A MULTI-ATTRIBUTE LARGE GROUP EMERGENCY DECISION MAKING METHOD BASED ON GROUP PREFERENCE CONSISTENCY OF GENERALIZED INTERVAL-VALUED TRAPEZOIDAL FUZZY NUMBERS

In this paper, a new decision making approach is proposed for the multi-attribute large group emergency decision-making problem that attribute weights are unknown and expert preference information is expressed by generalized interval-valued trapezoidal fuzzy numbers （GITFNs）. Firstly, a degree of similarity formula between GITFNs is presented. Secondly, expert preference information on different alternatives is clustered into several aggregations via the fuzzy clustering method. As the clustering proceeds, an index of group preference consistency is introduced to ensure the clustering effect, and then the group preference information on different alternatives is obtained. Thirdly, the TOPSIS method is used to rank the alternatives. Finally, an example is taken to show the feasibility and effectiveness of this approach. These method can ensure the consistency degree of group preference, thus decision efficiency of emergency response activities can be improved.

DIP-MOEA:a double-grid interactive preference based multi-ob jective evolutionary algorithm for formalizing preferences of decision makers

The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms(MOEAs)lies a certain distance away from the decision makers’preference information region.Therefore,we propose a multi-ob jective optimization algorithm,referred to as the double-grid interactive preference based MOEA(DIPMOEA),which explicitly takes the preferences of decision makers(DMs)into account.First,according to the optimization ob jective of the practical multi-ob jective optimization problems and the preferences of DMs,the membership functions are mapped to generate a decision preference grid and a preference error grid.Then,we put forward two dominant modes of population,preference degree dominance and preference error dominance,and use this advantageous scheme to update the population in these two grids.Finally,the populations in these two grids are combined with the DMs’preference interaction information,and the preference multi-ob jective optimization interaction is performed.To verify the performance of DIP-MOEA,we test it on two kinds of problems,i.e.,the basic DTLZ series functions and the multi-ob jective knapsack problems,and compare it with several different popular preference-based MOEAs.Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs,quickly provides the test results,and has better performance in the distribution of the Pareto front solution set.

Approaches to Improving Consistency of Interval Fuzzy Preference Relations

This article introduces a consistency index for measuring the consistency level of an interval fuzzy preference relation（IFPR）.An approach is then proposed to construct an additive consistent IFPR from a given inconsistent IFPR.By using a weighted averaging method combining the original IFPR and the constructed consistent IFPR,a formula is put forward to repair an inconsistent IFPR to generate an IFPR with acceptable consistency.An iterative algorithm is subsequently developed to rectify an inconsistent IFPR and derive one with acceptable consistency and weak transitivity.The proposed approaches can not only improve consistency of IFPRs but also preserve the initial interval uncertainty information as much as possible.Numerical examples are presented to illustrate how to apply the proposed approaches.

Multiparameter optimization and configuration comparison of supercritical CO_(2) Brayton cycles based on efficiency and cost tradeoff

Supercritical CO_(2)Brayton cycle has high efficiency,compactness,and excellent power generation potential.In the design of the cycle,some parameters,such as recuperator pinch point temperature difference(ΔTrec,pp),turbine inlet temperature(Ttur,in),and maximum cycle pressure(pmax),are often preset without optimization.Furthermore,different preferences on efficiency and cost tradeoff can significantly affect the optimal design of the cycle,and the influence of different parameters on the design condition and the optimum cycle configuration becomes unclear as the preference changes.In this study,different preferences on efficiency and cost tradeoff are considered,and the effects of cycle configuration and optimization parameter addition on the tradeoff are investigated.In addition,four configurations under different preferences on tradeoff are recommended.Results show that the design condition parametersΔT_(rec,pp) decrease and T_(tur,in) and pmax increase as the preference of thermal efficiency(W_(th))increases.Different optimized parameters affect the results of the design point and cycle performance.In addition,the simple recuperative cycle and reheating cycle are recommended when low cycle initial cost dominates(W_(th)<0.598),and the recompression cycle and intercooling cycle are recommended when high cycle thermal efficiency dominates(W_(th)>0.701).The decision maker can select appropriate configuration according to specific preferences.