We develop a modular and tractable framework for solving an adaptive distributionally robust linear optimization problem, where we minimize the worst-case expected cost over an ambiguity set of probability distributions. The adaptive distrbutaionally robust optimization framework caters for dynamic decision making, where decisions can adapt to the uncertain outcomes as they unfold in stages. Fo...

background and objectives: a sudden jump in blood demand during natural disasters may have strong negative impact on the performance of blood supply chain. appropriate response to the emergency situations, requires predictive approach to determining the optimal allocation of blood supply chain resources for various disaster scenarios. the present study, thus, presents an optimization model aime...

Object category detection, the task of determining if one or more instances of a category are present in an image with their corresponding locations, is one of the fundamental problems of computer vision. The task is very challenging because of the large variations in imaged object appearance, particularly due to the changes in viewpoint, illumination and intra-class variance. Although successf...

In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying minimum rate requirement for user and reliability user, attempt to maximize user’s throughput whilst ensuring a chance constraint D2D. Then, robust framework is proposed solving highly intractable constraint, where CSI uncertainties are represented a...

this paper discusses the use of box behnken design (bbd) approach to plan the experiments for turning the yield of cvd, thickness and layer number of graphene sheets with an overall objective of optimizing the process to provide higher graphene production volume, fewer layers and thinness structure of graphene. bbd is having the maximum efficiency for an experiment involving four factors such a...

The aim of the paper is to present an approach to optimal control of biotechnological processes, described by fuzzy sets which is based on genetic algorithms (GA). It allows more adequate, flexible and intelligent (human oriented) description of bioprocess dynamic and inclusion of this information in an effective and robust optimization procedure. Related algorithms and software are designed an...

We propose a novel, Bayesian framework for assessing the relative strengths of data-driven ambiguity sets in distributionally robust optimization (DRO). The key idea is to measure the relative size between a candidate ambiguity set and an asymptotically optimal set as the amount of data grows large. This asymptotically optimal set is provably the smallest convex ambiguity set that satisfies a s...

This paper addresses the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be un...