Parameterized Complexity of Critical Node Cuts

We consider the following natural graph cut problem called Critical Node Cut (CNC): Given a graph $G$ on $n$ vertices, and two positive integers $k$ and $x$, determine whether $G$ has a set of $k$ vertices whose removal leaves $G$ with at most $x$ connected pairs of vertices. We analyze this problem in the framework of parameterized complexity. That is, we are interested in whether or not this problem is solvable in $f(\kappa) \cdot n^{O(1)}$ time (i.e., whether or not it is fixed-parameter tractable), for various natural parameters $\kappa$. We consider four such parameters: - The size $k$ of the required cut. - The upper bound $x$ on the number of remaining connected pairs. - The lower bound $y$ on the number of connected pairs to be removed. - The treewidth $w$ of $G$. We determine whether or not CNC is fixed-parameter tractable for each of these parameters. We determine this also for all possible aggregations of these four parameters, apart from $w+k$. Moreover, we also determine whether or not CNC admits a polynomial kernel for all these parameterizations. That is, whether or not there is an algorithm that reduces each instance of CNC in polynomial time to an equivalent instance of size $\kappa^{O(1)}$, where $\kappa$ is the given parameter

A bicriteria approach to minimize number of tardy jobs and resource consumption in scheduling a single machine

We extend the classical scheduling problem of minimizing the number of tardy jobs in a single-machine to the case where the job processing times are controllable by allocating a continuous and non-renewable resource to the processing operations. Our aim is to construct an efficient trade-off curve between the number of tardy jobs and total resource consumption using a bicriteria approach. Most of the research on scheduling with controllable resource-dependent processing times is done either to a linear resource consumption function or to a specific type of convex resource consumption function. We, in contrast, analyze the problem for a more general type of convex decreasing resource consumption function, which guarantees a very robust analysis that can be applied to a wide range of problems. We present four different variations of the problem and prove them to be -hard. We then present a polynomial time algorithm to solve an important special case of the problem and also suggest and compare the performances of three different heuristic algorithms.Deterministic scheduling Controllable processing times Resource allocation Complexity