No abstract

No abstract

Due to urbon expansion and population increasing bus network design is an important problem in public transportation. Functional aspect of bus networks such as fuel consumption and the depreciation of buses and also spatial aspects of bus networks such as station and terminal locations or access rate to the buses are not proper conditions in most cities. Therefore, having an efficient method to evaluate the performance of bus lines considering both functional and spatial aspects is essential. In this paper, we propose a new model for bus terminal location problem (BTLP) using data envelopment analysis (DEA) with multi-objective programming approach. In this model, we want to find an efficient allocation patterns for assigning stations terminals and also we find the optimal locations for deploying terminals. So we use genetic algorithm for solving our model. By simultaneous combing the data envelopment analysis and bus terminal location problem, two types of

No abstract

The probabilistic hesitant fuzzy set (PHFS) is a worthwhile extension of the hesitant fuzzy set (HFS) which allows people to improve their quantitative assessment with the corresponding probability. Recently, in order to address the issue of difficulty in aggregating decision makers’ opinions, a probability splitting algorithm has been developed that drives an efficient probabilistic-unification process of PHFSs. Adopting such a unification process allows decision makers to disregard the probability part in developing fruitful theories of comparison of PHFSs. By keeping this feature in mind, we try to introduce a class of score functions for the notion of the single-valued extended hesitant fuzzy set (SVEHFS) as a novel deformation of PHFS. Interestingly, a SVEHFS not only belongs to a less dimensional space compared to that of PHFSs but also the proposed SVEHFS-based score functions satisfy a number of interesting properties. Eventually, some case studies of multiple criteria decision-making (MCDM) techniques under the PHFS environment are provided to demonstrate the effectiveness of proposed SVEHFS-based score functions.