Abstract
Facility location, particularly in the context of international investments by global enterprises, stands out as a paramount concern within the purview of top management's strategic decision-making process. The selection of a suitable location plays a pivotal role in determining the ultimate achievement of organizational objectives. The process of selecting an appropriate location requires the comprehensive analysis of a substantial volume of data, encompassing diverse tangible and intangible evaluation criteria that may exhibit inherent conflicts. This paper addresses the challenge of determining the best location for a manufacturing facility by employing alternative performance measures within the framework of the data envelopment analysis (DEA) model. In a performance evaluation process, not only positive but also negative aspects should be determined. This paper, therefore, proposes a double-frontier DEA-AR model, which is an integrated approach that incorporates the efficient frontier, anti-efficient frontier, and assurance region weight restrictions, with the aim of increasing the discrimination ability of the DEA method. An efficient frontier evaluates the information of each location from a positive viewpoint, while the worst side is evaluated by an anti-efficient frontier. The technique of weight restrictions, which allows incorporating expert opinion into the assessment, is also applied with both frontiers to restrict the regions of weights to some specific area. The prescribed approach is illustrated by a numerical example of selecting the best location among ten different countries under consideration of 22 selection criteria obtained from PEST analysis. The results show that the proposed alternative performance measures significantly improve discrimination capability, enabling the ranking of candidates based on their suitability for the optimal location.