The Efficiency of Double-Decked Elevators811172/... · 2015-05-11 · Abstract The purpose of this...

DEGREE PROJECT, IN , FIRST LEVELCOMPUTER SCIENCE

STOCKHOLM, SWEDEN 2015

The Efficiency of Double-DeckedElevators

A COMPARISON BETWEEN SINGLE-DECKEDAND DOUBLE-DECKED ELEVATORS IN ASKYSCRAPER ENVIRONMENT

WILLIAM SCHRÖDER AND JACK SHABO

KTH ROYAL INSTITUTE OF TECHNOLOGY

CSC SCHOOL

The Efficiency of Double-Decked ElevatorsA comparison between single-decked and double-decked elevators in a skyscraper

environment

Jack Shabo, William Schröder

Degree Project in Computer Science, First Cycle - DD143XSchool of computer science and communications

Royal Institute of Technology

Supervisor: Vahid MosavatExaminator: Örjan Ekeberg

AbstractThe purpose of this study was to investigate the efficiency of double-deckedelevators in a skyscraper environment. This was done by simulating elevatoractivity using different elevator types and elevator control algorithms. Theresults gained from the simulation suggested that double-decked elevators alwaysprovide better performance over using regular single-decked elevators. Somecontrol algorithms proved to have up to ten times better efficiency compared toothers using double-decked elevators.

Sammanfattning

Syftet med denna rapport var att undersöka hur effektiva dubbeldäckade hissarär i skyskrapsmiljö. Detta gjordes med att genomföra en simulation över olikahisstyper och kontrollalgoritmer. Resultatet från simulationen indikerar attdubbeldäckade hissar alltid ger bättre prestanda jämfört med vanliga enkeldäck-ade hissar. Vissa kontrollalgoritmer visade sig vara upp till 10 gånger så effek-tivare än andra med dubbeldäckade hissar.

Contents1 Introduction 12 Terminology 23 Background 3

3.1 Environments, Elevators and Efficiency . . . . . . . . . . . . . . . 33.1.1 Low to High Rise Buildings . . . . . . . . . . . . . . . . . 33.1.2 Skyscrapers . . . . . . . . . . . . . . . . . . . . . . . . . . 33.1.3 Measuring Elevator Efficiency . . . . . . . . . . . . . . . . 53.1.4 Passenger Traffic and Time . . . . . . . . . . . . . . . . . 6

3.2 Classical Control Algorithms . . . . . . . . . . . . . . . . . . . . 63.2.1 Single Automatic Control . . . . . . . . . . . . . . . . . . 63.2.2 Collective Control . . . . . . . . . . . . . . . . . . . . . . 73.2.3 Zone Control . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.3 Advanced Control Algorithms . . . . . . . . . . . . . . . . . . . . 73.3.1 Search Control . . . . . . . . . . . . . . . . . . . . . . . . 83.3.2 Destination Dispatch . . . . . . . . . . . . . . . . . . . . . 83.3.3 Double-decked Control . . . . . . . . . . . . . . . . . . . . 9

4 Problem Definition 114.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . 114.2 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

5 Method 125.1 Traffic Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

5.1.1 Interfloor Traffic . . . . . . . . . . . . . . . . . . . . . . . 135.1.2 Up-peak Traffic . . . . . . . . . . . . . . . . . . . . . . . . 135.1.3 Down-peak Traffic . . . . . . . . . . . . . . . . . . . . . . 135.1.4 Lunch time Traffic . . . . . . . . . . . . . . . . . . . . . . 13

5.2 Elevator Implementations . . . . . . . . . . . . . . . . . . . . . . 145.3 Simulating the Control Algorithms . . . . . . . . . . . . . . . . . 14

5.3.1 Local Elevators . . . . . . . . . . . . . . . . . . . . . . . . 145.3.2 Shuttle Elevators . . . . . . . . . . . . . . . . . . . . . . . 15

5.4 System Specifications . . . . . . . . . . . . . . . . . . . . . . . . . 155.5 Simplifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165.6 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165.7 Validity through Testing . . . . . . . . . . . . . . . . . . . . . . . 17

6 Results 186.1 Single Automatic . . . . . . . . . . . . . . . . . . . . . . . . . . . 186.2 Single Automatic Zone . . . . . . . . . . . . . . . . . . . . . . . . 196.3 Single Automatic Search . . . . . . . . . . . . . . . . . . . . . . . 206.4 Selective Collective . . . . . . . . . . . . . . . . . . . . . . . . . . 216.5 Selective Collective Zone . . . . . . . . . . . . . . . . . . . . . . . 226.6 Selective Collective Search . . . . . . . . . . . . . . . . . . . . . . 236.7 Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

7 Discussion 277.1 Interpretations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277.2 Error Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

8 Conclusion 31References 32

1 IntroductionEffective elevator control is far more sophisticated than serving one passengerat a time. Instead, programmers face the challenge of assigning multiple pas-sengers to elevators within certain time constraints. In fact, when assigning npassengers to elevators in the general case, there are n! potential pick-up or-ders. Furthermore, if all possible pick-up orders are allowed and considered bya scheduler, the corresponding problem of planning an optimal route has provento be NP hard[1].

In order to cope with this problem, there are multiple heuristic and ap-proximate elevator control strategies each performing differently according to avariety of parameters. Using the most efficient strategies, or more precisely con-trol algorithms, is vital to impressive buildings. One such example would be thetallest building in the world: Burj Khalifa [2] which has 57 elevators, 167 floorsand hundreds of thousands of visitors per year[3]. However, not only the controlalgorithms constitute a varying factor in the problem of time optimization; anelevator consisting of several elevator cars stacked upon each other serve as analternative to the single-decked version. The skyscraper Burj Khalifa [2], aswell as other buildings of the same scale, use such varying elevators. Thus withrespect to the plethora of control algorithms the issue is raised of whether ornot more complex elevator types provide better time efficiency for buildings ofhigh altitude.

