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Article
Publication date: 9 December 2021

Sifeng Liu, Tao Liu, Wenfeng Yuan and Yingjie Yang

The purpose of this paper is to solve the dilemma in the process of major selection decision-making.

Abstract

Purpose

The purpose of this paper is to solve the dilemma in the process of major selection decision-making.

Design/methodology/approach

Firstly, the group of weight vector with kernel has been defined. Then, the weighted comprehensive clustering coefficient vector was calculated based on the group of weight vector with kernel. Under the action of weighted comprehensive clustering coefficient vector, the information including in other components around component k and supporting object i to be classified into the k-th category has been gathered to component k. At last, a novel two-stage decision model based on the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector is put forward to solve the dilemma in grey clustering evaluation. Then the overall evaluation conclusion can be consistent with the clustering result according to the rule of maximum value.

Findings

A new way to solve the dilemma in the process of major selection decision-making has been found. People can obtain a consistent result with two-stage decision model at the case of dilemma. That is, the conclusion of the overall evaluation is consistent with the clustering result according to the rule of maximum value.

Practical implications

Several functional groups of weight vector with kernel have been put forward. The proposed model can solve the clustering dilemma effectively and produce consistent results. A practical application of decision problem to solve the dilemma in supplier evaluation and selection of a key component of large commercial aircraft C919 have been completed by the novel two-stage decision model.

Originality/value

The two-stage decision model, the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector were presented in this paper firstly. People can solve the dilemma in grey clustering evaluation effectively by the novel two-stage decision model based on the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector.

Article
Publication date: 1 August 2016

Li Li, Renxiang Wang and Xican Li

According to the grey uncertainty and the connotation of different types weights, the purpose of this paper is to establish the pattern of multi-dimensional grey fuzzy decision…

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Abstract

Purpose

According to the grey uncertainty and the connotation of different types weights, the purpose of this paper is to establish the pattern of multi-dimensional grey fuzzy decision making with feedback based on weight vector and weight matrix, and applies this pattern to evaluate the regional financial innovation ability.

Design/methodology/approach

At first, this paper analyzes the connotation of financial innovation ability and establishes the evaluation system of regional financial innovation ability. Second, the formula of computing the multi-objective weighted comprehensive value based on weight vector and weight matrix is put forward. In view of the object function with supervised factor and stability coefficient, this paper gives the formulas to compute weight vector and weight matrix. Moreover, the algorithm of the multi-dimensional grey fuzzy decision making pattern with feedback based on weight vector and weight matrix is expressed. At last, this paper uses the presented pattern to evaluate the financial innovation ability of thirty-one provinces in China.

Findings

The results are convincing: the development of regional financial innovation is not balanced in China, having obvious spatial clustering feature. The comparisons of evaluation results based on different forms of weights show that the calculating convergence speed of the pattern presented in this paper is fast. The pattern enhances the rationality of the demarcation point between categories, and the convergence within categories, making the evaluation more reasonable.

Practical implications

The method exposed in the paper can be used at evaluating the regional financial innovation ability and even for other similar evaluation problem.

Originality/value

The paper succeeds in realising both the pattern of multi-dimensional grey fuzzy decision making with feedback and evaluating the regional financial innovation ability by using the newest developed theories: weighted grey and fuzzy recognition theory based on weight vector and weight matrix.

Details

Grey Systems: Theory and Application, vol. 6 no. 2
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 16 November 2018

Masatoshi Muramatsu and Takeo Kato

The purpose of this paper is to propose the selection guide of the multi-objective optimization methods for the ergonomic design. The proposed guide enables designers to select an…

Abstract

Purpose

The purpose of this paper is to propose the selection guide of the multi-objective optimization methods for the ergonomic design. The proposed guide enables designers to select an appropriate method for optimizing the human characteristics composed of the engineering characteristics (e.g. users’ height, weight and muscular strength) and the physiological characteristics (e.g. brain wave, pulse-beat and myoelectric signal) in the trade-off relationships.

Design/methodology/approach

This paper focuses on the types of the relationships between engineering or physiological characteristics and their psychological characteristics (e.g. comfort and usability). Using these relationships and the characteristics of the multi-objective optimization methods, this paper classified them and constructed a flow chart for selecting them.

Findings

This paper applied the proposed selection guide to a geometric design of a comfortable seat and confirmed its applicability. The selected multi-objective optimization method optimized the contact area of seat back (engineering characteristic associated with the comfortable fit of the seat backrest) and the blood flow volume (physiological characteristic associated with the numbness in the lower limb) on the basis of each design intent such as a deep-vein thrombosis after long flight.

