Multi-attribute Group Decision Making Method of Ecological Water Compensation Program Based on Preference of Decision Makers

Abstract

Sun, B.; Wei, M.; Du, J., Ji, W., and Wen, M., 2015. Multi-attribute group decision making method of ecological water compensation program based on preference of decision makers.This paper presented a multi-attribute group decision making model of ecological water compensation program based on preference of decision makers. First, it constructed the interval indexes of ecological water compensation program and conducted general standardization on the initial decision matrix based on cost and efficiency indexes, which introduced the concept of attitude indexes and eliminated errors brought by preferences of decision-makers for the program. Second, it aimed for the minimum deviation between the expectation of decision-makers on the optimal solution and objective values, and the weighted value of unknown attributes of the optimal model was obtained. Finally, we used the ecological water compensation in Nantong City as an example to verify the validity of the model. ADDITIONAL INDEX WORDS:Ecological water compensation program, multi-attribute group decision making, attitude index, uncertain weights, interval index.

Group Decision-Making Method for Dynamic Cloud Model Based on Cumulative Prospect Theory

“…As modern science and technology develops rapidly, people start to pay more and more attention to group decision-making methods, which have important applicable values and developmental prospects in many fields such as investment decision-making, intelligent transportation and network planning, etc., [1][2]. Group decision-making methods mainly contain multiple attribute decision making(MADM) and multiple objective decision making (MODM), in which the former primarily makes a decision about the known solution and the latter produces a scheme about unknown solution [3][4][5].…”

“…If former is greater than latter, the former is regarded as the second best solution. Otherwise, the solution that has the biggest comprehensive prospect value in 2 L is set as the second best solution; (vi) If the former solution in step (iv) works out, then compare the solution that has the third biggest comprehensive prospect value to the solution from 1 L that has the biggest comprehensive prospect value from 2 L .If the former solution is greater than the latter one, the former will be the third best solution; If the latter solution in step (iv) works out, we will mutually compare the second biggest comprehensive prospect value in 1 L and 2 L to the biggest comprehensive prospect value in 3 L , and then the biggest one is the third best solution; By parity of reasoning, it comes to an end until all solutions are compared.…”