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2 TerminologyPick-up The state in which a passenger embarks on an elevator.

Drop-off The state in which a passenger disembarks from an elevator.

Control algorithm An algorithm allocating elevators in response to passen-ger calls, including the order in which pick-up and drop-off occur.

Elevator car A metallic box in which passengers ride the elevator. An eleva-tor consist of one or more of these boxes.

Single-decked An elevator consisting of one elevator car.

Double-decked An elevator consisting of two elevator cars stacked upon eachother.

Sky lobby A secondary lobby higher up in a building.

Shuttle elevator An elevator which only travel between lobbies, includingsky lobbies.

Local elevator An elevator that travel between a certain amount of floors,including only one lobby/sky lobby.

Waiting time The time a passenger has to wait before embarking on anelevator.

Travel time The time a passenger remains within an elevator before disem-barking.

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3 BackgroundThis section will go over the necessary background information needed to under-stand the remaining parts of the report. First, there will be an introduction ofdifferent buildings and the elevator systems used in these. Thereafter, differentcontrol algorithms for different types of buildings will be introduced. Finally,the control algorithms used in double-decked elevators will be presented.

3.1 Environments, Elevators and EfficiencyDifferent buildings require different types of elevator systems. A regular suburbresidential building of a few floors (typically no more than 20) tend to be servedby one or in some cases two elevators. At the same time there are buildingssuch as Burj Khalifa which have over 50 operating elevators. Obviously, thesevastly different buildings require some sort of categorization in consideration tothe elevator systems involved.

3.1.1 Low to High Rise Buildings

To begin with, consider regular buildings having less than 20 floors, a categorywhich represents the majority of buildings. The buildings of this category tendsto be referred to as low-, medium and high rise buildings depending on theamount of floors involved. Generally, they have a need for an elevator but tendsnot to have more than one or two shafts. Due to the simplicity of these systemsthey do not present a particularly challenging problem to operate [4].

3.1.2 Skyscrapers

In contrast, when buildings reach extreme heights problems with time con-straints arise due to the long travel distance of the elevators. This category ofbuildings, named more commonly as very tall buildings or skyscrapers [4], callsfor a more efficient solution. While more shafts and efficient control algorithmscould improve the situation, only a certain amount of space in the buildings canbe assigned to elevator shafts. For this reason, there are solutions that improvethe efficiency of each shaft by allowing multiple elevators to operate in eachshaft. The obvious problem with this would be that the elevators are in theway of each other. Today there are two different solutions to prevent this fromoccurring.

To begin with, there are double-decked elevators which solves the issueby simply attaching two elevator cars together so that two floors, one directlyabove the other, can be served simultaneously [6], see Image 3.1. This strategycan clearly increase performance since one shaft can serve two calls at the sametime. Consider for example two different groups of people making calls ontwo adjacent lower floors. A double-decked elevator would allow both thesegroups to embark on the two different elevator cars before traveling towardstheir destination floors. While it is true that passengers of one of the elevatorcars might have to wait for passengers boarding the other elevator car, such

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delay is negligible compared to the time it takes for a regular elevator to ascendwith one group of passengers and descend back again to pick up the next.

Image 3.1: Double-decked elevators [7]

Although double-decked elevators can as much as double the elevator per-formance, passengers traveling to high numbered floors are still likely to haveto stop on a number of intermediate floors to let other passengers on and off.To truly deal with this problem, having sky lobbies allow passengers to travelwith shuttle elevators directly to special floors, the sky lobbies, from which pas-sengers then switch elevator and use a local elevator to get to their respectivefinal destination floor [10]. As an example, Image 3.2 shows a schematic figureof a sky lobby implementation. In this, each sky lobby serve all floors from thesky lobby floor up to the floor below the next sky lobby using local elevators.Consequently, the local elevators for the different sky lobbies can share shaftssince each sky lobby as well as the main lobby only serve a separate range offloors. Each such interval can be treated as a separate sub-building which canbe operated as its own high-rise building. Therefore it can be concluded that asthe number of floors grows the amount of local elevator shafts does not need tobe increased, although extra shafts for shuttle elevators provides extra serviceto the different sky lobbies.

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Image 3.2: A schematic overview of the sky lobbies in the old World TradeCenter buildings [9]

Moreover, there is nothing that prevents double-decked elevators from beingused in conjunction with sky lobbies. One solution [11] involves using double-decked elevators both as shuttle and local elevators. This means that eachlobby includes two floors, one for boarding each elevator car in the double-decked elevators. Equally plausible is to have double-decked shuttle elevatorsthat takes the passengers to sky lobbies from where the local elevators are single-decked [4].

3.1.3 Measuring Elevator Efficiency

Elevator systems’ efficiency is measured on the performance of several param-eters, with the average waiting time(AWT) of passenger pick-up being themost common one to optimize [1]. The waiting time of a passenger is defined asthe time it takes for a passenger to make an elevator call and then board a givenelevator. With a set of passengers over a certain range of time, one can computethe waiting time of each of these passengers and then calculate an AWT. Thisnumber provide a rough measurement of how well an elevator system performs,in which low AWT is considered ’efficient’ while high AWT often is a sign ofbad scheduling [1].

