In today's economic environment, performance and efficiency assessment is essential for organizations in order to survive and raise their market share. Energy efficient consumption is a major issue in the energy planning of each country which is a big concern of managers, hence, exploitation of a strong approach for efficiency evaluation and assessment seems necessary in the energy section. In this study, a novel performance assessment model is proposed based on the concept of trust, using two popular fuzzy operators called T-norm and S-norm. The developed model is applied for a real case study of energy consumption efficiency assessment for 36 countries. An adaptive network based fuzzy inference system (ANFIS) is used to measure the efficiencies. Also, to predict efficiency rates of the future time periods, a regression model is applied as a time series model. The obtained results indicate the superiority and applicability of the proposed methodology. To the best of our knowledge, this is the first study that proposes a novel performance measurement approach based on trust context by using fuzzy T-norm and S-norm operators.

In this article, an integrated sales and leasing company is considered. This company remanufactures leased products at the end of operating lease contracts to make them as good as new ones and sell them to the customers. In order to satisfy customers' demand, required products are provided from a third-party when the company meets inventory shortage. Non-linear competitive demand functions are used which are sensitive to manufacturer suggested retail price (MSRP) and inflation rate. A mixed integer non-linear mathematical model (MINLP) is developed to determine optimal price of selling products, optimal amount of monthly payments in leasing contracts, and optimal inventory control planning, i.e. the optimal amount of manufacturing and remanufacturing products and optimal inventory levels. The main objective is to maximize net profit of the company. Small, medium and large-scale sizes of the model are solved to show the applicability of the model. To solve the large-scale problem, differential evolution (DE) algorithm is applied as a meta-heuristic solution approach. Numerical results show high sensitivity of model to demands. Also, optimal trend behaviors of some main variables of the problem seem similar to the competitive behavior of demands.