Table of Content

16 September 2020, Volume 32 Issue 3

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Articles

Fresh-keeping Strategy of Agricultural Products Supply Chain Considering Grey Game

Qing Zhang, Qiuyu Zhang, Zhichao Zhang

2020, 32(3):  1-10. 

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This paper uses grey number to identify the freshness degree of agricultural products. A contract to share profits between the farmer and the retailer is established based on their fresh-keeping efforts and we build their profits model respectively. We use game theory based on grey matrix to analyze their fresh-keeping strategy and get conclusions through numeric example: the retailer tends to take measures to keep products fresh whatever the farmer’s decision while the farmer’s fresh-keeping strategy is influenced by the proportion of profits-sharing. In given conditions, when we set the proportion a specific set, we can find a Nash equation. The result shows that in the end both the farmer and the retailer will keep products fresh.


Defective Recognition with MTD-based Non-Equigap Grey Models

Yao San Lin, Der-Chiang Li, Chien-Chih Chen, Hung-Yu Chen

2020, 32(3):  11-20. 

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The TFT-LCD (thin-film transistor liquid crystal display) industry in Taiwan has been developed for decades. To provide better service for customers, most Taiwan companies in the supply chain have set up their warehouse in mainland China for vendor managed inventory (VMI). However, over the last decade, owing to the industry issues of product diversity strategies and short product lifecycles, it has become difficult for suppliers to control well the stock level in VMI, especially the operation of defective product returns. In order to keep high customer satisfaction, it is considered an option by forecasting possible future defectives for preparing the returning products to satisfy customers. The non-equigap grey model (NGM) used to be applied to such short-term time series data and has shown to be an effective tool. However, NGM predictions still can be improved by making more appropriate background values by determining their parameter α values. Accordingly, this paper employs the mega-trend-diffusion (MTD) technique to estimate better α values, and the model is thus called MTD-NGM(1,1). In the experiments, two data sets taken from a supplier are examined for effectiveness validation. The experimental results indicate that MTD-NGM(1,1) can generally produce better predictions than NGM(1,1).

A Fuzzy-based Approach for Improving Accuracy of Grey Forecasting Models

Tung-I Tsai, Chun-Wu Yeh, Liang-Sian Lin, Che-Jung Chang, Der-Chiang Li

2020, 32(3):  21-33. 

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Over the past two decades, the grey model (GM) and its extensions have shown to be an effective tool to deal with short-term time series (STTS) data. To further improve the accuracy of GM models, this paper proposes a novel GM modelling procedure based on a fuzzy-set concept. In the procedure, STTS data is fuzzified by measuring the forces between existing data and a newly coming datum to form fuzzy time series for building GM models, and final predictions are defuzzified by aggregating the predictions of the GM models with the proposed weights. The experimental results of a real case indicate that the proposed procedure can further improve the accuracy of GM models in general.


A Remaining Useful Life Prediction Framework Integrating Multiple Time Window Convolutional Neural Networks

Ya Song, Tangbin Xia, Yu Zheng, Bowen Sun, Ershun Pan, Lifeng Xi

2020, 32(3):  34-47. 

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Efficiently predicting Remaining Useful Life (RUL) of equipment is fundamental for assessing system health and developing maintenance strategies. Considering the high degree of inconsistency among the length of degradation trajectories, a multiple time window Convolutional Neural Networks (CNN) based framework is introduced to improve prediction accuracy. In this proposed method, one-dimensional CNN is adopted to learn degradation trends from historical sensor data, and a multiple time window strategy is exploited to reduce training errors and increase the utilization rate of test data. The performance of this proposed framework is validated through an experimental study and compared with state-of-the-art models. The comparison and analysis have demonstrated that this framework can achieve the best overall performance, and thus it can provide strong support for preventive maintenance.

Grey Incidence Model for Multivariate Time Series with Different Length based on Spatial Pyramid Pooling

Ke Zhang, Yao Yin, Le Cui

2020, 32(3):  48-59. 

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Aiming at the problem that existing multivariate grey incidence models can not be applied to different length multivariate time series (MTS), a new model was proposed based on spatial pyramid pooling (SPP). Firstly, the local features of an MTS with different lengths were pooled and aggregated by SPP to construct feature pooling matrices on the same size. Secondly, classic multivariate grey incidence models were used to measure the degree of incidence between feature pooling matrices. Finally, the effectiveness of the models was verified on two data sets compared with a variety of algorithms.

Analysis of Eco-Efficiency in China's Key Provinces along the “Belt and Road” Based on Grey TOPSIS-DEA

Fanlin Meng, Wenping Wang

2020, 32(3):  60-79. 