In order to detect variances of passengers’ waiting times, which could besecluded when averaging, one can use average squared waiting time(ASWT)as an alternative performance parameter[1]. Much like AWT, the ASWT iscalculated by measuring the waiting time (w) of passengers and then squaringthe results according to the following formula: ASWT =

√w2

1 + w22 + ...+ w2

n

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3.1.4 Passenger Traffic and Time

The amount of passengers can vary from building to building depending on thebuilding’s layout and size. For example, consider the case where there is only ahandful of people in a building compared to where the building is almost full.In the first case there would be little or non-apparent practice of the elevatorswhile in the latter they would most likely be occupied without rest. As such, onecould categorize elevator traffic, based on passenger density, into low, medium orheavy traffic. These different markers of intensity are of interest when designingas well as selecting control algorithms.

Most control algorithms have a goal to minimize AWT, although othercontrol algorithms could aspire for minimizing average traveling time(ATT),the average time a passenger spend riding an elevator [14]. The first goal buildsupon how elevators are dispatched and controlled by the system to answer calls,while the latter depends more on how the elevator behaves in the traffic. In anenvironment with heavy traffic, elevators could get quite full and thus, in theaverage case, require more stops effectively increasing the ATT.

Not only does elevator traffic depend on the amount of passengers, but alsothe type of traffic. Passenger traffic can be divided into four different situations:uppeak traffic, lunchtime traffic, downpeak traffic and interfloor traffic [14].Uppeak traffic represents the arrival of passengers to the very first floor, thelobby, while on the contrary in downpeak traffic passengers travel from variousfloors down to the lobby. As one could guess, lunchtime traffic represents thehabit of passengers traveling down to the lobby and then up again in a very shorttime. Finally, passengers traveling between floors is the definition for interfloortraffic. [14]

3.2 Classical Control AlgorithmsElevator control began at a very primitive stage with human elevator opera-tors controlling levers and ropes. However, as the years progressed, so did theapproach for an automatic control system. Hence did the world’s early con-trol algorithms see the light of day. Even though these were invented in theearly stage of automatic elevator control, they are still widely used in modernelevators either in full or indirectly as enhanced versions. [12]

3.2.1 Single Automatic Control

One of the earliest developments of an automatic electrical control system wasthe Single Automatic operation strategy which was used for single-deckedelevators [12]. This elevator system consisted of single call buttons at each floor,and buttons on the operating panel inside the elevator car which representedeach floor destination. The elevator could be called from each floor but once inuse, it would not respond to calls from other floors until it had reached the givendestination floor. Due to this limitation, this strategy is more fit for exclusiveuse of elevators [12] such as for vehicle transportation in garages or sky lobbyshuttle elevators having only one other floor as a destination.

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3.2.2 Collective Control

In order to have a system which provides elevator service to multiple stops inone ride, thereby not providing exclusive use and effectively reducing the waitingtime of passengers, one can use the collective operation strategy. Generally, thisstrategy makes use of two landing call buttons for either upwards or downwardstravel instead of the aforementioned single button. By having passengers specifytheir desired direction on these landing call buttons, they would then be assignedto an elevator going in the specified direction. After a passenger embark on suchan elevator, any intermediate calls in the same direction results in the elevatorstopping and collecting the additional calls, thus serving calls along the way.When an elevator have answered all calls in one direction, it will reverse andanswer any upcoming calls in the opposite direction. Although this is the generalapproach for elevators using the collective strategy, most elevators used derivedversions. [12]

The Selective Collective operation is the most common version [13] whichbehaves as specified before with the addition that the elevator remembers calls inthe opposite direction and attempts to answer to them after a reversal. Shouldthere be no calls in the opposite direction, the elevator would by default parkat the last-served floor.

3.2.3 Zone Control

In order to cope with heavier passenger traffic as well as attempt to reduce theaverage passenger waiting time, the concept of zone control provides a simple,yet powerful approach. Zone control can be used as an extension for most multi-elevator algorithms and is all about distributing the labor in an elevator systemby dividing the given building into particular zones. Each elevator is assigned aparticular zone and answers calls primarily within it and, if sought, secondarilyoutside the zone.

A building can be zoned into stacked or interleaved zones [4]. Stackedzones consists of subsequent floors, meaning that (tall) buildings are divided intohorizontal layers. This is the ’recommended practice for office and institutionalbuildings’ [4](pg. 2) and thus of interest in the study. An interleaved zonedivision is defined as having elevators either serving odd or even floor, but sinceinterleaved zoning is common practice in public housing[4] such zoning will notbe further considered.

3.3 Advanced Control AlgorithmsModern elevators require better performance, in the form of better AWT andATT, than the classical control algorithms have to offer. As such, one can usesome more advanced control algorithms in which some take use of mathemat-ical models or computational concepts. These algorithms generally provide abetter distribution of elevators responding to calls, and hereby give better time-results[1]. While these advanced algorithms often provide optimized results,

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they require much more computational time. Such is the case of the Searchcontrol algorithm.

3.3.1 Search Control

Control algorithms which utilize the concept of searching through the systemand then deciding the best possible elevator assignments, by optimizing somecriteria such as AWT [15], could be an option as well in addition to classicalalgorithms. There are two different types of search-based strategies, greedyor non-greedy algorithms. A greedy search-based algorithm does not usuallyperform extensive search but rather assign hall calls to elevators when theyare first registered to the system [15] and then do not reconsider their choice.However while it requires little computational time, it does result in a lack inperformance [15].

A non-greedy search control algorithm require a lot of computational timesince it searches through all available allocations for a new passenger call to anyelevator. Although this approach does indeed give the most optimal assignment,it is not feasible for larger systems due to the computational requirement [15].However, if one were to use it on a smaller system such as a building divided bysky lobbies, preferably in an low-medium traffic environment, it could certainlybe worth the calculation time.

