Oil project selection in Iran: A hybrid MADM approach in an uncertain environment

Abstract

This research focusses on the selection of oil projects by using Multi-Attribute Decision Making (MADM) methods in an uncertain environment. Oil production plays a crucial role in the economy of Iran, a country with many opportunities for onshore oil exploration. Differently from other countries, however, oil producers in Iran face some constraints with respect to oil extraction because some areas are shared with other producers. Additionally, oil producers in Iran must also allocate scarce physical, human, and monetary resources among different projects. Inaccurate decision-making may not only yield sub-optimal revenue generation, but also adversely affect the national economy For these reasons, priority oil projects must observe a sequence of steps. In the first step, critical factors for selecting oil projects are collected from previous related studies and experts are interviewed. These factors are subsequently filtered using the Delphi method. The oil projects are then ranked using a comprehensive approach involving novel alternative MADM methods. The best-worst method (BWM) is a new MADM stream that relies on pairwise comparison. It presents several distinct advantages with respect to fewer computational steps and higher discriminatory power among alternatives. Differently from previous research, this paper couples BWM with Weighted Aggregated Sum–Product Assessment (WASPAS) to improve result sensitivity under uncertain decision-making environments as modelled by Z-numbers. A robustness cross-check against other MADM models is also presented. Results indicate that quality has the highest priority and that production technology has the lowest priority among ten factors for oil project selection, thus reflecting the impact of US sanctions on oil production in Iran. Managerial implications and future avenues of research are derived.