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This page describes the spacefilling curve heuristic for generating approximate solutions to the Traveling Salesman Problem, emphasizing its speed, simplicity, and practical applications in routing, logistics, and map drawing.
This paper proposes a reward-density heuristic for dynamic multi-vehicle routing problems that matches the solution quality of metaheuristics like ALNS, GA, and SA while requiring two to three orders of magnitude less planning time, demonstrated on drone task allocation and urban taxi dispatch scenarios.
Introduces front-to-attractors (F2A), a new heuristic class for bidirectional search that reduces computational cost by evaluating distances to a small set of attractors instead of the full opposite frontier, achieving up to 11.2x fewer pairwise evaluations and 4.8x fewer node expansions than existing methods.