Since the emergence of power market, the target of power generating utilities has mainly switched from cost minimization to revenue maximization. They dispatch their power energy generation units in the uncertain environment of power market. As a result, multi-stage stochastic programming has been applied widely by many power generating agents as a suitable tool for dealing with self-scheduling...

this paper discusses the problem of allocation of constrained renewable resource to splittable activities of a single project. if the activities of stochastic projects can be split, these projects may be completed in shorter time when the available resource is constrained. it is assumed that the resource amount required to accom-plish each activity is a discrete quantity and deterministic. the ...

The blood supply chain as a part of healthcare systems play a substantial role in health improvement within societies, and blood supply for daily needs and especially in disasters is the challenges requiring more attention. This paper presents a fuzzy-stochastic mixed integer linear programming model to design blood supply chain network for disaster relief. To deal with the uncertainty in model...

In the present master thesis, three different algorithms, for optimizing a medium-term hydro-power planning problem with consideration of stochastic values, are presented and compared. The uncertainty arises due to varying water inflows and uncertain evolution of prices. The solution methods yield robust control strategies, which indicate for every time step, e.g. every month in one year, the o...

In applications of geometric programming, some coefficients and/or exponents may not be precisely known. Stochastic geometric programming can be used to deal with such situations. In this paper, we shall indicate which stochastic programming approaches and which structural and distributional assumptions do not destroy the favorable structure of geometric programs. The already recognized possibi...

This paper proposes a general framework for integrating a constraint logic programming system with a stochastic constraint solver to solve constraint satisfaction problems eeciently. Stochastic solvers can solve hard constraint satisfaction problems very eeciently, and constraint logic programming allows heuristics and problem breakdown to be encoded in the same language as the constraints. Hen...

While deterministic optimization enjoys an almost universally accepted canonical form, stochastic optimization is a jungle of competing notational systems and algorithmic strategies. This is especially problematic in the context of sequential (multistage) stochastic optimization problems, which is the focus of our presentation. In this article, we place a variety of competing strategies into a ...

This paper proposes a number of models for integrating stochastic constraint solvers into constraint logic programming systems in order to solve constraint satisfaction problems eeciently. Stochastic solvers can solve hard constraint satisfaction problems very eeciently, and constraint logic programming allows heuristics and problem breakdown to be encoded in the same language as the constraint...

This paper proposes a number of models for integrating finitedomain stochastic constraint solvers into constraint logic programming systems to solve constraint-satisfaction problems efficiently. Stochastic solvers can solve hard constraint-satisfaction problems very efficiently, and constraint logic programming allows heuristics and problem breakdown to be encoded in the same language as the co...