Simulation-guided Beam Search for Neural Combinatorial Optimization

Choo, Jinho; Kwon, Y.; Kim, Jihoon; Jae, Jeongwoo; Hottung, André; Tierney, Kevin; Gwon, Youngjune

doi:10.48550/arxiv.2207.06190

Cited by 2 publications

(1 citation statement)

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“…Some studies train existing local search solvers such as 2-opt heuristic (da Costa et al 2020;Wu et al 2021), large neighborhood search (Hottung and Tierney 2020), iterative dynamic programming (Kool et al 2021), and LKH (Xin et al 2021b) using deep learning. Some studies use fine-tuning schemes focused on test-time adaptation in iterative learning (Hottung, Kwon, and Tierney 2021;Choo et al 2022). While a constructive solver is invaluable for quickly generating an initial feasible solution, an improvement solver plays a crucial role in refining the solution to achieve enhanced optimality.…”

Section: Related Work Vehicle Routing Problemsmentioning

confidence: 99%

Equity-Transformer: Solving NP-Hard Min-Max Routing Problems as Sequential Generation with Equity Context

Son,

Kim,

Choi

et al. 2024

AAAI

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Min-max routing problems aim to minimize the maximum tour length among multiple agents as they collaboratively visit all cities, i.e., the completion time. These problems include impactful real-world applications but are known as NP-hard. Existing methods are facing challenges, particularly in large-scale problems that require the coordination of numerous agents to cover thousands of cities. This paper proposes Equity-Transformer to solve large-scale min-max routing problems. First, we model min-max routing problems into sequential planning, reducing the complexity and enabling the use of a powerful Transformer architecture. Second, we propose key inductive biases that ensure equitable workload distribution among agents. The effectiveness of Equity-Transformer is demonstrated through its superior performance in two representative min-max routing tasks: the min-max multi-agent traveling salesman problem (min-max mTSP) and the min-max multi-agent pick-up and delivery problem (min-max mPDP). Notably, our method achieves significant reductions of runtime, approximately 335 times, and cost values of about 53% compared to a competitive heuristic (LKH3) in the case of 100 vehicles with 1,000 cities of mTSP. We provide reproducible source code: https://github.com/kaist-silab/equity-transformer.

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