On Index Policies for Stochastic Minsum Scheduling

Minimizing the sum of completion times when scheduling jobs on identical parallel machines is a fundamental scheduling problem. Unlike the well-understood deterministic variant, it is a major open problem how to handle stochastic processing times. We show for the prominent class of index policies that no such policy can achieve a distribution-independent approximation factor. This strong lower bound holds even for simple instances with deterministic and two-point distributed jobs. For such instances, we give an -approximative list scheduling policy

Degree-constrained orientations of embedded graphs

Abstract. We investigate the problem of orienting the edges of an em-bedded graph in such a way that the in-degrees of both the nodes and faces meet given values. We show that the number of feasible solutions is bounded by 22g, where g is the genus of the embedding, and all so-lutions can be determined within time O(22g|E|2 + |E|3). In particular, for planar graphs the solution is unique if it exists, and in general the problem of finding a feasible orientation is fixed-parameter tractable in g. In sharp contrast to these results, we show that the problem becomes NP-complete even for a fixed genus if only upper and lower bounds on the in-degrees are specified instead of exact values.

Approximation algorithms for capacitated location routing

An approximation algorithm for an optimization problem runs in polynomial time for all instances and is guaranteed to deliver solutions with bounded optimality gap. We derive such algorithms for different variants of capacitated location routing, an important generalization of vehicle routing where the cost of opening the depots from which vehicles operate is taken into account. Our results originate from combining algorithms and lower bounds for different relaxations of the original problem; along with location routing we also obtain approximation algorithms for multidepot capacitated vehicle routing by this framework. Moreover, we extend our results to further generalizations of both problems, including a prize-collecting variant, a group version, and a variant where cross-docking is allowed. We finally present a computational study of our approximation algorithm for capacitated location routing on benchmark instances and large-scale randomly generated instances. Our study reveals that the quality of the computed solutions is much closer to optimality than the provable approximation factor