Searching for data is one of the fundamental fields of computing. Often, the difference between a fast and slow one is the use of good algorithm for the data set. The efficiency of searching techniques used in a task will determine how fast such task can be completed. In this work, the efficiency of linear and binary searching techniques in terms of running time, memory usage and the numbers of...

Nowadays, managing a supply chain is turned to be one of the fundamentals of business process.  In doing so, investigating and analyzing each and every of the processes and selecting the best of each process is an important challenge for strategic managers. In this paper Data Envelopment Analysis (DEA) technique is used and a model is provided for selecting the best suppliers with flexible inpu...

For every finite n ≥ 4 there is a logically valid sentence φn with the following properties: φn contains only 3 variables (each of which occurs many times); φn contains exactly one nonlogical binary relation symbol (no function symbols, no constants, and no equality symbol); φn has a proof in first-order logic with equality that contains exactly n variables, but no proof containing only n − 1 v...

In this paper, a special class of redundancy optimization problem with fuzzy random variables is presented. In this model, fuzzy random lifetimes are considered as basic parameters and the Er-expected of system lifetime is used as a major type of system performance. Then a redundancy optimization problem is formulated as a binary integer programming model. Furthermore, illustrative numerical ex...

This review introduces logistic regression, which is a method for modelling the dependence of a binary response variable on one or more explanatory variables. Continuous and categorical explanatory variables are considered.

This paper presents techniques for handling symmetries in integer linear programs where variables can take integer values, extending previous work dealing exclusively with binary variables. Orthogonal array construction and coloring problems are used as illustrations.

Definitions and Distinctions First, it is important to distinguish between categorical variables and continuous variables. Categorical variables are those with two values (i.e., binary, dichotomous) or those with a few ordered categories. Examples might include gender, dead vs. alive, audited vs. not audited, or variables with few response options like “never,” “sometimes,” or “always.” Continu...

Definitions and Distinctions First, it is important to distinguish between categorical variables and continuous variables. Categorical variables are those with two values (i.e., binary, dichotomous) or those with a few ordered categories. Examples might include gender, dead vs. alive, audited vs. not audited, or variables with few response options like “never,” “sometimes,” or “always.” Continu...