3.3.2 Destination Dispatch

In order for the elevator control algorithms to have as much information as pos-sible available when assigning elevators, it would have to know the destinationfloors for the passengers when they call for the elevators. This type of system isknown as destination dispatch. The idea is that each floor has a panel of floorbuttons, corresponding to the different available floors from the elevator system.Passengers then call for elevators by selecting their destination floor. Finally,the control algorithm then decides which elevator should be assigned to servethe request, and informs the user which elevator to use [17], see Image 3.3.

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Image 3.3: A call panel for a destination dispatch system. The letter on thetop shows which elevator to use (A) in order to reach floor 50 which has just

been requested by the user. [17]

3.3.3 Double-decked Control

Since most elevator rides takes place to or from a lobby it is clear that lobbieshave to be constructed over two floors in order for double-decked elevators to beeffective. This way, passengers can board both the elevator cars of the elevatorsimultaneously which in turn could at most double the amount of passengerscarried in every ride. This is usually done by installing an escalator betweenthe two lobby floors (this include sky lobbies) and then direct passengers to usethe upper or lower deck depending if they are headed to an odd or even floor.Indeed this is the solution all current elevator manufacturers use [20]. Of course,passengers boarding at any intermediate floor cannot choose between floors andwill thus enter either the upper or lower elevator car depending on where theelevator stops.

Clearly any call system can be used for a double-decked elevator, i.e. sim-ple call, up/down call or destination dispatch. However, the most importantfactor when optimizing control of double-decked elevators is of course that bothelevator cars are served simultaneously as much as possible. For this reason,destination dispatch is well equipped to handle double-decked elevator controlsince it provides information about the destination floor of passengers at thetime the call is made. This allows the control algorithm to group passengersgoing to adjacent floors by assigning them to different cars of the same elevator,meaning both elevator cars can drop off the passengers simultaneously. As anexample, two passengers boarding at the lobby with destination floor 7 and 8respectively could be assigned to the same elevator, the passenger going to floor8 on the upper car and the passenger going to floor 7 on the lower car. Usingthese assignments, the passengers could board and disembark simultaneouslyand there would be no idle waiting time for one elevator car while only theother car is being served. Due to these reasons, destination dispatch is com-monly used in modern double-deck elevator systems, just as those developed bySchindler [11] and Otis [21], in conjunction with advanced control algorithmssuch as the following.

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The ETD (Estimated Time to Destination) algorithm used by ThyssenKrupputilizes a type of search algorithm to determine which of the elevators can mostefficiently serve a call [19]. This is done by calculating the amount of extrawaiting- as well as traveling time that would be generated for each of the avail-able elevators to serve this new call. For example: Assume there is a call foran elevator on floor 3 with destination floor 8. Elevator A is just leaving floor1 for floor 5 with two passengers. The total delay for elevator A can now becalculated. The stop at floor 3 to pick up the new passenger delays the elevatoron its trip to floor 5 by some time, say 10 seconds. Since there are two peoplein the elevator they both have to wait these 10 seconds so the total delay timeis so far 20 seconds. Now the passenger on floor 3 gets on and the elevatorsnext stop is on floor 5. However, the passenger boarding on floor 3 is going tofloor 8 so the stop on floor 5 delays him 10 seconds as well. Therefore the totalcost for elevator A to respond to this call is 30 seconds. This cost is calculatedfor all available elevators and the one with the least total cost will be chosen torespond to the call [19]. This assumes of course that the amount of passengersin each elevator is known. This is difficult to know exactly, but the elevatorscould have a scale inside to determine the total weight which gives an idea ofhow many people are inside the elevator. It is however clear that destinationdispatch is used by the algorithm to perform a more accurate search estimationthat a regular up/down elevator system could do.

Another solution, developed by Otis Compass Plus [18], utilizes a variationof the zone control algorithm rather than the search control algorithm. This isdone in an effort to avoid different elevators going to the same floor. Moreover,it is done by allocating each elevator to a group of adjacent floors, including thelobby floor. For example, if there are four elevators and nine floors, includingthe lobby, then each elevator could serve two floors each. Each elevator callis further responded to by the elevator assigned to the zone having either thepassengers destination floor or origin floor, whereby the amount of zones are 4in this example.[18]

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4 Problem DefinitionThis study investigates how efficient double-decked solutions are compared tosingle-decked variants, or combinations of these. It is done by finding an answerto a hypothetical scenario; to plan the construction of an elevator system for anew skyscraper. Its goal is to be the first 400 meter high skyscraper with 100floors, including a sky lobby, in northern Europe. With respect to the groundarea available, the owners have agreed to an elevator system of 40 shafts withelevator cars having a carrying capacity of 15 people. However, the owners areconcerned that the passengers’ elevator rides will be too slow, with respect towaiting time as well as travel time. They therefore consider if they have touse double-decked elevators in order to reduce passenger waiting time. Thesespecial elevators would certainly cost more than regular single-decked variants.To help with the trade-off, and at the same investigate if the use of double-decked elevators increase performance, this study aims to provide an answer tohow well different elevator types perform in the setting above, using differentcontrol algorithms.

4.1 Problem StatementCan something be said about whether single-decked or double-decked elevatorsare to be preferred in a skyscraper environment? Which control algorithms aremore time efficient in a skyscraper using single-decked and/or double-deckedelevators?

4.2 PurposeToday, multiple different combinations of double-decked and single-decked el-evators are used as shuttle and local elevators in different buildings [4]. Thisraises the question of why multiple different solutions are used. By answer-ing the problem statement, this study draws conclusions on the efficiency ofdouble-decked elevators using different control algorithms.