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Under the background of "Green Belt and Road", exploring the transition path from "high energy consumption and high pollution" to green development in regions along the “Belt and Road" is of great significance for achieving the healthy and sustainable development of the economy. The current study decomposes the industrial eco-economic system into manufacturing subsystems and environmental protection subsystems, separately establishes the eco-efficiency evaluation index system of the two subsystems, and introduces the grey system theory’s "small sample and poor information" uncertainty problem into the TOPSIS-DEA cross- efficiency model to construct Grey TOPSIS-DEA model to measure the eco- efficiency of China’s key provinces along the "Belt and Road" from 2005 to 2016. On this basis, this paper analyzes the differences in the contribution rate of production factor to eco-efficiency growth in various regions. The results show that: the eco-efficiency of the whole industrial eco-economic system and the manufacturing subsystem of China's key provinces along the "Belt and Road" presents a general downward trend, with a slight fluctuation in the middle, and the eco-efficiency of the "One Belt" regions is lower than that of the "One Road" regions. The eco-efficiency of environmental protection subsystems in most regions along the line shows an increasing trend, and it is significantly lower than that of the industrial eco-economic system and the manufacturing subsystem. In addition, for High Manufacturing-High Environmental Protection areas, the contribution of labor to the improvement of eco-efficiency is the largest, and for other three types of areas, capital plays the most crucial role in improving eco-efficiency.

Identifying the Influencing Factors of Patient’s Attitude to Medical Service Price by Combing Grey Relational Theory with CMH Statistical Analysis

Chongxu Zhang, Lizhong Duan, Hangyu Liu, Yinran Zhang, Lili Yin, Qi Lu, Guna Duan

2020, 32(3):  80-95. 

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The purpose of the paper is to explore the influencing factors of the patient’s attitude to the medical service price. Literature analysis and expert interviews are used to design the questionnaire. Using a convenient sampling method to extract 600 patients from the five areas of Beijing, Tianjin, Hebei, Shandong and Liaoning. Combing grey relational analysis with Cochran-Mantel-Haenszel (CMH) statistical analysis to explore the influencing factors of the attitude in the medical service price. The results show that region, gender, age, education, professional title, income by month, medical insurance, commercial medical insurance and work unit are related to the distribution of patients’ attitude of medical service price by CMH statistical analysis. The sorting of Grey relational grade is Region (0.997)> Gender (0.939)> Commercial Medical Insurance (0.828)> Level of Medical institution (0.736)> Income by month(RMB) (0.701)> Medical insurance (0.644)> Professional Title (0.598)> Work unit (0.545)> Age (0.481)> Education (0.462); The grey relational grade of those respondents surveyed who are living in Shandong province (0.821), female (0.797), don’t buy commercial medical insurance (0.846), go to the tertiary hospital lately (0.831) and have income of 4000-6000 yuan by month (0.840) are higher than others. The current medical service price is higher than the reasonable level, especially in the operation fee and examination fee involving large consumables. The patient’s satisfaction with the price of medical service items is different in different variables, and the main influencing factors are as follows: Region, Gender, Commercial Medical Insurance, Level of Medical institution and Income by month (RMB).

Multi-attribute Group Grey Target Decision-making Method Based on Three-parameter Interval Grey Number

Ye Li, Dongxing Zhang

2020, 32(3):  96-109. 

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Considering the value information of the three-parameter interval grey number and the risk attitude of decision-maker, a multi-attribute group grey target decision-making method based on a three-parameter interval grey number is advanced. Firstly, the nonlinear averaging operators are constructed to reflect the risk attitude of decision-makers. Then, the definition of the relative kernel and distance measure of the three-parameter interval number is given while considering the value characteristics. Next, the positive and negative clouts of the schemes are determined by the size of the grey number's kernel and the degree of accuracy, and two models are constructed based on the principle of maximum relative off-target distance and grey entropy to determine the weights of attributes and decision-makers. And then, the relative off-target distance of positive and negative off-target distance is used to rank and optimize the schemes. Lastly, an example is delivered to illustrate the rationality and feasibility of the proposed method.

Solving MDVRP with Grey Delivery Time based on Improved Quantum Evolutionary Algorithm

Xiaojian Yuan, Qishan Zhang, Hong Liu, Ling Wu

2020, 32(3):  110-123. 

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In the vehicle routing problem, the uncertainty of delivery time and customer expectation time greatly affects the selection of the delivery route and the customer service level. Therefore, the traditional vehicle routing optimization model and algorithm with time windows are no longer applicable. The author uses the travel budget time formula to generate a grey time window for vehicle distribution, and uses a probability density function to whiten the Grey Delivery Time, uses a fuzzy gradient function to represent customer satisfaction, and builds a multi-depot with maximum customer satisfaction and minimum cost as the goal. In the process of solving the model, the classical quantum evolutionary algorithm has a problem of a large amount of effective information being lost in the mapping processing between the quantum domain, the binary domain, and the problem domain. The author puts forward the concept of a quantum cell body and operation method of qubit alignment and constructs a new quantum evolution algorithm to solve the model. The author conducts experiments through specific examples to verify the correctness of the model and the effectiveness of the algorithm.

Taylor Series approximation based Unbiased GM(1,1) Hybrid Statistical Approach for Forecasting Stock Market

R. M. Kapila Tharanga Rathnayaka, D. M. K. N. Seneviratna

2020, 32(3):  124-133. 

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Due to the volatility with unstably in the modern finance, the ability of forecasting is notoriously embarrassing and represents a major challenge with traditional time series forecasting methods; especially, most of the available approaches are still weak to forecast future predictions under the unbalanced frameworks. The purpose of this current study is to propose a Taylor Series approximation based Unbiased GM(1,1) Hybrid approach (HTS_UGM(1,1)) to handle incomplete, noise and uncertain data in multidisciplinary systems.