International Journal of Operations Research and Optimization (IJORO)

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Gas and Oil exploitation is necessary for the country in order to improve the financial status and solve the day-to-day needs of the people with hundred percent ensured public safeties, in addition to environmental protection in and around the lake. Permanent measuring systems are also used to monitor the behavior of the lake during the exploitation of methane accumulation. In order to avoid the environment disaster after the gas extraction, the Kibuye-Platform-1 needs to re-inject the residual water at safety zone in the depth of Lake-Kivu. In this research, we present a novel application of ANOVA for 32 factorial design to identify the optimum level (in meters) of re-injection of residual water using pH value and salinity and this methodology is illustrated using a real life data.

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The concept of stability and maintainable structures in a graded manpower systems is of central importance in theoretical and applied study of populations. Predicting the manpower attrition using Stochastic approach has received much attention in recent literature. Stochastic model for the description, prediction and control of attrition from the various grades of a hierarchically structured manpower system of management staff is discussed by Vassiliou (1976). In this paper, it is proposed to study the manpower attrition using Stochastic approach in a global perspective. This approach is highly useful for not only predicting the human manpower but also formulating human resource strategy of Embryonic India

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This paper proposes a Bayesian estimator for a quick detection of “small shifts” in the control chart under the assumption that the Process characteristic follows normality. Bayesian estimate is compared to that of the classical Moving Average, CUSUM and EWMA control charts in detection of small shifts in the process mean in the case of individual observation through a simulation study.

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Many products and systems age, wear, or degrade over time before they fail or break down. In experiments where failure times are sparse, degradation analysis is useful for the analysis of failure time distributions in reliability studies. A relationship between component failure and amount of degradation makes it possible to use degradation models and data to make inferences and predictions about a failure-time distribution. Degradation modeling techniques have generated a great amount of research in reliability field. Relative to failure-based reliability, degradation-based reliability has received a modest amount of attention in the open literature. Also, degradation analysis for reliability has attracted considerable attention of statisticians, and engineers in recent years. For instance, the failures of many manufactured products are caused by certain degradation mechanisms. In particular, unit of such a product, degradation measurements can be made over time. Such degradation measurements can be used to make inference on the lifetime distribution of the product. This motivates the need for developing stochastic degradation models using accelerating variables for inference based on both observed failure level, and degradation measurement. In this contest, stochastic degradation models, based on Shock models and cumulative damage approach, using accelerating variables to characterize the degradation phenomenon are developed.

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Cox’s Proportional hazards models are a class of survival models in statistics. Survival models relate the time that passes before some event occurs to one or more covariates that may be associated with that quantity of time. In a proportional hazards model, the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate. The effect of covariates estimated by any proportional hazards model can thus be reported as hazard ratios. Cox observed that if the proportional hazards assumption hold (or, is assumed to hold) then it is possible to estimate the effect parameter(s) without any consideration of the hazard function. The Least Absolute Shrinkage and Selection Operator (LASSO) proposed by Tibshirani is a popular technique for model selection and estimation in linear regression model. Cox’s proportional hazard model for variable selection using LASSO method has been received much attention in the recent literature. In this paper an example of statistical analysis for the LASSO method for variable selection in the Cox’s model using Lung cancer and Liver data is highlighted. Numerical illustrations are also substantiated through real data example.

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In production and industrial sector, software sector and other branches of human activity, the proper training of persons before their placement in respective positions is essential. The fluctuating human for manpower requires an inventory . In this paper a Stochastic model is developed to determine the optimal size of reserve inventory of personnel, assuming that there are three nodes namely training, placement in two sections which are technical, administrative. The demand for manpower is of random character and hence random variables. The optimal size of reserve inventory is derived by referring the concept of cost of overages and shortages.

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