Supplier selection in the industry 4.0 era by using a fuzzy cognitive map and hesitant fuzzy linguistic VIKOR methodology.

Organizations will be increasingly concerned about maintaining their positions in today's changing world, the high-tech era, and the emergence of innovative technologies because of the industrial revolutions. Everyone has come to believe that to survive and continue their constructive roles, they must achieve competitive advantages by working based on the trends. It is undeniable that the introduction of Industry 4.0 has had a significant impact on enterprises, organizations, and, of course, supply chains. In the meantime, selecting a supplier is one of the main strategic decisions of the organization because choosing the right supplier leads to increasing profitability, improving market competition, better accountability, enhancing product quality, and reducing costs. While the issue of supplier evaluation has been one of the interesting topics for researchers in recent decades, its development in the fourth supply chain generation needs further consideration. In this regard, current technologies in the fourth-generation industrial revolution, methods, and criteria used in previous studies based on industry 4.0 and before that are reviewed separately. By reviewing previous articles and experts' opinions, thirteen sub-criteria considering industry 4.0 have been identified for selecting suppliers in three categories, economic, environmental, and social. The weight of each criterion has been determined using a set of fuzzy cognitive maps (FCMs) and considering the centrality of criteria in the concept of communication networks. To prioritize the suppliers, the hesitant fuzzy linguistic term sets (HFLTS) VIKOR method has been used in hesitant fuzzy linguistic terms. Finally, a case study is introduced to illustrate the effectiveness and usefulness of our integrated methodology and prioritize its four suppliers.

Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models.

Today, stock market has important function and it can be a place as a measure of economic position. People can earn a lot of money and return by investing their money in the stock exchange market. But it is not easy because many factors should be considered. So, there are many ways to predict the movement of share price. The main goal of this article is to predict stock price indices using artificial neural network (ANN) and train it with some new metaheuristic algorithms such as social spider optimization (SSO) and bat algorithm (BA). We used some technical indicators as input variables. Then, we used genetic algorithms (GA) as a heuristic algorithm for feature selection and choosing the best and most related indicators. We used some loss functions such as mean absolute error (MAE) as error evaluation criteria. On the other hand, we used some time series models forecasting like ARMA and ARIMA for prediction of stock price. Finally, we compared the results with each other means ANN-Metaheuristic algorithms and time series models. The statistical population of research have five most important and international indices which were S&P500, DAX, FTSE100, Nasdaq and DJI.

A hybrid fuzzy-stochastic multi-criteria ABC inventory classification using possibilistic chance-constrained programming.

Inventory classification is a fundamental issue in the development of inventory policy that assigns each inventory item to several classes with different levels of importance. This classification is the main determinant of a suitable inventory control policy of inventory classes. Therefore, a great deal of research is done on solving this problem. Usually, the problem of inventory classification is considered in a multi-criteria and uncertain environment. The proposed method in this paper inspired by the notion of heterogeneous decision-making problems in which decision-makers deal with different types of data. To this aim, a mathematical modeling-based approach is proposed considering different types of uncertainty in classification information. Demand information is considered to be stochastic due to its time-varying nature and cost information is considered to be fuzzy due to its cognitive ambiguity. A hybrid algorithm based on chance-constrained and possibilistic programming is proposed to solve the problems. Considering the stochastic nature of demand information, solving the proposed model using the hybrid algorithm, the classification of items to three classes of extremely important, class A, moderately important, class B, and relatively unimportant, class C, items are determined along with a minimum inventory level required to deal with the stochasticity of demands information. The proposed approach is applied to a case study of classifying 51 inventory items. The obtained results assigned 22%, 39%, and 39% of the items to A, B, and C classes, respectively.