Batching Scheduling in a Two-Level Supply Chain with Earliness and Tardiness Penalties

This paper studies the batch sizing scheduling problem with earliness and tardiness penalties which is closely related to a two-level supply chain problem. In the problem, there are K customer orders, where each customer order consisting of some unit length jobs has a due date. The jobs are processed in a common machine and then delivered to their customers in batches, where the size of each batch has upper and lower bounds and each batch may incur a fixed setup cost which can also be considered a fixed delivery cost. The goal is to find a schedule which minimizes the sum of the earliness and tardiness costs and the setup costs incurred by creating a new batch. The authors first present some structural properties of the optimal schedules for single-order problem with an additional assumption (a): The jobs are consecutively processed from time zero. Based on these properties, the authors give a polynomial-time algorithm for single-order problem with Assumption (a). Then the authors give dynamic programming algorithms for some special cases of multiple-order problem with Assumption (a). At last, the authors present some structural properties of the optimal schedules for single-order problem without Assumption (a) and give a polynomial-time algorithm for it.     

Orthogonal Arrays Robust to a Specified Set of Nonnegligible Effects

This paper considers experimental situations where the interested effects have to be orthogonal to a set of nonnegligible effects.It is shown that various types of orthogonal arrays with mixed strength are A-optimal for estimating the parameters in ANOVA high dimension model representation.Both cases including interactions or not are considered in the model.In particularly,the estimations of all main effects are A-optimal in a mixed strength(2,2)_3 orthogonal array and the estimations of all main effects and two-factor interactions in G_1×G_2 are A-optimal in a mixed strength(2,2)_4 orthogonal array.The properties are also illustrated through a simulation study.     

Approximating the Spectral Abscissa for Switched Linear Systems via Coordinate Transformations

In this paper,an approach of square coordinate transformation is proposed to approximate the spectral abscissa for continuous-time switched linear systems.By applying elementary transformations iteratively,a series of minimums of least μ1 matrix set measures are obtained,which are utilized to approximate the spectral abscissa of the switched system.The approach is developed into tractable numerical algorithms that provide upper bound estimates of the spectral abscissa.Numerical simulations show the effectiveness of the proposed method.     

Empirical likelihood for partially linear models under negatively associated errors

This paper proposes to use the blockwise empirical likelihood(EL) method to construct the confidence regions for the regression vector β in a partially linear model under negatively associated errors. It is shown that the blockwise EL ratio statistic for β is asymptotically χ~2 distributed. The result is used to obtain an EL-based confidence region for β. Results of a simulation study on the finite sample performance of the proposed confidence regions are reported.     

The equivalent representation of the breadth-one <Emphasis Type="Italic">D</Emphasis>-invariant polynomial subspace and its discretization

This paper demonstrates the equivalence of two classes of D-invariant polynomial subspaces, i.e., these two classes of subspaces are different representations of the breadth-one D-invariant subspace. Moreover, the authors solve the discrete approximation problem in ideal interpolation for the breadth-one D-invariant subspace. Namely, the authors find the points, such that the limiting space of the evaluation functionals at these points is the functional space induced by the given D-invariant subspace, as the evaluation points all coalesce at one point.     

Objective Bayesian analysis of degradation model with respect to a Wiener process

This paper proposes an objective Bayesian method to study the degradation model with respect to a Wiener process. The Jeffreys prior and reference prior for the parameters are derived, and the propriety of the posteriors under these priors is validated. Two sampling algorithms are introduced to compute the posteriors. A simulation study is conducted to investigate the performance of the objective Bayesian procedure. Finally, the authors apply the approach to a degradation data.     

Intersections of Critical Systems Thinking and Community Based Participatory Research: A Learning Organization Example with the Autistic Community

Critical systems thinking (CST) and community based participatory research (CBPR) are distinct approaches to inquiry which share a primary commitment to holism and human emancipation, as well as common grounding in critical theory and emancipatory and pragmatic philosophy. This paper explores their intersections and complements on a historical, philosophical, and theoretical level, and then proposes a hybrid approach achieved by applying CBPR’s principles and considerations for operationalizing emancipatory practice to traditional systems thinking frameworks and practices. This hybrid approach is illustrated in practice with examples drawn from of the implementation of the learning organization model in an action research setting with the Autistic community. Our experience of being able to actively attend to, and continuously equalize, power relations within an organizational framework that otherwise has great potential for reinforcing power inequity suggests CBPR’s principles and considerations for operationalizing emancipatory practice could be useful in CST settings, and CST’s vocabulary, methods, and clarity around systems thinking concepts could be valuable to CBPR practitioners.     

Unbiased condition of the dispersion effects estimator in unreplicated two-level factorial experiments

This article presents a general form of the estimator for identifying dispersion effects from unreplicated two-level factorial experiments, and shows that the widely used estimators such as the BH, MH, and AMH estimators are all special cases of the proposed one, designated as the G estimator. The unbiased condition of the G estimator is proved, and a lower bound of variance of the G estimator is provided. A simulation based on a realistic design illustrates the variation of the variance and MSE (mean square error) of the G estimator on different coefficients. This estimator may be more flexible and has better performance than other methods such as the BH and MH estimators by appropriately selecting the coefficients.     

Comparing two clustering-based techniques to track mobile nodes in industrial wireless sensor networks

Tracking mobile nodes in dynamic and noisy conditions of industrial environments has provided a paradigm for many issues inherent in the area of distributed control systems in general and wireless sensor networks in particular. Due to the dynamic nature of the industrial environments, a practical tracking system is required that is adaptable to the changes in the environment. More specifically, given the limited resources of wireless nodes and the challenges created by harsh industrial environments there is a need for a technique that can modify the configuration of the system on the fly as new wireless nodes are added to the network and obsolete ones are removed. To address these issues, two cluster-based tracking systems, one static and the other dynamic, are proposed to organize the overall network field into a set of tracking zones, each composed of a sink node and a set of corresponding anchor nodes. To manage the wireless nodes activities and inter and intra cluster communications, an agent-based technique is employed. To compare the architectures, we report on a set of experiments performed in JADE (Java Agent Development Environment). In these experiments, we compare two agent-based approaches (dynamic and static) for managing clusters of wireless sensor nodes in a distributed tracking system. The experimental results corroborate the efficiency of the static clusters versus the robustness and effectiveness of the dynamic clusters.     

A quantum multi-agent based neural network model for failure prediction

An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the Laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.