احسان میرزایی

In this paper, a new interval-valued fuzzy multi-criteria group decision-making model is proposed to evaluate each of the energy plans with sustainable development criteria for proper energy plan selection. The purpose of this study is divided into two parts: first, it is aimed at determining the weights of evaluation criteria for sustainable energy planning and second at rating sustainable energy alternatives by a group decision model under uncertainty. In the proposed method, given the concept of asymmetric data, possibilistic statistical concepts are used to make a more appropriate decision with less uncertainty consideration. A new rating system based on the reference point and a new improved version of Entropy method are introduced as the leading features of this model to determine the weight of criteria and possibilistic statistical concepts, including mean, standard deviation and cube root of skewness in the interval-valued fuzzy form, by considering positive and negative ideal points. Moreover, a practical example in the field of energy is presented and discussed, taking into account the experts experience in different fields and inaccurate concepts of information, efficiency and results of the proposed model.

The issue discussed in this paper is a bi-level problem in which two rivals compete in attracting customers and maximizing their profits which means that competitors competing for market share must compete in the centers that are going to be located in the near future. In this paper, a nonlinear model presented in the literature considering customer preferences is linearized. Customer behavior means that the customer patronizes the most attractive (most comfort) location that he/she wants to be served among the locations of the first-level decision maker (Leader) and the second-level decision maker (Follower). Four types of exact algorithms have been introduced in this paper which include three types of full enumeration procedures and a developed branch-and-bound procedure. Moreover, a clustering-based algorithm has been presented that can provide a good approximation (a good lower bound) to the mentioned binary problem. For this purpose, the numerical results obtained are compared with the results of the full enumeration, heuristic and the branch-and-bound procedure.