Marginal Analysis Method Research Articles

Multi-Echelon Inventory Allocation of Civil Aircraft Spare Parts Considering Maintenance Ratio

A multi-echelon inventory allocation technique with maintenance ratio for civil aircraft spare parts was proposed in this paper. Multi-echelon inventory allocation model was established, which integrates maintenance rate into METRIC (Multi-echelon technique for recoverable item control) model, and then marginal analysis method was applied to solve this model and accomplish multi-echelon inventory allocation of spare parts. Firstly, the theory of multi-echelon inventory allocation model with maintenance ratio was elaborated, and the mathematical model was established with the fleet availability as the optimization objective, the cost and the safeguard probability as the constraints. Secondly, the procedure of solving multi-echelon inventory allocation model considering the influence of maintenance ratio is discussed based on the marginal analysis method. Thirdly, landing gear LRU (Line repairable units) of a typical civil aircraft were selected as the research object, multi-echelon inventory allocation was investigated by considering maintenance ratio. The analysis results show that the minimum safeguard cost and fleet availability are respectively 2759833 dollars and 0.9806, and the number of LRU of multi echelon inventory allocation are obtained under the condition of satisfying the constrained optimization. The feasibility and effectiveness of the developed method are verified by comparing the traditional analysis method. This paper provides guidance for the accurate allocation of multi echelon inventory optimization for maintenance resources on the premise of reasonable cost reduction.

Optimization of three-echelon inventory project for equipment spare parts based on system support degree

Management of three-echelon maintenance and supply is a common support mode, in which the spare parts inventory project is one of the critical factors for system availability. Focus on the actual problem of the equipment support engineering, the system spare parts are divided into two levels, and based on the METRIC theory, the three-echelon support evaluation model for system spare parts is established. The three-echelon system support degree is used as the optimization target while the cost is taken as the restriction, and the margin analysis method is applied to inventory project optimization. In a given example, the optimal inventory project and the distribution rule for spare parts is got, and the influencing factors for spare parts inventory is analyzed. The optimization result is consistent with the basic principle under the three-echelon support pattern, which can supply decision assistance for equipment support personal to design a reasonable project.

Warship spare parts configuration optimization for stock control: Investigating the gap between qualitative and quantitative constraints

This paper investigates the gap between qualitative and quantitative constraints in spare parts stock control, with specific reference to warship spare parts support projects. A critical literature review of theoretical contributions about qualitative or quantitative factors for warship spare parts warehouse management is firstly provided, which allows to analyze the reasons for this qualitative-quantitative gap by addressing the limitations of spare parts models developed in the literature. Therefore a model including cloud model, marginal analysis and Lagrange multiplier method (CML) for study is proposed in this paper to bridge the gap. The model is used to solve the mix-constraints (both qualitative and quantitative constraints are considered) problem in a logic decision diagram particularly at the different decision nodes of the diagram. Finally, verifying test results show that the algorithm is feasible and its optimal support project meets the needs of engineering practices.

Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation.

Radiologists have used margin characteristics based on routine visual analysis; however, the attenuation changes at the margin of the lesion on CT images have not been quantitatively assessed. We established a CT-based margin analysis method by comparing a target lesion with normal lung attenuation, drawing a slope to represent the attenuation changes. This approach was applied to patients with invasive mucinous adenocarcinoma (n = 40) or bacterial pneumonia (n = 30). Correlations among multiple regions of interest (ROIs) were obtained using intraclass correlation coefficient (ICC) values. CT visual assessment, margin and texture parameters were compared for differentiating the two disease entities. The attenuation and margin parameters in multiple ROIs showed excellent ICC values. Attenuation slopes obtained at the margins revealed a difference between invasive mucinous adenocarcinoma and pneumonia (P<0.001), and mucinous adenocarcinoma produced a sharply declining attenuation slope. On multivariable logistic regression analysis, pneumonia had an ill-defined margin (odds ratio (OR), 4.84; 95% confidence interval (CI), 1.26–18.52; P = 0.02), ground-glass opacity (OR, 8.55; 95% CI, 2.09–34.95; P = 0.003), and gradually declining attenuation at the margin (OR, 12.63; 95% CI, 2.77–57.51, P = 0.001). CT-based margin analysis method has a potential to act as an imaging parameter for differentiating invasive mucinous adenocarcinoma and bacterial pneumonia.

