The main purpose of this study was to develop and apply a neural network (NN) approach and an adaptive neuro-fuzzy inference system (ANFIS) model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD) and mean absolute percent error (MAPE), were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose.

Perishable products run their life cycle in a short period of time due to the shortness of their shelf lives. Product efficiency falls when especially non-recyclable products are thrown away without being used. Furthermore, this kind of products that unnecessarily occupy shelves of supermarkets cause supermarkets to follow an insufficient stock management policy. Unconscious and unplanned use of our limited natural resources will deteriorate the product portfolio for future generations. Such unconscious production and consumption patterns will disrupt natural balance and damage sustainability of products. In addition to creating very high costs for producers, sellers and consumers alike, these unsold or stale products lead to environmental problems due to such pricing policies. In other words, although the products have to be thrown away without being sold is attributed by many managers to be attributable to the unplanned over-orders, the actual reason is something else. The real contributor of the problem is changing purchase attitudes of customers because of wrong pricing policies of wholesaler. In addition, limited resources are also consumed fast and in unnecessary amounts. The imbalance in respect to the sustainability of these products leads to increase in the production costs, procurement costs and failure to achieve balance among products to be kept in storage houses as some of the products occupy stocks unnecessarily. In the present study, a new pricing policy is developed for product stock whose shelf lives are about to expire and generally become waste to increase salability of these products in reference to fresher stocks of these products. The present study, which is designed to reduce the above-mentioned losses, will seek to minimize the cost of waste, maximize the profit earned by supermarkets from the product, maximize product utilization rates and ensure sustainability of products and stocks as well. Fulfillment of these objectives will increase productivity and enhance the significance of product efficiency and nature-friendly attitudes.

Environmental and social concerns force logistics firms to evaluate various risk factors for intermodal transportation systems. In this context, this paper takes into account economic, social, and ecological risk factors that have attracted considerable attention toward sustainable transportation. In this paper, the allocation of export containers to transportation modes, by incorporating social and ecological risks with a main focus on the minimization of transportation costs, was examined. A mixed-integer-programming-based mathematical model was proposed to decide how the containers can be allocated to different transportation modes. In addition, a fuzzy-based approach was performed to determine the social and ecological risk weights of the transportation modes within the decision process. Each mode was evaluated with social risks (e.g., human accidents and deaths) and ecological risks (e.g., emission values and noise pollution) by decision makers via the assignment of numerical scores. The obtained results show that optimal transportation modes more economical and environmentally friendly were provided by the proposed methods.

Fossil fuels are the primary energy sources and meet the global energy demands. However, environmental and health problems related with these sources boosted the demand for renewable energy sources. Hydrogen, as an energy carrier has a growing potential for solving these problems. In this article, a mathematical programming model that integrates the production, storage and transportation, safety, location, and staff assignment decisions is presented considering minimization of costs. Although most of the studies focus on location, distribution, storage decisions of hydrogen energy networks, the article also includes production, safety and staff assignment decisions to make this problem more practical. Furthermore, we also investigate the set covering constraint will ensure that each region is covered by minimum number of the hydrogen facilities. The developed model ensures a balance between location, distribution, storage, production, safety and staff decisions by installing two production facilities by assigning total 9 warehouses, 22 tank trucks, 12100 km pipeline, 35 staffs under distance constraint 2000 km in regions 1 and 5. The computational results indicate that the proposed model produces effective solutions for the coverage to all region and minimum total cost for real-case situations.