Robust optimization (RO) tackles data uncertainty by optimizing for the worst-case scenario
of an uncertain parameter and, in its basic form, is sometimes criticized for producing overly …

We study uncertain linear complementarity problems (LCPs), that is, problems in which the
LCP vector q or the LCP matrix M may contain uncertain parameters. To this end, we use the …

Data has been the topic of research in the last few decades. However, data collection
always comes with errors and uncertainties such that if data analysis is based on inaccurate …

Weather-sensitive resources are the main source of uncertainties in power systems.
However, the unpredictable climate change further introduces ambiguity into the system …

We define different concepts of robustness for combinatorial optimization problems and
introduce models for scenario sets in this chapter. From an axiomatic perspective, there is …

Since the early days of operational research, mathematical optimization models have been
used for the efficient planning of future processes. Inheritedly, uncertainty in the input …

In view of the fact that the market demand for remanufactured products does not obey the
probability distribution and does not fluctuate within a certain interval, this paper uses the …