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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

verified
Time: polynomial gapBudget factor: unknown

On a path, delivery with different budgets is weakly NP-complete; a quasi-pseudo-polynomial algorithm decides it.

Assumed (added on the last edge are outlined)

+Graph dynamicsstatic graph+Edge directionundirected+Graph classpaths

Not assumed

Budgetsbudgets may differ
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: any budgets
Each agent \(i\) has its own budget \(B_i\).

What is known

Time · running time of the planner
polynomial gap
Weakly NP-hard against a quasi-pseudo-polynomial algorithm; whether a pseudo-polynomial algorithm exists is open Chalopin et al. 2014.
lower bound
\(\text{weakly NP-hard}\) Chalopin et al. 2014
bottleneckcombinatorial hardness
holds when
  • NP-complete even when all positions and budgets are integers (Theorem 3), by a reduction from a weighted variant of 4-partition (Theorem 2)
  • budgets differ: with equal budgets a path is easy Anaya et al. 2016
introductory—
current best
\(O\big(\mathrm{dist}(v_s,v_t)^2\, k^{1+4\log \mathrm{dist}(v_s,v_t)}\big)\)
integer positions and budgets
Budget factor · factor by which a polynomial-time planner enlarges the budgets (resource augmentation), or its approximation ratio for the smallest equal budget
unknown
The factor \(\min\{3,\ 1+\max_{i,j}B_i/B_j\}\) of arbitrary graphs applies, as Chalopin et al. 2013 notes; no lower bound is known, and finding better planners is open Chalopin et al. 2014.
lower boundnone known
introductory—
current best—

Results, in order of publication

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

Introductory and current best (Theorem 1): a dynamic program over normalised schedules; the paper also states the bound \(O(\mathrm{dist}(v_s,v_t)^2\, k^{1+4\log(B_{\max}+1)})\), with \(B_{\max}\) the largest budget (their \(n\) is \(k\) here, and their \(\Delta\) is \(\mathrm{dist}(v_s,v_t)\)).

Open questions

  1. Is there a pseudo-polynomial algorithm for delivery on a path? Posed in Chalopin et al. 2014.

  2. Find good resource-augmented planners for delivery on a path. Posed in Chalopin et al. 2014.

Notes

Chalopin, Jacob, Mihalák and Widmayer answered a question of Anaya et al. and note that the hardness surprised everyone they had asked Chalopin et al. 2014. No agent needs to carry the message twice, so a schedule is an order of agents. Returning delivery on a path (and on trees) takes \(O(n + k\log k)\) time (Theorem 1 of Bärtschi et al. 2020); with energy exchange delivery on a path takes linear time (Theorem 3 of Czyzowicz et al. 2016). Checked against the authors' copy of Chalopin et al. 2014.

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

Arbitrary graphs
Time \(\text{poly}(n, m)\)Budget factor \(\min\{3,\ 1+\max_{i,j} B_i/B_j\}\)

Lower bounds from stronger settings hold here

Branches from here

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