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5 MethodThe defined problem consists of many parameters: a variety of control algo-rithms, elevator types and different intensities of traffic. Hence, an answer forthis problem is sought using a simulation of elevator activity. This simulationis written in Java and should be sufficient to provide answers, or at least hints,on which elevator setting provides better time efficiency. The simulation hassuch capability due to the fact that it attempts to imitate real-life behavior ofpassengers moving from one point to another via a system of elevators, underreal-life conditions.

To perform a simulation imitating real-life behavior, one would first needa collection of passengers representing a type of traffic. This collection is gen-erated by a traffic generator given a traffic type, such as up-peak traffic, and anumber representing how many different passengers one would wish to have inthe selected traffic type. The output of the traffic generator is a number of ele-vator calls that have an origin, a destination and a time stamp telling at whichtime in the simulation the call will be made. The generated traffic is then sentas input to the current elevator system, consisting of a set of elevators, whichmimics the elevators natural behavior (movement and opening/closing of doors)using the current control algorithm. The chosen control algorithm processes thegiven data by assigning incoming calls to elevators.

In order to measure the waiting- and travel time of passengers, each elevatordynamically calculates the time allocated passengers have to wait for, and ridewith, the elevator before reaching its destination. After each simulation, resultsare given in form of the AWT and ATT of the entire system. Furthermore, onewill also be given the ASWT, defined in Section 3.1.1, as well as the averagesquared travel time(ASTT) of the system in order to highlight passengersvariances not only in passenger waiting time, but in travel time as well.

The following sections specify the different parameters and settings of thesimulation.

5.1 Traffic GeneratorThe responsibilities of the traffic generator is to generate realistic passenger dataaccording to the different hours of a normal working day. These are interpretedto be the following:

• 7-9: Up-peak traffic where the vast majority of passengers are travelingfrom the lobby to their destination floor.

• 9-11: Interfloor traffic

• 11-13: Lunch time traffic where most passengers travel either to or fromthe lobby floor.

• 13-15: Interfloor traffic

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• 15-17: Down-peak traffic where the vast majority of passengers are trav-eling to the lobby.

The subsections below cover the different traffic-types which together forma day of simulation, a simulation day. Chosen numbers for the different periods,as well as the choice for having the periods themselves, are based upon the theoryin Section 3.1.4. Note that the sky lobby floors can never be the final destinationof any travel, and that unfinished calls from each period overlap onto the nextperiod. Should there be any remaining calls in the system after the last period,the simulation extend the day by keeping the simulation running with the samesettings, without having any new additional calls added to the system.

Each simulation day consist of a certain number of time units, in whichone time unit is represented as one "loop through the system", where one loopis simulated as one second of activity; each loop update elevator movement,elevator boarding, call allocations in the current control algorithm as well asthe update of waiting and travel time. Each random operation mentioned inthe following traffic states, as well as the exact time of which a call is made, isused with Javas pseudo-random linear distribution class.

5.1.1 Interfloor Traffic

There is interfloor traffic both in the morning between up-peak traffic and lunch-traffic as well as between lunch-traffic and down-peak traffic. Such traffic issimulated by having 33% of the total amount of passengers per period do aninterfloor elevator ride from a random floor to a random destination floor.

5.1.2 Up-peak Traffic

The up-peak traffic is generated by having 100% of the given amount of passen-gers take an elevator ride from the lobby floor at some point during the period.The destination floor of these is chosen at random.

5.1.3 Down-peak Traffic

During down-peak traffic, 100% of the given passenger amount travel from arandom originating floor down to the lobby sometime within the period. Whendouble-decked elevators are used, the floor above the lobby floor is also onepossible destination. Consequently, this floor is not a valid floor to originatefrom.

5.1.4 Lunch time Traffic

In lunch-time traffic, 50% of the given passenger amount does at some pointtravel down to the lobby from a random origin. After 30 simulation time units,these passengers travel back up again.

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5.2 Elevator ImplementationsThis section lists the different elevator implementations that are used in thesimulation. When a double-decked shuttle elevator is used, the lobby and skylobby consist of two floors, instead of just having one floor, namely the originallobby/sky lobby floor as well as the floor directly above it. The simulation runwith the following elevator structures:

• Double-decked locals with double-decked shuttles

• Single-decked locals with double-decked shuttles

• Single-decked locals with single-decked shuttles

The structure of ’Double-decked locals with single-decked shuttles’ is notused in the simulation. This is due to the fact that the double-decked localswould then only be able to have its lower half in the lobby/sky lobby floor,since there is a single-decked shuttle elevator between these floors. Thus thisstructure is interpreted as surrealistic according to the description of sky lobbiesin Section 3.1.2.

5.3 Simulating the Control Algorithms5.3.1 Local Elevators

All local elevators, regardless of whether they are single-decked or double-decked, will be simulated with the following control algorithms:

• Single Automatic with logical elevator assignments

• Single Automatic with a zone implementation

• Single Automatic with a search implementation

• Selective Collective with logical elevator assignments

• Selective Collective with a zone implementation

• Selective Collective with a search implementation

Logical elevator assignments are used on both Single Automatic and Se-lective Collective in order to investigate if a logical, but yet simple, allocationof elevator can prove to be useful. Each such logical elevator are chosen in thefollowing order:

1. The chosen elevator is the one having the least workload (amount of as-signed calls)

2. The chosen elevator is the one closest to the call

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3. If there should be multiple elevators with the same workload and prox-imity, one of these would be picked at random using Javas own linear,pseudo-random distribution

An algorithm implemented with zone control is given a list of elevators inwhich each elevator are assigned to a zone, consisting of the lobby/sky lobbyas well as two or three consecutive floors. Each passenger’s call is then handledby an elevator having the passenger’s origin floor within their zone. Thus, thealgorithm using zone is given a subset of the available elevators, rather than allof them, when performing their respective queue allocations.

