Evaluation of the Special Warehouse Handling Equipment (turret Trucks) Using Integrated FUCOM and WASPAS Techniques Based on Intuitionistic Fuzzy Dombi Aggregation Operators

Turret trucks used for special warehousing operations have exceptional abilities, qualifications, and working principles than other warehousing handling equipment. Also, at the same time, if decision-makers cannot select as proper to needs, the cost of being idle of these kinds of machines is very high. According to the comprehensive literature review and the paper's findings, evaluating turret trucks is complex, complicated, and time-consuming for decision-makers, as many conflicting criteria and uncertainties affect the evaluation processes. Hence, it is required to employ a practical, powerful, and practical multi-criteria decision-making (MCDM) approach that can handle ambiguities to solve these kinds of problems. For this purpose, the current paper proposes an extended version of a hybrid decision-making tool consisting of FUCOM (Full Consistency Method) and the WASPAS (Weighted Aggregated Sum Product Assessment) technique with the help of the intuitionistic fuzzy Dombi aggregation operators. It can help to select the appropriate turret trucks that can help reduce the costs of remaining idle and provide economic effectivity of logistics and warehousing operations. According to the outcomes of the suggested model, the width, lift motor power and lift height of the turret trucks are the most critical and influential criteria, as they determine the corridor width, accordingly space utilization, warehousing capacity, and unit warehousing costs. Finally, the intuitionistic fuzzy model is applied to solve the turret truck selection problems. A comprehensive sensitivity analysis consisting of three phases was performed to test the validation of the proposed model and its obtained results. The sensitivity analysis results approve the proposed model's applicability and validity. Thus, the analysis results validated that the proposed approach is a robust and practical MCDM framework, and its results are accurate and reasonable.