Jiang

A new consensus model for group decision making using fuzzy linguistic preference relations with heterogeneous experts

Group decision making (GDM) problems consist of finding the most acceptable solution based on a group of experts preferences. Sometimes, the experts experience difficulty expressing their preferences using crisp values or making comparisons between each pair of alternatives. Meanwhile, because of their different backgrounds or knowledge concerning a specific problem, the opinions of different experts may carry different weights along the decision process. Thus, a new consensus model is proposed to solve these problems. First, experts are required to express their opinions using fuzzy linguistic preference relations, and then, a new method is proposed for classifying the experts into three different importance levels according to these opinions. Then, consistency measures and proximity measures are used to guide the decision-making process. A new feedback mechanism that generates advice for experts according to their different levels is proposed. The importance degrees of experts are taken into consideration throughout the process, which is one of the main novelties of this model. Finally, a numerical example is conducted to illustrate the utilization and compared results are also presented to check the feasibility of the proposed model.

A hybrid approach for multi-weapon production planning with large-dimensional multi-objective in defense manufacturing:

Multi-weapon production planning contains multi-objective combinatorial optimization and decision-making problems with the NP-hard and large-dimensional natures, which are difficult to be attacked by one single technique successfully. A four-stage hybrid approach is proposed to solve this problem. In the first stage, the multi-weapon production planning problem is formulated with 2N (N > 5) objectives based on operational capability requirements and expected downside risk measure. In the second stage, the formulation addressed is converted into a bi-objective optimization model using goal programming. In the third stage, an algorithm DENS based on differential evolution and nondominated sorting genetic algorithm–II is developed to obtain the Pareto set. Finally, the multiple attribute decision-making method technique for order preference by similarity to ideal solution is employed to acquire the compromise solution from the Pareto set. A case study is given to demonstrate the effectiveness of the proposed approach. The concrete advantages of goal programming, DENS, and technique for order preference by similarity to ideal solution are also validated in this case. This approach can support the weapon production planning in defense manufacturing and is also applicable to solve the multi-level and multi-objective problem in other manufacturing fields.

Consensus building in group decision making based on multiplicative consistency with incomplete reciprocal preference relations

In this study, a new method is proposed to address group decision making (GDM) using incomplete reciprocal preference relations (RPRs). More specifically, the multiplicative transitivity property of RPRs is first used to estimate missing values and measure the consistency of preferences provided by experts. Following this, experts are assigned weights by combining consistency weights and trust weights. The former are derived by conducting a multiplicative consistency analysis of the opinions of each expert, whereas the latter are used to measure the degree of trust in an expert harbored by others. Experts with satisfactory consistency and large trust weights should typically be assigned large weights. The consensus level is then checked to determine whether the decision making process moves forward to the selection process. If it is negative, a hybrid method consisting of delegation and feedback mechanisms is used to improve the process of arriving at a consensus. The delegation occurs when some experts decide to leave the process, which is common in GDM involving large numbers of participants. The feedback mechanism, one of the main novelties of the proposed approach, generates different advice for experts based on their consistency and trust weights. Finally, a numerical example was studied to show the practicality and efficiency of the proposed method, and the results indicated that it can provide useful insights into the GDM process.

Automatic sleep and wake classifier with heart rate and pulse oximetry: Derived dynamic time warping features and logistic model

This paper presents a novel sleep/wake classification method based on heart rate and pulse oximetry, using logistic model with derived dynamic time warping and correlation features introduced, which were used to classify sleep stages. 100 sleep recordings obtained from the Sleep Heart Health Study dataset, which are available on websites, were used to validate the proposed method. Using the features extracted by our research, classification performance of a LD classifier and feedforward neural classifier were compared to the proposed logistic classifier. The classification accuracy and AUC of the logistic classifier was found to be better (83.8%, 0.924) than those of the two other classifiers (80.1%, 0.732 for LD and 64.0%, 0.801 for neural classifier). The result demonstrated that the proposed logistic classifier using the derived dynamic time warping and correlation features extracted from heart rate and pulse oximetry signals can classify sleep stages efficiently and effectively, which can provide a novel way to carry out automatic sleep stage classification with a wearable device.

