginningof at tem pts to solve more complicated problems such asthe development of the computer-aided system forinternational railroad-ferry transportation between theUSSR and Germany (Polyantsev and Kiselev 1987).The computer-aided system “Ferry” includes both rail-road and marine transportation and is characterized bythe various interactions between interactive systems.

244




Ferry also provides automatic support of the systemdatabase, forecast and control transportation pro-cesses, the estimation and analysis of ferry operation(including mutual calculations for transportation andservice), the official documentation of transportationprocesses and so on. The continuous character of the

transportation process and a high degree of variabilityof external factors, have resulted in the need to pro-duc e a complex system of constant efficient planning ofmarine transportation. This system is based on thesystematic inclusion of variations and additions intothe plans, with allowances for the p resent situation, theregular increase of plans for a certain period of t imeand the constant relationship of plans of various struc-tural units and plans with th e different planning periods(Leviy 1971, Av en et al. 1983). In this conne ction , it isof particular importance to work out methods andmodels that allow the automation of the processes of

continuous planning and control.Some scientists suggest an approach that facilitates

the process of composing the coordinated plans fordifferent periods of planning (Moiseenko 1978, Aven et

al. 1983, 1985). This approach has been considered asan example for th e united annual plan, w ith th e divisioninto quarters and the quarterly plan divided intomonths. Th e tasks of annual and quarterly planning areusually solved independently; this results in greatexpenditureof labour to coordinate these plans. Takinginto account the fact that th e initial information fo r thefirst qua rter is identical for an nual an d qua rterly plans,some authors suggest a model that allows both tasks tobe solved simultaneously. In the case when quarterlyplan indexes must be corrected in the intermediate

periods between corrections to the annual plan, themodel of qua rterly planning is used with flexible limi-tations to the annual plan. The suggested model repre-sents the task of large-scale linear programming. Inorder to solve it, decompositional methods based onlimitations are used. This approach is also used forsolving the TPSMF. However, the models, being thebasis of the TP SM F, differ from th e m odels suggestedby Moiseenko (1978) and Aven et al. (1983, 1985). Forthe TPSMF, the models are related to discrete rep-resentation of the transportation process, the necessityof forecast dynamics, difficulties in the formalization ofa 24 h period of planning, and criteria of optimization

that have situational character and depend on thecurrent situation in the transportation process.

3 . Formalization of the Problem

The dynamic nature of transportation processes, andthe randomness of external (load and truck arrival andhydrometeorological conditions) and internal (ship andport conditions) factors necessitate the TPSMF beingconsidered as a problem of multistage stochasticprogramming. However, making a numerical andanalytical decision is difficult because of the way theproblem is posed. If the role of accidental factors is

unimportant, the procedure of continuous planninggives an ap prox ima te optimum decision (Pervozvanskyand Gitsgory 1979).

The procedure of continuous planning takes place asfollows. The period of quarter planning is divided intosteps (Pervozvansky and Gitsgory 1979, Aven et al.

1985) which must presume com plete information aboutthe state of the system by the beginning of planning,when the realization of the efficient decision isdemanded. Then, the optimization of the multistepmodel is made with allowance for current data andfactor forecasting for the future. Because of the inac-curacies involved in forecasting the state, which isreached by the beginning of the next step, it willcontain discrepancies compared with the calculationand the procedure of efficient-planning decision mak-ing is repeated using current da ta.

Therefore, the TPSMF is regarded as a task to bedetermined by dynamic programming, which utilizesforecasts of accidental factors. These forecasts shouldbe slightly different from the realization of accidentalfactors. The accuracy of forecasts for the ferry isachieved by gathering information from the railroadnetwork ab out the location of trains a nd cars travellingto the p ort, coordinating forecasts of car arrivals at th eseaport between the ship company and the railroad andthe collection and processing of statistical data aboutthe effects of hydrometeorological conditions on fleetwork.

