ELECTRE TRI for Groups with Imprecise Information on Parameter Values

ELECTRE TRI is a well-known method to assign actions to predefined ordered categories, considering multiple criteria. Using this method requires setting many parameters, which is often a difficult task. We consider the case where a group of Decision Makers (DMs) is unsure of which values each parameter should take, which may result from insufficient, imprecise or contradictory information, as well as from lack of consensus among the group members. In a framework where DMs provide constraints bounding and interrelating the parameter values, rather than fixing precise figures, we discuss the problem of finding the best and worst category that each action may attain.     

PROMETHEE Group Decision Support System and the House of Quality

Quality function deployment (QFD) is a multi-step method that monitors customer needs throughout a product development process. The House of Quality (HOQ) exercise undertaken in the first phase of QFD is considered as the most important, since customer needs must be accurately translated into a set of technical requirements for the final product. This paper provides a PROMETHEE group decision support system (GDSS) approach that integrates the design preferences of the QFD team. We highlight the selection and ranking of the technical requirements in the HOQ exercise, where a group of multidisciplinary decision makers (DMs) in a globally dispersed QFD team is required to input their individual preferences. Our approach advances the HOQ group decision making context in three important areas. First, it treats each criterion and DM as unique in terms of the preference function and threshold levels. Second, it seeks a multi-criteria approach for the HOQ process, where some DMs may play a more important role than others on a certain criterion. Third, sensitivity analysis through the Geometrical Analysis for Interactive Assistance (GAIA) plane provides valuable information about the conflicts, similarities, or independencies between the criterion and the DMs, respectively. A case on an automotive part illustrates the performance of the PROMOTHEE approach with GAIA.     

Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation

Utility functions are often used to reflect decision makers’ (DMs’) preferences. They have the following two merits: one refers to the representation of the DM’s utility (satisfaction) level, the other one to the measuring of the consensus level in a negotiation process. Taking the background of China’s urban house demolition, a new kind of consensus model is established by using different types of multi-stage fluctuation utility functions, such as concave, convex, S-shaped, reversed S-shaped, reversed U-shaped as well as their combinations, to reveal negotiators’ dynamic physiological preferences and consensus level. Moreover, the effects of the decision-making budget and the individual compensation tolerance on the consensus level are also discussed in this paper. Compared with previous research, the proposed model takes both the negotiation cost and DM’s preference structure into consideration, and most importantly, it is computational less complex.     

Multiple Attribute Group Decision Making Method Based on Utility Theory Under Interval-Valued Intuitionistic Fuzzy Environment

In this paper, a kind of multiple attribute group decision making problem is studied, where there is no original information about the weights of importance of the attributes and the decision makers (DMs), and the attribute values are given in the form of interval-valued intuitionistic fuzzy numbers (IVIFNs). To solve this problem, a new method is proposed based on utility theory. In the proposed method, the weights of importance of the DMs and the attributes are all determined by using the intuitionistic indexes of related IVIFNs. And then, the alternatives are compared by using their composite interval indexes which are generated based on utility theory. Finally, two numerical examples are proposed to demonstrate the effectiveness of the proposed method.     

Selection of a Representative Value Function for Robust Ordinal Regression in Group Decision Making

In this paper, we introduce the concept of a representative value function in a group decision context. We extend recently proposed methods UTA^GMS-GROUP and UTADIS^GMS-GROUP with selection of a compromise and collective preference model which aggregates preferences of several decision makers (DMs) and represents all instances of preference models compatible with preference information elicited from DMs. The representative value function is built on results of robust ordinal regression, so its representativeness can be interpreted in terms of robustness concern. We propose a few procedures designed for multiple criteria ranking, choice, and sorting problems. The use of these procedures is conditioned by both satisfying different degrees of consistency of the preference information provided by all DMs, as well as by some properties of particular decision making situations. The representative value function is intended to help the DMs to understand the robust results, and to provide them with a compromise result in case of conflict between the DMs.     

An Extended PROMETHE II Multi-Criteria Group Decision Making Technique Based on Intuitionistic Fuzzy Logic for Sustainable Energy Planning

The implementations of Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) category to complex multi-criteria group decision making (MCGDM) scenarios have been included in thousands areas. Outranking methods such as PROMETHEE II are also greatly employed in energy planning application. In MCGDM methods if decision makers (DMs) are not able to treat precise data in order to define their preferences, the intuitionistic fuzzy set (IFS) theory enables them. IFS attributes are connected with the degree of membership and non-membership, and can be used to draw uncertainty in group decision-making situations. In this paper, a new version of the PROMETHEE II method is proposed, aiming at solving MCGDM problems. Linguistic variables are expressed in the membership function and non-membership function of IFS which are used to assess the weights of all criteria and the ratings of each alternative with respect to each criteria. Conditional normalized Euclidean distance measure is adopted to measure deviations between alternatives on intuitionistic fuzzy set. Then, a ranking algorithm is applied to indicate the order of superiority of alternatives. Finally, a practical example is given to an application of sustainable energy planning to verify our proposed method. Additionally, a comparative analysis is done among the proposed PROMETHEE II method and the intuitionistic fuzzy technique for order preference by similarity to ideal solution (IF-TOPSIS) method and elimination and choice translating reality method (IF-ELECTRE).     

