Modified PROMETHEE Approach for Solving Multi-Criteria Location Problems with Complex Criteria Functions

The specific problem that occurs in multi-criteria decision-making (MCDM) processes is ranking a number of alternatives using complex criteria functions (the hierarchical structure of criteria) whose values must consider the impacts of all-important characteristics and parameters of alternatives. The problem becomes more complex by increasing the number of levels of sub-criteria functions (degree of decomposition). This paper proposes an extended procedure based on the mean values conversion of the net outranking flow of sub-criterion functions obtained by modified PROMETHEE methods. The actual value of criterion functions is used only at the last level, and transformed values of the net outranking flow for generating a final rank of alternatives are introduced at other levels. This procedure provides a more objective comparison of the impact of various individual criteria to rank the alternatives and easier making of unique solution, where the impact of decision-maker (DM) experience and subjective estimation is minimised in the selection. Applicability and practicability of the presented procedure for solving the selection problem of a logistics warehouse location are demonstrated in the analysis of a case study example

Quantifying image distortion based on Gabor filter bank and multiple regression analysis

Image quality assessment is indispensable for image-based applications. The approaches towards image quality assessment fall into two main categories: subjective and objective methods. Subjective assessment has been widely used. However, careful subjective assessments are experimentally difficult and lengthy, and the results obtained may vary depending on the test conditions. On the other hand, objective image quality assessment would not only alleviate the difficulties described above but would also help to expand the application field. Therefore, several works have been developed for quantifying the distortion presented on a image achieving goodness of fit between subjective and objective scores up to 92%. Nevertheless, current methodologies are designed assuming that the nature of the distortion is known. Generally, this is a limiting assumption for practical applications, since in a majority of cases the distortions in the image are unknown. Therefore, we believe that the current methods of image quality assessment should be adapted in order to identify and quantify the distortion of images at the same time. That combination can improve processes such as enhancement, restoration, compression, transmission, among others. We present an approach based on the power of the experimental design and the joint localization of the Gabor filters for studying the influence of the spatial/frequencies on image quality assessment. Therefore, we achieve a correct identification and quantification of the distortion affecting images. This method provides accurate scores and differentiability between distortions

Pairwise comparison matrices and the error-free property of the decision maker

Pairwise comparison is a popular assessment method either for deriving criteria-weights or for evaluating alternatives according to a given criterion. In real-world applications consistency of the comparisons rarely happens: intransitivity can occur. The aim of the paper is to discuss the relationship between the consistency of the decision maker—described with the error-free property—and the consistency of the pairwise comparison matrix (PCM). The concept of error-free matrix is used to demonstrate that consistency of the PCM is not a sufficient condition of the error-free property of the decision maker. Informed and uninformed decision makers are defined. In the first stage of an assessment method a consistent or near-consistent matrix should be achieved: detecting, measuring and improving consistency are part of any procedure with both types of decision makers. In the second stage additional information are needed to reveal the decision maker’s real preferences. Interactive questioning procedures are recommended to reach that goal

Does ignoring multidestination trips in the travel cost method cause a systematic bias?

The present paper demonstrates that treating multidestination trips (MDT) as single‐destination trips does not involve any systematic upward or downward bias in consumer surplus (CS) estimates because the direct negative effect of a price increase (treating MDT as a single‐destination trip) is offset by a shift in the estimated demand curve. Still, ignoring MDT can greatly underestimate or overestimate the CS. In addition, we demonstrate that there is a sound theoretical basis for using preference information for allocating travel costs between different sites included in the MDT package. A novel extreme value approach is proposed, which does not require any overly restrictive assumptions about consumer preferences. This approach is applied to the zonal travel cost model of the Bellenden Ker National Park, Australia. Parametric and non‐parametric estimation techniques are used for calculating CS estimates, and the effects of different MDT treatments and estimation methods are compared.Research Methods/ Statistical Methods,

What's the Difference?! Gender, Personality, and the Propensity to Start a Business

Women start fewer businesses than men. The start-up rate among women in Germany falls short of males' start-up rate by one third. We scrutinize this gender gap using individual-level data from the KfW Start-up Monitor, a large-scale population survey on start-up activity in Germany. As a unique feature, the data combine socio-demographic characteristics, entrepreneurship-related attitudes, and general personality traits of both business starters and non-starters. Estimating binary choice models and employing decomposition techniques, we find that gender differences in socio-demographics alone would even be in favor of higher start-up rates among women, while the distribution of personality traits is less favorable for business start-ups among women and explains about one third of the entire gender difference. Most substantially, men opt for a start-up more often even given identical human capital and related endowments. Qualificational policies targeted towards higher educational attainments of potential entrepreneurs do thus not suffice to increase the number of female business starters.entrepreneurship, gender difference, start-up propensity, decomposition analysis, KfW Start-up Monitor, Germany

Evaluation of elicitation methods to quantify Bayes linear models

The Bayes linear methodology allows decision makers to express their subjective beliefs and adjust these beliefs as observations are made. It is similar in spirit to probabilistic Bayesian approaches, but differs as it uses expectation as its primitive. While substantial work has been carried out in Bayes linear analysis, both in terms of theory development and application, there is little published material on the elicitation of structured expert judgement to quantify models. This paper investigates different methods that could be used by analysts when creating an elicitation process. The theoretical underpinnings of the elicitation methods developed are explored and an evaluation of their use is presented. This work was motivated by, and is a precursor to, an industrial application of Bayes linear modelling of the reliability of defence systems. An illustrative example demonstrates how the methods can be used in practice

A heuristics approach for computing the largest eigenvalue of a pairwise comparison matrix

Pairwise comparison matrices (PCMs) are widely used to capture subjective human judgements, especially in the context of the Analytic Hierarchy Process (AHP). Consistency of judgements is normally computed in AHP context in the form of consistency ratio (CR), which requires estimation of the largest eigenvalue (Lmax) of PCMs. Since many of these alternative methods do not require calculation of eigenvector, Lmax and hence the CR of a PCM cannot be easily estimated. We propose in this paper a simple heuristics for calculating Lmax without any need to use Eigenvector Method (EM). We illustrated the proposed procedure with larger size matrices. Simulation is used to compare the accuracy of the proposed heuristics procedure with actual Lmax for PCMs of various sizes. It has been found that the proposed heuristics is highly accurate, with errors less than 1%. The proposed procedure would avoid biases and help managers to make better decisions. The advantage of the proposed heuristics is that it can be easily calculated with simple calculations without any need for specialised mathematical procedures or software and is independent of the method used to derive priorities from PCMs

Automated analysis of radar imagery of Venus: handling lack of ground truth

Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROCs) for evaluating detection performance when the “ground truth” is subjective. In particular, we evaluate the relative detection performance of humans and automatic algorithms. Our experimental results indicate that proper assessment of the uncertainty in “ground truth” is essential in applications of this nature