Clustering and selection of multiple criteria alternatives using unsupervised and supervised neural networks

There are decision-making problems that involve grouping and selecting a set of alternatives. Traditional decision-making approaches treat different sets of alternatives with the same method of analysis and selection. In this paper, we propose clustering alternatives into different sets so that different methods of analysis, selection, and implementation for each set can be applied. We consider multiple criteria decision-making alternatives where the decision-maker is faced with several conflicting and non-commensurate objectives (or criteria). For example, consider buying a set of computers for a company that vary in terms of their functions, prices, and computing powers. In this paper, we develop theories and procedures for clustering and selecting discrete multiple criteria alternatives. The sets of alternatives clustered are mutually exclusive and are based on (1) similar features among alternatives, and (2) preferential structure of the decision-maker. The decision-making process can be broken down into three steps: (1) generating alternatives; (2) grouping or clustering alternatives based on similarity of their features; and (3) choosing one or more alternatives from each cluster of alternatives. We utilize unsupervised learning clustering artificial neural networks (ANN) with variable weights for clustering of alternatives, and we use feedforward ANN for the selection of the best alternatives for each cluster of alternatives. The decision-maker is interactively involved by comparing and contrasting alternatives within each group so that the best alternative can be selected from each group. For the learning mechanism of ANN, we proposed using a generalized Euclidean distance where by changing its coefficients new formation of clusters of alternatives can be achieved. The algorithm is interactive and the results are independent of the initial set-up information. Some examples and computational results are presented.     

Convex cone-based partial order for multiple criteria alternatives

In this paper, we consider the problem of finding a preference-based strict partial order for a finite set of multiple criteria alternatives. We develop an approach based on information provided by the decision maker in the form of pairwise comparisons. We assume that the decision maker's value function is not explicitly known, but it has a quasi-concave form. Based on this assumption, we construct convex cones providing additional preference information to partially order the set of alternatives. We also extend the information obtained from the quasi-concavity of the value function to derive heuristic information that enriches the strict partial order. This approach can as such be used to partially rank multiple criteria alternatives and as a supplementary method to incorporate preference information in, e.g. Data Envelopment Analysis and Evolutionary Multi-Objective Optimization.     

A hybrid fuzzy multiple criteria group decision making approach for sustainable project selection

In this paper, a new hybrid fuzzy multiple criteria group decision making (FMCGDM) approach has been proposed for sustainable project selection. First, a comprehensive framework, including economic, social, and environmental effects of an investment, strategic alliance, organizational readiness, and risk of investment has been proposed for sustainable project selection. As the relative importance of the criteria of the proposed framework are hard to find through several conflictive preferences of a group of Decision Makers (DMs) so, a goal programming (GP) has been supplied to this aim considering multiplicative and fuzzy preference relation. Then, a fuzzy TOPSIS method has been developed to assess the fitness of investment chances. It is based on Preference Ratio (PR), which is known as an efficient ranking method for fuzzy numbers, and a fuzzy distance measurement. The properties of proposed hybrid approach make it robust for modeling real case of uncertain group decision making problems. The FMCGDM has been developed through a linkage between Lingo 11.0, MS-Excel 12.0, and Visual Basic 6.0. The proposed hybrid approach has been applied in a real case study called Iranian financial and credit institute for sustainable project selection.     

Clustering and group selection of interim product in shipbuilding

Cellular manufacturing of the interim products based on group technology (GT) is a useful way to increase the productivity and improving the process flows of shipbuilding. In this paper, we analyze the similarity relationship on interim product to form product families and establish requirement mode of similarity measurement based on shipbuilding GT. The classifier of ART2 artificial neural network is proposed on the basis of the adaptive resonance theory. It can classify and identify automatically the input data through analyzing the characteristics of interim product in shipbuilding. With the modified algorithm, the interim product can easily and efficiently be formed product families and controlled to group related interim product into families in reason by similar coefficient. Meanwhile, through analyzing the production constraint to form assembly cell, the set of evaluating indexes is founded whose weights are decided by the entropy weight method and the comprehensive assessment values of product families design schemes can be computed to judge the final optimization scheme through the grey correlation analysis. The formation of sub-assembly interim product family in a bulk carrier is taken as an example to verify the proposed method, and the results show that it is an effective method for solving the group manufacturing problem in shipbuilding activities.     

