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Metaheuristic algorithms for elevator group control system: a holistic review

  • Optimization
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Abstract

Optimization plays a crucial role in the elevator group control system (EGCS) since various unpredictable factors, such as future traffic demand of each floor, passengers’ random destinations, and indiscriminate starting-stopping of elevators, are incorporated in scheduling a group of elevators. When solving the optimization problem of EGCS, a number of dynamic performance indices, including average waiting time, average journey time, energy consumption, etc., have to be taken into account. Until now, numerous optimization approaches have been utilized to solve the car-dispatching problem of vertical transportation. Among those methods, in this study, the authors concentrate on various metaheuristic techniques that were implemented to optimize the metrics of EGCS. While establishing a metaheuristic approach, all the previous authors recognized various factors and limitations, which ought to analyze to develop a new metaheuristic-based EGCS. Owing to this, EGCS implemented via metaheuristic techniques is summarized in this review study, together with the underlying contributions, fitness functions, computational time, and limitations. What is more, performance comparisons of different previously implemented metaheuristic approaches are depicted in this study. This research will not only assist to figure out optimal elevator group optimization algorithms, but also will shrink the technological gap by outlining a number of potential future research lines and methodologies.

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Data availability

Enquiries about data availability should be directed to the authors.

Abbreviations

ABC:

Artificial bee colony

ACO:

Ant colony optimization

AFSO:

Artificial fish swarm optimization

AIA:

Artificial immune algorithm

AJT:

Average journey time

ALWP:

Average long waiting percentage

ANS:

Average number of stops

ART:

Average riding time

AST:

Average service time

ATT:

Average travelling time

AWT:

Average waiting time

BA:

Bat algorithm

CA:

Cellular automata

CT:

Computational time

DDES:

Double deck elevator system

EA:

Evolutionary algorithms

EC:

Energy consumption

EGCS:

Elevator group control system

ES:

Evolutionary strategies

FCM:

Fuzzy cognitive map

FL:

Fuzzy logic

FNN:

Fuzzy neural network

GA:

Genetic algorithm

GNP:

Genetic network programming

GWO:

Gray wolf optimizer

IPSO:

Immune particle swarm optimization

LWP:

Long waiting-time percentage

NC:

Nearest car

NN:

Neural network

PSO:

Particle swarm optimization

PTS:

Probabilistic tabu search

RPC:

Riding power consumption

SSCA:

Static sectoring-based control algorithm

SSZA:

Static zoning control algorithm

TS:

Tabu search

TSP:

Travelling salesman problem

VSA:

Viral system algorithm

\(t_{{\text{f}}}\) :

Transit time of a single floor

\(t_{{\text{s}}}\) :

Stopping time of elevator

\(t_{{\text{p}}}\) :

Passenger transfer time

\(S\) :

Expected number of stops

\(H\) :

Highest reversal floor

CC:

Rated car capacity

\(\emptyset_{1}\) :

Ground floor level

\(\emptyset_{2}\) :

Highest down hall call level

\(\emptyset_{3}\) :

Number of down hall call between \(\emptyset_{1} {\text{and }}\emptyset_{2}\)

\(\emptyset_{4}\) :

Highest up hall call level

\(\emptyset_{5}\) :

Number of up hall call between\(\emptyset_{1} {\text{and }}\emptyset_{4}\)

\(\emptyset_{6}\) :

Lowest down hall call level

t :

Opening and closing time of door

Hct:

Highest trip time of car

Lct:

Lowest trip time of car

f :

Total fitness

\( E_{{\text{a}}} \) :

Acceleration or deceleration energy

\( E_{{\text{v}}} \) :

Uniform running speed energy consumption

m :

Average weight of passenger

\( m_{{{\text{car}}}} \) :

Weight of elevator car

\( m_{{{\text{cwt}}}} \) :

Weight of counter weight

\( n_{1} \) :

Number of passengers

h :

Floor displacement

P :

Number of starting-stopping

q(r):

Total call answered by rth elevator

N :

Total number of passengers

\( t_{n} \) :

Waiting-time of nth passenger

\( t_{{\max }} \) :

Maximum waiting-time among N passengers

\( n_{{\text{c}}} \) :

Passengers’ sum experiencing one-cage service

\( n_{l} \) :

GNP loop-number in one-hour evaluation

\( w_{t} ,~w_{c} ,~w_{l} \) :

Weighting coefficients set by trial and error

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Authors and Affiliations

  1. Chittagong University of Engineering and Technology, Chittagong, Bangladesh

    Mohammad Hanif & Nur Mohammad

  2. University of Connecticut, Storrs, CT, USA

    Mohammad Hanif

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Conceptualization was contributed by M.H. and N.M.; methodology was contributed by M.H.; formal analysis was contributed by M.H; investigation was contributed by M.H.; resources were contributed by M.H.; data curation was contributed by M.H.; writing—original draft preparation was contributed by M.H.; writing—review and editing was contributed by N.M.; visualization was contributed by M.H.; supervision was contributed by N.M.

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Correspondence to Mohammad Hanif.

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Both authors voluntarily participate in this research. All of the previous research on EGCS are briefly summarized and analyzed in this study. Both authors have read and agreed to the version of the manuscript.

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Hanif, M., Mohammad, N. Metaheuristic algorithms for elevator group control system: a holistic review. Soft Comput 27, 15905–15936 (2023). https://doi.org/10.1007/s00500-023-08843-0

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