This study proposes a new scenario planning approach which consists of two main stages: evaluating scenarios under multiple criteria and selecting a manageable number of representative scenarios covering a wide range of future developments. In the evaluation stage, the interaction between criteria has been considered, which offers a significant contribution both to the scenario literature and practice. In the selection stage, a mathematical programming model has been developed to ensure the selection of distinct scenarios with high evaluation values. The approach is applied to an urban mobility system in a metropolitan area. The selected scenarios provide valuable insights into the future of urban mobility, serving as a basis for identifying strategies. The proposed approach does not offer a solution only for scenario planning problems, it can be effectively applied to similar problems in different areas.

Closed-loop supply chain management is an effective and efficient solution for a set of activities to retrieve a product from a customer and improve its value or to dispose it. Today, designing and planning a closed-loop chain is an inevitable but difficult task. In this research, a scenario-based modeling approach is presented by considering both forward and reverse flows as a closed-loop supply chains in steel industry. The proposed study also develops a multi-product and multi-period model based on a mixed integer linear programming (MILP) approach for profit maximization. The study also considers uncertainty in the amount of raw material, processing, storage and distribution of several products flow. Uncertainty is associated with the quantity and quality of the products in the reverse flow, which are directly affected by customers and sorting centers, respectively. Finally, the model is deployed in Steel industry with real data. The results show that by increasing the quality level of returned products the need for raw materials is reduced and the total profit of the supply chain is increased.

One of the primary assumptions in many project portfolio selection is the availability of all parameters. However, in real-world cases, many parameters are under uncertainty and the exact values are unknown in advance. This paper presents a scenario based mathematical model for project portfolio selection when parameters are under uncertainty. The problem considers two objective functions where the first one maximizes the net present value while the second objective function is the minimization of the positive deviations from the allocation of resources. The second objective function is looking for project resource leveling. The resulted model is formulated as mixed integer programming and the problem is analyzed under different conditions.