The paper presents a hybrid model based on the LBWA method and the fuzzy MABAC method, applied when selecting firing positions' locations of the Serbian Army's mortar units. Using a questionnaire, the experts determined the criteria for choosing the firing position. The LBWA method is used to determine the weighting coefficients of the criteria, while the fuzzy MABAC method is used to determine the most favorable location of the firing position by choosing between six specific options - alternatives. By changing the value of the elasticity coefficients, the sensitivity analysis of the developed model was performed, and by applying the Spearman coefficient, it was determined that there is an ideal positive correlation of ranks.
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<p>In real world uncertainty exist in almost every problem. Decision-makers are often unable to describe the situation accurately or predict the outcome of potential solutions due to uncertainty. To resolve these complicated situations, which include uncertainty, we use expert descriptive knowledge which can be expressed as fuzzy data. Pakistan, a country with a key geographic and strategic position in South Asia, relies heavily on irrigation for its economy, which involves careful consideration of the limits. A variety of factors can affect yield, including the weather and water availability. Crop productivity from reservoirs and other sources is affected by climate change. The project aims to optimize Kharif and Rabbi crop output in canal-irrigated areas. The optimization model is designed to maximize net profit and crop output during cropping seasons. Canal-connected farmed areas are variables in the crop planning model. Seasonal crop area, crop cultivated area, crop water requirement, canal capacity, reservoir evaporation, minimum and maximum storage, and overflow limits affect the two goals. The uncertainties associated with the entire production planning are incorporated by considering suitable membership functions and solved using the Multi-Objective Neutrosophic Fuzzy Linear Programming Model (MONFLP). For the validity and effectiveness of the technique, the model is tested for the wheat and rice production in Pakistan. The study puts forth the advantages of neutrosophic fuzzy algorithm which has been proposed, and the analyses derived can be stated to deal with yield uncertainty in the neutrosophic environments more effectively by considering the parameters which are prone to abrupt changes characterized by unpredictability.</p>
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This paper presents a new approach in the modification of the CRiteria Importance Through Intercriteria Correlation (CRITIC) method using fuzzy rough numbers. In the modified CRITIC method (CRITIC-M), the normalization procedure of the home matrix elements was improved and the aggregation function for information processing in the normalized home matrix was improved. By introducing a new way of normalization, smaller deviations between normalized elements are obtained, which affects smaller values of standard deviation. Thus, the relationships between the data in the initial decision matrix are presented in a more objective way. The introduction of a new way of aggregating the values of weights in the CRITIC-M method enables a more comprehensive view of information in the initial decision matrix, which leads to obtaining more objective values of weights. A new concept of fuzzy rough numbers was used to address uncertainties in the CRITIC-M methodology.
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