Regarding the essential functional task, solved byCAS PLAS, write t= 1 , T for numbers of the stages ofplanning which are in the current plan period. Theduration of the t-stages, measured in some unit of tim e,

can be different.For the TPSMF, a number of stages t = (1, o}

with a 24h period can be distinguished from a numberof stages t2={tl , . T whose period is, as a rule,longer. T he TPSM F can be formulated as:

T

T

r=l

yrEY r c 3, t = m (5)

n : , ~ n ~ , f o r t , ~ t 2 ,= 1 , 2 (6)

where t is a number of the stages of planning.Equation (1) reflects the necessity of obtaining the

maximum economic effect from the system. Equa tion(2) reflects the limitation of resources for the wholeprocess. Equa tion (3) is an equation of dynamic processstate. Equation 4) reflects the state limitations of the

245




process, including the conditions of fulfillment of theplan index at the stages t. Equation (5) reflects thelimitations on the choice of stage control.

The peculiarity of this mode is the presence of theoperator of state clustering A,, hence, the process atdifferent stages t is characterized by th e state vector x

changing in different R ;. This mode allows submodelsof the different details describing the process to beunited. For the ferry mod e, Eqns. (1)-(6) are a combi-nation of two submo dels: the prob lem of time planning(t Et2 nd the problem of scheduling (t E tl). Theseproblems involve different details of the processdescription. Combining them into one model (Eqns.(1)-(6)) ensures that none of th e system qu alities is lostand that all of the important processes are described inthe necessary detail.

T he problem h as a high dimension an d may be solvedby decompositional approaches. T he dynam ic planningme thod is quit e effective.

Consider a set of functions

where s, is the state vector of the system at the end ofstage t, whose components are the variables x andvariables which correspond to th e general process limitsgiven by Eqn. (2). F,(sr-J is the maximal profit fromthe beginning of stage t to the end of stage T.

Following th e optimization principle, recu rrent equa -tions can be written:

Fr sr-J= max [ tcVr) + F ~ + ~ ( G Y S ~ - ~ ? Y ~ ) ) I >Y , ~ Q ~ - I )

= l , T-1 (7)Fi sr-l)= max r c V r ) > t=T (8)

Y, E Q s,- 1)

where Q S , - ~ )s the limit for the equa tion ofy , at stage t

under the condition that at the beginning of stage t thesystem was at state

From the solution of these equations with originalstate So, values bl} re determined, running into amaximum of efficiency being th e solution of Eqn. (1).

In the stages t E z I , calculations are made based onscheduling problems and in the stage t e t 2 they aremade based on time-planning problems. Various meth-ods are used to avoid large-sized difficulties. Anapproximately optimal decision bp} is determined andthen additional limitations are introduced to b } nd{sl}; he area of - } is reduced (Bellman and Dreyfus1962, Moiseev 1974).

To determine by}, the method of modifiedLagrange's function turn s out to be effective (Gill et al.

1981), using the model of the calendar-planning prob-lem. Th e model given by Eq ns. (1)-(6) is used bo th inautomatic and dialog modes. While solving the task,the user has various opportunites: using solutionsobtained during previous calculations; giving and esti-mating any solution of the pro blem ; to correct problem

limitations; and giving various m odel inter preta tions bysetting a calculation on the d atabases, corresponding t ovarious modifications of the TPSM F problem.

4. Time Planning

Let the forecasts of freight arrival a,,he hydrometeor-ological forecasts p,, the state of system ( x ro )? n d t h eachieved values of the CPP indexes {P?} with J = 1,J beknown a t the end of stage to . Control variables are th emean intensity of ship operation, the quality of runsbetween the ports and the loading of the ships withfreight at stage t.

The problem of time planning can be formulated as:

under the condition of fulfillment of the CPP {Pi},ithj e J :

and fulfillment of balance and technological limitations:

x i = x i - a: Y;(y,), xo= x,A ; E t2;E I (11)

O s x f ~ x - , E I (12)

O s y f s y ; k ( p r ) , k c K (13)

where PR represents profit, I represents incomes, Erepresents expenditure and f , Y and y are knownfunctions.

Th e application of the m odel in the process of solvingEqns. (7) and (8), leads to a modification of theefficiency function and problem indexes according tothe demands of Eqns. (7) and (8).