Evaluating the economics of concentrate feeding decisions in grazing dairy cows

Purchased concentrates are a significant variable cost of a dairy business. Farm economic theory states that feeding supplements will enable a dairy farmer to improve profit as long as the marginal revenue received from the milk produced exceeds the marginal cost of the supplement. To do this, the quantities of milk, milk protein and milk fat produced from a unit of concentrate added to the diet are needed. Recent research has compiled results from short-term concentrate feeding experiments conducted in Victoria over a 30-year period. Using these data, relationships for the response of milk production to cereal grain supplements in dairy cows grazing temperate pastures have been developed and shown to be a better predictor than previous relationships. These response functions were used in the present study to investigate the economics of tactical (short-term; weekly, monthly or seasonally) and strategic (medium- to longer-term) supplementary feeding decisions in a pasture-based system, including, specifically, how much concentrate should be fed in a particular farm situation, given a certain feed cost and milk price. In the present paper, the relevant production economics method is explained and applied to determine the amount of supplement to feed that will maximise the margin of total extra milk income minus the total cost of supplement, thereby adding the most to farm profit. Currently, when dairy farmers make decisions about how much more supplement to feed their herd, they are making implicit judgements about the extra milk, and other potential benefits, that they expect to result as well as what the milk will be worth. More finely tuned decisions about feeding supplements based on comparing marginal cost and marginal revenue would add more to farm profit than decisions based on other common criteria, such as feeding supplement for maximum milk production. While some farmers may already be feeding supplements close to the point where marginal cost equals marginal revenue, the formal method of marginal analysis reported here makes explicit what is done implicitly at present and tests farmers’ intuitive decision-making. More detailed information about the responses to supplements and the costs and benefits of feeding supplements under particular circumstances at different times through the lactation has the potential to enable better, more profitable decisions to be made about feeding cows and managing the whole farm.

A United Allocation Method of Spare Parts and Ground Maintenance Equipment for Civil Aircraft

Aimed at multi-echelon inventory allocation problem with finite repair capacity for civil aircraft,a united planning method based on queuing theory and Vari-Metric model is presented to deploy the aircraft spare parts and ground maintenance equipment. Through the analysis of actual engineering, the effect of repair time is quantified by the number of maintenance equipment. On this basis, the available of fleet and the cost expense are selected as the objective and constraint respectively, marginal analysis method is set as the optimization, a new allocation scheme which was suit to civil aircraft’s maintenance resources is put forward. By applied examples analysis, it is shows that this method is reasonable and feasible.

A study on optimal ordering strategy for multi-product under contract constraint and stochastic demand an example on S-Kitchen Life Company

In this paper, based on historical data of sales, we proposed a total expected profit model for multi-product, stochastic demand and contract constraints. The constraints include only one time ordering in a year, annual ordering quantity must be greater than the lower bound of contract constraint. This study focuses on durable goods, so there will be no salvage. An optimal analysis technique was applied to search for the optimal ordering quantity for each commodity. Furthermore, a marginal profit analysis method was also used to determine the optimal incremental ordering quantity for each commodity. In addition, the sensitivity analysis was taken to realize the influence of each parameter on optimal ordering quantity and total expected profit. Finally, three conclusions were derived for practical applications and future studies.

