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Collective Fast Delivery by Energy-Efficient Agents

Authors Andreas Bärtschi, Daniel Graf, Matús Mihalák



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Author Details

Andreas Bärtschi
  • Department of Computer Science, ETH Zürich, Switzerland
Daniel Graf
  • Department of Computer Science, ETH Zürich, Switzerland
Matús Mihalák
  • Department of Data Science and Knowledge Engineering, Maastricht University, Netherlands

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Andreas Bärtschi, Daniel Graf, and Matús Mihalák. Collective Fast Delivery by Energy-Efficient Agents. In 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 117, pp. 56:1-56:16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.MFCS.2018.56

Abstract

We consider k mobile agents initially located at distinct nodes of an undirected graph (on n nodes, with edge lengths). The agents have to deliver a single item from a given source node s to a given target node t. The agents can move along the edges of the graph, starting at time 0, with respect to the following: Each agent i has a weight omega_i that defines the rate of energy consumption while travelling a distance in the graph, and a velocity upsilon_i with which it can move. We are interested in schedules (operating the k agents) that result in a small delivery time T (time when the item arrives at t), and small total energy consumption E. Concretely, we ask for a schedule that: either (i) Minimizes T, (ii) Minimizes lexicographically (T,E) (prioritizing fast delivery), or (iii) Minimizes epsilon * T + (1-epsilon)* E, for a given epsilon in (0,1). We show that (i) is solvable in polynomial time, and show that (ii) is polynomial-time solvable for uniform velocities and solvable in time O(n+k log k) for arbitrary velocities on paths, but in general is NP-hard even on planar graphs. As a corollary of our hardness result, (iii) is NP-hard, too. We show that there is a 2-approximation algorithm for (iii) using a single agent.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • delivery
  • mobile agents
  • time/energy optimization
  • complexity
  • algorithms

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