5. Scheduling Marine Fleet

The problem of scheduling marine fleet consists ofdetermining the start and en d of service for every ship

in port at stage t E r , according to the recurrent Eq ns.(7)-(8). This eq uation is:

Fr(sr-1)= max [ r(rJ + F r+ ~ (G (~ i -~ r i ) ) l14)~ , E Q , ( s , - I ) i er l

where r, is the schedule at stage t E t l ,(r,) is the profitas a function of ship schedule and Q r s , - l ) representsthe limitations in schedule.

An examination of Eqn . (14) as a discrete-programming problem allows the known methods ofdiscrete programming to be applied in order to solve it

246



Marine Fleet Phnning and Scheduling

(Mikhalevich and Kuska 1983, Aven et al. 1985, Avenand Alexeychuk 1986).

These m ethods ar e used for designing and examiningheuristic procedures to search for optimal and semi-optimal schedules. Scheduling process can be regardedas simulations of different kinds of ship operation

taking account of the limitation of Q,(S,-,). Decisionmaking in conflict situations is underta ken by me ans ofdifferent heuristic procedures, handling priorities ofships, their economic indexes, the states of the ports,and the hydrometeorological conditions.

The oriented graph of dynamic-system transitionfrom o ne state to anoth er is the Qasis for solving thisproblem. Ferry operation is divided into 12 stagesincluding detention of ships, loading, unloading, w harfwaiting, waiting for departure permission, and themotion of ships.

At every simulation step the following may bedetermined:

(a) the working index for period t,,

(b) the number of the ship state,

(c) the end of service time for this state,

(d) the recommended and maximum speeds,

(e) the quantity of freight for every ship in the cu rrentperiod, and

( f ) the maximum capacity of the ships.

The programme support includes the dialog with th emodel. The simulation ends with the scheduling of

ships for the period tE zl, the determination of thesystem clustered state s,-,) at the end of stage to and

the calculation of optimizational function variables.

6. Essential Functions of CASPLAS

CASPLAS performs the following main functions(Artynov and Vasilchenko 1988).

(a) Managing a database in an interactive mode-interactive and batch updating is permitted.

(b) Efficient plann ing of marin e fleet-the systempermits the calculation of the values of planindexes for a given number of steps comprising aCPP . CA SPL AS supports both an on-line mode of

decision of the TPSMF and an interactive mode,which allows the dispatcher’s experience to beutilized.

(c) Scheduling a fleet timetable-this is th e decisionstep at which additional technological constraints(e.g., ship location) are taken into account. A

main mode used in the scheduling of a fleet time-table is an interactive mode. The system obtainsoptimal plans and timetables (if they exist), evalu-ates them numerically and gives a message if aTPSMF cannot be solved as a whole for a selectedtimetable.

(d) Supporting an interactive operation mode-forcontrolling the solution process in an interactivemode, the system uses a dialog monitor whichsupports a hierarchical dialog scenario. For pro-cessing large bodies of information, new tools fordisplaying and manipulating information in tabu lar

form, as well as tools for printing desired recordforms, have been developed.

(e) The dialog monitor is used for controlling theprocess of calculation in a dialog mode-the con-troller can solve the necessary task and, with thehelp of commands, look through and correct theinformation tables with the subsequent usage ofcorrect information in the course of subroutinework. Every subroutine realizes, as a rule, one or

several simple functions of CASPLAS.

(f) Th e com puter outputs the results to the printer.

Th e system was realized in PL ll language.

6.1 Structure of C A S P L A S

The structure of CASPLAS is shown in Fig. 1. Theessential components of the system and the controlcommunication between components are reflected inthe figure.

Block 1.1 is the reading of the instruction. Thisprogramme reads the command from the display.

Block 1.2 produces the syntactical analysis of thecomm and. A comm and is correct, if it is in the vocabul-ary of commands and macrocommands. Th e analysis ofmacrocommands and instruction operands is realized.

Block 1.3 is the monitor. A subroutine governs thesystem op eration.The second-level programmes process first-level

commands, but they may include a regime of work formaking the second level of dialog. In this case, theconsumer will be in the env ironme nt of the processor ofthe first-level commands; hence, only the subroutineenvironment will be within reach. The essential com-pon ents of th e second are th e following.