Shrinkage and pretest estimators for longitudinal data analysis under partially linear models

In this paper, we develop marginal analysis methods for longitudinal data under partially linear models. We employ the pretest and shrinkage estimation procedures to estimate the mean response parameters as well as the association parameters, which may be subject to certain restrictions. We provide the analytic expressions for the asymptotic biases and risks of the proposed estimators, and investigate their relative performance to the unrestricted semiparametric least-squares estimator (USLSE). We show that if the dimension of association parameters exceeds two, the risk of the shrinkage estimators is strictly less than that of the USLSE in most of the parameter space. On the other hand, the risk of the pretest estimator depends on the validity of the restrictions of association parameters. A simulation study is conducted to evaluate the performance of the proposed estimators relative to that of the USLSE. A real data example is applied to illustrate the practical usefulness of the proposed estimation procedures.

Air Material Order Optimization Strategy Based on Aircraft Availability

To solve the problem of air material order problems of the station, this paper established under funding constraints to aircraft availability as a measure of the air material order optimization model, derivation the method of marginal analysis to solve the model, and use examples to illustrate the effectiveness and practicality of the method.

Similarity of markers identified from cancer gene expression studies: observations from GEO

Gene expression profiling has been extensively conducted in cancer research. The analysis of multiple independent cancer gene expression datasets may provide additional information and complement single-dataset analysis. In this study, we conduct multi-dataset analysis and are interested in evaluating the similarity of cancer-associated genes identified from different datasets. The first objective of this study is to briefly review some statistical methods that can be used for such evaluation. Both marginal analysis and joint analysis methods are reviewed. The second objective is to apply those methods to 26 Gene Expression Omnibus (GEO) datasets on five types of cancers. Our analysis suggests that for the same cancer, the marker identification results may vary significantly across datasets, and different datasets share few common genes. In addition, datasets on different cancers share few common genes. The shared genetic basis of datasets on the same or different cancers, which has been suggested in the literature, is not observed in the analysis of GEO data.

Cost-Sensitive Subspace Analysis and Extensions for Face Recognition

Conventional subspace-based face recognition methods seek low-dimensional feature subspaces to achieve high classification accuracy and assume the same loss from different types of misclassification. This assumption, however, may not hold in many practical face recognition systems as different types of misclassification could lead to different losses. Motivated by this concern, this paper proposes a cost-sensitive subspace analysis approach for face recognition. Our approach uses a cost matrix specifying different costs corresponding to different types of misclassifications, into two popular and widely used discriminative subspace analysis methods and devises the cost-sensitive linear discriminant analysis (CSLDA) and cost-sensitive marginal fisher analysis (CSMFA) methods, to achieve a minimum overall recognition loss by performing recognition in these learned low-dimensional subspaces. To better exploit the complementary information from multiple features for improved face recognition, we further propose a multiview cost-sensitive subspace analysis approach by seeking a common feature subspace to fuse multiple face features to improve the recognition performance. Extensive experimental results demonstrate the effectiveness of our proposed methods.

Robust gait recognition via discriminative set matching

In this paper, we propose a framework for gait recognition across varying views and walking conditions based on gait sequences collected from multiple viewpoints. Different from most existing view-dependent gait recognition systems, we devise a new Multiview Subspace Representation (MSR) method which considers gait sequences collected from different views of the same subject as a feature set and extracts a linear subspace to describe the feature set. Subspace-based feature representation methods measure the variances among samples, and can handle certain intra-subject variations. To better exploit the discriminative information from these subspaces for recognition, we further propose a marginal canonical correlation analysis (MCCA) method which maximizes the margins of interclass subspaces within a neighborhood. Experimental results on a widely used multiview gait database are presented to demonstrate the effectiveness of the proposed framework.

Convexity Results for the Erlang Delay and Loss Formulae When the Server Utilization Is Held Constant

This paper proves a long-standing conjecture regarding the optimal design of the M/M/s queue. The classical Erlang delay formula is shown to be a convex function of the number of servers when the server utilization is held constant. This means that when the server utilization is held constant, the marginal decrease in the probability that all servers are busy in the M/M/s queue brought about by the addition of two extra servers is always less than twice the decrease brought about by the addition of one extra server. As a consequence, a method of marginal analysis yields the optimal number of servers that minimize the waiting and service costs when the server utilization is held constant. In addition, it is shown that the expected number of customers in the queue and in the system, as well as the expected waiting time and sojourn in the M/M/s queue, are convex in the number of servers when the server utilization is held constant. These results are useful in design studies involving capacity planning in service operations. The classical Erlang loss formula is also shown to be a convex function of the number of servers when the server utilization is held constant.

