Predictive and simultaneous weighting of criteria and alternatives (PSWCA) in multi-criteria decision making based on past data

The emergence of complex technologies and economic competition in recent decades has led to the increasing importance of decisions about the future of organizations. Most of the former methods require a combination with the criterion weighting methods, which increases the complexity of the calculations. On the other hand, the importance of a criterion changes over time. Therefore, the provided weights must be proportional to the changes expected in the future. In this way, making future decisions based on past knowledge may not guarantee the best choice, but it can guide decision makers in the right direction. In this study, an innovative technique for simultaneously ranking options, and weighting criteria based on historical data is presented. In this method, it is possible that the reference weight is affected by other weighting methods and more accurate weights are assigned to the criteria. In addition, all the records are considered to evaluate the alternatives in relation to the criteria. After calculating the deviation and the starting point changes, a nonlinear mathematical model determines the coefficients of the reference weight (that is, the weight with the smallest difference from the values of the coefficients) and the final score of the options. Finally, the efficiency of the proposed method (PSWCA) is obtained on four real case studies, and the results are compared with other methods.