A Hybrid Approach of TOPSIS for Weapon Systems Selection

Selecting a weapon system is an unstructured, complex decision problem with lots of aspects that must be considered. This paper presents a hybrid approach for use in the selecting of weapon systems. The hybrid approach combines analytic hierarchy process (AHP) and Information Entropy Method (IEM) to assign weights to the criteria to be used in weapon systems selection. These weights are then inputted into a TOPSIS (the technique for preference by similarity to ideal solution) model to determine the best alternative among the weapon systems. At last, a case is studied to demonstrate the effectiveness of the proposed approach. Results indicate that decision support provided by the approach could offset the shortcomings better than AHP alone and therefore provide more reasonable decisions.

Similarity measures for time series data classification using grid representation and matrix distance

Two similarity measures are proposed that can successfully capture both the numerical and point distribution characteristics of time series. More specifically, a novel grid representation for time series is first presented, with which a time series is segmented and compiled into a matrix format. Based on the proposed grid representation, two matrix matching algorithms, matrix-based Euclidean distance (GMED) and matrix-based dynamic time warping (GMDTW), are adapted to measure the similarity of matrix-like time series. Last, to assess the effectiveness of the proposed similarity measures, 1NN classification and K-means experiments are conducted using 22 online datasets from the UCR time series datasets Web site. In general, the results indicate that GMDTW measure is apparently superior to most current measures in accuracy, while the GMED can achieve much higher efficiency than dynamic time warping algorithm with equivalent performance. Furthermore, effects of the parameters in the proposed measures are analyzed and a way to determine the values of the parameters has been given.

System Portfolio Selection Under Hesitant Fuzzy Information

System portfolio selection faces multi-criteria and multi-objective problems, which lead the decision-makers to build a decision model. Otherwise, the system evaluation value is not clear and the multi-objective of the system is difficult to outweigh. To solve the problem, a value-risk ratio model with Hesitant Fuzzy Set (HFS) is used for portfolio selection. To be specific, in this model, the HFS is used to evaluate the value and risk of systems; and the portfolio value and portfolio risk are calculated with HFS operation. Meanwhile, the value-risk rate is applied to address the problem of multi-objective for system portfolio. Finally, one numerical example for system portfolio selection is given to illustrate the applicability of the proposed model.

A Shape Based Similarity Measure for Time Series Classification with Weighted Dynamic Time Warping Algorithm

Time series similarity measure is an essential issue in time series data mining, which can be widely used in various applications. With an eye to the fact that most current measures neglect the shape characteristic of time series, this paper proposes a shape based similarity measure. By introducing a shape coefficient into the traditional weighted dynamic time warping algorithm, an improved version, shape based weighted dynamic time warping (SWDTW) algorithm is proposed. Specifically, the ways to measure univariate and multivariate time series similarity with SWDTW are presented. Finally, in order to verify the effectiveness of the proposed similarity measure, both 1NN classification and similarity search experiments are carried out using datasets derived from UCR Time Series Classification Homepage. By comparing the SWDTW similarity measure with other measures, the results show that the proposed SWDTW measure is more of accuracy and robust.

Group decision making for weapon systems selection with VIKOR based on consistency analysis

Weapon systems selection is an unstructured, complex multi-criteria decision analysis problem with a wide range of considerations. In this paper, a hybrid approach which uses VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian (VIKOR) technique, is presented for the screening of weapon systems. More specifically, a group of experts are first asked to make judgments on the criteria using fuzzy preference relations. Next, the experts are assigned different weights directly based on their opinions with consistency analysis, which is one of the main novelties in this paper. A collective comparison matrix is then constructed and the weights of the criteria are determined based on the dominance concept. VIKOR is used to rank and determine the best alternative. A case study demonstrates the usefulness and effectiveness of the proposed approach, in comparison with the technique for preference by similarity to ideal solution (TOPSIS).

A Differential Evolution Based Optimization Algorithm for Production Planning Model in Defense Industry

The production planning problem in defense industry has the non-regular nonlinearity of the objective function and the NP-hard nature of the solution space. A nonlinear integer programming model was proposed for this problem. A differential evolution based optimization algorithm was developed to solve the proposed model. A case study validated the effectiveness of the developed algorithm, and proved that it is superior to the standard genetic algorithm on the global searching capability. The algorithm can support the weapons production planning, and also can be applied to other industries.