An improved iterative stochastic multi-objective acceptability analysis method for robust alternative selection in new product development

Alternative selection in new product development (NPD) is a multi-criteria decision-making (MCDM) problem. It usually starts with incomplete, imprecise or even partially missing information. Currently, most existing methods in dealing with this problem cannot work well if required information is incomplete or missing. It is acknowledged that stochastic multi-objective acceptability analysis (SMAA) can be applied to address MCDM problem with incomplete preference information and uncertain criteria measurements. In SMAA, alternatives are evaluated based on SMAA measurements (acceptability index, central weight vector and confidence factor). The discriminability of SMAA for the optimum alternative heavily depends on differences of SMAA measurements among different alternatives. Usually, a large number of alternatives and high level of uncertainty are involved in alternative selection in NPD. In this situation, the differences among SMAA measurements are not obvious, and therefore SMAA cannot deal with such problem very well. To this end, this paper proposes an improved SMAA method called Iterative-SMAA (I-SMAA) for alternative selection in NPD. In the I-SMAA, an iterative multi-step decision-making process is suggested to improve differences of SMAA measurements among different alternatives, and thus assist decision makers (DMs) to positively discern from the most preferred alternative. To enhance the decision-making efficiency, sensitive criteria are acquired in each iteration by ranking sensitivity analysis. DMs are guided to provide partial preference information and give more accurate criteria measurements for sensitive criteria rather than all criteria. Eventually, to verify the proposed method, a numerical example of the existing literature is solved with the method, and the results are compared. And then, a practical example of a preparation equipment for coal samples is further employed to verify the practicability of the proposed I-SMAA.     

A distance-type-insensitive clustering approach

In this paper, we offer a method aiming to minimize the role of distance metric used in clustering. It is well known that distance metrics used in clustering algorithms heavily influence the end results and also make the algorithms sensitive to imbalanced attribute/feature scales. To solve these problems, a new clustering algorithm using a per-attribute/feature ranking operating mechanism is proposed in this paper. Ranking is a rarely used discrete, nonlinear operator by other clustering algorithms. However, it also has unique advantages over the dominantly used continuous operators. The proposed algorithm is based on the ranks of the data samples in terms of their spatial separation and is able to provide a more objective clustering result compared with the alternative approaches. Numerical examples on benchmark datasets prove the validity and effectiveness of the proposed concept and principles.     

Top-k-size keyword search on tree structured data

Keyword search is the most popular technique for querying large tree-structured datasets, often of unknown structure, in the web. Recent keyword search approaches return lowest common ancestors (LCAs) of the keyword matches ranked with respect to their relevance to the keyword query. A major challenge of a ranking approach is the efficiency of its algorithms as the number of keywords and the size and complexity of the data increase. To face this challenge most of the known approaches restrict their ranking to a subset of the LCAs (e.g., SLCAs, ELCAs), missing relevant results.In this work, we design novel top-k-size stack-based algorithms on tree-structured data. Our algorithms implement ranking semantics for keyword queries which is based on the concept of LCA size. Similar to metric selection in information retrieval, LCA size reflects the proximity of keyword matches in the data tree. This semantics does not rank a predefined subset of LCAs and through a layered presentation of results, it demonstrates improved effectiveness compared to previous relevant approaches. To address performance challenges our algorithms exploit a lattice of the partitions of the keyword set, which empowers a linear time performance. This result is obtained without the support of auxiliary precomputed data structures. An extensive experimental study on various and large datasets confirms the theoretical analysis. The results show that, in contrast to other approaches, our algorithms scale smoothly when the size of the dataset and the number of keywords increase.     

Negative statements considered useful

Knowledge bases (KBs) about notable entities and their properties are an important asset in applications such as search, question answering and dialog. All popular KBs capture virtually only positive statements, and abstain from taking any stance on statements not stored in the KB. This paper makes the case for explicitly stating salient statements that do not hold. Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities. Due to the abundance of such invalid statements, any effort to compile them needs to address ranking by saliency. We present a statistical inference method for compiling and ranking negative statements, based on expectations from positive statements of related entities in peer groups. Experimental results, with a variety of datasets, show that the method can effectively discover notable negative statements, and extrinsic studies underline their usefulness for entity summarization. Datasets and code are released as resources for further research.     

Hesitant Fuzzy Worth: An innovative ranking methodology for hesitant fuzzy subsets

We introduce a novel methodology for ranking hesitant fuzzy sets. It builds on a recent, theoretically sound contribution in Social Choice. In order to justify the applicability of such analysis, we develop two real implementations: (i) new metarankings of world academic institutions that build on real data from three reputed agencies, and (ii) a new procedure for improving teaching performance assessments which we illustrate with real data collected by ourselves.     

Progressively Censored Samples in Life Testing

In life and dosage-response studies, progressively censored samples arise when at various stages of an experiment, some though not all of the surviving sample specimens are eliminated from further observation. The sample specimens remaining after each stage of censoring are continued under observation until failure or until a subsequent stage of censoring. In this paper maximum likelihood estimators of the distribution parameters are derived for the normal, and for the exponential distribution when samples are progressively censored.     

Life Tests Under Dependent Competing Causes of Failure

In a life testing situation the failure of an individual, either a living organism or an inanimate object, may be classified into one of k(< 1) mutually exclusive classes, usually causes of failure. One often has dependent causes of failure in actual physical situations, i.e., the theoretical lifetime of an individual failing from one cause may be correlated with the theoretical lifetime of the same individual failing from a different cause. This paper i) discusses some properties of a bivariate Weibull distribution and ii) is concerned with estimating, by the method of maximum likelihood, the unknown parameters of life distributions belonging to two particular parametric families, viz., bivariate normal and bivariate Weibull, when the causes of failure are dependent. An example involving the failure of small electrical appliances is analyzed and compared with an analysis which assumes the causes of failure to be independent.     

东营凹陷沙河街组层序地层对成岩的控制作用

东营凹陷发育了沙四段底界面(T 7)、沙四段与沙三段分界面(T′6)及沙二下亚段顶界面(T′2)等3个二级层序界面。根据对储集层成岩作用的深入研究发现,原有的埋藏成岩作用序列不仅受深度的影响,而且受此层序地层所控制,在层序界面附近和层序体内部存在一定的规律性。据此确定了层序界面附近发育的淋滤、溶解成岩相带和层序体内部发育的黏土矿物转换和化学胶结成岩相带。认识到层序界面通常是流体运移的通道,影响界面上下低水位体系域和高水位体系域内储集层的成岩相展布,对改善储集层储集性能有利;层序体内部储集层成岩差异性明显,储集性往往受到制约。     

基于用户感知的3G网络规划方法探讨

为提升3G网络规划的准确性,做实做细网络规划,有效指导网络建设,山东联通提出一种基于用户感知的3G网络创新规划方法。文章对传统规划方法和创新的规划方法进行对比分析,并对创新规划方法的实现过程进行描述,详细说明规划指标的确定、站址库的选取以及规划方案的制定内容。     

A multi-objective selection procedure of determining a Pareto set

Ranking and selection (R&S) procedures have been widely studied and applied in determining the required sample size (i.e., the number of replications or batches) for selecting the best system or a subset containing the best system from a set of k alternatives. Most of the studies in the R&S have focused on a single measure of system performance. In many practical situations, however, we need to select systems based on multiple criteria. A solution is called Pareto optimal if there exists no other solution which is better in all criteria. This paper discusses extending a R&S procedure to select a Pareto set containing non-dominated systems. Computational results show that the proposed procedures are effective in obtaining non-dominated systems.