The facility layout problem belongs to typical multiple-attribute decision-making（MADM） problems. To make the information axiom fit to MADM problems, the original computation method for the information content is im...The facility layout problem belongs to typical multiple-attribute decision-making（MADM） problems. To make the information axiom fit to MADM problems, the original computation method for the information content is improved by increasing the satisfaction degree item. Attribute values are divided into precise type, uncertainty type and fuzzy type. For benefit, cost, fixation, and interval type attributes, the computation methods for the information content on the three types of attribute values are presented. The improved information content can reflect the system success probability and the decision-maker satisfaction degree simultaneously and evaluate the MADM problem including multiple type attribute values. Finally, as a case study, the facility layout alternatives of a welding assembly workshop are evaluated. The result verifies the validity and the feasibility of the improved information axiom on the MADM problems.展开更多
In this paper, for multiple attribute decision-making problem in which attribute values are interval grey numbers and some of them are null values, a decision model based on grey rough sets integration with incomplete...In this paper, for multiple attribute decision-making problem in which attribute values are interval grey numbers and some of them are null values, a decision model based on grey rough sets integration with incomplete information is proposed. We put forward incidence degree coefficient formula for grey interval, by information entropy theory and analysis technique, the method and principle is presented to fill up null values. We also establish the method of grey interval incidence cluster. Because grey system theory and Rough set theory are complementary each other, decision table with preference information is obtained by the result of grey incidence cluster. An algorithm for inducing decision rules based on rough set theory and the dominance relationship is presented. In some extent, this algorithm can deal with decision-making problem in which the attribute values are interval grey numbers and some of them are null values. Contrasted with classical model of cluster decision-making, the algorithm has an advantage of flexibility and compatibility to new information.展开更多
The TODIM(an acronym in Portuguese for interactive and multiple attribute decision making) method is a valuable tool to solve the multiple attribute decision making(MADM) problems considering the behavior of the decis...The TODIM(an acronym in Portuguese for interactive and multiple attribute decision making) method is a valuable tool to solve the multiple attribute decision making(MADM) problems considering the behavior of the decision maker(DM), while it cannot be used to handle the problem with unknown weight information on attributes. In this paper, a novel method based on the classical TODIM method is proposed to solve the hybrid MADM problems with unknown weight information on attributes,in which attribute values are represented in four different formats:crisp numbers, interval numbers, triangular fuzzy numbers and trapezoidal fuzzy numbers. Firstly, the positive-ideal alternative and negative-ideal alternative are determined, and the gain and loss matrices are constructed by calculating the gain and loss of each alternative relatived to the ideal alternatives concerning each attribute based on different distance calculation formulas, which may avoid the information missing or information distortion in the process of unifying multiform attribute values into a certain representation form. Secondly, an optimization model based on the maximizing deviation(MD) method, by which the attribute weights can be determined, is established for the TODIM method. Further, the calculation steps to solve the hybrid MADM problems are given. Finally, two numerical examples are presented to illustrate the usefulness of the proposed method, and the results show that the DM's psychological behavior, attribute weights and the transformed information would highly affect the ranking orders of alternatives.展开更多
基金Supported by the National Natural Science Foundation of China(50505017,50775111)the Qing Lan Project of China~~
文摘The facility layout problem belongs to typical multiple-attribute decision-making（MADM） problems. To make the information axiom fit to MADM problems, the original computation method for the information content is improved by increasing the satisfaction degree item. Attribute values are divided into precise type, uncertainty type and fuzzy type. For benefit, cost, fixation, and interval type attributes, the computation methods for the information content on the three types of attribute values are presented. The improved information content can reflect the system success probability and the decision-maker satisfaction degree simultaneously and evaluate the MADM problem including multiple type attribute values. Finally, as a case study, the facility layout alternatives of a welding assembly workshop are evaluated. The result verifies the validity and the feasibility of the improved information axiom on the MADM problems.
基金Supported by the NSF of Henan Province(082300410040)Supported by the NSF of Zhumadian City(087006)
文摘In this paper, for multiple attribute decision-making problem in which attribute values are interval grey numbers and some of them are null values, a decision model based on grey rough sets integration with incomplete information is proposed. We put forward incidence degree coefficient formula for grey interval, by information entropy theory and analysis technique, the method and principle is presented to fill up null values. We also establish the method of grey interval incidence cluster. Because grey system theory and Rough set theory are complementary each other, decision table with preference information is obtained by the result of grey incidence cluster. An algorithm for inducing decision rules based on rough set theory and the dominance relationship is presented. In some extent, this algorithm can deal with decision-making problem in which the attribute values are interval grey numbers and some of them are null values. Contrasted with classical model of cluster decision-making, the algorithm has an advantage of flexibility and compatibility to new information.
基金supported by the Ministry of Education Project of Humanities and Social Sciences(13XJC630011)the Specialized Research Fund for the Doctoral Program of Higher Education of China(20130203120024)+1 种基金the Soft Science Project of Shaanxi Province(2013KRZ25)the Xi’an Science and Technology Plan Projects(SF1404)
文摘The TODIM(an acronym in Portuguese for interactive and multiple attribute decision making) method is a valuable tool to solve the multiple attribute decision making(MADM) problems considering the behavior of the decision maker(DM), while it cannot be used to handle the problem with unknown weight information on attributes. In this paper, a novel method based on the classical TODIM method is proposed to solve the hybrid MADM problems with unknown weight information on attributes,in which attribute values are represented in four different formats:crisp numbers, interval numbers, triangular fuzzy numbers and trapezoidal fuzzy numbers. Firstly, the positive-ideal alternative and negative-ideal alternative are determined, and the gain and loss matrices are constructed by calculating the gain and loss of each alternative relatived to the ideal alternatives concerning each attribute based on different distance calculation formulas, which may avoid the information missing or information distortion in the process of unifying multiform attribute values into a certain representation form. Secondly, an optimization model based on the maximizing deviation(MD) method, by which the attribute weights can be determined, is established for the TODIM method. Further, the calculation steps to solve the hybrid MADM problems are given. Finally, two numerical examples are presented to illustrate the usefulness of the proposed method, and the results show that the DM's psychological behavior, attribute weights and the transformed information would highly affect the ranking orders of alternatives.