Marginal methods for correlated binary data with misclassified responses

Misclassification is a longstanding concern in medical research. Although there has been much research concerning error-prone covariates, relatively little work has been directed to problems with response variables subject to error. In this paper we focus on misclassification in clustered or longitudinal outcomes. We propose marginal analysis methods to handle binary responses which are subject to misclassification. The proposed methods have several appealing features, including simultaneous inference for both marginal mean and association parameters, and they can handle misclassified responses for a number of practical scenarios, such as the case with a validation subsample or replicates. Furthermore, the proposed methods are robust to model misspecification in a sense that no full distributional assumptions are required. Numerical studies demonstrate satisfactory performance of the proposed methods under a variety of settings. Copyright 2011, Oxford University Press.

Economy and Reliability Selection of Production Casing Thread Connections For Low pressure and Low permeability Gas Field

Abstract At present it is widely accepted that if gas reservoir pressure is more than 28 MPa, premium connections in production casing should be selected. However, the price of premium connections is about 1.5 to 2 times than traditional API round thread connections. Therefore, economy and reliability selection of casing thread connections type is one of effective measures to reduce development costs in low –potential gas field. Economy and reliability selection of casing thread connections type in sulige gas field (in China,Well depth at 3550 m, Gas reservoir pressure 35 MPa) was study. Through ϕ 139.7×9.17/7.72 mm N80/J55 LC casing gas-tight test under complex loads and statistical casing full-scale test data in past were analyzed, a minimum safety factor for casing string gas-tight design was determined by casing strength margin and reliability analysis method. Gas-tight reliability effect of load variability on casing was validated by Monte Carlo method. Two sets of casing string were successfully designed and applied to over 800 wells in the gas field, and all wells were reported running well until now. Over 4 hundred million Yuan(RMB) investment was reduced in developing these wells. Following conclusions were achieved by practice: Casing string of production using API long round thread and CATTS101 compound instead of premium connection was economical and feasible for low-pressure and low-permeability gas field development. Main effect factors on gas-tight with standoff and pitch were discoverer in full-scale test, which was different from the current standards on taper in evaluation of gas-tight performance. By statistics of full-scale test data in past, the casing string of design and check is a reliability method with advantage of economic and universal applicability.

Error margin analysis for feature gene extraction

BackgroundFeature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictive feature genes that can effectively and stably discriminate distinct types of disease conditions, e.g. tumors and normals. Since gene microarray data normally involves thousands of genes at, tens or hundreds of samples, the gene extraction process may fall into local optimums if the gene set is optimized according to the maximization of classification accuracy of the classifier built from it.ResultsIn this paper, we propose a novel gene extraction method of error margin analysis to optimize the feature genes. The proposed algorithm has been tested upon one synthetic dataset and two real microarray datasets. Meanwhile, it has been compared with five existing gene extraction algorithms on each dataset. On the synthetic dataset, the results show that the feature set extracted by our algorithm is the closest to the actual gene set. For the two real datasets, our algorithm is superior in terms of balancing the size and the validation accuracy of the resultant gene set when comparing to other algorithms.ConclusionBecause of its distinct features, error margin analysis method can stably extract the relevant feature genes from microarray data for high-performance classification.

