In this paper, we compare four ORDERING policies in a lost sales inventory model with zero ORDERING cost, constant lead time, and Poisson demand process. These ORDERING policies are 1) base stock policy, 2) full delay policy, 3) simple delay policy and 4) a recently developed ORDERING policy called (1; T) or one for one period policy. Our work can be considered as an expansion of a previous research which compared the first three policies. We show that, for any fixed value of the ratio of unit lost sales cost over unit holding cost, there is a specific value of lead time demand beyond which the cost of (1; T) policy is lower than the costs of the other three policies. Furthermore, the superiority of (1; T) policy is more significant for low values of the above ratio and becomes more pronounced as the lead time demand increases.

With considering a ‘triangle of three major currency pairs’, there is a tiny difference between multiplication of exchange rate for the first two currency pairs and the third. To discover whether this little difference can lead to a neutral arbitrage or not, I took portfolios of 35 baskets of three major currency pairs (combinations of all 7 major currencies). There are eight approaches (different cases of short and long positions) in each basket; for example buying 1st currency pairs and selling two others, etc.Historical monthly FX rates were gathered from January 1990 until July 2011. Profit or loss derived from eight approaches in all baskets has been calculated. Number of months with a profit has been compared with the months with a loss. Covariances of all approaches of FX rates between growth rates were calculated.I found that this ‘triangle ORDERING’ of three currency pairs will not always eventuate to a profit. In the other meaning, this is not a neutral strategy. The results showed that in 94% of 280 cases, the probability of gaining profit is almost equal to gaining loss. Also it was found that the most profitable approach is not the best in probability of profit.Standard deviations of results (as indicators of risk of the approaches) were diagramed with the amount of profits. This figured ‘efficient frontier’ of approaches, the best combinations of risks and profits.Covariance were often positive (in 70% of 280 cases), showing probability of simultaneous effectiveness of external factors on all currencies

Topological aspects of meshless element free Galerkian method (EFGM) as the nodal connectivity of matrices are studied. Four different graphs are defined for meshless discretizations of EFGM and their connectivity is investigated. Graphs are used for nodal ORDERING of meshless models in order to reduce the bandwidth, profile, maximum front width, and root-mean-square wave front of the corresponding matrices.      

The topological ORDERING algorithm sorts nodes of a directed graph such that the order of the tail of each arc is lower than the order of its head. In this paper, we introduce the notion of covering between nodes of a directed graph. Then, we apply the topological ORDERING algorithm on graphs containing the covering nodes. We show that there exists a cut set with forward arcs in these graphs and the order of the covering nodes is successive.

One of the main techniques used in data mining is data clustering, which has many applications in computer science, biology and social sciences. Constrained clustering is a type of clustering in which side information provided by the user is incorporated into current clustering algorithms. One of the well researched constrained clustering algorithms is called microaggregation. In a microaggregation technique, the algorithm divides the dataset into groups containing at least  members, where  is a user-defined parameter. The main application of microaggregation is in Statistical Disclosure Control (SDC) for privacy preserving data publishing. A microaggregation algorithm is qualified based on the sum of within-group squared error, . Unfortunately, it has been proven that the optimal microaggregation problem is NP-Hard in general, but the special univariate case can be solved optimally in polynomial time. Many heuristics exist for the general case of the problem that are founded on the univariate case. These techniques order multivariate records in a sequence. This paper proposes a novel method for record ORDERING. Starting from a conventional clustering algorithm, the proposed method repeatedly puts multivariate records into a sequence and then clusters them again. The process is repeated until no improvement is achieved. Extensive experiments have been conducted in this research to confirm the effectiveness of the proposed method for different parameters and datasets.