Affiliations: Center of Operations Research and Econometrics (CORE), Louvain School of Management, Université catholique de Louvain, Louvain-la-Neuve, Belgium | Business Systems and Analytics, La Salle University, Philadelphia, PA, USA | Department of Mathematics, Tehran-North Branch, Islamic Azad University, Tehran, Iran | Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Note: [] Corresponding author. Madjid Tavana, Business Systems and Analytics, La Salle University, Philadelphia, PA19141, USA. Tel.: +1 215 951 1129; Fax: +1 267 295 2854; E-mail: tavana@lasalle.edu
Abstract: Although crisp data are fundamentally indispensable in the conventional linear programming technique for multidimensional analysis of preference (LINMAP), the observed values in the real-world problems are often imprecise or vague. These imprecise or vague data can be suitably characterized by linguistic terms which are fuzzy in nature. LINMAP has been widely used to solve multi-attribute decision making (MADM) problems. This paper extends the conventional LINMAP model to a fuzzy group decision making framework using trapezoidal fuzzy numbers. The ranking approach is used to transform the fuzzy model into a crisp model. The fuzzy LINMAP method proposed in this paper is a simple and effective tool for tackling the uncertainty and imprecision associated with the group MADM problems. A case study in fast food industry is presented to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures.
Keywords: LINMAP, fuzzy linear programming, multi-attribute decision making, group decision making, fast food industry
