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A complete information PCA-imprecise DEA approach for constructing composite indicator with interval data: an application for finding development degree of cities

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  • Kolsoom Zamani
  • Hashem Omrani

Abstract

Composite indicator approach is widely used for finding development degree of regions. One of the most important models for constructing composite indicator is data envelopment analysis (DEA) model. This paper presents a complete information principal component analysis (CIPCA)-imprecise DEA (IDEA) approach for finding development degree of cities with uncertain data. CIPCA is applied to reduce the number of indicators. The output of the CIPCA is a set of new indicators with lower and upper bounds. These indicators are considered as indicators of IDEA and final ranks of cities are calculated by IDEA model. To illustrate the capability of CIPCA-IDEA approach, the development degrees of cities in Kurdistan province of Iran are calculated. First, 62 development indicators are selected and the related interval data for year 2015 are gathered. Then, the proposed approach is applied for nine cities of Kurdistan province and development degree for each city is finally calculated. The results indicate that in the overall ranking, Bijar City is ranked first in the development and Baneh is ranked as ninth city.

Suggested Citation

  • Kolsoom Zamani & Hashem Omrani, 2022. "A complete information PCA-imprecise DEA approach for constructing composite indicator with interval data: an application for finding development degree of cities," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 44(4), pages 522-549.
    • Handle: RePEc:ids:ijores:v:44:y:2022:i:4:p:522-549
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