Supplier Selection Model Based on D Numbers and Transformation Function

Selecting reasonable suppliers can effectively improve the efficiency of enterprise supply chain management. Among them, expert evaluation is an important part of supplier selection problem, but the uncertainty, fuzziness and incompleteness of expert opinions make supplier selection problem difficult to solve. In order to systematically and effectively solve the uncertainty, ambiguity and incompleteness in supplier selection problem, this paper presents a new supplier selection method based on D numbers and transformation function. First, fuzzy preference relation is generated based on the decision matrix of pairwise comparisons given by experts. D numbers which can effectively deal with uncertain information extend fuzzy preference relation (D matrix). Second, the D matrix is converted into a crisp matrix form based on the integration representation of D numbers according to different situations whether or not the information in D matrix is complete. Third, the crisp matrix is converted into judgement matrix by using the transformation functions. Finally, analytic hierarchy process (AHP) method is applied based on the judgment matrix to give a priority weights for decision making. Three numerical examples and application of the supplier selection are used to show the feasibility and effectiveness of the proposed method

A Mikhlin--H\"ormander multiplier theorem for the partial harmonic oscillator

We prove a Mikhlin--H\"ormander multiplier theorem for the partial harmonic oscillator H_{\textup{par}}=-\pa_\rho^2-\Delta_x+|x|^2 for $(\rho, x)\in\R\times\R^d$ by using the Littlewood--Paley $g$ and $g^\ast$ functions and the associated heat kernel estimate. The multiplier we have investigated is defined on $\mathbb R \times \mathbb N$.Comment: 14 pages, no figure. All comments are welcom

Target Recognition Based on Fuzzy Dempster Data Fusion Method

Data fusion technology is widely used in automatic target recognition system. Problems in data fusion system are complex by nature and can often be characterised by not only randomness but also by fuzziness. To accommodate complex natural problems with both types of uncertainties, it is profitable to construct a data fusion structure based on fuzzy set theory and Dempster Shafer evidence theory. In this paper, after representing both, the individual attribute of target in the model database and the sensor observation or report as fuzzy membership function, a likelihood function was constructed to deal with fuzzy data collected by each sensor. The method to determine basic probability assignments of each sensor report is proposed. Sensor reports are fused through classical Dempster combination rule. A numerical example is illustrated to show the target recognition application of the fuzzy-Dempster approach.Defence Science Journal, 2010, 60(5), pp.525-530, DOI:http://dx.doi.org/10.14429/dsj.60.57

Is the status of gold threatened by Bitcoin?

This paper evinces the ability of gold to avoid risks during periods with great fluctuations in the Bitcoin market. We apply bootstrap full- and subsample rolling-window Granger causality tests to explore the causal relationship between Bitcoin price (BCP) and gold price (GP). The empirical results show that an increase in BCP can cause GP to decrease, indicating that the prosperity of the Bitcoin market undermines the hedging ability of gold. However, a decrease in BCP causes GP to increase, and it also emphasizes that the ability of gold to avoid risks persists. Hence, the status of gold will not be completely threatened by Bitcoin, and they are complementary to each other instead of in competition. In turn, both positive and negative influences of GP on BCP suggest that fluctuations in BCP can be predicted through the gold market. In situations of severe global uncertainty and complicated investment environments, investors can benefit from complementary markets to optimize their asset allocation. Additionally, countries can grasp the trends in Bitcoin and gold prices to prevent large fluctuations in both markets and to reduce the uncertainty of the financial system