BT2: Mathematical Optimization in the Presence of Uncertainties

The official Call for Papers for this session is available here: Call for Papers BT2

Description

Decisions in the context of forestry and agriculture often have to be made without the knowledge of all relevant data. For example, one might face the risk of a pest infestation, the weather conditions in the following year can only be assumed, future market prices are unknown at the time of decision making, or the quality and quantity of available products might be influenced by uncertain factors.
In order to model and hedge against these uncertainties in mathematical optimization problems, the fields of stochastic, robust and online optimization have evolved over the last years.
Stochastic optimization assumes a probability distribution of the unknown parameters. Its goal is to find some policy that is feasible for (almost) all possible data instances and to optimize the expectation of some function of the decisions and the random variable. In contrast, robust optimization hedges against a worst case of the objective function in some sense and therefore requires no probability distribution of the unknown parameters. Finally, in online optimization a sequence of decisions has to be made without secure information about the future: Relevant input data arrives bit by bit and with each new piece of input a decision has to be made.
In order to establish optimization systems in areas faced with uncertainties, such as the sector of renewable resources, the mathematical community is challenged to contribute to further progress and developments in the fields mentioned above. We invite contributions to the field of mathematical optimization with applications to resource efficiency, in particular concepts, theoretical results and solution approaches for optimization in the presence of uncertainty.


Suggested Topics

Topics of interest include but are not limited to:


Applications of operations research in the context of

• renewable resources and
• resource constraints,

in particular when dealing with uncertain future knowledge. Techniques include

• robust optimization,
• online optimization,
• stochastic optimization,
• multi-objective optimization,
• algorithmic game theory and (online) mechanism design, and
• discrete optimization, especially network flows.