A modular fuzzy inference system approach in integrating qualitative and quantitative analysis of store image

The image situation in a store includes various stimuli, such as color, sound, scent, taste, layout and space, which are important clues for buyers. This paper describes store image response and a fuzzy logic model developed by comprehensive literature studies on image measurements (including atmospheric factors) and perceptual measures. Here, a fuzzy inference system is proposed as an alternative approach to handle effectively the impreciseness and uncertainty that are normally found in store image selection processes. This paper also shows that the proposed decision-making model is application to modified stimulus?Corganism?Cresponse (S?CO?CR) framework for integrating qualitative and quantitative analysis. The result of the simulation indicates a numerical and linguistic change in the store image perception after analyzing three input parameters. This finding is able to provide a solid foundation on which retailers and decision makers can base suitable strategies for ensuring the efficiency and stability of store image management system.     

Quantitative analysis of effect of Pilates exercises on psychological variables and academic achievement using fuzzy logic

Exercise techniques, such as Pilates, have beneficial effects on physical and neuropsychological characteristics. The effects of exercises on physical and neuropsychological features have been heavily examined throughout the previous years. The purpose of this study is to analysis impact of Pilates exercises on academic achievement with regards to motivation, attention, anxiety by using the fuzzy logic and comparing the results with traditional statistical methods. Ninety students taking the course titled “Adolescent Psychology” from the Faculty of Education of Near East University (North Cyprus) participated in this study. Psychological variables were gathered using the internationally recognized tests of Test Anxiety Inventory, d2 Test of Attention and the Academic Motivation Scale. The results indicate that using the fuzzy logic allows researchers to handle the imprecision and vagueness inherence of input data in depth and develop the more reliable model for computing input–output relations.     

A Mathematical Programming Approach for Measuring Technical Efficiency in a Fuzzy Environment

A three stage approach is proposed to measure technical efficiency in a fuzzy environment. This approach uses the traditional data envelopment analysis framework and then merges concepts developed in fuzzy parametric programming (Carlsson and Korhonen, 1986). In the first stage, vague input and output variables are expressed in terms of their risk-free and impossible bounds and a membership function. This membership function represents the degree to which a production scenario is plausible. In the second stage, conventional efficiency measurement models (Banker, Charnes and Cooper, 1984; Deprins, Simar and Tulkens, 1984) are re-formulated in terms of the risk-free and impossible bounds and the membership function for each of the fuzzy input and output variables. In the third stage, technical efficiency scores are computed for different values of the membership function so as to identify uniquely sensitive decision making units. The approach is illustrated in the context of a preprint and packaging manufacturing line which inserts commercial pamphlets into newspapers.     

Application of fuzzy mathematical programming to imprecise project management decisions

In real-world project management (PM) decisions, the input data and environmental coefficients are generally imprecise/fuzzy because of incompleteness and unavailability of relevant information over the project planning horizon. This work aims to present a fuzzy mathematical programming approach to solve imprecise PM decision problems with fuzzy goal and fuzzy cost coefficients. The designed PM decision model attempts to minimize total project costs with reference to direct costs, indirect costs, contractual penalty costs, duration of activities and the constraint of available budget. The proposed approach achieves greater computational efficiency by employing the simplified triangular fuzzy number to represent imprecise goal and cost coefficients, and provides a systematic framework that facilitates the decision-making process, enabling a decision maker to interactively modify the imprecise data and related parameters until a satisfactory solution is obtained. An industrial case is implemented to demonstrate the feasibility of applying the proposed approach to practical PM problems. The computational methodology developed in this work can easily be extended to any other situations and can handle the realistic PM decisions in fuzzy environments.     

Technically and cost-efficient centralized allocations in data envelopment analysis

In data envelopment analysis and with a variable returns to scale production-technology, we apply Banker's [2] approach to determine the relationship between technically and cost-efficient industry structures, featuring reallocation of outputs and a variable number of firms. The interpretation based on the most productive and optimal scale-size notions allows us to both establish an inequality relationship between the corresponding industry-efficiency measures and provide adequate information on optimal solutions. At the applicative level, we introduce an exact algorithm to solve related non-linear programming problems, thus providing the decision maker with an accurate method for computing and comparing the input and output mixes and the optimal number of units obtained in the two allocations. Empirical illustration, given with reference to the Italian local-public-transit sector and employing a multiple inputs and outputs technology, reveals striking differences with regard to the managerial and regulatory implications of the two centralized allocations.     