Synchronization criteria and pinning control for complex networks with multiple delays

By linearizing complex networks with multiple coupling delays to some time-delayed subsystems, for the first time, some new criterions are given to ensure the global synchronization of the system with multiple delays. Then, based on the proposed criterions and Lyapunov stability theory, pinning control schemes for this system are developed to achieve global synchronization. The obtained conditions are expressed within the framework of linear matrix inequalities and can be easily checked in practice. Finally, several numerical examples are provided to show the effectiveness of the proposed results.     

Ranking and screening multiple criteria alternatives with partialinformation and use of ordinal and cardinal strength of preferences

This paper consists of three parts: 1) some theories and an efficient algorithm for ranking and screening multicriteria alternatives when there exists partial information on the decision maker's preferences; 2) generation of partial information using variety of methods; and 3) the existence of ordinal and cardinal functions based on and strengths of preferences. We demonstrate that strengths of preference concept can be very effectively used to generate the partial information on preferences. We propose axioms for ordinal and cardinal (measurable) value functions. An algorithm is developed for ranking and screening alternatives when there exists partial information about the preferences and the ordering of alternatives. The proposed algorithm obtains the same information very efficiently while by solving one mathematical programming problem many alternatives can be ranked and screened. Several examples are discussed and results of some computational experiments are reported     

Dynamic RAT selection for multiple calls in heterogeneous wireless networks using group decision-making technique

Existing radio access technology (RAT)-selection algorithms for heterogeneous wireless networks (HWNs) do not consider the problem of RAT selection for a group of calls from a multimode terminal (MT). Multimode terminals (MTs) for next generation wireless networks have the capability to support two or more classes of calls simultaneously. When a new call is initiated on an MT already having an ongoing call in an HWN, the current RAT may no longer be suitable for the two calls (incoming call and the existing call). Thus, a new RAT may be more suitable for the two calls. The problem of RAT selection for two or more calls from an MT in an HWN is a group decision problem. This paper addresses the problem of RAT selection for a group of calls from an MT in an HWN by using the modified TOPSIS group decision-making technique. The paper proposes a dynamic RAT-selection algorithm that selects the most suitable RAT for a single call or group of calls from an MT in an HWN. The algorithm considers users’ preferences for individual RATs, which vary with each class of calls, in making RAT selection decisions in an HWN. A user’s preference for each of the available RATs is specified by weights assigned by the user to RAT selection criteria for different classes of calls. Based on the assigned weights, the proposed algorithm aggregates individual calls’ weights specified by the user to make a RAT-selection decision for a group of calls. In order to reduce the frequency of vertical handover, the proposed algorithm uses RAT preference margin in making RAT selection decisions. RAT preference margin is a measure of the degree to which the newly preferred RAT is better than the current RAT. Performance of the proposed algorithm is evaluated through numerical simulations. Results are given to show the effectiveness of the proposed RAT-selection algorithm.     

Clustering algorithm for solving group technology problem with multiple process routings

Cell formation is an important problem in the design of a cellular manufacturing system. Most of the cell formation methods in the literature assume that each part has a single process plan. However, there may be many alternative process plans for making a specific part, specially when the part is complex. Considering part multiple process routings in the formation of machine-part families in addition to other production data is more realistic and can produce more independent manufacturing cells with less intercellular moves between them. A new comprehensive similarity coefficient that incorporates multiple process routings in addition to operations sequence, production volumes, duplicate machines, and machines capacity is developed. Also, a clustering algorithm for machine cell formation is proposed. The algorithm uses the developed similarity coefficient to calculate the similarity between machine groups. The developed similarity coefficient showed more sensitivity to the intercellular moves and produced better machine grouping.     

An interactive approach for multiple criteria selection problem

In this study, we develop an interactive algorithm for the multiple criteria selection problem that aims to find the most preferred alternative among a set of known alternatives evaluated on multiple criteria. We assume the decision maker (DM) has a quasi-concave value function that represents his/her preferences. The interactive algorithm selects the pairs of alternatives to be asked to the DM based on the estimated likelihood that one alternative is preferred to another. After the DM selects the preferred alternative, a convex cone is generated based on this preference information and the alternatives dominated by the cone are eliminated. Then, the algorithm updates the likelihood information for the unselected pairwise questions. The aim of the algorithm is to detect the most preferred alternative by performing as few pairwise comparisons as possible. We present the algorithm on an illustrative example problem. We also develop a mathematical model that finds the minimum number of questions that can be asked to the DM to determine the most preferred alternative under perfect information. We use the minimum number of questions to develop strategies for interactive algorithm and measure its performance.     

