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Dalal, J, Mohapatra, P K J and Mitra, G C (2010) Prioritization of rural roads: AHP in group decision. Engineering, Construction and Architectural Management, 17(02), 58.

  • Type: Journal Article
  • Keywords: analytical hierarchy process; decision making; India; rural areas
  • ISBN/ISSN: 0969-9988
  • URL: http://www.emeraldinsight.com/10.1108/09699981011024669
  • Abstract:
    Purpose – Poor conditions of rural roads in most developing countries have hindered the growth of their rural economy. Investments from government and other sources in rural road projects are inadequate, leading often to political, rather than socio-economic, considerations in allocating funds to specific projects. Realizing this, many donor organizations demand an unbiased study for prioritizing rural roads for funds allocation. This requires prioritizing rural roads on a rational basis. This paper seeks to examine this issue. Design/methodology/approach – A two-stage AHP model is developed considering several socio-economic criteria relevant to a road and the region through which it passes. The model is exercised in a group-decision environment. The final scores indicate a list of roads in decreasing order of priority. Findings – The framework, when applied to 178 rural roads in two districts of Orissa, a relatively backward state of India, yields a ranked-order list of roads.Research limitations/implications – Experts' judgments during pair-wise comparison are associated with imprecision. Fuzzy AHP is a distinct alternative. Possible dependency among criteria has been ignored. Use of Analytic Network Process (ANP) can overcome problems arising from such dependencies. Practical implications – Funds that are limited can be allotted to the highly ranked roads. Originality/value – The paper presents application of AHP for ranking of rural roads in a developing country; development of a generic road prioritization framework; and devising a group decision-making procedure that combines the best features of the group consensus and geometric mean methods.