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Delivery by energy-constrained agents

Agents with energy budgets, placed in a weighted graph, must carry a message from a source node to a target node, handing it over when they meet: when can a planner find a schedule quickly, and by how much must the budgets grow?

Problem statement and measuresexpand

\(k\) agents start at nodes of a connected undirected graph \(G\) with \(n\) nodes and \(m\) edges; every edge \(e\) has a positive weight \(\mathrm{wt}(e)\), its length. Agent \(i\) has a budget \(B_i\): it can travel a total distance of at most \(B_i\), also along parts of edges. A message lies at a source node \(v_s\) and must reach a target node \(v_t\). An agent can pick the message up where it is, carry it, and drop it anywhere (at a node or inside an edge), where another agent can pick it up; agents need not return to their start nodes. A planner that knows \(G\), the start nodes and the budgets computes a schedule: which agents move, and where each one picks up and drops the message. The task is to decide whether a schedule exists and to find one. Time is the running time of the planner. The budget factor \(\gamma\) is that of a planner that runs in polynomial time and either correctly reports that no schedule exists or finds one in which every agent may travel \(\gamma B_i\) (resource augmentation); with equal budgets \(B_i = B\), it is the ratio between the budget the planner uses and the smallest budget that admits a schedule.

Measures: Time, running time of the planner; Budget factor, factor by which a polynomial-time planner enlarges the budgets (resource augmentation), or its approximation ratio for the smallest equal budget.

Paths, equal budgets

verified
Time: tightBudget factor: tight

With equal budgets, a greedy schedule in which each agent pushes the message as far as it can decides delivery on a path in linear time.

Assumed (added on the last edge are outlined)

+Graph dynamicsstatic graph+Edge directionundirected+Graph classpaths+Budgetsequal budgets

Not assumed

Formal definitions of this setting
Graph dynamics: static graph
\(G_t=G\) for all \(t\): the edge set never changes.
Edge direction: undirected
\(G=(V,E)\) with \(E\subseteq\binom V2\); every edge can be traversed in both directions.
Graph class: path
\(G\) is a path, equivalently a segment of the line with the agents, \(v_s\) and \(v_t\) on it.
Budgets: equal budgets
All agents have the same budget \(B\).

What is known

Time · running time of the planner
tight
Linear in the number of agents.
lower boundnone known
introductory—
current best
\(O(k)\)
agents sorted by position
Budget factor · factor by which a polynomial-time planner enlarges the budgets (resource augmentation), or its approximation ratio for the smallest equal budget
tight
The planner is exact (factor 1).
lower boundnone known
introductory—
current best—

Results, in order of publication

Anaya et al. 2016 current best
Time \(O(k)\)
agents sorted by position

Introductory and current best (Lemma 1 and Remark 1): if any schedule exists, so does a regular one in which the agents, in order of position, each push the message as far as their budget allows; computing it agent by agent decides the task, which the paper calls carry. It is a building block of convergecast and broadcast, for which the paper computes the smallest equal budget on a path in linear time (Theorems 1 and 2).

Open questions

  1. Is delivery with equal budgets between two nodes of a tree NP-hard or polynomial? Posed in Anaya et al. 2016 and again in Chalopin et al. 2013; Chalopin et al. 2021 states that on trees delivery is known to be weakly NP-hard, without a reference, in a paper about equal budgets.

Notes

Anaya, Chalopin, Czyzowicz, Labourel, Pelc and Vaxès also show that the smallest equal budget for convergecast and for broadcast is NP-hard to compute on trees, approximate it within 2 and 4 on arbitrary graphs, and give distributed algorithms on trees that need 2 and 4 times the budget of a planner, 2 being best possible already on paths Anaya et al. 2016. Checked against the full text (arXiv v1). Chalopin, Das, Mihalák, Penna and Widmayer state the same greedy rule: start with the agent closest to \(v_s\), never overtake an unused agent, and use the closest unused agent next Chalopin et al. 2013.

What carries over from neighbouring settings

Read off the order of assumptions, not checked case by case: results may carry extra assumptions (shown on their pages), and asynchronous time is measured in epochs.

Algorithms from weaker settings work here

Paths
Time \(O\big(\mathrm{dist}(v_s,v_t)^2\, k^{1+4\log \mathrm{dist}(v_s,v_t)}\big)\)

Lower bounds from stronger settings hold here

No catalogued stronger setting.

Know a better result for this setting, or found a mistake? Suggest a result · Report an error. Accepted contributions are acknowledged.