On the estimation of reliabilty function in a Weibull lifetime distribution

The estimation of the reliability function of the Weibull lifetime model is considered in the presence of uncertain prior information (not in the form of prior distribution) on the parameter of interest. This information is assumed to be available in some sort of a realistic conjecture. In this article, we focus on how to combine sample and non-sample information together in order to achieve improved estimation performance. Three classes of point estimatiors, namely, the unrestricted estimator, the shrinkage estimator and shrinkage preliminary test estimator (SPTE) are proposed. Their asymptotic biases and mean-squared errors are derived and compared. The relative dominance picture of the estimators is presented. Interestingly, the proposed SPTE dominates the unrestricted estimator in a range that is wider than that of the usual preliminary test estimator. A small-scale simulation experiment is used to examine the small sample properties of the proposed estimators. Our simulation investigations have provided strong evidence that corroborates with asymptotic theory. The suggested estimation methods are applied to a published data set to illustrate the performance of the estimators in a real-life situation.     

Asymptotic properties of the score test for varying dispersion in exponential family nonlinear models

Wei et al. [B.C. Wei, J.Q. Shi, W.K. Fung, and Y.Q. Hu, Testing for varying dispersion in exponential family nonlinear models, Ann. Inst. Statist. Math. 50 (1998), pp. 277–294.] developed the score diagnostics for varying dispersion in exponential family nonlinear models, such as the normal, inverse Gaussian, and gamma models, and investigated the powers of these tests through Monte Carlo simulations. In this paper, the asymptotic behaviours, including asymptotic chi-square and approximate powers under local alternatives of the score tests, are studied and examined by Monte Carlo simulations. The methods to estimate local powers of the score tests are illustrated with Grass yield data [P. McCullagh, and J.A. Nelder, Generalized Linear Models, Chapman and Hall, London (1989).].     

A consistent test for heteroscedasticity in semi-parametric regression with nonparametric variance function based on the kernel method

It is important to detect the variance heterogeneity in regression models. Heteroscedasticity tests have been well studied in parametric and nonparametric regression models. This paper presents a consistent test for heteroscedasticity for nonlinear semi-parametric regression models with nonparametric variance function based on the kernel method. The properties of the test are investigated through Monte Carlo simulations. The test methods are illustrated with a real example.     

基于行政大数据的失业率估计:以某四百万人口城市为例

我国城镇登记失业率指标稳定在4%左右,难以较为准确反映就业动态;而劳动力调查样本量有限,城镇调查失业率对省以下各级行政区域代表性不足。本文将针对大数据的机器学习算法与针对传统统计数据的核算思想结合起来,基于某四百万人口城市2016—2018年的全样本行政大数据,利用机器学习算法,对每个城镇居民每个月的就业状态进行预测,再利用统计核算方法,估计出该城市的失业率。在个人层面,本文的模型在样本外测试集上的准确率达到96.7%。经过统计核算加总,本文估计的当地失业率在合理区间范围内,并表现出明显的周期性特征,对就业形势动态变化的刻画明显优于当地一年发布一次的登记失业率数据。本文基于个人层面的预测结果,进一步探讨了当地失业人口 的性别与文化程度特征,以及再就业的时间规律。本文针对如何使用行政大数据辅助经济决策提出了新的范式,对大数据时代如何理解经济与制定政策具有参考意义。     

DEA with streaming data

DEA can be interpreted as a tool for the identification of “frontier outliers” among data points. These are points that are potentially interesting because they exhibit extreme properties in that the values of their attributes, either alone or combined, are at the upper or lower limits of the data set to which they belong. A real challenge for this type of frontier analysis arises when data stream in at high rates and the DEA analysis needs to be performed quickly. This paper extends DEA into this dynamic data environment. The purpose is to propose a formal theoretical framework to handle streaming data and to answer the question of how fast data can be processed using this new framework. Potential applications involving large data sets include auditing, appraisals, fraud detection, and security. In such settings the situation is likely to be dynamic with the data domain constantly changing as new entities arrive in the course of time. New specialized tools to adapt DEA to deal with streaming data will be explored.     

