An evolutionary strategic weight manipulation approach for multi-attribute decision making: TOPSIS method

Weight information of the attributes plays a pivotal role in multi-attribute decision making (MADM) problems. Oftentimes, a decision maker may try to manipulate this weight information to persuade a particular rank order of the alternatives of his/her interest. In the literature, this type of manipulation is known as strategic manipulation of the weight information. In this study, we consider the manipulation of weight information strategically in a TOPSIS MADM method under two scenarios: (1) completely unknown weight information i.e. the decision maker does not provide any weight information; (2) incomplete weight information i.e. the decision maker provides only partial preference information over the attributes. This weight manipulation problem is formulated as a mixed integer non-linear programming (MINLP) problem which is highly constrained. Therefore, for solving the MINLP model, a genetic algorithm based solution procedure is developed. A practical example is presented to illustrate the strategic manipulation procedure. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

This study investigates strategic manipulation of weight information in a TOPSIS MADM method under two scenarios and formulates the problem as a mixed integer non-linear programming (MINLP) problem. A genetic algorithm based solution procedure is developed to solve this highly constrained problem.