Block 2.1 is the TPSMF. Th e programs of this blockallow the T PSMF t o be solved in both the automaticand th e dialog regimes. Also, in the environment of theTPSMF, there is the possibility of working with thedatabase for the printing of results, and so on.

Block 2.2 contains the program s for working with thedatabase. They allow the user, to correct, in dialogmod e, the information in the system d atabase.

Block 2.3 contains the programs that simplify theoperation in dialog mod e. The se subroutines, as a rule,are used in the environment of other second-leveloperations for the realization of the dialog. Also, theblock 2.3 programs allow some system param eters to becorrected.

Block 2.4 contains the printing programs. Theseprograms allow the information in the buffer to beprinted as tables with th e help of printing param eters.

247



Marine Fleet Phnning and Scheduling

tlock 1 1reading of command

Block 1.2syntoctical onalysis

of command

Block 1.3

the system monitor

1 fI ’

Block 2.1 Block 2 .2

program of workwith datobose

CASPLASuigure I

Control structure of C ASP LAS

Block I

hime planningp K q of marine

forecasting fleets for the

Block 2atabaseI‘ scheduling of

marine fleets

,‘ I

4 t

I ferrv (obiect of control)

Figure2Functional structure of C ASPL AS

6.2 Functional Structure

T he functional struc ture of t he system is given in Fig. 2.The essential components of CA SPL AS, which solvesthe TPSMF and information relations between the

components, are shown in the figure. The informationenvironment is formed by the forecasting system andthe dynamic-information tracking system for the trans-portation system on-board the ferry. The forecastingsystem forecasts the arrival time of the ships into theports, as well as forecasting the hydrometeorologicalconditions. These forecasts cover different time per-iods, depending on the stage of solution of the TPSMF.Th e information system of ferry (see Fig. 2) reflects thecurrent st ate of ports, ships and the fulfillment of plansand so on. Details of this information also depend onthe solution stage.

Using the methods of decomposition for determining

Block 2 4table

pr int ing

Block 2 3display and c orre ctio n

of toblesin diolog

the optimal plans and schedule (Bellman’s optimalprinciple), two blocks in the structure of continuousplanning of the system must be distinguished: timeplanning marine fleets and scheduling marine fleets.Such decomposition of the original problem not onlymakes its solution possible, but allows each block t o beinterpreted m ore clearly.

Block 1, tim e planning m arine fleets solves the o pti-mization problem of plan determination of the indexes

in stages of planning interval and evaluation of the CPPfor the time remaining using clustered data.

The problem is solved on the basis of forecastinginformation con cerning the arrival of freight and c ars tothe ports, the current state of the ports resources (i.e.,ships) taking into account repair plans, the detention of

ships and hydrometeorological conditions for the per-iod t, he clustered forecasts of hydrometeorologicalconditions and the arrival of freight in the periodremaining.

The optimal plans for the transportation of freight,with details of the planning stages and suitable criteriaare passed to block 2. Block 2 solves the problem of

scheduling the movements of a ship for a perio d, takingaccount of the original ship location, the state of theports an d oth er technological limitations to their work.The capacity of the transportation levels of the plans,profits, an d exploitation expenses ar e calculated for thechosen schedule.

This scheduling process is realized every day. Itallows the deviation of the real working indexes fromthose planned t o be taken into account and corrected,as well as coordinating the resolution of problems indifferent planning intervals (days, months or quarters)and coordinating the decisions of interacting transportsystems.

248




Table 6Analysis of solutions for ports

Stage Initial Cars Cars Resultingnumber state arriving departing state Limitations

Port 1

stage 01 120 170 210 80 300stage 02 80 250 210 120 300

stage 01 150 130 210 70 400

stage02 70 330 210 190 400

Port 2

Models and methods that allow the problem to be

solved in the dialog regime have been worked out. A

peculiarity of these models and methods is the coordi-

nating of solutions obtained using different details and

durations. The exploitation of the system has con-

firmed both its expendiency and efficiency. At present,

CASPLAS is designed for computers such as the

IBM360 and the IBM370. The development of thesystem is proceeding in the direction of personal

computers, and automated communication with ships,

ports and users of transportation systems. In order to

expand the opportunities of the system and increase its

efficiency, it is intended that the mechanism of infor-

mation exchange between interacting t ransport systems

and coordinated decision making on the efficient

control of transportation proceses be worked out .