Supplier selection and supply quantity allocation of common and non-common parts with multiple criteria under multiple products

With limited capacity of suppliers, how to reduce the total operating cost of the enterprise by determining the most suitable production capacity allocation has become the major issue faced by various enterprises in producing multiple types of products. In addition, when manufacturing multiple types of products, due to the high demand of common and non-common parts, which is applicable to various products, enterprises will place special emphasis on the procurement of common and non-common parts, to select most suitable suppliers of parts with the highest quality and minimum time and costs, in order to cut down on operating costs of enterprises. This research first lists parts of various products through bill of material (BOM), and constructs an optimal mathematical model suitable for multi-phase products’ parts, in order to assess the assembling relationship of various parts; it makes use of the linkage among those to select the supplier of common and non-common parts when assessing multiple products. Then considering the limited production capacity of suppliers, it selects the best combination of suppliers of special common and non-common parts. To solve the optimal mathematical model, a genetic algorithm (GA) is proposed to find the acceptable results of the supply selection and quantity allocation problem. It then provides a benchmark for enterprise in current diversified market to purchase and assess common and non-common parts, and makes such benchmark a normal standard for selection of suppliers in the future.     

TOPSIS with belief structure for group belief multiple criteria decision making

The technique for order performance by similarity to ideal solution(TOPSIS)is one of the major techniques in dealing with multiple criteria decision making(MCDM)problems, and the belief structure(BS)model has been used successfully for uncertain MCDM with incompleteness, impreciseness or ignorance. In this paper, the TOPSIS method with BS model is proposed to solve group belief MCDM problems. Firstly, the group belief MCDM problem is structured as a belief decision matrix in which the judgments of each decision maker are described as BS models, and then the evidential reasoning approach is used for aggregating the multiple decision makers' judgments. Subsequently, the positive and negative ideal belief solutions are defined with the principle of TOPSIS. To measure the separation from ideal solutions, the concept and algorithm of belief distance measure are defined, which can be used for comparing the difference between BS models. Finally, the relative closeness and ranking index are calculated for ranking the alternatives. A numerical example is given to illustrate the proposed method.     

TOPSIS with fuzzy belief structure for group belief multiple criteria decision making

TOPSIS is one of the major techniques in dealing with multiple criteria decision making (MCDM) problems, and Belief Structure (BS) model and Fuzzy BS model have been used successfully for uncertain MCDM with incompleteness, impreciseness or ignorance. In this paper, the TOPSIS method with Fuzzy BS model is proposed to solve Group Belief MCDM problems. Firstly, the Group Belief MCDM problem is structured as a fuzzy belief decision matrix in which the judgments of each decision maker are described as Fuzzy BS models, and then the Evidential Reasoning approach is used for aggregating the multiple decision makers’ judgments. Subsequently, the positive and negative ideal belief solutions are defined with the principle of TOPSIS. In order to measure the separation from the ideal belief solutions, the concept and algorithm of Belief Distance Measure are introduced to compare the difference between Fuzzy BS models. Using the Belief Distance Measure, the relative closeness and ranking index can be calculated for ranking the alternatives. A numerical example is finally given to illustrate the proposed method.     

边相似性聚类分析及其在生物网络功能模块识别中的应用

生物网络的功能模块识别有助于理解网络的结构和功能关系,有助于发现网络中的隐藏规律以及预测网络的行为等,是当前生物网络研究方面的一个重要研究主题。然而,当前的功能模块识别方法大多依赖于对网络中的节点进行聚类分析,其主要缺陷在于:每个节点只能属于某一个模块,而生物网络中的节点很难划分到某个独立的模块中。本文介绍了新近发展的基于边相似性的聚类方法,通过2个具体的生物网络功能模块识别实例,考察了这种新兴的方法在生物网络研究中的应用,结果表明了该方法的高效性。     

Six-Sigma project selection using national quality award criteria and Delphi fuzzy multiple criteria decision-making method

