In Brief
Flexible task reassignment with localized path repairs finds better team plans than fixing which robot does each job — improving 89.1% of benchmark instances and yielding up to a median 12.2% cut in travel cost for the best variant.
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Key Findings
A two-level search that repeatedly reassigns groups of related tasks and repairs the affected paths produces large, reliable gains over fixed assignments. This aligns with the Planning Pattern. Defining neighborhoods over tasks (not agents) and respecting task precedence lets the method fix local bottlenecks without re-planning the whole team. A global repair strategy using a priority-style path solver gave the best overall gains, while a local repair strategy can outperform it when task dependencies are very dense. The approach scales to large benchmarks — the suite includes runs with up to 500 agents and 1,000 tasks — though most runtime is spent inside the path-repair solver.
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By the Numbers
1Flexible reassignment improved 89.1% of instances compared to fixed-assignment seeds.
2The best method (global repair with a priority-style path solver) achieved a median relative sum-of-costs reduction of 12.2% overall.
3Benchmarks include up to 500 agents and 1,000 tasks; on the hardest warehouse tier that global variant improved 75.0% of instances with a median 1.5% reduction.
Implications
Robotics and logistics engineers who coordinate robot fleets can use this to cut wasted travel and waiting caused by rigid task assignment. The approach aligns with the Human-in-the-Loop Pattern to catch errors before deployment. Platform and tool builders who stitch assignment and motion planners together can treat strong path planners as ‘repair engines’ inside a higher-level reassignment loop to gain consistent improvements. Researchers exploring multi-agent scheduling and planning will find the neighborhood-based approach a practical way to trade optimality for scalability.
Key Figures
Fig 2: Figure 2: Main method comparison across seeded benchmark subsets. Each panel reports relative sum of costs reduction over the fixed-assignment seed (higher is better). Top row : By benchmark tier. Bottom row : By map family.
Fig 3: Figure 3: Improvement frequency and absolute sum of costs reduction. Left : Fraction of improved instances, Right : Boxplots of absolute SoC reduction across all instances. Larger values are better.
Fig 4: Figure 4: Search behavior on PBS-seeded runs. Curves show median best-so-far relative SoC reduction over wall-clock time, and shaded bands show the interquartile range. The x-axis shows wall-clock time including seed generation and post-refinement.
Fig 5: Figure 5: Scalability on PBS-seeded runs. Curves show median relative SoC reduction, and shaded bands show interquartile range. Left column : Varying agents with fixed tasks and precedence constraints. Right column : Varying precedence constraints with fixed agents and tasks. Top row : Random map. Bottom row : Warehouse map.
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Learn MoreKeep in Mind
The method is heuristic: it finds many practical improvements but does not guarantee optimality. High-density precedence (many strict dependencies) reduces the benefit of global reassignment — local repairs may work better there. Runtime is dominated by the inner path-repair solves, so overall scalability depends on the efficiency of the embedded path planner and seed quality; online or lifelong task streams were not evaluated here. Consider integrating Agent Service Mesh Pattern for scalable coordination across components.
Methodology & More
A two-level search strategy produces practical gains by separating ‘who does what’ from ‘how they move.’ Start with a feasible assignment and collision-free paths, then repeatedly pick a neighborhood of related tasks (chosen to respect precedence links), erase assignments inside that neighborhood, and invoke a path-and-order repair solver constrained by the frozen exterior. Neighborhoods are defined over tasks because removing or changing one task often forces reordering of its successors and predecessors; repairing those localized clusters avoids re-planning the whole team. Market-Based Coordination Pattern can complement this approach by aligning incentives and flows across agents, while Mutual Verification Pattern can help ensure robustness under uncertainty. Across a large benchmark suite (different map families, task/agent scales up to 500 agents and 1,000 tasks), the neighborhood-repair framework improved 89.1% of instances over fixed-assignment seeds. A global repair variant that used a priority-style path solver as the repair engine showed the strongest median gains (12.2% reduction in team travel cost), while a local repair variant was sometimes better when precedence constraints were very dense. Most runtime is spent inside the embedded path-repair solves, so gains are practical but hinge on efficient lower-level planners. The method is a solid engineering tool to boost multi-agent coordination: plug a good path planner into the outer reassignment loop to reduce waiting and travel without redesigning the entire system. Future work includes stronger repair policies for dense precedence graphs and extending the approach to online, lifelong task streams.
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Credibility Assessment:
Authors affiliated with MIT (top university) which maps to a 4-star rating, despite low h-indices and arXiv venue.