Algorithms using search control are based on ETD and use a greedy searchcontrol variant which, as mentioned in Section 3.3.3, does not consider differentqueue allocations. Instead, such algorithms calculate the performance of placingnew calls into each possible elevator. When all possibilities are accounted for,the algorithm then allocates the new call to the elevator taking the least amountof waiting time and travel time.

5.3.2 Shuttle Elevators

Shuttle elevators run with the Single Automatic algorithm with logical elevatorassignments, regardless of whether double-decked elevators are used or not. Thisis due to that shuttle elevators only travel in between two floors (or set of floors)and thus does not require advanced elevator assignments.

5.4 System SpecificationsThe building layout consist of a single lobby floor with multiple destinationfloors including exactly one sky lobby. Garage floors and other entrance floorsare not considered present. The following list describe the fixed or alternativeparameters to the simulation and represent a simplification of the One WorldTrade Center building and elevator specifications.

• Floors: 100

• Building Height: 400 m

• Distance between floor: 4 m

• Daily amount of people riding elevators: 2500 / 6000 / 7200 (low traf-fic/medium traffic/heavy traffic)

• Shafts: 40

• Amount of shuttle elevators: 16

• Amount of local elevators: 24 in each part of the building (top and bottom)

• Elevator car capacity: 15 passengers

• Average elevator speed: 20 m/s

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• Sky lobby floor: 66

• Floor delay time (time spent embarking/disembarking passengers): 10seconds

• Number of simulation days: 14

• Time per period: 7200 seconds (two hours per period)

• Extended time after each simulation day: unlimited

5.5 SimplificationsTo simplify the simulation, elevator acceleration and deceleration are not consid-ered. Instead, the average speed of the elevator are used for elevator movement.Furthermore, the elevators keep track of the amount of passengers currentlyinside them at all times in order to check whether they are full or not. If suchis the case, elevators will at first drop-off a passenger before picking up anotherregardless of the current queue. Additionally, elevators are only open for em-barking/disembarking for a fixed period of time (see above) and are not delayedfurther. Moreover, a passenger cannot be allocated with another elevator oncethe call has been processed by the control algorithm. Some of the consequencesof this simplification is that passengers cannot ride with another elevator, shouldone arrive at the same floor, or be reassigned to another elevator if the allocatedelevator happens to be full. Finally, algorithms using search based elevator allo-cations use a simplified version of the real ETD, whereas algorithms using zonecontrol use a derived version of Compass.

5.6 AssumptionsThe simulation is based on a number of assumptions, namely the following:

• Destination dispatch is assumed to be used on all floors regardless of eleva-tor setting. This has the consequence that the simulated elevator controlsystem is able to perform elevator allocations with the destination floorsof passengers taken into account as soon as a call for an elevator is made.

• Passengers know their whole route, including lobby interchange (if any)and position in lobby floor should double-decked shuttle elevators be used.

• Eventual movement between the ground lobby floors or sky lobby floors ina double-decked shuttle elevator scenario is assumed to take zero seconds.Such is the case when a passenger ride from the lobby/sky lobby and hasto embark on the upper or lower elevator car of the double-decked elevatorin order to reach their respective destination.

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5.7 Validity through TestingIn order to ensure that the given results are creditable, the program was runwith a number of test classes. More specifically, the expected behavior of thefollowing parts of the program was validated:

• Elevator movement and embarkment/disembarkment

• Passenger movement and tracking

• The selective collective algorithm

• Passenger traffic generation

Other parts of the program was validated through debugging and error printingalong with multiple runs of the program.

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6 ResultsThe following subsections display the simulation results for each control al-gorithm. In these, there are tables for each respective algorithm variant for14 simulation-days of simulation in low, medium and heavy traffic passengeramounts. At the end of section, one can find diagrams that graphically presentdifferences in waiting time and squared waiting time for the different trafficsand algorithms. There are no such diagrams for travel time or squared traveltime due to it having few significant differences. Furthermore, computationaltime is not an issue in the simulation and is thus not investigated upon.

• AWT = Average Waiting Time

• ASWT = Average Squared Waiting Time

• ATT = Average Traveling Time

• ASTT = Average Squared Traveling Time

6.1 Single AutomaticLow Traffic

Elevator Type AWT ASWT ATT ASTTSingle/Single 12,55 15,04 17,77 18,36Double/Single 12,59 15,07 17,81 18,39Double/Double 12,48 15,00 17,77 18,36

Medium Traffic


Heavy Traffic


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6.2 Single Automatic ZoneLow Traffic


Medium Traffic


Heavy Traffic


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6.3 Single Automatic SearchLow Traffic


Medium Traffic


Heavy Traffic


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6.4 Selective CollectiveLow Traffic


Medium Traffic


Heavy Traffic


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6.5 Selective Collective ZoneLow Traffic


Medium Traffic


Heavy Traffic


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6.6 Selective Collective SearchLow Traffic


Medium Traffic


Heavy Traffic


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6.7 Diagrams

Diagram 6.1: AWT for low traffic

Diagram 6.2: AWT for medium traffic

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Diagram 6.3:AWT for heavy traffic

Diagram 6.4: ASWT for low traffic

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Diagram 6.5: ASWT for medium traffic

Diagram 6.6: ASWT for heavy traffic

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7 Discussion

7.1 InterpretationsTo maintain elevator traffic in the extreme environment of a skyscraper is hard,yet again NP-hard in general when a scheduler consider all possible pick-uporders. This has however not been the case in this study, due to the usage ofthe heuristics Single Automatic and Selective Collective. These has proven togive different, but feasible results in accordance to their respective theoreticalaspect. Single Automatic has proven to be on par with Selective Collectivein a number of different traffics and alternative structures in terms of AWTas well as for ASWT, although only in the low and medium traffics. A certainequality seem to arise in Diagram 6.4 and Diagram 6.5 as well. Nevertheless, thedifferences become majorly apparent in heavy traffic with Selective Collectivehaving better waiting time with results being from 20 up to hundreds of secondsbetter than the algorithms utilizing Single Automatic. Indeed, utilizing theconcept of collection provides better overall results even with the simple logicalelevator allocation.