A Distance-Based Consensus Model with Flexible Choice of Rank-Position Weights

In this paper we propose a preference aggregation procedure for those cases in which the decision-makers express their preferences by means of a ranking of alternatives. Among the most applied methods for this purpose are those inspired by the Borda–Kendall rule, which attach to each alternative an aggregated value of the votes received in the different rank positions, and those based on distance measures between individual and collective preferences, which look for the solution that maximizes the consensus. The main idea here is to integrate these two approaches. Taking into account that the information about the values of weights or utilities assigned to each rank position is imprecise, we propose an evaluation of the alternatives using that vector of weights that minimizes the disagreement between DMs. In order to solve the problem, mixed-integer linear programming models are constructed. Two numerical examples are examined to illustrate the applicability of the proposed procedure.     

The Use of Ranking Veto Concept to Mitigate the Compensatory Effects of Additive Aggregation in Group Decisions on a Water Utility Automation Investment

The use of additive models for aggregating group decisions implies they have a compensatory effect in the process of aggregating all decision makers’ (DMs’) preferences. In this kind of model, the final result may produce some extremely undesirable alternatives for one or more DMs. Such alternatives may emerge with a higher ranking than desirable ones, thus generating conflicts and regrets. To overcome this problem the concept of ranking veto is introduced based on a reduction factor combined with the utility of the alternative in order to penalize conflicting alternatives and reduce disagreements in an additive model. A water utility problem was considered as a numerical application to illustrate the model. A decision group method based on MAUT, utility thresholds and a reduction factor is proposed to support group decision in selecting regions that will receive investments in automation over the next 4 years.     

An Integrated Group Decision Making Support Model for Corporate Financing Decisions

The essential goal of corporate finance is to maximize corporate value while reducing a firm’s financial risks. Corporate financing decision is a kind of multi-criteria based group decision making that embodies major approaches to handle qualitative criteria and quantitative limitations. However, in literature related to financing decision making, very little research uses decision making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) methods to consider the impact and dependency of its factors, or uses Goal programming (GP) to find the satisfactory financing decision under the related financial constraints. This study proposes an integrated group decision making support (GDMS) model to assist corporate financing group decision makers (DMs) in obtaining a satisfactory group solution. ANP, DEMATEL and GP are combined in this GDMS model. By using this model, the group DMs can systemically structure a multi-criteria network framework and derive priority weights of those criteria, and then deal with the quantitative financial constraints for a satisfactory group solution. An illustrative case is demonstrated for the effectiveness and practicability of this GDMS model.     

Comparing two diverse methods for generating perceptual maps: Multidimensional scaling versus the hand-drawn technique

Multidimensional scaling techniques have lately been subjected to criticism because of a supposed lack of reliability and validity. Yet, no alternative cognitive map-producing techniques have been suggested. The present research describes a graphic technique for developing cognitive maps and compares this technique to multidimensional scaling in terms of reliability and validity. The two techniques are also compared in terms of perceived subject difficulty in completing the tasks and the ability of each technique to predict future brand behavior.     

Monte Carlo Simulation Techniques in a Decision Support System for Group Decision Making

This paper describes a group decision support system based on an additive multi-attribute utility model for identifying a consensus strategy in group decision-making problems where several decision-makers or groups of decision-makers elicit their own preferences separately. On the one hand, the system provides procedures to quantify the DMs or group of DMs preferences separately. This involves assessing the DMs or group of DMs component utilities that represent their preferences regarding the respective possible attribute values and objective weights that represent the relative importance of the criteria. On the other hand, we propose Monte Carlo simulation techniques for identifying a consensus strategy. An iterative process will be carried out, where, after the simulations have been performed, the imprecise component utilities and weights corresponding to the different DMs or groups of DMs are tightened to output more meaningful information in the next simulations to achieve a consensus strategy. Finally, an application to the evaluation of remedial strategies for restoring contaminated aquatic ecosystems illustrates the usefulness and flexibility of this decision support tool.     