Nonparametric Methods for Evaluating Economic Efficiency and Imperfect Competition

This article unifies and extends ideas from nonparametric production analysis and DEA for testing organizational efficiency. We show how the admissible price set can be restricted to account for prior information on prices. These restrictions may relate prices to input and output quantities in order to test noncompetitive behavior of the evaluated decision making unit. While the resulting efficiency tests cannot always be cast into linear programming problems, we discuss various solution strategies for the tests. Thereby we consider the question when does local optimality of the result guarantee global optimality. We also show how the decision maker's preferences, for example ranking information, can be adopted into DEA models in a simple manner. Finally, the approach with price restrictions is illustrated with an application to test noncompetitive behavior of the pulp and paper industries in Finland.     

The analysis of risk perception with fuzzy means-end approach

Visitors’ risk perceptions have been found to influence the on-site behavior of tourists and their intention to return to a destination or to recommend it to others. This study discusses how the uses of a means-end approach with fuzzy conceptualization in eliciting the perception of tourism risks in a better understanding of the visitors’ perceptual orientation toward the tourism values. We provide a hierarchy value map that fuses the attribute–consequence–value (A–C–V) and fuzzy linguistics to effectively and efficiently understand vacation risks and risk characteristics. Fuzzy logic is also adopted to deal with the ill-defined nature of the tourist linguistic judgments required in the proposed means-end chain. This research findings suggest that additionally to managing the most likely risks, tourist resorts should be prepared to cope with worst case scenarios such as “Thunderstorm”, “Bus accident”, “Food poisoning” and “Cable car accident”. From an overall risk perceptive, tourists are most concerned with dominant perceptual orientation of risk delivers being “Bus accident” → “Decrease of trust in the safety management as a result of the event of damage” → “Anger”.     

An Aggregated Regional Economic Input–Output Analysis within a Fuzzy Environment

Abstract

Imprecision within economic input–output frameworks can be a problem, particularly when investigating the characteristics of defined industry sectors. This study undertakes an aggregated regional economic input–output analysis, within a fuzzy environment. The dearth of studies encompassing a fuzzy approach within input–output analysis largely concerns the problematic representation of imprecision. Results pertaining to the fuzzy output multipliers associated with each sector group are described, including possibilistic mean and variance; also identified is a specific ranking of the sector groups. Where appropriate, results are compared with those from a Monte Carlo simulation based stochastic analysis.     

Decision making framework for tender evaluation and contractor selection in public organizations with risk considerations

Selecting the best construction contractor plays a critical role in the success of any construction project. Moreover, in the public sector, the traditional lowest bid method is still broadly used and contracts are often awarded based on the lowest price. However, this method has been criticized by many researchers because even if it might guarantee the lowest cost for a project, it does not guarantee the maximum value in terms of time and quality. More particularly, the risk exposure during the tendering process is usually very high and the success of the construction project is strongly related to managing this risk in an appropriate way. Therefore, the selection of the most appropriate contractor should be based on a set of criteria such as technical capability, financial stability, risk, safety, etc., in addition to cost in order to avoid problems that may happen after the contract is awarded. This study aims therefore to develop a decision making framework (DMF) to assist the public organizations in selecting the most appropriate construction contractor(s). The proposed DMF uses a combination of multi-criteria decision making (MCDM) tools and fuzzy logic theory and consists of three stages. In the first stage, data envelopment analysis (DEA) is used to reduce the number of contractors, based on multiple criteria, such that only efficient contractors are considered. In the second stage, the risk factors that are related to each efficient contractor are identified and assessed using a fuzzy logic based approach. Finally, in the third stage, a bi-objective mixed integer linear programming (MILP) model is developed to select the best contractor(s) based on cost and risk and to determine the optimal quantity of work to be assigned to each selected contractor. Indeed, the proposed approach considers the multiple award contract case in which more than one contractor can be selected to share the amount of work to be performed. We assume that prior to using the proposed DMF, a pre-qualification study is conducted using multiple criteria chosen by the public organization, and only pre-qualified contractors are included in the first stage of the proposed DMF. Moreover, a case study inspired from the contractor selection process in a public organization in the United Arab Emirates (UAE) is used to show the effectiveness of the proposed approach.     

Choosing among alternative technologies: conditions for assuring the feasibility of an input–output database or scenario

The conceptual reach of the basic input–output modeling framework is substantially extended by new models that incorporate the economic logic of comparative advantage as the basis for the endogenous choice among alternative production technologies. This paper establishes procedures that define the conditions under which the database used for scenario analysis in this extended framework assures the existence of an economically feasible solution. We provide a criterion for structural feasibility, the property established by the Hawkins–Simon condition for the basic input–output model, and introduce a criterion for scale feasibility. The logic underlying the tests is illustrated by numerical examples based on the Rectangular Choice-of-Technology model and database. These procedures can be particularly useful for incorporating engineering and other technical sources of information into multi-regional input–output databases; they can also provide substantial underlying detail about individual technologies, sectors, and factors of production for both feasible and infeasible scenarios.     