Originality/value

Because of the lack of the selection guide of the multi-objective optimization methods, an inappropriate method is often applied in industry. This paper proposed the selection guide applied in the ergonomic design having a lot of the multi-objective optimization problem.

Details

Journal of Engineering, Design and Technology, vol. 17 no. 1
Type: Research Article
ISSN: 1726-0531

Keywords

Abstract

Details

Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

Article
Publication date: 22 February 2021

Jinshan Ma, Di Tian and Jinmeng Yue

This paper is to propose a novel generalized grey target decision method (GGTDM) with index and weight both containing mixed types of data.

Abstract

Purpose

This paper is to propose a novel generalized grey target decision method (GGTDM) with index and weight both containing mixed types of data.

Design/methodology/approach

The decision-making steps of the proposed approach are as follows. First, all mixed attribute values of alternatives and weights are transformed into binary connection numbers and also comprised two-tuple (determinacy, uncertainty) numbers. Then, the two-tuple (determinacy, uncertainty) numbers of target center indices are calculated. Next, the certain weights are determined by the Gini–Simpson (G–S) index-based method. Following this, the comprehensive-weighted Kullback–Leibler distances (CWKLDs) of all alternatives and the target center are obtained. Finally, the alternative ranking relies on the CWKLD considering the smaller value as the better option.

Findings

The certain weights determined by the improved Gini–Simpson index (IGSI) based method are more accurate in compared with that by the proximity-based method and the weight function method. The discrimination ability of alternatives ranking of the proposed approach is stronger than that of the compared comprehensive-weighted proximity (CWP) based method and comprehensive-weighted Gini–Simpson index (CWGSI) based method.

Research limitations/implications

The proposed method fulfills the decision-making task relying on CWKLD, which solves the uncertain measurement from the viewpoint of entropy.

Originality/value

The proposed approach adopts the IGSI to transform uncertain weights into certain ones and takes the CWKLD as the basis for the decision-making.

Details

Grey Systems: Theory and Application, vol. 12 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 10 August 2010

Shamsuddin Ahmed

The proposed algorithm successfully optimizes complex error functions, which are difficult to differentiate, ill conditioned or discontinuous. It is a benchmark to identify…

Abstract

Purpose

The proposed algorithm successfully optimizes complex error functions, which are difficult to differentiate, ill conditioned or discontinuous. It is a benchmark to identify initial solutions in artificial neural network (ANN) training.

Design/methodology/approach

A multi‐directional ANN training algorithm that needs no derivative information is introduced as constrained one‐dimensional problem. A directional search vector examines the ANN error function in weight parameter space. The search vector moves in all possible directions to find minimum function value. The network weights are increased or decreased depending on the shape of the error function hyper surface such that the search vector finds descent directions. The minimum function value is thus determined. To accelerate the convergence of the algorithm a momentum search is designed. It avoids overshooting the local minimum.

Findings

The training algorithm is insensitive to the initial starting weights in comparison with the gradient‐based methods. Therefore, it can locate a relative local minimum from anywhere of the error surface. It is an important property of this training method. The algorithm is suitable for error functions that are discontinuous, ill conditioned or the derivative of the error function is not readily available. It improves over the standard back propagation method in convergence and avoids premature termination near pseudo local minimum.

Research limitations/implications

Classifications problems are efficiently classified when using this method but the complex time series in some instances slows convergence due to complexity of the error surface. Different ANN network structure can further be investigated to find the performance of the algorithm.

Practical implications

The search scheme moves along the valleys and ridges of the error function to trace minimum neighborhood. The algorithm only evaluates the error function. As soon as the algorithm detects flat surface of the error function, care is taken to avoid slow convergence.

Originality/value

The algorithm is efficient due to incorporation of three important methodologies. The first mechanism is the momentum search. The second methodology is the implementation of directional search vector in coordinate directions. The third procedure is the one‐dimensional search in constrained region to identify the self‐adaptive learning rates, to improve convergence.

Details

Kybernetes, vol. 39 no. 7
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 27 May 2024

Li Li and Xican Li

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute…

Abstract

Purpose

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Design/methodology/approach

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

Findings

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

Practical implications

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

Originality/value

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 17 September 2018

Mahamaya Mohanty, Rashmi Singh and Ravi Shankar

The purpose of this paper is to investigate ways to improve operational efficiency of outbound retail logistics considering retailers and consumers by using clustering approach…

Abstract

Purpose

The purpose of this paper is to investigate ways to improve operational efficiency of outbound retail logistics considering retailers and consumers by using clustering approach. The retailers are allocated to serve a cluster of consumers. This study demonstrates economic and environment benefits that are achieved in terms of reduced delivery time, transportation cost and carbon emissions.

