In the process of establishing supply chain partnership, partner selection is the key and main step. If the enterprise selects the appropriate supply chain partner, in the material equipment unit price, the material equipment production and the supply ability, the product quality appraisal, the brand meets the demand, the new product development ability, has the bad record, the historical project performance, management system and management level, service level, human resource level, internal information processing level, historical cooperation situation, cooperation will, corporate culture and strategic fit degree, financing support ability, information transmission ability, Enterprise green idea propaganda, product energy consumption or energy consumption ratio, green building project participation in construction, environmental protection and energy-saving product development investment, toxic and harmful raw materials use, green matter The use of flowing green packaging, the use of recycled materials/product recycling, and the treatment of industrial “three wastes” will all produce a series of advantages that cannot be matched by traditional relationships. Then, the whole supply chain competitiveness will be improved. This paper studies the selection and evaluation of the partners in the supply chain environment from the point of view of the current research situation of the cooperative relationship in the supply chain environment at home and abroad. Based on the relevant research results at home and abroad, according to certain principles and methods, combined with experts’ evaluation of the future trend of qualitative indicators, a set of evaluation index system of supply chain partners is constructed. The standardized treatment of evaluation index is given. By comparing and analyzing the advantages and disadvantages of the common evaluation methods, the mature BP neural network is applied to the artificial neural network by using the artificial neural network evaluation method. MATLAB software is used to construct neural network. BP neural network is used for training and the trained neural network is used to evaluate an example. The results show that this method can solve the problem of partner selection and evaluation in supply chain environment, and improve the evaluation efficiency.

Research on supply chain partner selection method based on BP neural network

With the increasing number of customer reviews on the Web, there is a growing need for effective methods to retrieve valuable information hidden in these reviews, as sellers need to gain a deep understanding of customers’ preferences in a timely manner. With the continuous enhancement of opinion mining or sentiment analysis research, researchers have proposed many automatic mining and classification methods. However, how to choose a trusted method is a difficult problem for companies, because customer reviews (or opinions) contain a lot of uncertain information and noise. This article reports on a detailed survey of recent opinion mining literature. It also reviews how to extract text features in opinions that may contain noise or uncertainties, how to express knowledge in opinions, and how to classify them. Through this extensive study, this paper discusses open questions and recommends future research directions for building the next generation of opinion mining systems.

Mining and classifying customer reviews: a survey

Assessing banking sectors’ efficiency of financially troubled Eurozone countries

Vogel’s Approximation Method (VAM) is known as the best algorithm for generating an efficient initial feasible solution to the transportation problem. We demonstrate that VAM has some limitations and computational blunders. To overcome these limitations we develop an Improved Vogel’s Approximation Method (IVAM) by correcting these blunders. It is compared with VAM on obtained initial feasible solutions to a numerical example problem. Reduction in the total transportation cost over VAM by IVAM is found to be 2.27%. Besides, we have compared IVAM with each of twelve previously developed methods including VAM on solutions to numerical problems. IVAM leads to the minimal total cost solutions to seven, better solutions to four and the same better solution to the remaining one. Finally, a statistical analysis has been performed over the results of 1500 randomly generated transportation problems with fifteen distinct dimensions, where each of them has 100 problems instances. This analysis has demonstrated better performance of IVAM over VAM by reducing the total transportation cost in 71.8% of solved problems, especially for large size problems. Thus IVAM outperforms VAM by providing better initial feasible to the transportation problem.

A heuristic for obtaining better initial feasible solution to the transportation problem

This paper deals with the solution of the fuzzy fractional transportation problem in which the parameters of the transportation problem, supply, and demand, are stochastic in nature and considered as a fuzzy random variable that follows the exponential distribution with fuzzy mean and fuzzy variance. In a fuzzy fractional objective function, the costs are taken as a triangular fuzzy number. As the parameters are imprecise in nature, the obtained objective value should be a fuzzy number. To obtain the fuzzy objective value, one has to find out its lower and upper bounds, which represent the level of uncertainty. The mathematical form of bounds is expressed by applying dual formulation and variable substitution. Also, for converting the fuzzy constraints into deterministic, the chance-constrained and fuzzy programming was applied. The values of the bounds, which are calculated at different values of 
$$\lambda$$

, the membership function of the objective value is approximated. A numerical example illustrates the considered methodology.

Fuzzy fractional stochastic transportation problem involving exponential distribution

In a transportation problem, the parameters like unit cost of transportation of goods or services from source to destination, supplies from the sources and demands at destinations depend on many factors which may not be deterministic in nature. To deal with the uncertainty of the parameters, random variables and fuzzy variables were used previously. Sometimes, in absence of sufficient sample observations, the uncertain parameters are estimated by the belief degree of the experts. In this paper, the aim is to investigate the transportation problem where the unit cost of transportation, supplies, demands are initially taken as rough variables based on subjective estimation of experts. Further, these rough estimates are suitably approximated as uncertain normal variables and the conceptual uncertain programming model has been developed. The model is then transformed to a deterministic linear programming model by minimizing the expected value of the uncertain objective function under the constraints at certain confidence level.

Subhakanta Dash

Papers

Uncertain transportation model with rough unit cost, demand and supply