Six-Sigma is a tactical tool of significant value in achieving operational excellence. The project selection decision, under a resources constraint, is the early stage of implementation for a Six-Sigma intervention. The project selection decision is challenging due to its fuzzy group decision-making aspect inherent to the problem. The present study proposes to adopt national quality award criteria as the Six-Sigma project selection criteria, and proposes a hierarchical criteria evaluation process. The strategic criteria are evaluated by the management team using a Delphi fuzzy multiple criteria decision-making method. Then, the tactical sub-criteria which contain additional operational issues are evaluated by the Six-Sigma Champion. The proposed methodology is successfully applied in solving the project selection problem deriving from a component manufacturer. The empirical outcomes are promising. Moreover, the results show that the higher a project’s priority is, the greater the financial gains will be on completion of the project. Accordingly, the proposed methodology can prioritize the financial gain – which is the key performance indicator for a Six-Sigma project. Additionally, the quality status of the case company has been significantly improved through implementation of the Six-Sigma project. The systematic evaluation process also influences employees to adopt an analytical operations philosophy. Moreover, the commercial objectives of the company are brought into focus by the proposed methodology.     

Clustering based multiple branches deep networks for single image super-resolution

Since the limitation of optical sensors, it’s often hard to obtain an image with the ideal resolution. Image super-resolution (SR) technology can generate a high-resolution image from the corresponding low-resolution image. Recently, deep learning (DL) based SR methods draw much attention due to their satisfying reconstruction results. However, these methods often neglect the diversity of image patches. Therefore, the reconstruction effect is limited. To fully exploit the texture variability across different image patches, we propose a universal, flexible, and effective framework. The proposed framework can be adopted to any DL based methods. It can significantly improve the SR accuracy while maintaining the running time. In the proposed framework, K-means is employed to cluster image patches into different categories. Multiple CNN branches are designed for these different categories to reconstruct the SR image. Each branch is weighted in accordance with the Euclidean distance to the cluster centers. Experimental results demonstrate that by applying the proposed framework, performance of the DL based SR method can be significantly improved.

     

FIOWHM operator and its application to multiple attribute group decision making

To study the problem of multiple attribute decision making in which the decision making information values are triangular fuzzy number, a new group decision making method is proposed. Then the calculation steps to solve it are given. As the key step, a new operator called fuzzy induced ordered weighted harmonic mean (FIOWHM) operator is proposed and a method based on the fuzzy weighted harmonic mean (FWHM) operator and FIOWHM operators for fuzzy MAGDM is presented. The priority based on possibility degree for the fuzzy multiple attribute decision making problem is proposed. At last, a numerical example is provided to illustrate the proposed method. The result shows the approach is simple, effective and easy to calculate.     

New delay-dependent stability criteria for cohen-grossberg neural networks with multiple time-varying mixed delays

This paper investigates the globally asymptotical stability problem for a general class of Cohen-Grossberg neural networks with multiple mixed time-delays. Before proving the main theorem, a more generalized convex combination inequality is proposed. A new stability criterion for Cohen-Grossberg neural networks with multiple time-varying delays is obtained by the employed general inequality technique. Two examples are included to illustrate the effectiveness of the presented results.     

Wavelet feature selection based neural networks with application to the text independent speaker identification

A wavelet packets feature selection derived by using neuro-fuzzy evaluation index for speaker identification is described. The concept of a flexible membership function incorporating weighed distance is introduced in the evaluation index to make the modeling of clusters more appropriate. Experimental evaluation of the systems performance was conducted on three speech databases. Our results have shown that this feature selection introduced better performance than the wavelet features with respect to the percentages of recognition.     

Macro-star networks: efficient low-degree alternatives to stargraphs

We propose a new class of interconnection networks, called macro-star networks, which belong to the class of Cayley graphs and use the star graph as a basic building module. A macro-star network can have node degree that is considerably smaller than that of a star graph of the same size, and diameter that is sublogarithmic and asymptotically within a factor of 1.25 from a universal lower bound (given its node degree). We show that algorithms developed for star graphs can be emulated on suitably constructed macro-stars with asymptotically optimal slowdown. This enables us to obtain through emulation a variety of efficient algorithms for the macro-star network, thus proving its versatility. Basic communication tasks, such as the multimode broadcast and the total exchange, can be executed in macro-star networks in asymptotically optimal time under both the single-port and the all-port communication models. Moreover, no interconnection network with similar node degree can perform these communication tasks in time that is better by more than a constant factor than that required in a macro-star network. We show that macro-star networks can embed trees, meshes, hypercubes, as well as star, bubble-sort, and complete transposition graphs with constant dilation. We introduce several variants of the macro-star network that provide more flexibility in scaling up the number of nodes. We also discuss implementation issues and compare the new topology with the star graph and other popular topologies