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Heuristics for Vehicle Routing Problem: A Survey and Recent Advances
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Vehicle routing is a well-known optimization research topic with significant practical importance. Among different approaches to solving vehicle routing, heuristics can produce a satisfactory solution at a reasonable computational cost. Consequently, much effort has been made in the past decades to develop vehicle routing heuristics. In this article, we systematically survey the existing vehicle routing heuristics, particularly on works carried out in recent years. A classification of vehicle routing heuristics is presented, followed by a review of their methodologies, recent developments, and applications. Moreover, we present a general framework of state-of-the-art methods and provide insights into their success. Finally, three emerging research topics with notable works and future directions are discussed.
Forward citations
Cited by 3 Pith papers
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Learning to Optimize: Joint Routing and Flow Allocation on Sparse Non-Euclidean Networks
A double-channel graph-attention RL policy with constraint masks solves joint cyclic routing and flow allocation on sparse non-Euclidean networks faster and better than strong baselines at large scale.
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EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization
EALG uses LLMs in an evolutionary adversarial loop to generate increasingly hard TSP instances and heuristics that beat existing LLM-designed solvers on those instances and on TSPLIB.
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Solving Large-Scale Vehicle Routing Problems with Hybrid Quantum-Classical Decomposition
A standard graph partitioner and circuit-cutting toolkit shrink a 13-node VRP from 156 qubits to 6-qubit subcircuits, but the quality of the 13-node solution is not reported.
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