Multi Criteria Decision Making Methods in Terms of Information Measures

Abstract

This thesis will help assist people in making tough decisions. In this study, two novel strategies for choosing the MCDM methods have been developed. These methods are based on an examination of MCDM issues, procedures, and the reliability of findings. This thesis introduces two novel MCDM methods, namely the Sum Weighted Information (SWI) method, and the Sum Weighted Exponential Information (SWEI) method, which integrate information measures into the decision-making process. The SWI and SWEI methods leverage Shannon entropy information measures to assess the significance of criteria and alternatives, thereby facilitating more informed and robust decision-making outcomes. The application of these methods is demonstrated through a real-world case study involving the selection of optimal materials for engineering applications newlineThe SWI method incorporates Shannon entropy to quantify the uncertainty and diversity in the criteria and alternatives, allowing decision-makers to prioritize criteria based on their information content. By assigning weights to criteria proportional to their information content, the SWI method enables a more nuanced consideration of diverse decision factors, leading to more balanced and informed decisions. Additionally, the SWI method offers a flexible framework that accommodates various decision scenarios, including uncertain and incomplete information. On the other hand, the SWEI method extends the SWI method by incorporating weighted exponential information measures, which exhibit enhanced sensitivity to variations in criteria importance. By applying weighted exponential functions to the information content of criteria and alternatives, the SWEI method offers a more flexible and customizable approach to decision-making, allowing decision-makers to adjust the level of sensitivity to information content based on the specific requirements of the decision problem.