Scenario decomposition algorithms for stochastic programs compute bounds by dualizing all nonanticipativity constraints and solving individual scenario problems independently. We develop an approach that improves upon these bounds by re-enforcing a carefully chosen subset of nonanticipativity constraints, effectively placing scenarios into ‘groups’. Specifically, we formulate an optimization pr...

In conservation planning, the data related to size, growth and diffusion of populations is sparse, hard to collect and unreliable at best. If and when the data is readily available, it is not of sufficient quantity to construct a probability distribution. In such a scenario, applying deterministic or stochastic approaches to the problems in conservation planning either ignores the uncertainty c...

Scenario planning is a method for learning about the future by understanding the nature and impact of the most uncertain and important driving forces affecting that future. However, most scenarios, being mostly stories, lack validation, dynamism and fail to acknowledge all relations between actors, activities and resources. In this paper, we propose an agent-based model for scenario development...

We address the problem of body pose tracking in a scenario of multiple camera setup with the aim of recovering body motion robustly and accurately. The tracking is performed on three-dimensional (3D) space using 3D data, including colored volume and 3D optical flow, which are reconstructed at each time step. We introduce strategies to compute multiple camera-based 3D optical flow and have attai...

In this paper, the optimizations problems to seek robust solutions under uncertainty are considered. The light robust approach is one of the strong and new methods to achieve robust solutions under conditions of uncertainty. In this paper, we tried to improve the quality of the solutions obtained from the Light Robust method by introducing a revised approach. Considering the problem concerned, ...

In order to address major changes in the operational environment, companies can (i) define scenarios that characterize different alternatives for this environment, (ii) assign probabilities to these scenarios, (iii) evaluate the performance of strategic actions across the scenarios, and (iv) choose those actions that are expected to perform best. In this paper, we develop a portfolio model to s...

This paper presents ongoing research in the field of application and further development of scenario-methods in strategic R&D processes. The core purpose of this study is to investigate how scientific and technological fields of innovation can be supported through participatory work with normative-narrative scenarios. The normative-narrative scenario approach focuses on a participatory communic...

We study two-stage robust variants of combinatorial optimization problems like Steiner tree, Steiner forest, and uncapacitated facility location. The robust optimization problems, previously studied by Dhamdhere et al. [9], Golovin et al. [15], and Feige et al. [13], are two-stage planning problems in which the requirements are revealed after some decisions are taken in stage one. One has to th...