Even though the waiting times seem to differ between the algorithms, thereare no real significant change in travel time. In the majority of the results, ATTseem to be a (rounded) constant value of 18 seconds while ASTT seem to beconstant to approximately 20 seconds. Thus, it would seem that the chosenalgorithm or state of traffic has negligible effect on how long a passenger has toride in order to get to its destination. The only particular changes in travel timeoccur in the squared travel time of section 6.3 and section 6.6 which provide theresult for the two algorithms’ respective search variant. Indeed, the differencebetween Single Automatic and Selective Collective as a control algorithm doesnot appear to be diversified by the core algorithm type but rather around thedifferent variants of these.

There is a clear difference between the performance of the logical, zone andsearch based algorithms from the diagrams. The observation that first meets theeye is that the search based algorithms performs very well in terms of waitingtime compared to the other algorithms, such as in Diagram 6.3. In fact, it per-forms best in terms of waiting time compared to all the other algorithm/traffic-intensity combinations. The probable explanation for this would be that searchbased actually finds the best suited elevator for each call by analyzing the run-ning time for each possible allocation. While this is true, one has to keep inmind that new passengers arriving between the time of allocation and the timeof arrival of the first passenger could affect which elevator would perform thebest in terms of waiting time. Yet, passengers can not be allocated to a newelevator once they have been assigned to one in the first place since destinationdispatch is used. For this reason, one could argue that search based algorithmsperforms the best in terms of waiting time from the given information. This isnot necessarily true though, since it could be possible for the algorithm to guessthe flow of incoming traffic and thus allocate passengers in a more efficient wayto begin with.

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Upon further inspection of the results in the diagrams it is also clear thatthe search based algorithms performs much worse compared to other algorithmsin terms of squared waiting time. This would indicate that the waiting timedistribution between the different passengers is unevenly distributed which inturn suggest search based algorithms makes some passengers wait longer in orderfor more passengers to be served with less waiting time. One might express itas sacrificing the few for the greater good. The reason for this is difficult toanalyze since search based algorithms operates on nothing but runtime specificcalculations. A possible analysis would be that new calls originating from distantlocations would be allocated to empty but not necessarily nearby elevators.This would save time since an elevator that might be close, but contains severalpassengers, would cause an increase in the total delay time. On the other hand,the passenger who is making the new call might have to wait for a longer timesince the chosen elevator could be further away which would affect the squaredwaiting time negatively.

Another algorithm with surprising results is the zone algorithm, which per-formed worse in terms of waiting time than the logical version in all scenarios inthe diagrams. It can also be seen from the same diagrams that while the single-decked version of zoned algorithms performs considerably worse than the logicalversions, double-decked implementations seems to be a somewhat closer race.This would indicate that zone control algorithms works well with double-deckedimplementation, even if still slightly slower than the logical versions in terms ofwaiting time. This property seems to be unique for zone control; neither logicalnor search based algorithms have a performance increase with double-decked.While logical algorithms seems to have about half the improvement comparedto zone algorithms, search based practically reach the same waiting time usingboth single- and double-decked systems. This could be explained by the factthat double-decked zone control in the implementation for this simulation con-sisted of two adjacent floors in conjunction with the relevant lobby floor. Thiswould mean a double-decked elevator achieves grouping with passengers travel-ing to the upper of the two adjacent floors boarding the upper elevator car andsimilar for the lower elevator car, meaning these passengers make efficient useof the fact that these elevator cars travel together.

The question remains as to why the zone algorithm performed worse thelogical algorithm in all cases. This result indicate that the logical algorithm’sway of choosing elevator is superior to the zone version, which is worth investi-gating since the very reason zone algorithms exist are to improve performance.There are two factors to consider, they way the logical algorithm choose anelevator and the exact way the zone control is set up in the simulation. Firstly,the logical algorithm select elevator by finding the closest non-empty elevator.While this might appear as a very effective algorithm at first glance it has theissue of bunching up a lot of people from possibly different floors, meaning thefirst passengers to enter the elevator might have to wait a very long time. Theseissues are illustrated by the way the search algorithm performs the elevator allo-cation much more efficiently in terms of AWT. Secondly, the zone control of theelevators was implemented in such a manner that as few elevators as possible

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were to serve each zone. This means that in a system of 40 elevators and 64floors, the first 32 elevators serve two floors each and the remaining 8 help servethe top 16 floors.

This type of distribution have the consequence that there are only someelevators serving some of the zones and thus passengers calling from the floorswithin a particular zone might have to wait a very long time if the elevatoralready have left the zone. It is possible zone control would have performedbetter if there were more elevators in each zone, but this was not investigated.Another aspect worth considering is that while rides to or from the lobby floorswork well with zone control, interfloor traffic is more difficult to handle. Thereason for this is that the origin and destination are unlikely to be in the samezone and thus zone control is not really achieved.

