Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada     

Redesigning a Cellular Manufacturing System to Handle Long-Term Demand Changes: A Methodology and Investigation*

Cellular manufacturing systems have been proposed as an alternative to the job shop since they provide some of the operational benefits of a flow line production process, while retaining to some extent the flexibility of job shops. However, this must be balanced against the possibility of additional initial investments in equipment to form the cells and a certain loss in manufacturing flexibility, particularly in terms of the ability to deal with long-term demand changes. This paper presents a model-based heuristic cell system redesign methodology to deal with such demand changes. The methodology is validated and applied to system designs generated from several data sets published in the literature. Results show that different kinds of demand changes incur distinct kinds of costs. Further, characteristics of cell designs that can handle long-term demand changes at least cost are identified.     

Resource Flexibility Issues in Multistage Manufacturing

We test the situational impact of two types of resource flexibility, machine flexibility and labor flexibility, in a material requirements planning (MRP)-driven production system. Machine flexibility has not been treated in prior multistage research, only labor flexibility. Machine flexibility is closely related to a plant's positioning strategy. A process-focused plant opts for considerable machine flexibility by choosing general-purpose equipment. Resource flexibility, if effective, can be an attractive alternative to two other types of buffers, inflated inventories and costly capacity cushions. Our simulation results, using factor settings established earlier by a panel of managers, show that resource flexibility is indeed an effective buffer against uncertainties such as end-item demand variability, capacity bottlenecks, equipment failures, and yield losses. Machine flexibility is especially helpful in environments characterized by high uncertainties, tight capacities, and large lot sizes. Worker flexibility has a similar, but less dramatic, impact. Benefits are most striking with customer service, rather than with inventory or labor productivity. Finally, we show that simultaneous introduction of both machine and labor flexibility yields only marginal improvements over either kind of flexibility alone.     

The Impact of a Structured-Argument Approach on Group Problem Formulation*

Despite advances in decision analysis and decision support systems, few formulaic procedures exist for undertaking problem formulation, particularly in group settings. This leaves managers with little procedural support for the important task of carefully structuring problems. In a laboratory experiment of 29 intact student teams, we contrasted two problem formulation methodologies: a structured argument approach (based on application of formal reasoning) and a group process approach (based on private idea generation prior to public sharing and evaluation of ideas). The structured argument approach took more time to use and failed to bring about more information search and equivocality reduction in group discussions. On the positive side, however, the structured argument approach led to a greater combination of both coverage of critical issues and consensus on those issues. Use of the structured argument approach also resulted in higher satisfaction with the problem definition and commitment to implementing results of the group meeting. Overall, the results reveal cost/benefit tradeoffs associated with developing argumentation-based procedures for problem formulation.     

The contingent futures of the mobile present: automation as possibility

In this article we outline and demonstrate a design anthropological approach to investigating automated mobile futures as a processual opening up of possibilities, rather than as a process of technological innovation. To undertake this we investigate the example of how the car-smartphone relationship is configuring in the contingent circumstances of the mobile present and the implications of this for automated mobile futures. Our discussion is set in the context of the growing possibility that automonous driving (AD) features are increasingly part of everyday mobilities (even if unequally distributed globally) and in which personal mobile smart technologies and artificial intelligence (AI) will exist in some form and will interface with humans and be interoperable with other technologies. In developing this we draw on ethnographic understandings of how people live with the possibilities afforded by technologies in everyday life.     

论公共政策效能的衰减机制

公共政策效能衰减分为有效衰减和无效衰减两种形式。公共政策效能有效衰减是公共政策效能运行规律的反映,而无效衰减是在公共政策效能没有正常释放的情况下发生的,是影响公共政策目标实现的主要因素。公共政策共同体中,不同行为主体之间行为取向的多元化和行为方式的分散化是公共政策效能无效衰减的主要原因。公共政策共同体中各行为主体以理解为目的的交往活动,是减少公共政策效能无效衰减的重要途径。现有制度设计上的不完善,导致公共权力一定程度上的垄断化、碎片化和异质化,这是造成公共政策效能无效衰减的根本的原因。打造有利于公共政策共同体中各行为主体在平等互动基础上的有效的和全方位、深层次交往的制度性平台,实现公共权力在组织上、资源上和功能上的整合是克服公共政策效能无效衰减的根本途径。     

数控加工与编程数字化教学资源建设

为培养高职数控技术专业学生的数控加工与编程能力,通过调研典型制造型企业,以其岗位能力需求为基础,以企业的实践过程为任务流程,规划了数控加工与编程课程的教学体系,并构建了基于网络化教学平台的数字化教学资源库。     

Bayesian credible intervals for response surface optima

In response surface methodology, one is usually interested in estimating the optimal conditions based on a small number of experimental runs which are designed to optimally sample the experimental space. Typically, regression models are constructed from the experimental data and interrogated in order to provide a point estimate of the independent variable settings predicted to optimize the response. Unfortunately, these point estimates are rarely accompanied with uncertainty intervals. Though classical frequentist confidence intervals can be constructed for unconstrained quadratic models, higher order, constrained or nonlinear models are often encountered in practice. Existing techniques for constructing uncertainty estimates in such situations have not been implemented widely, due in part to the need to set adjustable parameters or because of limited or difficult applicability to constrained or nonlinear problems. To address these limitations a Bayesian method of determining credible intervals for response surface optima was developed. The approach shows good coverage probabilities on two test problems, is straightforward to implement and is readily applicable to the kind of constrained and/or nonlinear problems that frequently appear in practice.     

Generalized minimum aberration mixed-level orthogonal arrays: A general approach based on sequential integer quadratically constrained quadratic programming

Orthogonal fractional factorial designs and in particular orthogonal arrays (OAs) are frequently used in many fields of application, including medicine, engineering, and agriculture. In this article, we present a methodology and an algorithm to find an OA, of given size and strength, which satisfies the generalized minimum aberration criterion. The methodology is based on the joint use of polynomial counting functions, complex coding of levels, and algorithms for quadratic optimization and puts no restriction on the number of levels of each factor.     

Extended scaled prediction variance optimality for modified central composite design

Robust parameter designs (RPDs) enable the experimenter to discover how to modify the design of the product to minimize the effect due to variation from noise sources. The aim of this article is to show how this amount of work can be reduced under modified central composite design (MCCD). We propose a measure of extended scaled prediction variance (ESPV) for evaluation of RPDs on MCCD. Using these measures, we show that we can check the error or bias associated with estimating the model parameters and suggest the values of α recommended for MCCS under minimum ESPV.