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Multi-criteria decision making approach is one of the most troublesome tools for solving the tangled optimization problems in the machining area due to its capability of solving the complex optimization problems in the production process. Turning is widely used in the manufacturing processes as it offers enormous advantages like good quality product, customer satisfaction, economical and relatively easy to apply. A contemporary approach, MOORA coupled with PCA, was used to ascertain an optimal combination of input parameters (spindle speed, depth of cut and feed rate) for the given output parameters (power consumption, average surface roughness and frequency of tool vibration) using L27 orthogonal array for turning on ASTM A588 mild steel. Comparison between MOORA-PCA and TOPSIS-PCA shows the effectiveness of MOORA over TOPSIS method. The optimum parameter combination for multi-performance characteristics has been established for ASTM A588 mild steel are spindle speed 160 rpm, depth of cut 0.1 mm and feed rate 0.08 mm/rev. Therefore, this study focuses on the application of the hybrid MCDM approach as a vital selection making tool to deal with multi objective optimization problems.

The paper aims to present an integrated approach to solve the decision-making problem under the probabilistic hesitant fuzzy information (PHFI) features, which is an extension of the hesitant fuzzy set. The considered PHFI not only allows multiple opinions, but also associates occurrence probability to each opinion, which increases the reliability of the information. Motivated by these features of PHFI, an approach is presented to solve the decision problem with partial known information about the attribute and expert weights. In addition, an algorithm for finding some missing values in the preference information is presented and stated their properties. Afterward, the Hamy mean operator has been used to aggregate the different collective information into a single one. Also, we presented a COPRAS method to the PHFI for ranking the given alternatives. The presented algorithm has been demonstrated through a case study of cloud vendor selection and its validity has been revealed by comparing the approach results with the several existing algorithm results.

Sustainable site selection for electric vehicle charging station (EVCS) is a significant process in the promotion of electric vehicle system development. The assessment and selection of suitable EVCS site is a very critical decision, involving complexity due to the presence of several associated criteria. Furthermore, uncertainty is an inevitable component of the information in the decision-making procedure and its significance in the selection process is relatively high and needs to be cautiously measured. Single-valued neutrosophic set (SVNS) is one of the valuable and flexible tools for handling such type of uncertain information arising in multi-criteria decision-making (MCDM) applications. Thus, the objective of this study is to introduce novel single-valued neutrosophic information-based additive ratio assessment (ARAS) approach for evaluating and prioritizing the sustainable EVCS sites. In this method, novel single-valued subjective and objective weighted integrated approach (SVN-SOWIA) is developed to compute the criteria by aggregating the objective weights resulted from a similarity measure-based procedure and the subjective weights given by the experts. For this purpose, an innovative similarity measure is proposed for SVNSs.To display the performance of the present methodology, a computational study of EVCS sites evaluation is conferred under single-valued neutrosophic environment. Comparative and sensitivity analyses are further performed to verify the strength of the developed approach. The outcome illustrates EVCS site EvUrjaa-Electric Vehicle Charging Station is the most optimal EVCS site in Indore region, India. Also, the environmental (0.324) and social (0.273) criteria are more important than technological (0.236) and economical (0.167) criteria in assessing the EVCS sites. The sensitivity analysis outcomes signify the EVCS option EvUrjaa-Electric Vehicle Charging Station always acquires its highest ranking in spite of how sub-criteria weights fluctuate. The outcome of this study indicates that the developed approach can suggest more realistic performance under uncertain environment and therefore, provides a wide range of applications. K E Y W O R D Sadditive ratio assessment, electric vehicle charging station, multi-criteria decision-making, similarity measure, single-valued neutrosophic set | INTRODUCTIONDue to the rapid urbanization and depletion of natural resources, environmental degradation is a vital issue in today's world. 1 Rapid growth in population, urban development, modernization of agriculture, rapid industrialization, multiplicity of the means of transport, and deforestation are main causes of the environmental degradation. Energy is currently a crucial constraint on transport and transport is a foremost determinant of energy demand. To reduce air pollution and protect the environment, several countries have implemented different strategies to promote electric vehicle (EV) production. 2 EV is regarded as an innovative eco-friendly means of transportation which encourages the secu...

The dual probabilistic linguistic (DPL) term sets are considered superior to probabilistic linguistic term sets. The power average operator can lessen the effects of the extreme assessing data from some decision-makers with prejudice. Further, the Dombi operators are quite flexible with the general parameter during the aggregation process. Moreover, based on deviation from the maximum consistency by the exclusion of the concern of the redundancy of the comparisons made in criteria pairs, FUCOM (full consistency method) is utilized as a subjective criteria weight computing model. Besides, MARCOS (Measurements alternatives and ranking according to compromise solution) method is based on the determination of utility degrees according to the distance from anti-ideal and ideal solutions and their aggregations. In this study, we combine the merits of power average operator, Dombi operator, FUCOM technique and MARCOS for dealing with multi-criteria group decision-making (MCGDM) problems under a DPL setting where rank of the alternatives are obtained through MARCOS method. For aggregating decision-experts preferences, we propose two types of operators, namely- DPL Dombi power weighted averaging and DPL Dombi power weighted geometric aggregation operators. We discuss the elegant properties of these proposed aggregation operators. We provide a case study regarding open source software learning management system selection to focus the practicability and usefulness of the proposed approach. Furthermore, we perform a sensitivity assessment on diverse criteria weight sets in order to test the stability of our developed intriguing approach. To this effect, we also provide a comparison between our approach with various extant methods.