European Transport Research Review (Jan 2021)

Sensitivity enriched multi-criterion decision making process for novel railway switches and crossings − a case study

  • Hitesh C. Boghani,
  • Ramakrishnan Ambur,
  • Marcelo Blumenfeld,
  • Louis Saade,
  • Roger M. Goodall,
  • Christopher P. Ward,
  • Otto Plášek,
  • Neil Gofton,
  • Miquel Morata,
  • Clive Roberts,
  • Roger Dixon

DOI
https://doi.org/10.1186/s12544-020-00467-x
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 14

Abstract

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Abstract Background Despite their important role in railway operations, switches and crossings (S&C) have changed little since their conception over a century ago. It stands now that the existing designs for S&C are reaching their maximum point of incremental performance improvement, and only a radical redesign can overcome the constraints that current designs are imposing on railway network capacity. This paper describes the process of producing novel designs for next generation switches and crossings, as part of the S-CODE project. Methods Given the many aspects that govern a successful S&C design, it is critical to adopt multi criteria decision making (MCDM) processes to identify a specific solution for the next generation of switches and crossings. However, a common shortcoming of these methods is that their results can be heavily influenced by external factors, such as uncertainty in criterium weighting or bias of the evaluators, for example. This paper therefore proposes a process based on the Pugh Matrix method to reduce such biases by using sensitivity analysis to investigate them and improve the reliability of decision making. Results In this paper, we analysed the influences of three different external factors, measuring the sensitivity of ranking due to (a) weightings, (b) organisational and (c) discipline bias. The order of preference of the results was disturbed only to a minimum while small influences of bias were detected. Conclusions Through this case study, we believe that the paper demonstrates an effective case study for a quantitative process that can improve the reliability of decision making.

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