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Given a graph with “uncertainty intervals” on the edges, we want to identify a minimum spanning tree by querying some edges for their exact edge weights which lie in the given uncertainty intervals. Our objective is to minimize the number of edge queries. It is known that there is a deterministic algorithm with best possible competitive ratio 2 [T. Erlebach, et al., in Proceedings of STACS, Schloss Dagstuhl, Dagstuhl, Germany, 2008, pp. 277--288]. Our main result is a randomized algorithm with expected competitive ratio $1+1/\sqrt{2}\approx 1.707$, solving the long-standing open problem of whether an expected competitive ratio strictly less than 2 can be achieved [T. Erlebach and M. Hoffmann, Bull. Eur. Assoc. Theor. Comput. Sci. EATCS, 116 (2015)]. We also present novel results for various extensions, including arbitrary matroids and more general querying models.

The Ring Loading Problem is an optimal routing problem arising in the planning of optical communication networks which use bidirectional SONET rings. In mathematical terms, it is an unsplittable multicommodity flow problem on undirected ring networks. We prove that any split routing solution to the Ring Loading Problem can be turned into an unsplittable solution while increasing the load on any edge of the ring by no more than $+\frac{19}{14} D$, where $D$ is the maximum demand value. This improves upon a classical result of Schrijver, Seymour, and Winkler (1998), who obtained a slightly larger bound of $+\frac32 D$. We also present an improved lower bound of $\frac{11}{10} D$ (previously $\frac{101}{100} D$) on the best possible bound and disprove a famous long-standing conjecture of Schrijver, Seymour, and Winker in this context.

We consider online versions of the minimum spanning tree (MST) problem and the traveling salesman problem (TSP) where recourse is allowed. The nodes of an unknown graph with metric edge cost appear one by one and must be connected in such a way that the resulting tree or tour has low cost. In the standard online setting, with irrevocable decisions, no algorithm can guarantee a constant-competitive ratio. In our model we allow recourse actions by giving a limited budget of edge rearrangements per iteration. It has been an open question for more than 20 years whether an online algorithm equipped with a constant (amortized) budget can guarantee constant-approximate solutions. As our main result, we answer this question affirmatively in an amortized setting. We introduce an algorithm that maintains a nearly optimal tree when given a constant amortized budget. Unlike in classical TSP variants, the standard double-tree and shortcutting approach does not give constant guarantees in the online setting. We propose...

Flows over time (also called dynamic flows) generalize standard network flows by introducing an element of time. They naturally model problems where travel and transmission are not instantaneous. Traditionally, flows over time are solved in time-expanded networks that contain one copy of the original network for each discrete time step. While this method makes available the whole algorithmic toolbox developed for static flows, its main and often fatal drawback is the enormous size of the time-expanded network. We present several approaches for coping with this difficulty. First, inspired by the work of Ford and Fulkerson on maximal $s$-$t$-flows over time (or “maximal dynamic $s$-$t$-flows”), we show that static length-bounded flows lead to provably good multicommodity flows over time. Second, we investigate “condensed” time-expanded networks which rely on a rougher discretization of time. We prove that a solution of arbitrary precision can be computed in polynomial time through an appropriate discretization leading to a condensed time-expanded network of polynomial size. In particular, our approach yields fully polynomial-time approximation schemes for the NP-hard quickest min-cost and multicommodity flow problems. For single commodity problems, we show that storage of flow at intermediate nodes is unnecessary, and our approximation schemes do not use any.

More than forty years ago, Ford and Fulkerson studied maximum s-t-flows over time (also called "dynamic" flows) in networks with fixed transit times on the arcs and a fixed time horizon. Here, flow on arcs may change over time and transit times specify the amount of time it takes for flow to travel through a particular arc. Ford and Fulkerson proved that there always exists an optimal solution which sends flow on certain s-t-paths at a constant rate as long as there is enough time left for the flow along a path to arrive at the sink; a flow over time featuring this simple structure is called "temporally repeated." Although this result does not hold for the more general and also more realistic setting where transit times depend on the current flow situation, we show that there always exists a provably good temporally repeated solution. Moreover, such a solution can be determined very efficiently by only one minimum convex cost flow computation. Our results rest upon a new model of flow-dependent transit times. It is based on two assumption on the pace of flow on a particular arc. First, the pace of flow on an arc is assumed to be uniform for all flow units on an arc for each point in time. Second, this uniform pace is for each moment determined by the actual amount of flow on this arc. Finally, we show that the resulting flow-over-time problem is strongly NP-hard and cannot be approximated with arbitrary precision in polynomial time, unless P=NP.

In many scheduling applications it is required that the processing of some job be postponed until some other job, which can be chosen from a pregiven set of alternatives, has been completed. The traditional concept of precedence constraints fails to model such restrictions. Therefore, the concept has been generalized to so-called AND/OR precedence constraints which can cope with this kind of requirement. In the context of traditional precedence constraints, feasibility, transitivity, and the computation of earliest start times for jobs are fundamental, well-studied problems. The purpose of this paper is to provide efficient algorithms for these tasks for the more general model of AND/OR precedence constraints. We show that feasibility as well as many questions related to transitivity can be solved by applying essentially the same linear-time algorithm. In order to compute earliest start times we propose two polynomial-time algorithms to cope with different classes of time distances between jobs.

We consider the scheduling problem of minimizing the average weighted completion time of n jobs with release dates on a single machine. We first study two linear programming relaxations of the problem, one based on a time-indexed formulation, the other on a completion-time formulation. We show their equivalence by proving that a O(n log n) greedy algorithm leads to optimal solutions to both relaxations. The proof relies on the notion of mean busy times of jobs, a concept which enhances our understanding of these LP relaxations. Based on the greedy solution, we describe two simple randomized approximation algorithms, which are guaranteed to deliver feasible schedules with expected objective function value within factors of 1.7451 and 1.6853, respectively, of the optimum. They are based on the concept of common and independent $\alpha$-points, respectively. The analysis implies in particular that the worst-case relative error of the LP relaxations is at most 1.6853, and we provide instances showing that it is at least $e/(e-1) \approx 1.5819$. Both algorithms may be derandomized; their deterministic versions run in O(n2) time. The randomized algorithms also apply to the on-line setting, in which jobs arrive dynamically over time and one must decide which job to process without knowledge of jobs that will be released afterwards.

We present a new class of randomized approximation algorithms for unrelated parallel machine scheduling problems with the average weighted completion time objective. The key idea is to assign jobs randomly to machines with probabilities derived from an optimal solution to a linear programming (LP) relaxation in time-indexed variables. Our main results are a $(2+\varepsilon)$-approximation algorithm for the model with individual job release dates and a $(3/2+\varepsilon)$-approximation algorithm if all jobs are released simultaneously. We obtain corresponding bounds on the quality of the LP relaxation. It is an interesting implication for identical parallel machine scheduling that jobs are randomly assigned to machines, in which each machine is equally likely. In addition, in this case the algorithm has running time O(n log n) and performance guarantee 2. Moreover, the approximation result for identical parallel machine scheduling applies to the on-line setting in which jobs arrive over time as well, with no difference in performance guarantee.