- Published 2013 in ACM Trans. Algorithms

In the <i>Movement Repairmen (MR)</i> problem, we are given a metric space (<i>V</i>, <i>d</i>) along with a set <i>R</i> of <i>k</i> repairmen <i>r</i><sub>1</sub>, <i>r</i><sub>2</sub>, …, <i>r<sub>k</sub></i> with their start depots <i>s</i><sub>1</sub>, <i>s</i><sub>2</sub>, …, <i>s<sub>k</sub></i> ∈ <i>V</i> and speeds <i>v</i><sub>1</sub>, <i>v</i><sub>2</sub>, …, <i>v<sub>k</sub></i> ⩾ 0, respectively, and a set <i>C</i> of <i>m</i> clients <i>c</i><sub>1</sub>, <i>c</i><sub>2</sub>, …, <i>c<sub>m</sub></i> having start locations <i>s</i>′<sub>1</sub>, <i>s</i>′<sub>2</sub>, …, <i>s</i>′<sub><i>m</i></sub> ∈ <i>V</i> and speeds <i>v</i>′<sub>1</sub>, <i>v</i>′<sub>2</sub>, …, <i>v</i>′<sub><i>m</i></sub> ⩾ 0, respectively. If <i>t</i> is the earliest time a client <i>c<sub>j</sub></i> is collocated with any repairman (say, <i>r<sub>i</sub></i>) at a node <i>u</i>, we say that the client is served by <i>r<sub>i</sub></i> at <i>u</i> and that its latency is <i>t</i>. The objective in the (S<scp>um</scp>-MR) problem is to plan the movements for all repairmen and clients to minimize the sum (average) of the clients’ latencies. The motivation for this problem comes, for example, from Amazon Locker Delivery [Amazon 2010] and USPS gopost [Service 2010]. We give the first <i>O</i>(log<i>n</i>)-approximation algorithm for the S<scp>um</scp>-MR problem. In order to approximate S<scp>um</scp>-MR, we formulate an LP for the problem and bound its integrality gap. Our LP has exponentially many variables; therefore, we need a separation oracle for the dual LP. This separation oracle is an instance of the Neighborhood Prize Collecting Steiner Tree (NPCST) problem in which we want to find a tree with weight at most <i>L</i> collecting the maximum profit from the clients by visiting at least one node from their neighborhoods. The NPCST problem, even with the possibility to violate both the tree weight and neighborhood radii, is still very hard to approximate. We deal with this difficulty by using LP with geometrically increasing segments of the timeline, and by giving a <i>tricriteria</i> approximation for the problem. The rounding needs a relatively involved analysis. We give a constant approximation algorithm for S<scp>um</scp>-MR in Euclidean Space where the speed of the clients differs by a constant factor. We also give a constant approximation for the makespan variant.

@inproceedings{Hajiaghayi2013ApproximationAF,
title={Approximation Algorithms for Movement Repairmen},
author={Mohammad Taghi Hajiaghayi and Rohit Khandekar and M. Reza Khani and Guy Kortsarz},
booktitle={ACM Trans. Algorithms},
year={2013}
}