Supplier selection in risk consideration: a fuzzy based topsis approach

Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain. In classical Multi-Criteria Decision Making (MCDM) methods, the ratings and the weights of the criteria are known precisely. Owning to vagueness of the decision data, the crisp data are inadequate for real-life situations. Since human judgments including preferences are often vague and cannot be expressed by exact numerical values, the application of fuzzy concepts in decision making is deemed to be relevant. On the other hand, it is a hard problem since supplier selection is typically a MCDM problem involving several conflicting criteria on which decision maker’s knowledge is usually vague and imprecise. In the present work, a risk-based suppliers’ evaluation module is proposed. Linguistic values are used to assess the ratings and weights for the risk based supplier selection factors. These linguistic ratings can be expressed in triangular fuzzy numbers. Then, a hierarchy MCDM model based on fuzzy-sets theory is proposed to deal with the supplier selection problems in the supply chain system. According to the concept of the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), a closeness coefficient is defined to determine the ranking order of all suppliers by calculating the both fuzzy positive-ideal solution and fuzzy negative-ideal solution, simultaneously. Empirical data have been analysed and results obtained thereof, have been reported to exhibit application potential of the decision-support systems in appropriate situation

The decision tree approach to classification

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers

Layered classification techniques for remote sensing applications

The layered classifier method is outlined and several applications to pattern classification for which the approach is suited are discussed

On the accuracy of pixel relaxation labeling

There are no author-identified significant results in this report

Pixel labeling by supervised probabilistic relaxation

There are no author-identified significant results in this report

Processing techniques development, volume 3. Part 2: Data preprocessing and information extraction techniques

There are no author-identified significant results in this report

Processing techniques development, volume 3

The author has identified the following significant results. Analysis of the geometric characteristics of the aircraft synthetic aperture radar (SAR) relative to LANDSAT indicated that relatively low order polynominals would model the distortions to subpixel accuracy to bring SAR into registration for good quality imagery. Also the area analyzed was small, about 10 miles square, so this is an additional constraint. For the Air Force/ERIM data, none of the tested methods could achieve subpixel accuracy. Reasons for this is unknown; however, the noisy (high scintillation) nature of the data and attendent unrecognizability of features contribute to this error. It is concluded that the quadratic model would adequately provide distortion modeling for small areas, i.e., 10 to 20 miles square

A case study using ECHO(Extraction and Classification of Homogeneous Objects) for analysis of multispectral scanner data

There are no author-identified significant results in this report

Implementation of ILLIAC 4 algorithms for multispectral image interpretation

Research has focused on the design and partial implementation of a comprehensive ILLIAC software system for computer-assisted interpretation of multispectral earth resources data such as that now collected by the Earth Resources Technology Satellite. Research suggests generally that the ILLIAC 4 should be as much as two orders of magnitude more cost effective than serial processing computers for digital interpretation of ERTS imagery via multivariate statistical classification techniques. The potential of the ARPA Network as a mechanism for interfacing geographically-dispersed users to an ILLIAC 4 image processing facility is discussed

ECHO user's guide

There are no author-identified significant results in this report