Design/methodology/approach

This study is based on modeling the outbound logistics of a retail chain by using Kohonen self-organizing map (KSOM). KSOM is an unsupervised learning and data analysis method for vector quantization, which is based on Euclidean distance method to form clusters.

Findings

Appropriate clustering of retailers and consumers provides efficient locations of retailers that are identified using the KSOM training algorithm. It provides optimum distance with lesser delivery time, transportation cost and carbon emissions.

Research limitations/implications

The implication of research includes modeling of operational procedures in a retail supply chain, which is a crucial task for a business. These operations positively affect the reduction in inventory and distribution costs, improvement in customer service and responsiveness to the ever-changing markets of consumer durables. Overall results are insightful and practical in the sense that implementation would result in consumer convenience, eco-friendly environment, etc.

Originality/value

There is not enough research available on outbound retail logistics considering retailers and consumers using clustering approach.

Details

Journal of Modelling in Management, vol. 13 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 17 April 2020

Barkha Bansal and Sangeet Srivastava

Aspect based sentiment classification is valuable for providing deeper insight into online consumer reviews (OCR). However, the majority of the previous studies explicitly…

Abstract

Purpose

Aspect based sentiment classification is valuable for providing deeper insight into online consumer reviews (OCR). However, the majority of the previous studies explicitly determine the orientation of aspect related sentiment bearing word and overlook the aspect-context. Therefore, this paper aims to propose an aspect-context aware sentiment classification of OCR for deeper and more accurate insights.

Design/methodology/approach

In the proposed methodology, first, aspect descriptions and sentiment bearing words are extracted. Then, the skip-gram model is used to extract the first set of features to capture contextual information. For the second category of features, cosine similarity is used between a pre-defined seed word list and aspects, to capture aspect context sensitive sentiments. The third set of features includes weighted word vectors using term frequency-inverse document frequency. After concatenating features, ensemble classifier is used using three base classifiers.

Findings

Experimental results on two real-world data sets with variable lengths, acquired from Amazon.com and TripAdvisor.com, show that the advised ensemble approach significantly outperforms sentiment classification accuracy of state-of-the-art and baseline methods.

Originality/value

This method is capable of capturing the correct sentiment of ambiguous words and other special words by extracting aspect-context using word vector similarity instead of expensive lexical resources, and hence, shows superior performance in terms of accuracy as compared to other methods.

Details

Information Discovery and Delivery, vol. 48 no. 3
Type: Research Article
ISSN: 2398-6247

Keywords

Article
Publication date: 11 October 2019

Yen-Liang Chen, Cheng-Hsiung Weng, Cheng-Kui Huang and Duo-Jia Shih

As researchers are writing a draft paper with incomplete structure or text, one of burdensome tasks is to deliberate about which references should be cited for one sentence or…

Abstract

Purpose

As researchers are writing a draft paper with incomplete structure or text, one of burdensome tasks is to deliberate about which references should be cited for one sentence or paragraph of this draft. In view of the rapid increase in the number of research papers, researchers desire to figure out a better way to do citation recommendations in developing their draft papers. The purpose of this paper is to propose citation recommendation algorithms that enable the acquisition of relevant citations for research papers that are still at the drafting stage. This study attempts to help researchers to select appropriate references among the vast amount of available papers and make draft papers complete in reference citation.

Design/methodology/approach

This study adopts a model for recommending citations for incomplete drafts. Four algorithms are proposed in this study. The first and second algorithms are unsupervised models, applying term frequency-inverse document frequency and WordNet technologies, respectively. The third and fourth algorithms are based on the second algorithm to integrate different weight adjustment strategies to improve performance.

Findings

The proposed recommendation method adopts three techniques, including using WordNet to transform vector and setting adjustment weights according to structural factors and the information completeness degree, to generate citation recommendation for incomplete drafts. The experiments show that all these three techniques can significantly improve the recommendation accuracy.

Originality/value

None of the methods employed in previous studies can recommend articles as references for incomplete drafts. This paper addresses the situation that a draft paper can be incomplete either in structure or text or both. Recommended references, however, can be still generated and inserted into any desired sentence of the draft paper.

Details

Data Technologies and Applications, vol. 53 no. 4
Type: Research Article
ISSN: 2514-9288

Keywords

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