826 research outputs found
Fair allocation of indivisible goods under conflict constraints
We consider the fair allocation of indivisible items to several agents and
add a graph theoretical perspective to this classical problem. Thereby we
introduce an incompatibility relation between pairs of items described in terms
of a conflict graph. Every subset of items assigned to one agent has to form an
independent set in this graph. Thus, the allocation of items to the agents
corresponds to a partial coloring of the conflict graph. Every agent has its
own profit valuation for every item. Aiming at a fair allocation, our goal is
the maximization of the lowest total profit of items allocated to any one of
the agents. The resulting optimization problem contains, as special cases, both
{\sc Partition} and {\sc Independent Set}. In our contribution we derive
complexity and algorithmic results depending on the properties of the given
graph. We can show that the problem is strongly NP-hard for bipartite graphs
and their line graphs, and solvable in pseudo-polynomial time for the classes
of chordal graphs, cocomparability graphs, biconvex bipartite graphs, and
graphs of bounded treewidth. Each of the pseudo-polynomial algorithms can also
be turned into a fully polynomial approximation scheme (FPTAS).Comment: A preliminary version containing some of the results presented here
appeared in the proceedings of IWOCA 2020. Version 3 contains an appendix
with a remark about biconvex bipartite graph
Approximation Algorithms for Round-UFP and Round-SAP
We study Round-UFP and Round-SAP, two generalizations of the classical Bin Packing problem that correspond to the unsplittable flow problem on a path (UFP) and the storage allocation problem (SAP), respectively. We are given a path with capacities on its edges and a set of jobs where for each job we are given a demand and a subpath. In Round-UFP, the goal is to find a packing of all jobs into a minimum number of copies (rounds) of the given path such that for each copy, the total demand of jobs on any edge does not exceed the capacity of the respective edge. In Round-SAP, the jobs are considered to be rectangles and the goal is to find a non-overlapping packing of these rectangles into a minimum number of rounds such that all rectangles lie completely below the capacity profile of the edges.
We show that in contrast to Bin Packing, both problems do not admit an asymptotic polynomial-time approximation scheme (APTAS), even when all edge capacities are equal. However, for this setting, we obtain asymptotic (2+?)-approximations for both problems. For the general case, we obtain an O(log log n)-approximation algorithm and an O(log log 1/?)-approximation under (1+?)-resource augmentation for both problems. For the intermediate setting of the no bottleneck assumption (i.e., the maximum job demand is at most the minimum edge capacity), we obtain an absolute 12- and an asymptotic (16+?)-approximation algorithm for Round-UFP and Round-SAP, respectively
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