We consider nonlinear multistage stochastic optimization problems in the spaces of integrable functions. allow for dynamics and general objective functionals, including dynamic risk measures. study causal operators describing system derive Clarke subdifferential a penalty function involving such operators. Then we introduce concept subregular recourse establish subregularity resulting systems t...

Berth allocation problems are amongst the most important occurring in port terminals, and they greatly affected by several unpredictable events. As a result, study of these under uncertainty has been target more researchers. Following this research line, we consider berth problem uncertain handling times. A distributionally robust two-stage model is presented to minimize worst-case expected sum...

Optimizing multi-product assemble-to-order (ATO) inventory systems is a long-standing difficult problem. We consider ATO systems with identical component lead times and a general ‘bill of materials’. We use a related two-stage stochastic program (SP) to set a lower bound on the average inventory cost and develop inventory control policies for the dynamic ATO system using this SP. We apply the f...

This research demonstrates the applicability of parallel computing to Air Mobility Command’s aircraft allocation problem. The Tanker Airlift Control Center (TACC) is responsible for worldwide tasking of DoD airlift in a manner that is robust against random future events. Our work seeks to improve this process using a parallel stochastic mathematical program that allocates aircraft to various mi...

Cloud computing has become a new era technology that has huge potentials in enterprises and markets. By using this technology, Cloud user can access applications and associated data from anywhere. It has many application for example, Companies are able to rent recourses from cloud for storage and other computational purposes so that infrastructure cost can be reduced significantly. For managing...

We review convex approximations for stochastic programs with simple integer recourse. Both for the case of discrete and continuous random variables such approximations are discussed, and representations as continuous simple recourse problems are given.

Endogenous, i.e. decision-dependent, uncertainty has received increased interest in the stochastic programming community. In robust optimization context, however, it rarely been considered. This work addresses multistage mixed-integer with decision-dependent sets. The proposed framework allows us to consider both continuous and integer recourse, including recourse decisions that affect set. We ...

Mathematical programming has been applied to various problems. For many actual problems, the assumption that the parameters involved are deterministic known data is often unjustified. In such cases, these data contain uncertainty and are thus represented as random variables, since they represent information about the future. Decision-making under uncertainty involves potential risk. Stochastic ...

We present a unified and tractable framework for distributionally robust optimization that could encompass a variety of statistical information including, among others things, constraints on expectation, conditional expectation, and disjoint confidence sets with uncertain probabilities defined by φ-divergence. In particular, we also show that the Wasserstein-based ambiguity set has an equivalen...