7.2 Error SourcesThe perhaps most obvious flaw with the performed simulation was that theelevator speed was set to an average value at all times. This speed was alsothe same for local and shuttle elevators, something that is far from true in realsystems. The effects of these simplifications is that elevators traveling shorterdistances are faster than they should be while elevators traveling long distancesare slower than they should be. This means the simulated systems favors shorterelevator rides and thus also that multiple stops on close floors are conductedfaster than should be possible. This flaw could also have been a factor of the slowefficiency of the zone algorithms, since these depend on traveling long distanceswith passengers between the lobby and the designated zoned floors.

Another simplification made for the simulation is that each elevator knowsthe exact numbers of passengers currently inside at all times. While this is notentirely incorrect (there have been attempts with scales in elevators as describedin the background) it in unlikely the elevator knows its current passenger amountwith such precision, which thus makes the simulated elevators more efficientsince they allow more passengers to board even though the elevator is almostfull. While this different might provide different results, the simulated elevatorsstill behaves just as real ones do by traveling to the next drop-off destination,ignoring pick-ups, when the elevator is considered full.

It is worth to consider how elevators behaves when they stop at a floor toeither let passengers embark or disembark. The time this process takes is set toten seconds in the simulation. Yet, in real systems it is clear this number variesheavily depending on the situation. While ten seconds might be a reasonableaverage value it could possibly be more realistically represented by a stochasticvariable ranging from 5-30 seconds with the highest probability being around5-15 seconds. This type of change would let the system be affected by theoccasional passenger that requires extra time boarding and disembarking theelevator.

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7.3 ApplicationsDespite whether zone control, search or logical Selective Collective and SingleAutomatic are used, the results suggest that having a double-decked elevatorincrease the overall performance of the elevator system. Although these gainsare mostly apparent in heavy traffic, having double-decked locals and shuttleelevators improve the AWT with up to 6 seconds (Diagram 6.3, Selective Col-lective Zone) in the best case, excluding the results in single automatic searchwhich although took very long time but shows a difference of nearly 20 seconds.While 6 seconds might appear insignificant to a single waiting passenger, havingsix seconds better waiting time in the average case has the consequence of con-siderable less waiting time in the system. For example, having 7200 passengerswith 30 seconds of AWT in single/single (single-decked shuttles and locals) and6 seconds less in double/double has the consequence that the system runningwith 7200 passengers in single/single has approximately the equivalent totalwaiting time of double/double with (7200 + (7200 ∗ 6/30)) = 8440 passengers.Although total waiting time is not looked upon in this study, it suggest thatdouble-decked elevators for both local elevators and shuttle elevators seem tobe the more sought structure.

In conjunction with the general performance increase of double-decked el-evators, it is crucial to mention that double-decked elevators are more than tentimes more efficient in zone algorithms compared to search algorithms. Thiscan be seen by looking at Diagram 6.3 where selective collective with searchyields about 0,5 seconds better AWT for double-decked elevators whereas thesame number for selective collective zone is about six seconds. Thus, the gainof using double-decked elevators is much more important in zone based imple-mentations, such as the Otis Compass system. At the same time, search basedimplementations such as the ETD by ThyssenKrupp is not nearly as dependenton the use of double-decked elevators to perform efficiently. Therefore it canbe concluded that zone based implementations should always strive to consistof double-decked elevators. The problem can also be viewed from the oppositeangle; assuming we have a double-decked system, should a zone or search algo-rithm be used? This does lie outside of the scope of this report though, andan answer can not be derived from the results of the simulations, due to theproblems in the zone implementations as well as the high squared waiting timesof the search implementation.

With respect to the acquired results of this study, there seem to be multipleoptions on which control algorithm to use in the hypothetical scenario definedin the problem definition. As mentioned earlier, the best option between usingSingle Automatic or Selective Collective would be the latter. As for whichversion of Selective Collective to use, the results, backed up by the theory behindthe concept, would at first suggest that the search algorithm could be the moreoptimal choice as it does decrease the AWT significantly. However, the problemof having some passengers wait a lot longer than the average in order to achieveoverall better results could be seen to be ethically wrong for realistic buildingconditions. Henceforth, the theory would suggest that zone control would be the

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next best option, but yet again the results deemed zone control to be worse thanlogical Selective Collective. As such, Selective Collective with logical logicalelevator allocations turns out to be best heuristic approach to be taken forskyscrapers with a (single) sky lobby implementation.

As previously stated, double-decked elevators seem to be the better elevatorsetting. Then again, if the building does not expect far to much traffic one couldquestion the cost of having double-decked local elevators over the single-deckedvariant. This assumes of course that a double-decked elevator cost more thana single-decked elevator, which should be true since a double-decked elevatoris theoretically twice as large as a single-decked elevator. By looking at theresults, one can see that double-decked shuttle and single-decked local elevatorsare more effective than only using single-decked elevators as well as not far fromthe efficiency of the solid double-decked variant. Thus, double-decked shuttleand single-decked local elevators could be a good alternative if one wish to haveelevators that are more effective than regular ones but cheaper than only havingdouble-decked elevators. Beyond shadow of doubt, double-decked locals anddouble-decked shuttle elevators remain the better elevator setting with regardto only efficiency.

8 ConclusionJudging from the results of this study, Selective Collective with logical elevatorallocations are more effective for a skyscraper having a sky lobby implementa-tion. Search-based Selective Collective could be an even more effective option,but cannot be used as it is right now due to unfair distribution. Zone did notappear to be an effective option, but has potential to be more effective withbetter zone distribution.

The value of using double-decked elevators depend on which algorithm isused. However, they proved to very efficient when used together with zone whilenot as effective with search. In any case, double-decked performed better thansingle-decked elevators in all scenarios.

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