Robust supply chain network design that considers supply resiliency, plays vital role in supply chain risk management in dealing with various operational and disruption risks. This study developed a novel three-stage decision approach to consider two echelons robust and resilient supply chain networks. We present a mixed-integer non-linear programming model with two objective functions. The objectives are maximization of SCN profit and maximization of resiliency, where robustness, agility, leanness, flexibility, and integrity can be defined as the five resiliency criteria. Fuzzy Simultaneous Evaluation of Criteria and Alternatives (FSECA) and Simple Multi-Attribute Rating technique (SMART) have been used to obtain the supplier resiliency and weighted importance of resilience criteria. Then, a robust optimization model is built based on uncertainty parameters considering supplier resiliency. A Non-dominated Sorting Genetic Algorithm (NSGAII) and Multi Objective Particle Swarm optimization (MOPSO) were used to solve the robust model on a large scale. parameters calibrated by the Taguchi method and five metrics of performance evaluation were considered to compare the meta-heuristic algorithms. We demonstrate the proposed NSGAII algorithm over a competing method based on five performance metrics. The research findings reveal the optimal level of robust supply chain networks based on algorithm performance and Taguchi analyses. Moreover, the results indicate that when profit increases, resilience can increase simultaneously.

In supply chain management, supplier performance is evaluated based on several criteria. In this paper, a fuzzy multi-objective mathematical programming model is presented to consider different qualitative and quantitative factors to choose appropriate suppliers and the optimal order quantity allocated to them. The proposed study uses analytical hierarchy process to rank different suppliers and a fuzzy multi-objective mathematical programming is presented to choose the best suppliers. The study uses NSGAII to solve the resulted problem and the model is analysed using some sample results under various circumstances. The study considers different Pareto solution set obtained by TOPSIS ranking algorithm, and eventually determines the best possible solutions.

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.