IRANIAN JOURNAL OF OPERATIONS RESEARCH
Year:2009 | Volume:1 | Issue:2|Papers Count:8

On behalf of the board of editors of Iranian Journal of Operations Research, I am pleased to present the second issue of Iranian Journal of Operations Research. The first issue of this journal was published in 2008. The objective was to introduce it to Iranian scholars, researchers and students. Now, after gathering the views of the members of operations research society of Iran, we are planning to publish this journal twice a year regularly.

This survey paper is intended for the graduate students and researchers who are interested in Operations Research, have solid understanding of linear optimization but are not familiar with Semidefinite Programming (SDP). Here, provide a very gentle introduction to SDp, some entry points for further look into the SDP literature, and brief introductions to some selected well-known applications which may be attractive to such audience and in turn motivate them to learn more about semidefinite optimization.

Two main results of necessary conditions of optimality for two kinds of problems, bilevel optimization and quasidifferentiable MPEC, are presented via Demyanov sum of quasidifferentials. The result that Lagrange multipliers are independent of the choices of quasidifferentials and super gradients is given.

In this paper we present a real application of ANP in entertainment business, the expansion of Disney amusing parks by establishing a new theme park in greater China. The Analytic Network Process (ANP) is an efficient approach for this critical decision making. ANP is a generalization of the Analytic Hierarchy Process (AHP). The basic structure is an influence network of clusters and nodes contained within the clusters. Priorities are established in the same way they are in the AHP using pairwise comparisons and judgment.

Reliability models based on Markov chain (Except in queuing systems) have extensive applications in electrical and electronic devices. Here, we consider a k-out-of-n:G system with n parallel and identical elements with increasing failure rates (failure rates are Weibull distributed) and non repairable elements. The failure rate of remaining elements increases when some elements fail. The system works until at least k elements work. The system of equations are established and the exact equations are sought for the parameters like MTTF and the probability of system working at the time t . A numerical example is solved to demonstrate the procedure clarifying the theoretical development.

Fuzzy set theory has been applied to many fields, such as operations research, control theory, and management sciences. We consider two classes of fuzzy linear programming (FLP) problems: Fuzzy number linear programming and linear programming with trapezoidal fuzzy variables problems. We state our recently established results and develop fuzzy primal simplex algorithms for solving these problems. Finally, we give illustrative examples.

We propose a new approach to monitor an overall mean shift of a bi-variate quality control system. To do this, we first define beliefs on deciding whether the quality characteristics are in out-of-control state. Then, by taking new observations, in an iterative approach we update the belief of each quality characteristics being out-of-control. This task is performed using a recursive method and prior beliefs. Finally, we introduce a statistics in combination with Bivariate Exponentially Weighted Moving Average (BEWMA) statistics to improve the performance of the proposed method. In order to understand the proposed methodology and to evaluate its performance, we perform a simulation study. Moreover, we compare in- and out-of-control average run lengths of the proposed method with the ones from the well-known MCUSUM and MEWMA procedures in different scenarios of mean shifts. The results of the simulation study show that the proposed methodology performs better than the other methods for small shifts of the process mean.

We propose a mechanism to deal with the asymmetric information which increases the phenomenon of underpricing in the Initial Public Offering (IPO) transactions. In this regard, we develop a truthful screening mechanism by which a screening agent could assess a firm that is going to be public during an IPO. A mathematical model is developed and solved to determine the incentives of these agencies so that they find it optimal to perform truthfully. We also pursue the case of cooperation of n such agents and compare it with the situation in which each agent works independently.