A mulTi-noRmalization mUlti-distance aSsessmenT (TRUST) approach for locating a battery swapping station for electric scooters

Abstract Due to high popularity of electric scooters, cities with high population have faced challenges regarding establishment of battery swapping stations (BSS) along populated areas of the urban districts. However, locating a BSS in a big city is a multi-dimensional problem which require reliable tools to efficiently address. This paper proposes a novel robust decision-making tool, named mulTi-noRmalization mUlti-distance aSsessmenT (TRUST), to tackle location selection problem for BSS considering sustainability criteria. The proposed approach applies a multi-normalization procedure using three linear normalization techniques, logarithmic-normalization, and constraint-based normalization which are integrated through an aggregation operator. Then, Euclidean, Manhattan, Lorentzian, and Pearson distance measures are used to determine distance of alternatives from the negative ideal solution in order to calculate the final score. Advantages of the proposed approach are consideration of a multi-normalization algorithm to minimize subjectivity in normalized data, consideration of constraint-based normalization technique to ensure that specific standards, and utilization of four distance measures through a two-stage process to determine a relative distance score. To show the feasibility and applicability of the new approach, a real-life case study is investigated to locate a BSS in Istanbul. Results show that the best alternative is Beyoglu for locating a BSS for electric scooters.