矩阵算法在多目标投资决策中的应用

用于解决多目标决策的传统方法有线性加权法、理想点法、平方和加权法等方法,但采用不同的方法得到的结果并不完全相同。为了克服传统方法的不足之处,提出了基于估计相对位置的方案排队法来解决多目标决策问题。     

Some aspects of intuitionistic fuzzy sets

We first discuss the significant role that duality plays in many aggregation operations involving intuitionistic fuzzy subsets. We then consider the extension to intuitionistic fuzzy subsets of a number of ideas from standard fuzzy subsets. In particular we look at the measure of specificity. We also look at the problem of alternative selection when decision criteria satisfaction is expressed using intuitionistic fuzzy subsets. We introduce a decision paradigm called the method of least commitment. We briefly look at the problem of defuzzification of intuitionistic fuzzy subsets.     

Party–state systems and their dynamics as networks

The dynamics of expansion and retreat of a network are detailed in the example of the birth and demise of party–state systems. This systemic network is described with the help of the interactive party–state (IPS) model through the interrelationship of individual party–state and economic decision-makers. The model also describes the causes of the internal dynamics of this network: its generators, its self-similarities and differences, as well as the dynamics of its operation and transformation. In view of the IPS model, I elaborate on the specific structure and dynamics of the Chinese party–state.     

Coupled disease–behavior dynamics on complex networks: A review

It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease–behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease–behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.     

A collective choice method based on individual preferences relational systems (p.r.s.)

In group decision-making literature, several procedures are proposed in order to establish a collective preference from the different individual ones. The majority of these procedures, however, reveal that the individual preferences are always expressed in total pre-orders (or ranking). Indeed, until now very few have considered individual preferences which are expressed in partial pre-orders or, more generally, in preferences relational systems (p.r.s.). Moreover, many of these procedures generate collective preferences which are expressed in total pre-orders (ranking decision-making problematic). The efforts reported in the literature to develop procedures which treat other decision-making problematics—such as choice problematic—remain insufficient. In this paper, we propose a method which would determine from individual p.r.s. at least one collective subset containing the “best” alternatives. Each of these collective subsets results from the exploitation—according to the choice problematic—of a collective p.r.s. obtained from the aggregation of the individual p.r.s. Furthermore, each collective p.r.s. has two main characteristics: (i) it is at a minimum distance from all individual p.r.s. and (ii) it takes into account the members’ relative importance.     

A two-stage fuzzy robust integer programming approach for capacity planning of environmental management systems

In this study, a two-stage fuzzy robust integer programming (TFRIP) method has been developed for planning environmental management systems under uncertainty. This approach integrates techniques of robust programming and two-stage stochastic programming within a mixed integer linear programming framework. It can facilitate dynamic analysis of capacity-expansion planning for waste management facilities within a multi-stage context. In the modeling formulation, uncertainties can be presented in terms of both possibilistic and probabilistic distributions, such that robustness of the optimization process could be enhanced. In its solution process, the fuzzy decision space is delimited into a more robust one by specifying the uncertainties through dimensional enlargement of the original fuzzy constraints. The TFRIP method is applied to a case study of long-term waste-management planning under uncertainty. The generated solutions for continuous and binary variables can provide desired waste-flow-allocation and capacity-expansion plans with a minimized system cost and a maximized system feasibility.     

Setting up a decision rule from estimated uncertainty: emission limit value for PCDD and PCDF incineration plants in Wallonia, Belgium

Complex analytical procedures are often required to prove the non-compliance with a specific legislation. In the case of a small overlap of the limit, integration of the method uncertainty in the decision-making process is essential. The decision rule proposed in Wallonia, Belgium, for the non-compliance of waste incineration plants with the EU limit value for PCDD and PCDF emissions is presented. The method uncertainty was estimated annually over 6 years from duplicate measurements using two top-down approaches. Depending on the congener, the standard uncertainty varies from 30 to 85%, with a good correlation between calculations. The analytical contribution was estimated using a bottom-up evaluation. The impact of the sampling step was deduced from the whole estimation and represents more than 80% of the total uncertainty budget. No optimisation is foreseen at this time because of practical field constraints. Based on the average fraction of each congener, the uncertainty associated with the measurement result has been established and shows a high stability over the years. Using this value, a guard band has been calculated and will be proposed to the regulatory body. Presented at the Measurement Uncertainty Symposium, Berlin, Germany, April 2008.     

Comparison of two multicriteria decision aid methods: Net Flow and Rough Set Methods in a high yield pulping process

This investigation presents a synthesis of two multicriteria analysis methods, Rough Set Method (RSM) and Net Flow Method (NFM), applied to the multicriteria optimisation for the manufacture of paper using jack pine as the source of fibres. This work is the result of a collaboration between different Canadian and French laboratories.     

基于VIKOR算法的区域经济发展状况评价

区域经济发展状况评价是多属性方案决策,为防止个别较差指标的消极影响被其他指标中和,提高决策的合理性,采用简化的VIKOR算法完成决策矩阵的规范化处理,在此基础上对区域经济发展状况进行排序.并通过算例说明VIKOR算法的有效性和可行性.     

Time to consensus

The dynamics of a two-state decision-making model (DMM) with a tunable control parameter K$K$ is described. On all-to-all (ATA) networks, the system undergoes a phase transition at a critical value of _Kc=1$K_c=1$. Scale-free networks were also found to undergo phase transitions, but the value of _Kc$K_c$ increases as the scale-free exponent increases. Time to consensus is defined as a first passage time to a specified threshold of the global mean field, which represents a level of majority for the network. At criticality, the time to consensus for sparse networks with broad degree distributions (e.g., scale-free networks) approaches that of the ATA networks, although somewhat more cooperation (higher _Kc$K_c$) is required.