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.     

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.     

Implicit Riquier Bases for PDAE and their semi-discretizations

Complicated nonlinear systems of pde with constraints (called pdae) arise frequently in applications. Missing constraints arising by prolongation (differentiation) of the pdae need to be determined to consistently initialize and stabilize their numerical solution. In this article we review a fast prolongation method, a development of (explicit) symbolic Riquier Bases, suitable for such numerical applications. Our symbolic-numeric method to determine Riquier Bases in implicit form, without the unstable eliminations of the exact approaches, applies to square systems which are dominated by pure derivatives in one of the independent variables.     

Distance-based tree models for ranking data

Ranking data has applications in different fields of studies, like marketing, psychology and politics. Over the years, many models for ranking data have been developed. Among them, distance-based ranking models, which originate from the classical rank correlations, postulate that the probability of observing a ranking of items depends on the distance between the observed ranking and a modal ranking. The closer to the modal ranking, the higher the ranking probability is. However, such a model basically assumes a homogeneous population and does not incorporate the presence of covariates.To overcome these limitations, we combine the strength of a tree model and the existing distance-based models to build a model that can handle more complexity and improve prediction accuracy. We will introduce a recursive partitioning algorithm for building a tree model with a distance-based ranking model fitted at each leaf. We will also consider new weighted distance measures which allow different weights for different ranks in formulating more flexible distance-based tree models. Finally, we will apply the proposed methodology to analyze a ranking dataset of Inglehart’s items collected in the 1999 European Values Studies.