INTERNATIONAL JOURNAL OF APPLIED OPERATIONAL RESEARCH
Year:2011 | Volume:1 | Issue:2|Papers Count:8

Today, quality improvement and cost reduction are key factors for achieving business success, growth and position. One of the primary tools for quality improvement and cost reduction in online activities of statistical process control is control charts. As the need for monitoring several correlated quality characteristics is extensively growing, the use of multivariate control charts becomes more popular. Among these, the T2 control chart is the most popular due to many advantages. In this paper, the variable sampling scheme is studied to increase the power of the fixed sampling rate (FRS) T2 control chart to show small shifts. In this study, one of the parameters of T2control chart, e.g., the sample size, vary between three values, depending on the most recent T2 value.The design of T2 control chart with three variable sample sizes (3VSS) from the statistical and economic perspective has been investigated, when the shift in the process mean does not occur at the beginning but at some random time in the future. Therefore, the Markov Chain approach used to develop the cost function. Then, the genetic algorithm (GA) is applied to search for the optimal values of the parameters of the3VSS control chart. Finally, numerical comparisons among FRS and 3VSS schemes are made and discussed.

In this paper, there are two competitors in a planar market. The first competitor, called, the leader, opens p new facilities. After that, the second competitor, the follower, reacts to the leader's action and opens r new facilities. The leader and the follower have got some facilities in advance in this market. The optimal locations for leader and follower are chosen among predefined candidate locations. In this paper, it is assumed that the demand is inelastic. Considering the huff model, customers share their demand among all facilities, probabilistically. The leader's objective function is the maximization of its market share after the follower's new facilities entry. The leader and follower problems are solved via an exact solution method.

Flow shop scheduling problem with missing operations is studied in this paper. Missing operations assumption refers to the fact that at least one job does not visit one machine in the production process. A mixed-binary integer programming model has been presented for this problem to minimize the make span. The genetic algorithm (GA) and tabu search (TS) are used to deal with the optimization problem. According to computational experiments on data sets, it is suggested that GA is a more appropriate method to solve this problem. GA can reach good-quality solutions in short computational time, and can be used to solve large scale problems effectively.

Risk management is one of the most prominent concepts which has recently been brought into sharp focus regarding security issues in computer networks. Scientifically speaking, risk in the field of network security is a generalized matter leading the organization to the provision of resolutions which target resources and profits of the organization. This paper has discussed what methods are to be adopted with respect to identification and classification of risk assessment and evaluation of computer networks using data mining. The proposed method uses support vector machine to generate and analyze results of risk evaluation. Considering available resources in the organization, the tasks performed by means of this method are of high accuracy, therefore, results are hereby in accordance with theories of management and security and based on resources and organizational profits depending on adjustable parameters as inputs. Outcomes of this study can be implemented in a dynamically real-time defensive system.

Association rule mining process can be visualized as a multi-objective problem rather than as a single objective one. Measures like support, confidence and other interestingness criteria which are used for evaluating a rule, can be thought of as different objectives of association rule mining problem. Support count is the number of records, which satisfies all the conditions that exist in the rule. This objective represents the accuracy of the rules extracted from the database. Confidence represents the proportion of records for which the prediction of the rule (or model in the case of a complete classification) is correct, and it is one of the most widely quoted measures of quality, especially in the context of complete classification. Interestingness measures how much interesting the rule is. Using these three measures as the objectives of rule mining problem, this article uses a Simulated Annealing algorithm to extract some useful and interesting rules from any Market-basket type databases. The experimental results show that the algorithm may be suitable for large and noisy datasets but don't stay in local minimum.

Earlier methods in classic DEA investigate congestion for DMUs with crisp data. Although in real world we face data which are not crisp necessarily, available methods in this situation are not enough. In this paper for investigating congestion, a new method with fuzzy data has been promoted.In order to evaluate the performance of the method it is shown how our new method lead in exact results by an numerical example.

This paper proposes a new mathematical model for multi-objective redundancy allocation problem (RAP) without component mixing in each subsystem when the redundancy strategy can be chosen for individual subsystems. Majority of the mathematical model for the multi-objective redundancy allocation problems (MORAP) assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. The proposed model for MORAP simultaneously maximizes the reliability and the net profit of the system. And finally, to clarify the proposed mathematical model a numerical example will be solved.

In data envelopment analysis, anyone can do classification decision units with efficiency scores. It will be interesting if a method for classification of DMUs without regarding to efficiency score is obtained. So in this paper, the classification of Decision Making Units (DMUs) is done according to the additive model without being solved for obtaining scores efficiency. This is because it is known that the additive model is the simplest non-radial model in DEA. In fact, the classification of DMUs to a set of efficient, weak efficient, and inefficient units, based upon feasibility concept is done here. Especially, the models and theorems for this aim are presented.