Recomputing Solutions to Perturbed Multi-Commodity Pickup and Delivery Vehicle Routing Problems using Monte Carlo Tree Search
CoRR(2023)
摘要
The Multi-Commodity Pickup and Delivery Vehicle Routing Problem aims to
optimize the pickup and delivery of multiple unique commodities using a fleet
of several agents with limited payload capacities. This paper addresses the
challenge of quickly recomputing the solution to this NP-hard problem when
there are unexpected perturbations to the nominal task definitions, likely to
occur under real-world operating conditions. The proposed method first
decomposes the nominal problem by constructing a search tree using Monte Carlo
Tree Search for task assignment, and uses a rapid heuristic for routing each
agent. When changes to the problem are revealed, the nominal search tree is
rapidly updated with new costs under the updated problem parameters, generating
solutions quicker and with a reduced optimality gap, as compared to recomputing
the solution as an entirely new problem. Computational experiments are
conducted by varying the locations of the nominal problem and the payload
capacity of an agent to demonstrate the effectiveness of utilizing the nominal
search tree to handle perturbations for real-time implementation.
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关键词
vehicle routing problems,monte carlo tree search,multi-commodity
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