Exploration of Offshore Drilling for Oil and Gas Operations Based on the Probabilistic Linguistic Dombi Aggregation Decision Model

This study focuses on optimizing offshore drilling operations through Multi-Criteria Decision-Making (MCDM). It recognizes MCDM as a crucial framework for evaluating alternatives in the complex offshore drilling landscape. The objective is to identify the most suitable alternative among four approaches: Advanced Drilling Technologies, Drilling Process Optimization, Human Factors and Safety Enhancement, and Environmental Impact Mitigation. Evaluation criteria include Operational Efficiency, Safety Performance, Environmental Impact, and Cost-effectiveness. The research aims to contribute insights and recommendations for stakeholders in the offshore oil and gas industry. In the field of information aggregation and fusion, there is a growing interest among researchers in the domain of probabilistic linguistic expression sets, which are particularly effective in consolidating uncertain data. This article aims to explore various methodologies for information aggregation using probabilistic linguistic expressions. To achieve this goal, we have introduced procedural principles based on the Dombi (D) framework, specifically designed for managing probabilistic linguistic term elements (PLTEs). These principles are firmly grounded in both the product and sum of Dombi operations. As a result, we have developed a range of techniques for probabilistic linguistic aggregation, including entities such as the Probabilistic Linguistic Dombi Average (PLDA) and the Probabilistic Linguistic Dombi Geometric (PLDG). Additionally, we have created weighted aggregation operators (AOs) such as PLDWA and PLDWG, along with ordered AOs like PLDOWA and PLDOWG. By utilizing the D
$\tau $
-norm and
$\tau $
-conorm, we have designed versatile aggregation tools that support information reinforcement in both ascending and descending directions. Furthermore, we provide a comparative analysis between our proposed methodologies and the MARCOS approach. Additionally, we offer a detailed explanation of the distinctive attributes associated with these operators. Through the application of PLDA, PLDG, PLDWA, PLDWG, PLDOWA, and PLDOWG, we present strategies for effectively integrating probabilistic linguistic term sets (PLTs) into the realm of MCDM.