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A modified genetic algorithm applied to the elevator dispatching problem

  • Methodologies and Application
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Abstract

Reduction of passenger waiting time in a multiple elevator system is an important goal in the lift industry. Genetic algorithms (GAs) have been applied to the dispatching problem in vertical transportation. In this paper, we present an approach based on a GA with several relevant adjustments to adapt this type of algorithm to this problem. The algorithm serves calls currently registered in the system to create a dispatch plan, under the assumption that just one passenger has made each call (i.e. without passenger forecasting). We develop and investigate various versions of the GA incorporating one or more adjustments in this research area. The algorithms were implemented and evaluated using ELEVATE, for two different building configurations, in terms of incoming, outgoing and interfloor profiles. To compare results, one-factor analysis of variance tests were applied to passenger waiting times. The performance of the basic GA was significantly improved upon by making these adjustments. These adjustments turn out to be essential for a successful implementation of a GA in the dispatching problem.

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Abbreviations

ANN:

Artificial neural networks

ANOVA:

Analysis of variance

ATT:

Average transit time

ATTD:

Average time to destination

AWT:

Average waiting time

C1:

Building configuration [6 cars, 12 floors, average population/floors for populated floors, car capacity (Kg) 1600], 400 landing calls to be attended

C2:

Building configuration [6 cars, 33 floors, average population/floors for populated floors, car capacity (Kg) 850], 350 landing calls to be attended

EGCS:

Elevator group control system

ELEVATE:

simulation software for vertical transportation http://www.elevateconsulting.co.uk/

ETA:

Estimated time arrival. A rule based dispatching algorithm

EWT:

Estimated waiting time

GA:

Genetic algorithm

GA \(+\) ALL:

The algorithm GA with all the next adjustments LbI \(+\) Sd \(+\,S+\,P3\)

GA \(+\) ALL:

GA with all adjustments (GA \(+\) LBI \(+\) Sd \(+\,S\,+\,P3\))

GA \(+\) LBI:

The GA algorithm with the Last Best Individual adjustment

GA \(+\,P\) :

The GA algorithm with the P penalty adjustment

GA \(+\,P3\) :

The GA algorithm with the P3 penalty adjustment

GA \(+\,S\) :

The GA algorithm with the stability adjustment

GA \(+\,S\,+\) LBI \(+\) Sd \(+\,P\) :

The GA algorithm with the S, the LBI, the Sd and the P adjustments

GA \(+\,S\,+\)LBI \(+\) Sd \(+\,P3\) :

The GA algorithm with the S, the LBI, the Sd and the P3 adjustments

GA \(+\,S\,+\) LBI \(+\) Sd:

The GA algorithm with the S stability, the LBI and the Sd adjustments

GA \(+\,S\,+\,P\) :

The GA algorithm with the S and the P adjustments

GA \(+\,S\,+\,P3\) :

The GA algorithm with the S and the P3 adjustments

GA \(+\) Sd:

The GA algorithm with the seeding adjustment

GC:

Group collective, a rule Based dispatching algorithm

HC:

Handling capacity, a percentage of the population of the building in a vertical transportation system that the system can move in up-peak mode in a 5 min period

LBI:

Last best individual adjustment

LTT:

Longest transit time

LTTD:

Longest time to destination

LWT:

Longest waiting time

NSF:

Next stopping floor

P :

Penalization

P3:

P3 Penalization

RWT:

Real waiting time. The average of the real waiting times for the passengers already in the system. The real waiting time of passengers in the system is the waiting time from the moment they press the button until the lift’s arrival in the simulation

S :

Stability adjustment

Sd:

Seeding adjustment

STEP1:

Mixed passenger profiles where 45 % of the landing calls were for incoming, 45 % were for outgoing and 10 % were for interfloor with increasing handling capacity from 11 % to 13 %

STEP2:

Mixed passenger profiles where 0 % of the landing calls were for incoming, 100 % were for outgoing and 0 % were for interfloor with increasing handling capacity from 11 to 13 %

STEP3:

Mixed passenger profiles where 80 % of the landing calls were for incoming, 15 % were for outgoing and 5 % were for interfloor, with increasing handling capacity from 11 to 13 %

TSP:

Travelling salesman problem

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Author information

Authors and Affiliations

  1. Department of Electronics and Computing, University of Mondragon, Arrasate, Spain

    M. Beamurgia & R. Basagoiti

  2. Faculty of Economics and Business Science, University of Navarra, Pamplona, Spain

    I. Rodríguez & V. Rodriguez

Authors
  1. M. Beamurgia
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  2. R. Basagoiti
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  3. I. Rodríguez
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  4. V. Rodriguez
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Corresponding author

Correspondence to M. Beamurgia.

Additional information

Communicated by V. Loia.

Practical application A genetic algorithm can be used to solve the elevator dispatching problem. Adjustments can optimize the solution. The current paper lists and describes possible adjustments, and evaluates their effects on performance in isolation and in combination.

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Beamurgia, M., Basagoiti, R., Rodríguez, I. et al. A modified genetic algorithm applied to the elevator dispatching problem. Soft Comput 20, 3595–3609 (2016). https://doi.org/10.1007/s00500-015-1718-1

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  • DOI: https://doi.org/10.1007/s00500-015-1718-1

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