In this note we study the complexity of the tool switching problem with non-uniform tool sizes. More specifically, we consider the problem where the job sequence is given as part of the input. We show that the resulting tooling problem is strongly NP-complete, even in case of unit loading and unloading costs. However, we show that if the capacity of the tool magazine is also given as part of the input, the problem is solvable in polynomial time.

Classical measures of network connectivity are the number of disjoint paths between a pair of nodes, and the size of a minimum cut. For standard graphs, these measures can be computed efficiently using network flow techniques. However, in the Internet on the level of Autonomous Systems (ASs), referred to as AS-level Internet, routing policies impose restrictions on the paths that traffic can take in the network. These restrictions can be captured by the valley-free path model, which assumes a special directed graph model in which edge types represent relationships between ASs. We consider the adaptation of the classical connectivity measures to the valley-free path model, where it is N P-hard to compute them. Our first main contribution consists of presenting algorithms for the computation of disjoint paths, and minimum cuts, in the valley-free path model. These algorithms are useful for ASs that want to evaluate different options for selecting upstream providers to improve the robustness of their connection to the Internet. Our second main contribution is an experimental evaluation of our algorithms on four types of directed graph models of the AS-level Internet produced by different inference algorithms. Most importantly, the evaluation shows that our algorithms are able to compute optimal solutions to instances of realistic size of the connectivity problems in the valley-free path model in reasonable time. Furthermore, our experimental results provide information about the characteristics of the directed graph models of the AS-level Internet produced by different inference algorithms. It turns out that (i) we can quantify the difference between the undirected AS-level topology and the directed graph models with respect to fundamental connectivity measures, and (ii) the different inference algorithms yield topologies that are similar with respect to connectivity, and are different with respect to the types of paths that exist between pairs of ASs.

I n this paper we discuss a special paiiet-loading probiem, which we encountered at a manufacturing company.In graph-theoreticai terms, the probiem is equivalent to partitioning a permutation graph into bounded-size cliques. We formulate the problem as an integer program, and present two exact algorithms for solving it. The Erst algorithm is a branch-and-price algorithrti based on the integer-programming formulation; the second one is an algorithm based on the concept of bounded clique width. The latter algorithm was motivated by the structure present in the real-world instances. Test results are given, both for real-world instances and randomly generated instances. As far as we are aware, this is the first implementation of an algorithm based on bounded clique width.

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The problem of partitioning a partially ordered set into a minimum number of chains is a well-known problem. In this paper we study a generalization of this problem, where we not only assume that the chains have bounded size, but also that a weight w i is given for each element i in the partial order such that w i ≤ w j if i ≺ j. The problem is then to partition the partial order into a minimum-weight set of chains of bounded size, where the weight of a chain equals the weight of the heaviest element in the chain. We prove that this problem is APX-hard, and we propose and analyze lower bounds for this problem. Based on these lower bounds, we exhibit a 2-approximation algorithm, and show that it is tight. We report computational results for a number of real-world and randomly generated problem instances.