This paper investigates the configuration of a closed-loop supply chain (CLSC) network, which involves suppliers, a single manufacturer, customers, collection/disassembly centers, disposal centers, a single recovery center and subcontractors. Due to the importance of green issues in the proposed supply chain, the efforts mainly focus on the suitable parts that form more durable and more sustainable products, reduce costs and help the environmental protection. To do so, an integrated framework is introduced which consists of a multi-attribute decision making (MADM) method and a multi-objective mathematical model that determines the material flow in the dynamic consecutive time segments of the network. This flow consists of parts and products which pass through the extant or potential facilities so that ultimately organize a CLSC network. Thereafter, a numerical example is examined by the augmented ε-constraint method and the results are demonstrated in a specific time horizon as an efficient solution. The results show the effects of time horizon in finding different solutions for each time segment. Furthermore, it shows how subcontractors can facilitate the flow of materials.

Evaluating and prioritizing appropriate renewable energy sources is inevitably a complex decision process. Various information and conflicting attributes should be taken into account. For this purpose, multi-attribute decision making (MADM) methods can assist managers or decision makers in formulating renewable energy sources priorities by considering important objective and attributes. In this paper, a new extension of compromise ranking method with interval numbers is presented for the prioritization of renewable energy sources that is based on the performance similarity of alternatives to ideal solutions. To demonstrate the applicability of the proposed decision method, an application example is provided and the computational results are analyzed. Results illustrate that the presented method is viable in solving the evaluation and prioritization problem of renewable energy sources.

A crucial issue in humanitarian relief operations is vehicle routing because there are many difficulties in roads that people face when a disaster is occurred. In such circumstances, selecting more reliable routes could help rescue team act more efficacious. Therefore, we propose a mathematical model that maximizes the reliability of selected routes in disaster relief routing process using a multiple attribute decision making (MADM) technique. Furthermore, the proposed model minimizes the total cost that imposed to system. For this reason, the model is a multi-objective vehicle routing problem (VRP). The problem considered here is NP-hard and its objective is to find near optimal solutions, rather than optimal ones, using the non-dominated sorting genetic algorithm.