This paper deals with the multiple experts multiple criteria decision making (MEMCDM) problem. To help experts in a committee in reaching a satisfactory decision outcome, a three phase decision making method is deve...This paper deals with the multiple experts multiple criteria decision making (MEMCDM) problem. To help experts in a committee in reaching a satisfactory decision outcome, a three phase decision making method is developed in this study. We describe the procedure of the proposed three phase method and its application in a case study. Three phases—the initial comparison test, criteria aggregation, and experts’ aggregation, are concerned with fuzzy evaluation of the multiple criteria, which is based on fuzzy preference ordering and membership functions of the fuzzy set theory. The algorithms are presented thoroughly for each phase of overall procedure of MEMCDM. The three phase method combines the Delphi interactive method and the evaluation of minimal separation measure from the ideal solution. Finally worthwhile directions for future research are summarized.展开更多
The main purpose of this paper is to build a new approach for solving a fuzzy linear multi-criterion problem by defining a function called “error function”. For this end, the concept of level set is used to co...The main purpose of this paper is to build a new approach for solving a fuzzy linear multi-criterion problem by defining a function called “error function”. For this end, the concept of level set is used to construct the error function. In addition, we introduce the concept of deviation variable in the definition of the error function. The algorithm of the new approach is summarized in three main steps: first, we transform the original fuzzy problem into a deterministic one by choosing a specific level . second, we solve separately each uni-criteria problem and we compute the error function for each criteria. Finally, we minimize the sum of error functions in order to obtain the desired compromise solution. A numerical example is done for a comparative study with some existing approaches to show the effectiveness of the new approach.展开更多
The VIKOR method is a multi-criteria decision making aid, which employs linear normalization to offer compromise solu- tions and has been successfully applied to various group decision making problems. However, the co...The VIKOR method is a multi-criteria decision making aid, which employs linear normalization to offer compromise solu- tions and has been successfully applied to various group decision making problems. However, the conventional VIKOR techniques used to integrate group judgments and the information loss arising from defuzzification are problematic and distort final outcomes. An improved integration method, which is optimization-based, is proposed. And it can handle fuzzy criteria values and weights. The precondition for accurately defuzzifying triangular fuzzy num- bers is identified. Several effective defuzzification procedures are proposed to improve the extant VIKOR, and a comprehensive evaluation framework is offered to aid multi-criteria group decision making. Finally, a numerical example is provided to illustrate the practicability of the proposed method.展开更多
文摘对计及经济、环境因素的电力系统发电调度问题(Economic Environmental Dispatching,EED)进行研究,提出一种采用改进多目标灰狼算法的发电调度规划方案。构建基于发电燃料成本、污染气体排放量和节点电压偏移量等指标的多目标EED模型,并采用改进的多目标灰狼算法进行求解,以得到更优的Pareto前沿和折中解。设计多度量自适应FCM算法对灰狼算法(Gray Wolf Algorithm,GWA)种群多样性进行分析,重新定义狼群层级结构和Pareto前沿规模控制策略,并在此基础上提出反向学习和变异进化策略,以提升GWA全局收敛性能。仿真结果表明,改进的GWA具有优秀全局寻优能力,而且基于改进多目标灰狼算法得到的Pareto前沿和折中解更具可行性和优越性。
文摘This paper deals with the multiple experts multiple criteria decision making (MEMCDM) problem. To help experts in a committee in reaching a satisfactory decision outcome, a three phase decision making method is developed in this study. We describe the procedure of the proposed three phase method and its application in a case study. Three phases—the initial comparison test, criteria aggregation, and experts’ aggregation, are concerned with fuzzy evaluation of the multiple criteria, which is based on fuzzy preference ordering and membership functions of the fuzzy set theory. The algorithms are presented thoroughly for each phase of overall procedure of MEMCDM. The three phase method combines the Delphi interactive method and the evaluation of minimal separation measure from the ideal solution. Finally worthwhile directions for future research are summarized.
文摘The main purpose of this paper is to build a new approach for solving a fuzzy linear multi-criterion problem by defining a function called “error function”. For this end, the concept of level set is used to construct the error function. In addition, we introduce the concept of deviation variable in the definition of the error function. The algorithm of the new approach is summarized in three main steps: first, we transform the original fuzzy problem into a deterministic one by choosing a specific level . second, we solve separately each uni-criteria problem and we compute the error function for each criteria. Finally, we minimize the sum of error functions in order to obtain the desired compromise solution. A numerical example is done for a comparative study with some existing approaches to show the effectiveness of the new approach.
基金supported by the National Natural Science Foundation of China(71271116)
文摘The VIKOR method is a multi-criteria decision making aid, which employs linear normalization to offer compromise solu- tions and has been successfully applied to various group decision making problems. However, the conventional VIKOR techniques used to integrate group judgments and the information loss arising from defuzzification are problematic and distort final outcomes. An improved integration method, which is optimization-based, is proposed. And it can handle fuzzy criteria values and weights. The precondition for accurately defuzzifying triangular fuzzy num- bers is identified. Several effective defuzzification procedures are proposed to improve the extant VIKOR, and a comprehensive evaluation framework is offered to aid multi-criteria group decision making. Finally, a numerical example is provided to illustrate the practicability of the proposed method.
