No abstract

There may not always be actual data available for planning. Predicted data are used especially for future planning. Due to errors in such planning based on prediction, many products enter the reverse logistics network without completing the shelf life. Especially in textile sector, because of fashion, it is the most important point of planning to be able to make accurate estimates in order to avoid unnecessary resource utilization and to provide minimum cost. It is difficult to establish a mathematical model because the prediction problems in real life have multivariate structure and unknown parameters. Most of the studies in literature have been based on time series prediction. But due to changing fashion and demands of consumers, there are significant differences between demand forecasts and real data. So, in the problems with unknown parameters and multivariate structure, Ensemble Machine Learning (EML) methods are preferred recently because they give more accurate results than other prediction methods. Unlike other studies, the product return rate in textile sector has been predicted with the Stacking and Vote algorithms from EML methods in this paper. In this direction, it is aimed to concentrate on the returns of the products sold with the preferences of the customers and to predict the returns more accurately. In this way, the consumer information obtained as a result of the analyzes can provide more accurate planning in avoiding unnecessary production, transportation and storage activities, reducing costs, resource utilization and environmental pollution. In addition, it is one of the main aims of the study to contribute to the literature by determining the parameters that can be used in predicting the return rates. Highest performance results were obtained with Stacking algorithm. The obtained results were given comparatively and the correlation coefficient of 86.07% was reached.

Many textile products are in reverse logistics network due to mistakes made in activities such as sales forecasting, inventor y planning and distribution. In order to reduce resource usage and cost at first step, in addition to producing the correct quantity, these products must be sent to branches, in correct properties (amount, color, size, model…) and transportation planning and stock planning should be done correctly. Statistical methods, artificial intelligence and machine learning methods are used because of the difficulty of establishing mathematical models in multi-parameter and multi-variable problems. In general, all these activities are based on demand forecasts by time series, but there are important differences between these demand predictions and the actual demands because of fashion and consumers' requests change very quickly. Artificial intelligence and machine learning methods provide faster and more accurate results in complex data sets. The difference of this study from other studies is to estimate the product return rates in Reverse Logistics with Machine Learning. In this direction, it is aimed to predict the claims accurately by concentrating on the customers' preferences, their reasons and the replies of the products which are sold to the customers. Thus, the consumer information obtained as a result of these analyzes can provide us with more accurate planning in terms of avoiding unnecessary production, transportation and storage activities, and sending the products with the correct properties; amount, color, size and model, to the branches. Best results (the correlation coefficient value is 82.35% and lowest error metrics) of this study are obtained with M5P algorithms of machine learning techniques

The wasted amount of energy has become a critical problem for many countries due to limited energy sources and energy production costs. These forced countries to increase energy usage awareness by making regulations in the construction sector, which might dramatically decrease energy consumption cause of the size of the domain. One of them is standardizing heating and cooling loads (HL/CL) to avoid energy waste. HL and CL need an advanced engineering process because of different parameters such as the thermal characteristics of the building, hot water supply, passive solar systems, etc. Hence, it can only be carried out by expert engineers in calculations. In this paper, a classification model as the decision support system is proposed for predicting the energy consumption of residents which is an efficient indicator of architectural features of the construction about energy consumption concept. The data is collected from architectural projects and energy performance certificates. Multilayer Perceptrons, Bagging, and Random subspace are used to predict the energy class of buildings. Based on findings, the most accurate results were achieved by Bagging. Moreover, main input features affecting the prediction performance of HL were revealed and classification success was observed.