An enhanced TOPSIS method based on equality constrained optimization

TOPSIS is an effective multiple attributes decision making method. How to determine the weights of indicators and how to calculate proximity degree of each scheme are very crucial in TOPSIS. In this paper, we propose a weight combination model based on equality constrained optimization, which combines subjective weights and objective weights to calculate the optimal weights according to the distance difference degree. Then, we introduce the generalized weighted distance to replace the Euclidean distance and construct a novel proximity degree model. Our method takes the factual information and subjective experience into account, whilst overcomes the shortcomings of Euclidean distance. We evaluate the proposed method using the data of oilfield injection wells profile control selection layer. Results show that our method is much more effective than the traditional TOPSIS method.