Sensitivity Assessment of Multi-Criteria Decision-Making Methods in Chemical Engineering Optimization Applications

TL;DR

This study evaluates how different multi-criteria decision-making methods in chemical engineering are affected by changes in decision data, identifying which methods are more sensitive or robust to such modifications.

Contribution

It provides a comprehensive sensitivity analysis of various MCDM methods and weighting techniques, highlighting their stability and reliability in chemical engineering optimization.

Findings

Entropy weights are more sensitive to data modifications.

Reciprocal objective reformulation has the largest impact on rankings.

Certain methods like GRA without weights and MABAC are more robust.

Abstract

This chapter assesses the sensitivity of multi-criteria decision-making (MCDM) methods to modifications within the decision or objective matrix (DOM) in the context of chemical engineering optimization applications. Employing eight common or recent MCDM methods and three weighting methods, this study evaluates the impact of three specific DOM alterations: linear transformation of an objective (LTO), reciprocal objective reformulation (ROR), and the removal of alternatives (RA). Our comprehensive analysis reveals that the weights generated by entropy method are more sensitive to the examined modifications compared to the criteria importance through intercriteria correlation (CRITIC) and standard deviation (StDev) methods. ROR is found to have the largest effect on the ranking of alternatives. Moreover, certain methods, gray relational analysis (GRA) without any weights, multi-attributive border approximation area comparison (MABAC), combinative distance-based assessment (CODAS), and simple additive weighting (SAW) with entropy or CRITIC weights, and CODAS, SAW, and technique for order of preference by similarity to ideal solution (TOPSIS) with StDev weight are more robust to DOM modifications. This investigation not only corroborates the findings from the previous study, but also offers insights into the stability and reliability of MCDM methods in the context of chemical engineering.