Decision Making in Location Selection : An Integrated Approach with Clustering and TOPSIS

Selection of the location is one of the most important decision-making processes which requires considering several criteria based on the mission and the strategy. In supply chain management, determining optimal facility location is a crucial problem. This study aims to provide a decision support model in order to help the decision makers to select the best location. It proposes a hybrid method, which incorporates K-means and Fuzzy C-Means (FCM) clustering techniques and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) into an evaluation process to solve the Facility Location Selection (FLS) problem. These methods effectively shortlist a set of viable locations out of several alternatives and choose the most suitable option effectively. The results of hybrid method are validated with other approaches, demonstrated for effectiveness and feasibility of the model to real world applications. As a result, this application shows that the method can be an important supportive decision-making tool for FLS. Further, the suggested methodology can be applied for any type of selection problems involving any number of attributes