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Elevator planning with stochastic multicriteria acceptability analysis

Tommi Tervonen, Henri Hakonen, Risto Lahdelma, Elevator planning with stochastic multicriteria acceptability analysis. Omega 36(3), 352-362, 2008.

Abstract:

Modern elevator systems in high-rise buildings consist of groups of elevators with centralized control. The goal in elevator
planning is to configure a suitable elevator group to be built. The elevator group must satisfy specific minimum requirements
for a number of standard performance criteria. In addition, it is desirable to optimize the configuration in terms of other criteria
related to the performance, economy and service level of the elevator group. Different stakeholders involved in the planning
phase emphasize different criteria. Most of the criteria measurements are by nature uncertain. Some criteria can be estimated by
using analytical models, while others, especially those related to the service level in different traffic patterns, require simulations.
In this paper we formulate the elevator planning problem as a stochastic discrete multicriteria decision-making problem. We
compare 10 feasible elevator group configurations for a 20-floor building. We evaluate the criteria related to the service level in
different traffic situations using the KONE Building Traffic Simulator, and use analytical models and expert judgments for other
criteria. The resulting decision problem contains mixed type criteria. Some criteria are represented by the multivariate Gaussian
distribution, others by deterministic values and ordinal (ranking) information. To identify configurations that can best satisfy the
goals of the stakeholders, we analyze the problem using the stochastic multicriteria acceptability analysis (SMAA) method.

BibTeX entry:

@ARTICLE{jTeHaLa08a,
  title = {Elevator planning with stochastic multicriteria acceptability analysis},
  author = {Tervonen, Tommi and Hakonen, Henri and Lahdelma, Risto},
  journal = {Omega},
  volume = {36},
  number = {3},
  pages = {352-362},
  year = {2008},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI)

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