Sometimes in the sense of intuitionist 2-tuple linguistic (I2TL) sets, experts may not be able to decide the most suitable criterion weight vector for multicriteria decision making (MCDM). To avoid this situation, the...
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Sometimes in the sense of intuitionist 2-tuple linguistic (I2TL) sets, experts may not be able to decide the most suitable criterion weight vector for multicriteria decision making (MCDM). To avoid this situation, the decisionmakers (DMs) can use the best-worstmethod (BWM), in which DMs choose the best (most significant) criterion and the worst (least significant) criterion and then provide two preference vectors by comparing criteria best to other (BO) and other to worst (OW). In the Nonlinear best-worst method (NBWM) it is more complicated to find the unique solution of the model. Therefore, the main goal of this study is to propose two approaches to BWM, namely, linear best-worst method (LBWM) and Euclidean best-worstmethod (EBWM) to achieve the best criteria priority vector for Multi-Criteria Group Decision Making (MCGDM) problems in the context of I2TL information. In the computational process of MCDM problems, we have to aggregate I2TL elements into a global one. Consequently, under certain critical properties, we are creating some operational laws for I2TL elements based on Hamacher operations. Also, the intuitionistic 2-tuple linguistic Hamacher weighted average (I2TLHWA), and the intuitionistic 2-tuple linguistic Hamacher weighted geometric (I2TLHWG) operators are introduced with the assistance of Hamacher operations and I2TL elements. Subsequently, we analyze some of the I2TLHWA operator's related properties and we propose MCGDM framework under I2TL information. Finally, we demonstrate the validity and efficiency of our method and operations.
Maximal covering location problems have been widely studied, due to the practical applications of their solutions in real-life scenarios where it is not possible to fulfill the total demand. For example, these solutio...
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Maximal covering location problems have been widely studied, due to the practical applications of their solutions in real-life scenarios where it is not possible to fulfill the total demand. For example, these solutions can be used to provide humanitarian relief or to allocate fire stations, hospitals, and commercial services. However, coverage is commonly based on the ability of clients to reach the facilities or on the ability of facilities to serve clients within a reasonable area (or radius) or in a limited service time. In this study, we assume that facilities have a limited service area, while people in demand centroids have a degree of mobility encompassing a reasonable travel distance to look for their demand. Based on the latter assumption, we define a maximum covering location problem that optimizes an accessibility measure. This is a weighted sum of accessibility indicators based on the coverage of demand centroids, the number of demand centroids with access to opportunities within their mobility radius, the number and location of opportunities, a travel cost function, and spatial disaggregation. We formulate our optimization problem through a mixed-integer linear program; an experimental stage on randomly-generated instances shows that a commercial solver is capable of obtaining near-optimal solutions in reasonable computational times for large instances. In addition, we use data from an economically-deprived region in Mexico to perform a sensitivity analysis for different service and mobility radii. Finally, we implement the linearbestworstmethod to obtain the value of weights parameters representing subjective preferences for different indicators of accessibility.
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