Catching trash that drifts away.

Two collection boats start at the centre of the map, plastic hotspots drift around — and the clock is ticking: after 100 hours the mission ends. Unlike the TSP, you don't have to visit everything. It's an orienteering problem: pick the subset that maximizes the collected value (bigger = more valuable). The catch: hotspots and boats both drift with the current — travel times are never certain, and the plan from a minute ago is already stale.

Every free boat scores all hotspots with a single formula: α weighs proximity, 1−α weighs value. α = 1 only chases the nearest, α = 0 only the most valuable. Test 0 of the thesis shows a non-linear curve: a dip around α ≈ 0.3–0.4 and a peak at α ≈ 0.6 — balanced beats extreme. Set your α and race both extremes. All three boats fish in the same waters.

The online greedy (OG) has a weakness: once assigned, it stubbornly sticks to its target — even if it drifts away or something better pops up next door. The OGR therefore checks at every time step with a second rule (factor β) whether switching pays off — but only if the new score beats the old one by the threshold r, so the boat doesn't jitter back and forth. Same scene, same currents, same α (value-leaning here, as in the thesis's own example) — watch who collects more. Every ↻ is a target switch.

The simulation didn't use toy waves but hourly surface currents from the Copernicus Marine Service (June 2024) around the Great Pacific Garbage Patch, implemented in Python with OpenDrift. Tested across 10 scenarios — from “Short Time” to “Vast & Dense” — and compared against exact Gurobi baselines. The findings: balanced weighting beats both extremes, and retargeting significantly improves adaptability. A foundation for adaptive route planning in dynamic maritime environments.