AI-Driven Neighborhood Selection in Large Neighborhood Search for Representative Container Vessel Stowage Planning

The representative container vessel stowage planning problem (CSPP) is a large-scale combinatorial optimization challenge with numerous constraints and decision variables. Due to its complexity, exact solution methods often struggle to find feasible or optimal solutions within a reasonable timeframe, necessitating the use of heuristic approaches. A widely used metaheuristic is the large neighborhood search (LNS) framework, which has demonstrated effectiveness across various combinatorial optimization problems with a manageable number of neighborhoods. However, a full-featured CSPP requires a large set of neighborhoods, rendering conventional selection heuristics ineffective in identifying promising neighborhoods during the search. This reduces the performance of LNS, highlighting the need for more intelligent selection strategies.

In this planned work, we leverage an AI-driven neighborhood selection heuristic within the LNS framework to solve representative instances of the CSPP. The search will be formulated as a Markov decision process, where state features capture solution characteristics, neighborhood selection serves as the action, stochastic transitions update solutions based on neighborhood operators, and rewards are based on objective value and feasibility satisfaction. Our AI-assisted LNS framework will be evaluated on real-life instances. Its performance will be compared to a baseline vanilla LNS to assess improvements in solution quality and computational costs.