Integrated Fuzzy AHP–TOPSIS for selecting the best plastic recycling method: A case study

Due to the rapid depletion of natural resources and undesired environmental changes in a global scale, it is necessary to conserve the natural resources and protect the environment. Industries which manufacture plastic based products have the necessity to recycle plastics. There are number of methods to recycle plastics. Since the selection of the best recycling method involves complex decision variables, it is considered to be a multiple criteria decision-making (MCDM) problem. This article develops an evaluation model based on the fuzzy Analytic Hierarchy Process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) to enable the industry practitioners to perform performance evaluation in a fuzzy environment. The purpose of the study is to determine the best method for recycling plastics among the various plastic recycling processes. By observing the results, it is identified that mechanical recycling process is found to be the best plastic recycling process using the integrated approach.     

色散长波方程簇的Hamilton结构

本文导出了色散长波方程簇,并利用屠规彰迹恒等式方法,讨论它们的Hamil-ton结构。     

Fuzzy logic modifications of the Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP) is a measurement methodology based on pair-wise comparisons that relies on judgment to derive priority scales. During its implementation, one constructs hierarchies, then makes judgments or performs measurements on pairs of elements with respect to a criterion to derive preference scales, which are then synthesized throughout the structure to select the preferred alternative.One of the areas where the AHP finds application is in the subjective phases of risk assessment (RA), where it is used to structure and prioritize diverse risk factors, including the judgments of experts. Since fuzzy logic (FL) has been shown to be an effective tool for accommodating human experts and their communication of linguistic variables, there has been research aimed at modeling the fuzziness in the AHP (FAHP), and recently the focus of some of that modeling has been with respect to RA.The literature discusses more than one FAHP model, which raises the question as to which are the prominent models and what are their characteristics. In response to this question, we examine three of the most influential FAHP models. The article proceeds as follows. It begins with a brief overview of the AHP and its limitations when confronted with a fuzzy environment. This is followed by a discussion of FL modifications of the AHP. A RA-based likelihood score example is used throughout. The article ends with a commentary on the findings.     

Nested hierarchies in planar graphs

We construct a partial order relation which acts on the set of 3-cliques of a maximal planar graph G and defines a unique hierarchy. We demonstrate that G is the union of a set of special subgraphs, named ‘bubbles’, that are themselves maximal planar graphs. The graph G is retrieved by connecting these bubbles in a tree structure where neighboring bubbles are joined together by a 3-clique. Bubbles naturally provide the subdivision of G into communities and the tree structure defines the hierarchical relations between these communities.     

基于区间AHP与粗糙集的决策系统的排序

针对决策系统中属性值以区间数形式给出的多指标决策问题,根据区间层次分析法(IAHP)与粗糙集的特点,提出了一种合理的排序方法.在保持分类能力不变的前提下,利用粗糙集理论中的知识约简方法,删除其中不相关或不重要的知识,结合区间层次分析法计算权重进行有效的排序.最后,给出算例说明了该方法的可行性和有效性.     

定量型多指标决策的层次分析法探讨

本文采用等级标度与层次分析综合的方法对定量型多指标决策问题进行了讨论，它既是层次分析法在定量型多指标决策中的合理推广，同时也为定量型多指标决策问题提供了一种行之有效的方法．     

区间判断的凸锥模型与排序方法

本文用凸锥模型对层次分析法中区间判断条件下的排序问题进行研究，给出了权重向量的合理算法．并对区间判断下权重向量的合理位置，产生逆序的可能性等重要问题进行了探讨和研究，给出了计算实例．     

The Hierarchical compromise programming

This paper presents a multicriteria decision making method, the Hierarchical Compromise Programming (HCP), which combines a hierarchical structure for the modelization of a problem with the minimization of the distance to an ideal point as a resolution procedure. The proposed method allows the application of a model based on the minimization of a distance function, with tangible and intangible aspects. This research has been partially supported by the project SISDEMA: UN SISTEMA DECISIONAL AMBIENTAL (ref.: PMA-2196 CONSID-DGA)     

On effectively computable realizations of choice functions: Dedicated to Professors Kenneth J. Arrow and Anil Nerode

Let X be a compact, convex subset of R_n, and let $\text{〈R(X),}\text{F}{}_{\mathrm{R}}\text{〉}$ be a recursive space of alternatives, where R(X) is the image of X in a recursive metric space, and $\text{F}{}_{\mathrm{R}}$ is the family of all recursive subsets of R(X). If $\text{C:}\text{F}{}_{\mathrm{R}}\text{→}\text{F}{}_{\mathrm{R}}$ is a non-trivial recursively representable choice function that is rational in the sense of Richter, we prove that C has no recursive realization within Church's Thesis. Our proof is not a diagonalization argument and uses no paradoxical statements from formal systems. Instead, the proof is a Kleene-Post reduction style argument and uses the Turing equivalence between mechanical devices of computation and the recursive functions of Gödel and Kleene.     

Structural modeling in a class of systems using fuzzy sets theory

On the basis of fuzzy sets theory, we propose a method for structuring hierarchy for the several complex problems, and call it Fuzzy Structural Modeling (FSM) method. An important requirement for structural modeling of complex systems is that the necessary data is acquired and organized into a form such that a structural model can be developed. The main purpose of this method is to describe and illustrate a formal procedure for constructing the graphic presentation of the hierarchical arrangement given the necessary information concerning the relation of each element to each other element. The procedure permits an automatic development of the graphic structure that portrays the hierarchy.