## 1 Introduction  
The location of a brick and mortar business plays a vital role in its success or failure. In order to keep investors happy and to avoid any financial losses, it is necessary to select an optimal site for a new business. The term \"optimal\" refers to a location that may be suitable for a new business and that yields paybacks. However, the identification of optimal sites does not depend on any one factor. There are several aspects that require consideration, such as competition in the area, the target customers' convenience in terms of accessibility, the convenience of suppliers, traffic congestion in the area, etc. The selection of an optimal business site is a multi-criteria decision-making (MCDM) [1,2] problem. MCDM involves dealing with decisions where the choice of an alternative site is provided by several potential candidates while considering several criteria [3, 4, 5]. Since this problem poses great challenges, there are several related research papers that suggest different algorithms for the identification of an ideal location to open new business. Most of these approaches are either data based [6] or survey based [7; 8]. Data-based approaches take into consideration only a single criteria in their evaluation, which results in biased decisions, whereas survey-based approaches lack the use of real data and are based on the opinion of a small group of people. Our argument is that, in big cities, for the selection of an optimal business site such as gas stations, convenience stores, restaurants, etc., a multi-criteria-based approach must be employed, and it should be applied on some real data for evaluation. There is disagreement upon the decision of a suitable site for a new commercial opening as there are several criteria to be considered and some of them are more important/significant than others. For instance, for a gasoline station, ease of access for vehicles is an extremely important criteria, whereas for a convenience store, it is not so important. Based on the significance, different weights need to be assigned to different criteria. Hence, in this work, we present an AHP (Analytic Hierarchy Process) /TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution)-based approach [9; 10], which choses an optimal site for a commercial opening when given a set of candidate sites. The techniques of AHP and TOPSIS are termed multi-criteria decision-making (MCDM) [1; 2]. They are employed to derive criteria/factors' weight and to provide the ranking of alternatives/options. Although both approaches can be used to solve the MCMD problem separately, individually, each approach has some limitations. For instance, AHP alone is a very flexible and powerful MCDM tool and the computations made by AHP are always guided by the decision maker's experience. However, if the decision maker's understanding about the alternatives is not good enough, it can lead to inaccurate results. On the other hand, AHP alone requires a large number of evaluations by the user, especially for problems with many criteria and alternatives [11]. In fact, the number of pairwise comparisons grows quadratically with the number of criteria and options. This is discussed with an example in Section 4. To tackle this issue, [PERSON] et al. proposed an optimized-AHP (O-AHP) method for the generation of a judgment matrix by using a mathematical programming formulation. According to the authors, O-AHP exhibits the same effectiveness as the standard AHP and can be easily applied to a large number of alternatives [12]. In [13], the authors use augmented reality-based decision-making (AR-DM) for the multicriteria analysis approach. The approach starts with problem structuring using a flowchart similar to that of the AHP and proceeds with new phases inspired from the SRF method. On the other hand, TOPSIS does not support criteria weight computation and must be combined with some technique to compute the weights. Usually AHP or entropy weights are used with TOPSIS [14].