Searching for a target in an unfamiliar environment requires acquiring, storing, processing, and recalling spatial information. We use datasets from two virtual reality navigation experiments to design and validate a simple biologically-inspired computational model of these processes. Subjects in both datasets play taxicab drivers, picking up and delivering passengers in a series of small virtu...

The formulas of cost and allocative efficiencies of decision making units (DMUs) with positive data cannot be used for DMUs with negative data. On the other hand, these formulas are needed to analyze the productivity and performance of DMUs with negative data. To this end, this study introduces the cost and allocative efficiencies of DMUs with negative data and demonstrates that the introduc...

A configuration of lattice vectors is supernormal if it contains a Hilbert basis for every cone spanned by a subset. We study such configurations from various perspectives, including triangulations, integer programming and Gröbner bases. Our main result is a bijection between virtual chambers of the configuration and virtual initial ideals of the associated binomial ideal.

Suporte financeiro: CNPq e Capes. Abstract: The global market for automotive parts is typically characterized by the strong presence of global suppliers, which are continually pressured to reduce costs, and increase productivity and competitiveness. In this context, this paper describes a new FGPDEA model that combines Data Envelopment Analysis (DEA) and Fuzzy Goal Programming, thereby aiming t...

For better guiding a system, senior managers should have accurate information. Using Data Envelopment analysis (DEA) help managers in this objective. Thus, many investigations have been made in order to find the most productive scale size (MPSS) for the evaluating decision making units (DMUs). In this paper we consider this case where there exist subsets of input and output variables to be inte...

Data Envelopment Analysis (DEA) requires that the data for all inputs and outputs are known exactly. When some outputs and inputs are known decision variables, such as interval data and ordinal data, the DEA models becomes a nonlinear programming problem and is called imprecise DEA (IDEA). When data assume to be interval, decision making units (DMUs) can divide to three classes as follows: (I) ...

Data Envelopment Analysis (DEA) cannot provide adequate discrimination among efficient decision making units (DMUs). To discriminate these efficient DMUs is an interesting research subject. The purpose of this paper is to present a Cross-Efficiency Profiling (CEP) model which can be used to improve discrimination power of DEA and conduct a methodological comparison of CEP and the other develope...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently DEA has been extended to examine the efficiency of two-stage processes, where all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. The resulting two-stage DEA model provides not only an overall efficiency score for the ent...

Classic data envelopment analysis (DEA) models determine the efficiency of productive units, called decision making units (DMUs). DEA uses as its methodology the equiproportional reduction of inputs or increase of outputs and the finding of a single target for each DMU. This target does not incorporate the preference of the decision maker. Later works propose obtaining alternative targets based...

This paper studies the inverse data envelopment analysis using the nonradial enhanced Russell model. Necessary and sufficient conditions for inputs/outputs determination are introduced based on Pareto solutions of multiple-objective linear programming. In addition, an approach is investigated to identify extra input/lack output in each of input/output components (maximum/minimum reduction/incre...