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management information system for transportationindustry. Mini and Microcomputers and Applications,Proc. ISMM Int. Symp., Paris

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Aven 0 I, Alexeychuk A Ye 1986 Interactive computer-ized system for dynamic scheduling in transportationapplications. Prep. 5th IFAC Int. Conf. ContainerTransport Systems. International Federation ofAutomatic Control, Dusseldorf, Germany, pp. 97-100

Aven I, Alexeychuk A Ye, Lovetsky S Ye 1983Computer-aided system of continuous planning of

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Optimization of Transport Flows. Nauka, Moscow (inRussian)

Bellman R, Dreyfus S 1962 Applied DynamicProgramming. Princeton University Press, Princeton,NJ

Cashman J P 1983 Worldwide shipping informationsystem. Int. Association of Ports and Harbors, 13th Int.Con IAPH, Tokyo pp. 28-38

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Etschmaier M M 1986 Operational planning and control

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Gill P Ye, Murray W, Wright M 1981 PracticalOptimization. Academic Press, New York

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Polyantsev Yu D, Kiselev A V 1987 The improvement ofcontrol for transport-Technological systems by means

of computers. Express Information, Mar. Transp.Organ. Control 3, 1-13 (in Russian)

Pritulsky V S, Sukolenov A Ye 1986a Ferry operationcontrol at the international ferry Ilyichevsk-Varna.Express Information, Mar. Transp. Organ. Control 5, 1-12 (in Russian)

Pritulsky V S, Sukolenov A Ye 1986b The main principlesand methods of the annual, quarterly and monthlyplanning of the international ferry Ilyichevsk-Varna.Express Information, Mar. Transp. Organ. Control 5,12-18 (in Russian)

Schonknecht K 1983 Automatische Informations Systemein Seehafen. Ein Uberblick zum Entwicklungsstand inkapitalisticschen Seehafen. D D R V e r k. 16(7), 205-8

A. P. Artynov and A. I . Vasilchenko[USSR Academy of Sciences, Vladivostok, USSR]

Marine Propulsion Plants: ControlMarine propulsion machinery has increased in com-

plexity as steam driven plant has gradually given way to

diesel or gas turbine powered systems. This change

towards greater complexity has also been accompanied

by an increase in both the difficulty and the number of

control problems that need to be resolved. The

demands that are placed on the control equipment have

also increased. The use of digital computer controlsystems has further contributed to system complexity

by virtue of the opportunities for advanced control and

monitoring systems that need to be implemented if the

computer is to be used in a cost effective and efficient

way.

1 . Gas Turbine Powered M arine PropulsionPlantsGas turbine powered marine propulsion plants are

typically found in modern warships, container vessels

and some other types of large ships. The gas turbine is

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Marine Propulsion Plants: Con trol

normally a version of an established and proven aeroengine (e.g., a Rolls-Royce Olympus gas turbineengine) which has been marinized, that is, made suit-able for operation in the salt-laden highly corrosiveenvironment in which ships have to operate.Marinization involves modifying the engine to run on

diesel oil instead of the k erosene burnt in a ero engines,minimization of the effects of corrosion by the appli-cation of corrosive-resistant coatings, modifications tothe prop erties and types of metals used in the construc-tion of the engine co mpo nents, and the installation offilters in the air intake to the engine and cleaningsystems in th e fuel supply.

Gas turbine designs can vary, but basically a typicalengine consists of a gas generator that produces a gasflow which drives a power turb ine. T he gas generato rcomprises a compressor, a combustion chamber and acompressor turbine which is coupled t o the compressor.Air is drawn into the gas generator, passing through thecompressor, the combustion chamber and the com-

pressor turbine, and then the high-temperature outp utflow from the gas generator passes into the powerturbine, from where it is discharged into the atmos-phere via the exhaust system.