Characteristics of fuzzy synthetic decision methods for measuring student achievement

Traditional method of student achievement evaluation only use arithmetic mean and convert them to rankings, but this does not provide further explanatory information to proceed with more reasonable evaluations, decisions, and interpretations for the learning achievements of students, and provide a fair and appropriate consideration of the evaluation results. Therefore, this study attempts to introduce four types of fuzzy synthetic decision methods in actual scores for evaluating and ranking student’s academic achievement. Using the synthetic decision method of fuzzy theory, the four types of composite operations are used in conjunction with the membership function, and finally fuzzy means are used to express the diverse evaluation results of students. This study uses junior high school first year students in central Taiwan as research subjects, selecting the actual mid-term exam results of the gifted class and general class as research data. The fuzzy synthetic decision method is applied through four types of composite operations, proposing a ranking system that is more diverse and precise than the traditional average method. Finally, this study proposes the characteristics of the four types of fuzzy composite operations, considering the most suitable composite operations for classes with different characteristics. Results of this study can be used as a reference for educators in the field and future researchers. The contribution of this study is to provide the fuzzy grade calculation methods that are suited to students with different characteristics, in order to achieve diverse and precise ranking evaluations.     

A satisficing method for fuzzy goal programming problems with different importance and priorities

For treating multiple objectives decision making problems with fuzzy goals and different importance, various kinds of fuzzy goal programming (FGP) models have been developed in the past three decades. Among them, two most widely used methods are: (1) weighted FGP, where the importance of the objectives is represented by weights (2) preemptive priority (also known as “Lexicographic”) FGP, where the priority levels of the goals are set in advance, and the trade-offs among goals placed at different priority levels are implemented strictly. This article presents a satisficing method based on FGP model, which holds that a more important objective has to be achieved as much as possible. The relaxed preemptive priority requirement in the proposed model provides a more efficient, flexible and practicable decision support compared to the weighted and the lexicographic models. In addition, the trade-off between optimization and importance requirement can be realized by the regulation parameter in the presented method. The performance of this method is evaluated by comparing its result with those of the six existing models in the literature.     

桩基础选型的模糊决策

基于模糊数学理论，运用模糊决策方法，对定性指标量化，建立模糊评价矩阵，利用专家评议法(Delphi)确定评价因素的权重值，对桩基础设计方案选型进行模糊综合评价，为决策者提供决策依据。     

Multiple Objective Approach as an Alternative to Radial Projection in DEA

Radial projection is a standard technique applied in data envelopment analysis (DEA) to calculate efficiency scores for input and/or output variables. In this paper, we have studied the appropriateness of radial projection for target setting. We have created a situation where the decision making units (DMUs) are free to choose their own target values on the efficient frontier and then compared the results to those of radial projection. In practice, target values are primarily used for future goal attainment; hence, not only preferences but also, and on the whole, change in time frame, affect the choice of target values. Based on that, we conducted an empirical experiment with an aim to study how the DMUs choose their most preferred target values on the efficient frontier. The subjects, who all were students of the Helsinki School of Economics, were given the freedom to explore their personalized efficient frontiers by using a multiple objective linear programming (MOLP) approach. To study various and relevant scenarios, the personalized efficient frontiers for all students were constructed in such a way that the current position of each student in relation to the frontier made him/her inefficient, efficient, or super-efficient. The results show that the use of radial projection for target setting is too restrictive.     

Efficiency aggregation with enhanced Russell measures in data envelopment analysis

In aggregation for data envelopment analysis (DEA), a jointly determined aggregate measure of output and input efficiency is desired that is consistent with the individual decision making unit measures. An impasse has been reached in the current state of the literature, however, where only separate measures of input and output efficiency have resulted from attempts to aggregate technical efficiency with the radial measure models commonly employed in DEA. The latter measures are “incomplete” in that they omit the non-zero input and output slacks, and thus fail to account for all inefficiencies that the model can identify. The Russell measure eliminates the latter deficiency but is difficult to solve in standard formulations. A new approach has become available, however, which utilizes a ratio measure in place of the standard formulations. Referred to as an enhanced Russell graph measure (ERM), the resulting model is in the form of a fractional program. Hence, it can be transformed into an ordinary linear programming structure that can generate an optimal solution for the corresponding ERM model. As shown in this paper, an aggregate ERM can then be formed with all the properties considered to be desirable in an aggregate measure—including jointly determined input and output efficiency measures that represent separate estimates of input and output efficiency. Much of this paper is concerned with technical efficiency in both individual and system-wide efficiency measures. Weighting systems are introduced that extend to efficiency-based measures of cost, revenue, and profit, as well as derivatives such as rates of return over cost. The penultimate section shows how the solution to one model also generates optimal solutions to models with other objectives that include rates of return over cost and total profit. This is accomplished in the form of efficiency-adjusted versions of these commonly used measures of performance.     