Salient Features of a Lecture Course on Reliability Theory

The module ‘oReliability Modelling and Analysis’ has been taught by the author at the University of Bradford in the School of Informatics over several years. In this paper, four aspects of the lecture course are described. After reviewing the general course structure, we examine within the field of maintenance modelling the method of marginal cost analysis. The application of this technique is deemed to be novel in the present (teaching) context and it is intended to make a basically mathematical subject amenable also to engineers and economists. Next, we outline the use of the computer algebra package Maxima in teaching ‘reliability’. This is exemplified with a classroom example, an exercise and a coursework assignment. Experience shows that the use of Maxima enriches the teaching of reliability to aspiring practitioners. Finally, some general principles are reviewed which help the learner to keep an eye on the reasonableness of purported results, using properties such as model structure, model symmetry, dimensional correctness, boundary conditions, specialization and generalization.

Wordlength Optimization of Linear Time-Invariant Systems With Multiple Outputs Using Geometric Programming

This paper proposes two new methods for optimizing hardware resources in finite wordlength implementation of multiple-output (MO) linear time-invariant (LTI) systems. The hardware complexity is measured by the exact internal wordlength used for each intermediate data. The first method relaxes the wordlength from integer to real-value and formulates the design problem as a geometric programming, from which an optimal solution of the relaxed problem can be determined. The second method is based on a discrete optimization method called the marginal analysis method, and it yields the desired wordlengths in integer values. By combining these two methods, a hybrid method is also proposed, which is found to be very effective for large scale MO LTI systems. To illustrate the effectiveness of the proposed methods, wordlength optimization problems of two-channel structural perfect reconstruction filter banks and multiplier-less fast Fourier transforms are studied in detail. Design results show that the proposed algorithms offer better results and a lower design complexity than conventional methods

Design and Complexity Optimization of a New Digital IF for Software Radio Receivers With Prescribed Output Accuracy

This paper studies the design, signal round-off noise, and complexity optimization of a new digital intermediate frequency (IF) architecture for a software radio receiver (SRR). The IF under study consists of digital filters with fixed coefficients, except for a limited number of multipliers required in the Farrow-based sampling rate converter (SRC). The fixed-coefficient filters can be implemented efficiently using sum-of-power-of-two (SOPOT) coefficients and the multiplier-block technique, which gives minimum adder realization. Apart from the multipliers required in the SRC, the digital IF can be implemented without any multiplications. While most multiplier- less filter design and realization methods address only the coefficient round-off problem by minimizing the number of SOPOT terms used, the proposed design methodology aims to minimize more realistic hardware complexity measure, such as adder cells and registers, of the digital IF subject to a given spectral and accuracy specifications. The motivation is that the complexity is closely related to the target output accuracy, which is specified statistically by its total output noise power generated by rounding the intermediate data. Two novel algorithms for optimizing the internal wordlengths of linear time-invariant systems are proposed. The first one relaxes the solution to real valued and formulates the design problem as a constrained optimization. A closed-form solution can be determined by the Lagrange multiplier method. The second one is based on a discrete optimization method called the Marginal Analysis method, and it yields the desired wordlengths in integer values. Both approaches are found to be effective and suitable to large scale systems. A design example and the field programmable gate array (FPGA) realization of a multi-standard receiver are given to demonstrate the proposed method

Bottom-up optimization approach and nonlinear model for oilfield output programming

In the process of oilfield development, it is unable to separate the marginal effect of each developing measure from the aggregate effect of all developing measures, so both marginal analysis method and Markowitz portfolio theory are helpless for the oilfield output programming. Function Simulation is proved to be a feasible tool to model the input-output relationship of oilfield development. By training with historical development data, it possesses extrapolation capability in some range, which forms the basis for the oilfield output programming. The purpose of this paper is to report on the use of bottom-up optimization mechanism which is embedded by Function Simulation to optimize the distribution of output in oilfield. Following presentation of the features of oilfield development, the top-down approach and the Function Simulation as a tool for oilfield output programming are reviewed. The process of designing the bottom-up optimization framework embedded with Function simulations, determining the embedding protocol and coordinating the corresponding relationship between the subentry outputs and the subentry costs is described. Subsequently, models and an example are given. Finally, the paper concludes that above optimization method is suitable to solve a class of programming problems.