扩频通信系统相位的捕获与跟踪技术

本文讨论扩展频谱通信系统中有关相位的捕获与跟踪技术问题，道德在扩频通信系统原理的基础上结合实际系统中的应用条件，详细分析了捕获的方法、性能特点，给出了比较适用的2种捕获与跟踪原理图。理论分析与实验结果表明，所采用的分析方法和捕获方案是可行的。     

我国国有外贸企业当务之急是解决体制障碍

加入WTO后 ,我国国有外贸企业面临的竞争对手更多、竞争压力更大。如何在新的环境下求生存、求发展是每一个国有外贸企业都要深思的问题。回顾以往 ,国有外贸企业也进行了各种各样的改革尝试 ,试图走出困境 ,但都成效不大。新形势下 ,国有外贸企业无法摆脱困境的根本原因在于体制障碍。国有外贸企业的当务之急就是解决这一问题。     

一种新的用户随机选择平分功率协作协议

大量的研究结果证明,无线网络中一个或者多个中继节点具有协助传输信息的优点。在实际情况中,参与协作的每个用户均要决定协作时序及协作对象。为此,提出一种传输速率只降低一半,而分集指数可达到满分集的协议。在分布式网络中,每个节点根据接收信号的即时信噪比独立决定所协助的用户对象,并在协作时隙内将功率平均分配给被协助对象。由于协作时隙采用平分功率协作,无论参与协作的用户数目如何变化,网络的数据速率都只降低为不协作情况的1/2。     

一种流星突发通信中联合自适应功率速率方法

提出一种新的用于流星突发信道的联合自适应功率速率方法,该方法在实现自适应改变速率的同时自适应的改变发送功率。仿真结果表明,与固定功率、固定速率方法相比,该方法可以在提高流星突发通信系统平均吞吐量性能3倍左右的同时,节省40%左右的能量。     

Bank Competition and its Determinants: Evidence from Indian Banking

Abstract

This paper measures the degree of bank competition in India using a sample of 70 commercial banks over the period 1996–2016. To assess the degree of competition, we estimate the market power of each bank in our sample employing three nonstructural measures: the Lerner index, the adjusted Lerner index, and the Boone indicator. Bank-wise and year-wise estimates of the marginal cost required in all these measures are obtained using the semi-parametric method. The paper further attempts to undertake a comprehensive assessment of competition in Indian banking and identifies various bank-specific, macroeconomic, structural, and contestability indicators, which are supposed to explain level and variation of the degree of competition over time. Empirical findings reveal that public-sector banks in India exercise a relatively higher degree of bank competition compared to private and foreign-sector banks. However, aggregate results support that the Indian banking system is competitive in general. Unlike the structure-conduct-performance paradigm, which advocates that a concentrated banking system impairs competitiveness, our findings reveal that concentration measures hardly exert any effect on bank competition. Rather, contestability measures play a significant role in the determination of bank competition.     

Assessing new empirical industrial organization (NEIO) methods: The cases of five industries

The methods proposed in the new empirical industrial organization (NEIO) literature have made significant contributions to our understanding of competitive behavior. However, these methods have yet to be compared with each other for their performance in explaining and diagnosing competitive market conduct. This inter-method comparison is important because conclusions about competitive behavior based on these methods have significant strategic as well as policy implications for firms. Our objective in this paper is to examine the performance of these different NEIO methods in terms of their discriminatory power, ability to identify strategic variables, and robustness in estimation. For empirical demonstration, we use data from diverse industries such as microprocessors, personal computers, facial tissue, disposable diapers and automobiles. Our results suggest that two commonly used NEIO methods-conjectural variation and non-nested model comparison-exhibit quite good convergence with each other and are consistent with a traditional time series method. This suggests that simpler methods such as conjectural variations deserve more credit. We also find that using these methods in tandem provides valuable additional information that may not be available when using any one method alone. While the emphasis in this study is on comparing different methods of analyzing competitive interaction, the findings also reveal some substantive insights about each market studied.     

Matrix Representation of Conflict Resolution in Multiple-Decision-Maker Graph Models with Preference Uncertainty

Explicit matrix representations of solution concepts in a graph model of a multiple-decision-maker conflict with preference uncertainty are developed. In a graph model, the relative preferences of each DM over the available states are crucial in determining which states are stable according to any stability definition (solution concept). Unfortunately, it is often difficult to obtain accurate preference information in practical cases, so models that allow preference uncertainty can be very useful. In this work, stability definitions are extended to apply to graph models with this feature. The extension is easiest to implement using the matrix representation of a conflict model, which was developed to ease the coding of logically-defined stability definitions. Another benefit of matrix representation is that it facilitates modification and extension of the definitions.