International Journal of Industrial Engineering & Production Research

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A periodic maintenance policy through either an imperfect repair or replacement is proposed for a repairable system. It is assumed that the system is subject to an inverse Gaussian degradation process. The effect of imperfect repair is modeled through both arithmetic reduction of degradation and arithmetic reduction of age approaches. The degradation level of the system is measured after each imperfect repair in periodic time intervals. The system is replaced if its deterioration level  exceeds a pre-determined technical threshold or at the nth inspection time, whichever occurs first. The main goal of the paper is to find the optimal value of n based on cost rate function. Some theoretical results are derived and then the optimal policy is obtained numerically. The effect of imperfect repair, the inspection time interval, the value of technical degradation threshold, and the costs of interest are all studied on the optimal policy.  

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The hydrocyclone has a very important roll in industrial separation. The consideration of its behavior is very important for design. In this investigation, behavior of water flow and particles trajectory inside a hydrocyclone has been considered by means of numerical and experimental methods, and results have been compared together. To have a numerical simulation, a CFD software was used, and for modeling flow the RNG k – model applied. Finally, the effect of particle size on hydrocyclone performance has been studied. It was found that the grade efficiency and number of particle that exit from underflow of the hydrocyclone is increased when bigger particles is used.

A series of experiments has been carried out in a laboratory with a hydrocyclone. Comparison shows that, there is a good agreement between the CFD models and experimental result.

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Main result of this paper is to derive the exact analytical expressions of information and covariance matrix for multivariate Pareto, Burr and related distributions. These distributions arise as tractable parametric models in reliability, actuarial science, economics, finance and telecommunications. We showed that all the calculations can be obtained from one main moment multidimensional integral whose expression is obtained through some particular change of variables. Indeed, we consider that this calculus technique for that improper integral has its own importance.

 

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In the evaluation of non-efficient units by Data Envelopment Analysis (DEA) referenced Decision Making Units (DMU’s) have an important role. Unfortunately DMU’s with extra ordinary output can lead to a monopoly in a reference set, the fact called abnormality due to the outliers' data. In this paper, we introduce a DEA model for evaluating DMU’s under this circumstance. The layer model can result in a ranking for DMU’s and obtain an improving strategy leading to a better layer.

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  M. and M.

 

Abstract: Existence and uniqueness of solution for singular 2-D systems depends on regularity condition. Simple regularity implies regularity and under this assumption, the generalized wave model (GWM) is introduced to cast singular 2-D system of equations as a family of non-singular 1-D models with variable structure.These index dependent models, along with a set of boundary constraint relations, forming the admissible subspace, led to the recursive solution of the GWM.

 

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  In this paper, authors tackle three very important questions that need to be answered if a theory of design is to be constructed. The first is what designers do, Which we attempt to illustrate with the help of case studies and theories of design practice. The second question is what guides designers. Here, authors try to present some of the proposed normative positions about design, to show the similarities and differences between positions and a framework of how they can be categorized. The main (third) question is how the design thinking process can be represented drawing upon on a review of recent studies of design practice and designer's creativity.

  One approach to design thinking is to extract the features of the designers' strategic knowledge, for which comparative studies between expert designers and novices are useful. Also, controlled experimental studies may be adopted in order to understand the nature of the idea generation process.

Finally, the methods of research and representation of design thinking in order to gain a deeper understanding of the designers' creativity are proposed .

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Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.

  In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact, a new numerically method was introduced for finding the maximum a posterior estimation by using EM-algorithm and Gaussians mixture distribution. In this algorithm, we were made a sequence of priors, posteriors were made and then converged to a posterior probability that is called the reference posterior probability. Maximum a posterior estimated can determine by the reference posterior probability which can make labeled image. This labeled image shows our segmented image with reduced noises. We presented this method in several experiments.

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Abstract : A tandem automated guided vehicle (AGV) system deals with grouping workstations into some non-overlapping zones , and assigning exactly one AGV to each zone. This paper presents a new non-linear integer mathematical model to group n machines into N loops that minimizes both inter and intra-loop flows simultaneously. Due to computational difficulties of exact methods in solving our proposed model, a threshold accepting (TA) algorithm is proposed. To show its efficiency, a number of instances generated randomly are solved by this proposed TA and then compared with the LINGO solver package employing the branch-and-bound (B/B) method. The related computational results show that our proposed TA dominates the exact algorithm when the size of instances grows.

  

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  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.

  In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, we introduce a new numerically method of finding maximum a posterior estimation by using EM-algorithm and Gaussians mixture distribution. In this algorithm, we have made a sequence of priors, posteriors and they converge to a posterior probability that is called the reference posterior probability. Maximum a posterior estimated can determine by the reference posterior probability that will make labeled image. This labeled image shows our segmented image with reduced noises. We show this method in several experiments.

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  Abstract : The main objective of this study is estimating the strong motion for the Bam region using the stochastically based seismological models. The two widely used synthetic techniques namely point-source and finite-fault were utilized incorporating the source-path and site parameters into simple function. The decay factor kappa was estimated based on the data obtained from recorded strong motions to be used as an appropriate factor for the region. The results were validated against those of the recorded data during the destructive 26 December 2003 Bam earthquake in south east of Iran. The efficiency of these methods and estimating the appropriate regional model parameters are the main objectives of this study. The results of the synthesized ground motion, such as acceleration time history, PGA and elastic response spectra were compared /assessed with those of observed data. The Bias model (MB) is used to assess the validation of the simulated earthquakes against recorded horizontal acceleration time histories. The %90 confidence interval of the means averaged over the whole stations using t-student distribution was evaluated and it was shown to be in an acceptable range. The elastic response spectra of the simulated strong motion are showed to be in a good agreement between the recorded waveforms confirming the acceptability of the selected/evaluated source-path-site model parameters. The sensitivity of the simulated PGA and response spectra against kappa factor as well as the path-averaged frequency-dependent quality factor Q, is studied and discussed.

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  In this paper, we present the stochastic version of Maximal Covering Location Problem which optimizes both location and allocation decisions, concurrently. It’s assumed that traveling time between customers and distribution centers (DCs) is uncertain and described by normal distribution function and if this time is less than coverage time, the customer can be allocated to DC. In classical models, traveling time between customers and facilities is assumed to be in a deterministic way and a customer is assumed to be covered completely if located within the critical coverage of the facility and not covered at all outside of the critical coverage. Indeed, solutions obtained are so sensitive to the determined traveling time. Therefore, we consider covering or not covering for customers in a probabilistic way and not certain which yields more flexibility and practicability for results and model. Considering this assumption, we maximize the total expected demand which is covered. To solve such a stochastic nonlinear model efficiently, simulation and genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed algorithm.