Journal Of Industrial Engineering International
Volume:7 Issue: 1, Jan 2011

The present paper deals with development of a simulation model for the performance evaluation of feed water system of a thermal power plant using Markov Birth-Death process and probabilistic approach. In present paper, the feed water system consists of four subsystems. After drawing transition diagram for feed water system, differential equations are developed and then solved recursively using probabilistic approach. Then to predict the steady state availability i.e. measure of performance of feed water system, normalizing conditions are used. Thus availability simulation model has been developed. After that, the availability matrix and plots of failure/repair rates of all subsystems are prepared to decide the availability trends. Based upon various availability values in the availability matrix, performance of feed water system has been evaluated. Further the optimum values of failure/repair rates for maximum system availability have also been determined. The finding of this paper might be helpful to the plant management for futuristic maintenance decisions.

Fuzzy set based methods have been proved to be effective in handling many types of uncertain-ties in different fields, including reliability engineering. This paper presents a new approach on fuzzy relia-bility, based on the use of beta type distribution as membership function. Considering experts` ideas and by asking operators linguistic variables, a rule base is designed to determine the level of reliability of each component. Hence, we can determine the level of reliability of components with expending low costs. Also in this work a simple approach is presented for reducing the number of rules. The outputs of the presented model are fuzzy sets representing the reliability levels of components. In order to determine the level of re-liability as linguistic variables, a new method is presented. Also the validity of the model is investigated by two methods. After determining the level of reliability of each component, the reliability of the composed system can be determined by using t-norm and s-norm functions. The system can be parallel, series, paral-lel-series or series-parallel. The presented model has been applied in a glass manufacturing company.

Business all around the world uses different approaches to know their customers, segment them and formulate suitable strategies for them. One of these approaches is calculating the value of each customer for the company. In this paper by calculating Customer Lifetime Value (CLV) for individual customers of an online toy store named Alakdolak, three customer segments are extracted. The level of profitability for customers is identified, and finally suitable marketing strategies for each segment are developed. The results indicate that the company should increase its low price products and develop special programs for those that buy high price products and have high loyalty. Logistic regression as a data mining technique is used to present the customer defection and future purchase probability models and for each model, verification and validation is done.

Technology selection is an important part of management of technology. Recently Karsak and Ahiska (2005) proposed a novel common weight multiple criteria decision making (MCDM) methodology for selection of the best Advanced Manufacturing Technology (AMT) candidates based on a number of attributes. However, Amin et al. (2006), by means of a numerical example demonstrated the convergence difficulty of the Karsak and Ahiska algorithms, and then introduced an improvement model to rectify that running problem. This paper presents an MCDM-DEA methodology in order to evaluate the relative effi-ciency of AMTs with respect to multiple outputs and a single exact input. Using displaced ideal methodolo-gy, a practical common weight is developed and its robustness and discriminating power are illustrated via a previously reported robot evaluation problem by comparing the ranking obtained by the proposed MCDM framework with that obtained by a data envelopment analysis (DEA) classic model

This paper presents the steady state behavior and maintenance planning of the bleaching system in a paper plant. The paper plant comprises of various systems including feeding, chipping, digesting, wash-ing, bleaching, screening, stock preparation and paper making, etc. One of the most important functionaries of a paper plant, on which quality of paper depends, is the bleaching system, where removal of coloring constituents is done to obtain the desired degree of brightness. The bleaching system consists of two subsystems A and B arranged in series with two states; good and failed. The mathematical modeling is carried out on the basis of Markov birth-death process using a probabilistic approach. An expression for steady state availability is also developed. Based upon the maintenance data available from medium sized paper plant, the effect of each working unit on the system availability behavior has been explained. In this regard, the behavioral chart for bleaching system has been given. Besides, a maintenance schedule has also been suggested and utilized for the purpose of maintenance planning of the bleaching system in a paper plant. The findings of this paper have been discussed with the concerned plant personnel and found to be highly beneficial for enhancing the performance level of the plant concerned.

This paper introduces discretionary imprecise data in Data Envelopment Analysis (DEA) and discusses the efficiency evaluation of Decision Making Units (DMUs) with non-discretionary imprecise data. Then, suggests a method for evaluation the efficiency of DMUs with non-discretionary imprecise data. When some inputs and outputs are imprecise and non-discretionary, the DEA model becomes non-linear programming problem. By a Theorem, we use the translation of imprecise data into exact data and then use the standard linear DEA model for evaluating DMUs with non-discretionary and imprecise data, which is the generalized form of envelopment form in input oriented of CCR model. To illustrate the proposed method, a numerical example with non-discretionary imprecise data is solved.

In this paper, we focus on a continuously deteriorating critical equipment which its failure cannot be measured by cost criterion. For these types of systems like military systems, nuclear systems, etc it is extremely important to avoid failure during the actual operation of the system. In this paper we propose an approach which constrains failure probability to a pre-specified value. This value guarantees a chance of failure less than or equal to the pre-specified value during real operation of the system. The inspection periods and maintenance policy are found in two phases. Failure probability is limited to a pre-specified value In the first phase, and in the second phase optimum maintenance thresholds and inspection periods are obtained in such a way that minimize long-run expected costs. Due to the complexity of the model, Monte Carlo simulation is used to obtain optimum results

Emergency department has become a useful way to the access to hospital and it is a subject of study for many researchers. The research developed in this paper aims to improve the performance of the emergency department(ED) of Sfax Hospital. Simulation results showed that to manage to increase the number of treated patients and thus to decrease the patient cycle time, it is important to add a specialist physician or a formed general physician. To choose what physician the managers do add, the researchers used the fuzzy PROMETHEE method.