Output complexity, environmental conditions, and the efficiency of municipalities

Over the last few years, many studies have analyzed the efficiency of local governments in different countries. An accurate definition of their output bundles—i.e., the services and facilities they provide to their constituencies—is essential to this research. However, several difficulties emerge in this task. First, since in most cases the law only establishes the minimum amount of services and facilities to provide, it may well be the case that some municipalities go beyond the legal minimum and, consequently, might have an uncertain effect on efficiency when compared to other municipalities which stick to the legal minimum. Second, municipalities face very different environmental conditions, which raises some doubts about the plausibility of an unconditional analysis. This study tackles these problems by proposing an analysis in which the efficiency of municipalities is evaluated after splitting them into clusters according to various criteria (output mix, environmental conditions, level of powers). We perform our estimations using order-m frontiers, given their robustness to outliers and immunity to the curse of dimensionality. We provide an application to Spanish municipalities, and results show that both output mix and, more especially, environmental conditions, should be controlled for, since efficiency differences between municipalities in different groups are notable.     

Incorporating support vector machines with multiple criteria decision making for financial crisis analysis

Feature selection is an essential pre-processing technique in data mining that eliminates redundant or unrepresentative attributes and improves the performance of classifiers. However, a classifier with different feature selection approaches results in diverse outcomes. Thus, determining how to integrate feature selection methods and yield an appropriate feature set is an issue worth further study. Based on ensemble learning, this investigation develops a SVMMCDM (support vector machines with multiple criteria decision making) model that employs various feature selection techniques as data preprocessing schemes and then uses SVM for financial crisis prediction. The study uses MCDM to determine the most suitable feature selection mechanism when many performance criteria are considered. After the feature selection mechanism has been determined, the study decomposes the SVM to obtain support vectors and predicted labels which are then fed into a decision tree to generate rules. The numerical results for the ex-ante and ex-post periods relative to the financial tsunami show that the proposed SVMMCDM model is an effective way to predict a financial crisis and can provide useful rules for decision makers.     

Ranking of sectors based on fuzzy importance measure

The changing nature of policy variables specific to any planned developmental programme often leads to conflicting decisional problems regarding the identification of thrust areas. Hence the inherent requirement is for a composite index which eases out such ambiguous choice issues. The present paper introduces the measure of sectoral importance which is capable of encompassing different variables with their associated weights and ranks sectors in an economy based on such a measure. However, the term importance suggests the qualitativeness and subjectivity involved in defining such a concept and thus establishes the need for the concepts of fuzzy mathematics. The theory of fuzzy subsets is capable of dealing with qualitative variables within a quantitative framework. The sectoral importance measures derived from the sectoral output linkages, employment multipliers and value added multipliers, have been represented as fuzzy subsets, or to be precise, as fuzzy numbers. A comparison of these numbers through the binary approach of determination of the measure of relative strength provides the basis for the ranking of sectors. The novelty of the approach lies in its simplicity and flexibility in treating qualitative factors which characterise most decision support socio economic planning problems. The validity of the exercise has been tested by applying it to the economy of West Bengal, a State of India.     

基于GIS的启发式物流配送中心选址方法的研究

本文针对配送中心选址规划问题，综合考虑影响选址决策的各种因素。利用GIS的空间分析技术，在电子地图上分析定性因素得出了一系列选址候选点，在此基础上建立整数规划选址模型，采用聚类的方法对客户分群，用贪婪取走启发算法得出最佳配送中心选址地点。最后通过实例证明方法的有效性。     

Do entrepreneurs from institutionally distinct countries apply different decision logic when internationalizing their companies? A multiple-case analysis

Literature on entrepreneurs’ decision logic has either focused on effectuation or causation, lacking comparison and analysis of logics when founders come from different institutional environments. This paper presents an exploratory study examining the behaviors, beliefs, and decisions of entrepreneurs (all typical drivers for new market entries), considering those institutional differences. Research was based on the comparison of two case studies of young software companies, one from Brazil and one from Germany, each founded by two entrepreneurs with similar academic and professional backgrounds. Results indicate that apparently, business environment affects founders’ decision logics with much more strength than the institutional environment.