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Elevator Scheduling Ingrid Cheh Xuxu Liu 05/05/09

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Elevator Scheduling Problem Elevator as a control system Response time and behavior depends on programmed algorithm(s) Different solution depending on building type and number of elevators working together In algorithm, assignment of job: External elevator request Internal floor request

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Our Problem Context Setting: 15 floor school administration building 2 elevators Goal: Analysis of possible elevator scheduling algorithms through simulation Find most optimal in given problem Suggest future directions

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Tools Basic graphical user interface & software Programmed in Pascal in Delphi programming environment Strategies, labeled A, B, C & D

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User Interface & Software

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Strategy A Elevators calculate most requested floor Each elevator heads to most requested floor, unless: Other elevator already heading there Other elevator contains passenger who want to go there If no passengers in elevator, elevator will position itself on floor 8 Reacting to modal distribution of requests

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Strategy B If no passenger in elevator, it waits and goes to the floor where earliest request is made Otherwise, elevator will head to floors requested in order of passenger entry into elevator If elevator passes a floor where current passenger has requested to get off, elevator stops and picks up new passengers on direction of travel External requests in FIFO manner

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Strategy C Both elevators do the same If elevator is empty, it heads towards the earliest external request Otherwise, elevator would head towards desired destination of occupants When elevator opens, it picks up passengers in either direction Internal before external requests in FIFO manner

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Strategy D Elevator A Initially goes to floor 15 and descends one floor at a time picking up passengers who are going down Heads straight back up to floor 15 when reaches floor 1 Elevator B Initially goes to floor 1 and ascends one floor at a time picking up passengers who are going up Heads straight back down to floor 1 when reaches floor 15 Round Robin scheduling method

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Performance Metrics Metrics Average Wait (w) Passengers Using Stairs (s) Power Efficiency (e) Service Profile (chart) Passenger’s Satisfaction (p) Passengers Carried (c) w: time request submission to final destination arrival c: total passengers carried s: total passengers who gave up elevator wait and used stairs p: c/(c+s) e: energy efficiency level in elevator operation chart: running profile of customers served in different time brackets

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Inputs to Simulation Available Inputs Scenario Particular pattern of passengers waiting and choice of floor Peak Hours Choice of more or less passenger traffic Simulation Length Total simulated of each simulation run Boredom Level How much time before switching to stair use Our Selection of Inputs Scenario 30 different scenarios Peak Hours Peak chosen Simulation Length 5 minutes Boredom level Level of 20

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Results 61.40 84.27 75.93 34.57 24.67 29.70 12.07 19.2719.73 70.57 57.27 61.20 79.93 27.60 20.33 57.53 38.90 34.77 41.07 73.70

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Analysis of Results Strategy A is the best strategy Least average waiting time Least number of passengers using stairs Maximum passengers carried Greatest percentage of passenger’s satisfaction Ranking of strategies from best to worst: A, C, D, B

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Limitations of Strategies Strategy A Efficiency may be related to its position on 8 th floor Overlooks the potential heavy skew of requests around the lower floors Difficult for Elevators A and B both to calculate the most requested floor Interlacement of strategies B & C necessary “Smart” strategies are less power efficient

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Extensions within Problem Context Investigation into other variables Simulation length Peak hours to non-peak option Boredom level Scenario Uniform probability distribution is not realistic Poisson arrival processes might be more reflected More requests assigned to ground level Different setting ± Floors ± Elevators

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Inspiration from Literature Passenger behavior (Susi, Sorsa and Sikonen) Modeling of diverse traffic flows with passenger compositions that incorporate physical and behavioral characteristics Complex controllers (Bartz-Beielstein, Preuss and Markon) Fujitec Neural network structure to determine control strategy Zoning Policy (Chu, Lin and Lam) Set of floors divided into blocks Goal of increasing overall handling capacity

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Summary Insight into elevator scheduling simulation for a particular setting Compromise between running efficiency and power efficiency Very simple and limited model Extensions possible, as shown by literature research

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Thank You!

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