Using simulation to manage project supply chain in the offshore oil and gas industry

This paper is aimed at comparing simulation against spreadsheets as decision support tools to properly manage project supply chain in the offshore oil and gas industry. The paper presents a case study related to the problem of sizing a chain for pipeline laying from an offshore field in the Barents sea to the Russian coast. Results obtained through a spreadsheet developed by an oil and gas company have been compared to the ones gathered from an ad hoc simulation model. A simulation model with no stochastic variable has been introduced: results are quite similar to the ones of the spreadsheet, which allowed to validate the simulation model. However, the spreadsheet cannot take into account the continuous move of the pipe-lay vessel while laying the pipes and it does not consider stochastic variables whose effect in real life is not negligible. Both weaknesses above are discussed.     

Integrating Kanban principles in a pharmaceutical campaign production system

Centralised planned campaign production is the predominant production system in process industries. In this study, we investigate whether a decentralised Kanban control system, which has proven to offer advantages in other industries, can be successfully integrated in a campaign production environment. The used research methodology combines model-based and case-study-based elements. Using the example of a pharmaceutical internal five-stage supply chain, we conceive a Kanban concept that integrates campaign formation, develop a discrete-event simulation model and conduct a range of explorative simulation experiments. We find that a Kanban campaign production system is not merely feasible but would also be favourable: throughput times can be reduced without increasing customer lead times. Sensitivity analysis shows that the system’s performance is relatively robust to changes in Kanban key configuration parameters such as number of Kanban cards or campaign size. We conclude by discussing our findings and formulating three propositions that might stimulate future research.     

Simulation analysis of cold chain performance based on time–temperature data

Perishable goods are a fundamental source of revenue for the retail sector; their management, however, constitutes a severe challenge for retailers and supply chain partners. A significant cost in particular is the fraction of products perished through the supply chain, which also constitutes an ethical and environmental concern. Supply chain organisation and operative characteristics have a significant influence on this matter, as in fact ensuring suitable temperature conditions for the stock-keeping units throughout the supply chain is mandatory for perishable products. Recent developments in sensing and communication technologies allow detailed monitoring and control of cold chain; however, depending on the characteristics of the supply chain, an inherent risk of perished products is often inevitable, even in the hypothesis of perfect control. This article proposes a methodology to evaluate the performance of a cold chain in terms of expected product quality at the retail store, and to estimate the expected fraction of perished products, according to the supply chain configuration. The approach is based on Monte Carlo simulation, and implements referenced shelf-life models. A real application is also presented, involving a preliminary analysis and mapping of the supply chain activities based on time–temperature data, in order to demonstrate the practicability of the approach proposed.     

Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system

The competitive nature of the business environment requires the productivity-driven organization to be aware of its relative level of effectiveness and efficiency vis-à-vis its competitors. This suggests the need, first, for an effective mechanism that allows for discovering appropriate productivity models for improving overall organizational performance, and, second for a feedback-type mechanism that allows for evaluating multiple productivity models in order to select the most suitable one. In this paper our focus is on organizations that consider the states of their internal (e.g., possibly exemplified by resource-based view) and external (e.g., possibly exemplified by positioning) organizational environment in the formulation of their strategies. We propose and test a DEA-centric Decision Support System (DSS) that aims to assess and manage the relative performance of such organizations.     

Line balance analyses for system assembly lines in an electronic plant

This research analysed and optimised the assembly line balance of notebook computer system production lines in a major computer plant. The research developed a mathematical model for balancing assembly lines of a specific notebook computer system. Precedence relationship charts were created to enforce the required sequences and ILOG CPLEX software was used to obtain the optimal solution of the task assignments to the workstations. The new task assignments were simulated to verify the improvement recommendations and predict the potential benefits.The estimated improvements include: (1) 5.9% increase in average daily production; (2) 29% decrease in average daily WIP; (3) 67.9% reduction in cycle time; (4) 16.7% reduction in operator headcount over the whole system assembly area. The contributions of this research include: (1) Establishing line balancing mathematical model suitable for notebook computer factories; (2) Optimising the system assembly lines for the subject company; (3) Transferring the line balance modelling and optimisation techniques for company use.