The output from the power turbine is high speed,thus the gas turbine is connected to th e propeller and itsshafting via a reduction gearbox. Th e actual connectionof the power turbine t o the gearbox is mad e through atorque tube and a self-synchronizing clutch, thepurpose of which is to enable the engine to be con-nected t o and disconnected from the rest of the propul-sion machinery.

The alternative to a gas turbine engine is a marine

diesel. T he main advantages of the gas turb ine over thediesel are its power output-weight and power outpu t-bulk ratios. T he main disadvantage of the gas turbin e isits high fuel consumption relative to the diesel engine.A gas turbine such as the Rolls-Royce Olympus has anoutput power of the order of 20MW, thus providingthe ship with a high-speed capability. H ow ever , the fuelconsumption of such a high-power en gine at par t load isvery poor. Typically, a modern naval vessel of 4000 tneeds about 36MW to achieve a full speed of about15m s-’. However, for about 80 of the life of a ship,a cruising speed of about 9 m s-’ will not be exceeded,which coincides with a power requirement of about6 M W . From typical fuel consumption vs part-loadperformance characteristics of a high-power gas tur-bine, it can be demonstrated that a doubling of fuelconsumption can occur when operating at such partloads.

For reasons of fuel economy it is normal to use high-power gas turbines only for high speed or “sprint”maneuvering, and to use a second, more economical,low-power gas turbine running at full power for“cruise” maneuvering. A Rolls-Royce Tyne gas turbi newhich has a power out put of about 4MW can be used inthis role. This results in an installation known as acombined gas or gas (COGOG) propulsion-plant

arrangem ent, where either the sprint engine is in use or

the cruise engine is operating. Th e two types of enginescannot however be used simultaneously to drive thepropeller.

In some cases, a marine diesel is used for the cruiseengine instead of the low-power gas turbine. This

results in a combined diesel or gas turbine (CODOG)propulsion-plant arrangem ent. Sometimes, two marinediesels are installed instead of one, and while thediesels canno t be used t o drive the system at the sametime as the high-power gas turbine, it is normallypossible to run either on e diesel alone o r both dieselssimultaneously. It is also not uncommon in naval ves-sels to find twin shaft propulsion systems, where eachshaft set consists of a sprint gas turbine, a cruise gasturbine engine (or diesel engines), a reduction gearb ox,clutches and a propeller and associated shafting. It isalso possible to have twin screw propulsion driven by acommon engine system.

Ga s turbines (and marine diesels) a re unidirectional

prime movers, thus it is necessary to provide somemeans of generating reverse thrust (e.g., to stop theship quickly or for harbor maneuvering). Reversethrust can be achieved either by the use of a reversinggearbox or by a controllable pitch propeller whichallows the pitch angle to be reversed. A reversinggearbox is more complex and bulkier than a unidirec-tional gearbox. O n the othe r hand, a controllable pitchpropeller is less efficient than a fixed pitch propeller,but controllable pitch propellers improve the maneu-verability of the ship an d, for this reason , controllablepitch propellers tend to be fitted to naval ships andother types of vessels such as car ferries where maneu-

verability is important.The remainder of this article focuses on gas turbine

powered controllable pitch propeller propulsionsystems and the associated control problems and avail-able solutions.

2. Modelling and Simulation

Digital computer simulation now plays an importantrole in the design of ship propulsion systems. It is usedin initial feasibility design studies, right through subse-quent stages of design, to sea trials and beyond.Typically, computer simulation is used (a) to evaluateship and propulsion-plant perform ance, (b) as an aid topropulsion-plant selection and development, (c) forcontrol-system functional design and development, (d)for failure analysis, a nd ( e) as an aid to control-systemtesting and commissioning.

The primary advantage of computer simulationis that it gives designers a flexible tool to predictsteady-state and dynamic performance. It assistsdesigners to establish and optimize gas turbine fuel-valve opening and closing times, propeller pitch strok-ing times, ship maneuvering performance and mainmachinery torque, thrust and rotational speeds under awide range of maneuvering conditions.

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