Setting targets with interval data envelopment analysis models via wang method

Data envelopment analysis (DEA) is a mathematical programming for evaluating the relative efficiency of decision making units (DMUs). The first DEA model (CCR model) assumed for exact data, later some authors introduced the applications of DEA which the data was imprecise. In imprecise data envelopment analysis (IDEA) the data can be ordinal, interval and fuzzy. Data envelopment analysis also can be used for the future programming of organizations and the response of the different policies, which is related to the target setting and resource allocation. The existing target model that conveys performance based targets in line with the policy making scenarios was defined for exact data. In this paper we improved the model for imprecise data such as fuzzy, ordinal and interval data. To deal with imprecise data we first established an interval DEA model. We used one of the methods to convert fuzzy and ordinal data into the interval data. A numerical experiment is used to illustrate the application to our interval model

Measuring efficiency of a hierarchical organization with fuzzy DEA method

The paper analyses how the data envelopment analysis (DEA) and fuzzy set theory can be used to measure and evaluate the efficiency of a hierarchical system with n decision making units and a coordinating unit. It is presented a model for determining the of activity levels of decision making units so as to achieve both fuzzy objectives of achieving global target levels of coordination unit on the inputs and outputs and individual target levels of decision making units, and then some methods to resolve fuzzy models are proposed.fuzzy DEA, policy making in multi-level organisations, efficiency analysis

A Risk Management Model for Merger and Acquisitio

In this paper, a merger and acquisition risk management model is proposed for considering risk factors in the merger and acquisition activities. The proposed model aims to maximize the probability of success in merger and acquisition activities by managing and reducing the associated risks. The modeling of the proposed merger and acquisition risk management model is described and illustrated in this paper. The illustration result shows that the proposed model can help to screen the best target company with minimum associated risks in the merger and acquisition activity

Fuzzy Logic and Singular Value Decomposition based Through Wall Image Enhancement

Singular value decomposition based through wall image enhancement is proposed which is capable of discriminating target, noise and clutter signals. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy logic. Fuzzy inference engine is used to assign weights to different spectral components. K-means clustering is used for suitable selection of fuzzy parameters. Proposed scheme significantly works well for extracting multiple targets in heavy cluttered through wall images. Simulation results are compared on the basis of mean square error, peak signal to noise ratio and visual inspection

Optimal Forest Strategies for Addressing Tradeoffs and Uncertainty in Economic Development under Old-Growth Constraints

In Canada, governments have historically promoted economic development in rural regions by promoting exploitation of natural resources, particularly forests. Forest resources are an economic development driver in many of the more than 80% of native communities located in forest regions. But forests also provide aboriginal people with cultural and spiritual values, and non-timber forest amenities (e.g., biodiversity, wildlife harvests for meat and fur, etc.), that are incompatible with timber exploitation. Some cultural and other amenities can only be satisfied by maintaining a certain amount of timber in an old-growth state. In that case, resource constraints might be too onerous to satisfy development needs. We employ compromise programming and fuzzy programming to identify forest management strategies that best compromise between development and other objectives, applying our models to an aboriginal community in northern Alberta. In addition to describing how mathematical programming techniques can be applied to regional development and forest management, we conclude from the analysis that no management strategy is able to satisfy all of the technical, environmental and social/cultural constraints and, at the same time, offer aboriginal peoples forest-based economic development. Nonetheless, we demonstrate that extant forest management policies can be improved upon.forest-dependent aboriginal communities, boreal forest, compromise and fuzzy programming, sustainability and uncertainty, International Development, Resource /Energy Economics and Policy, R11, Q23, Q01, C61,

ARTMAP-FTR: A Neural Network For Fusion Target Recognition, With Application To Sonar Classification

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657