Sometimes a complex stochastic decision system undertakes multiple tasks called events, and the decision-maker wishes to maximize the chance functions which are defined as the probabilities of satisfying these events. Originally introduced by Liu and Iwamura [6], dependent-chance programming is aimed at maximizing some chance functions of events in an uncertain environment. In this work we show...

The sequence mining problem consists in finding frequent sequential patterns in a database of timestamped events. Some application domains require limiting the maximum temporal gap between events in the input sequences. However concentration on such constraint is critical for most sequence mining algorithms. In this paper we describe CCSM (Cache-based Con-strained Sequence Miner), a new level-w...

If there are grey variables in the constraint conditions in uncertain programming, the programming is called grey chance constrained programming. In this paper we provide the concept of grey chance constrained programming. When the whitenization weight functions of grey variables are known and the distribution functions are constructed, we studied the certainty equivalence solution of grey chan...

In this paper, we deal with a hydraulic reservoir optimization problem with uncertainty on inflows in a joint chance constrained programming setting. In particular, we will consider inflows with a persistency effect, following a causal time series model, and examine the impact of the ”Gaussian” assumption for such inflows. We present an iterative algorithm for solving similarly structured joint...