1643. AI-Driven Neighborhood Selection in Large Neighborhood Search for Representative Container Vessel Stowage Planning
Invited abstract in session MC-32: Stowage Planning and Vessel Operations, stream Maritime and Port Logistics.
Monday, 12:30-14:00Room: Maurice Keyworth 1.09
Authors (first author is the speaker)
| 1. | Jaike van Twiller
|
| Computer Science, IT University of Copenhagen | |
| 2. | Djordje Grbic
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| IT University of Copenhagen | |
| 3. | Rune Jensen
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| IT-University of Copenhagen |
Abstract
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 and diverse set of neighborhoods, rendering conventional selection heuristics ineffective in identifying promising neighborhoods during the search process. This reduces the overall performance of LNS, highlighting the need for more intelligent neighborhood selection strategies.
In this planned work, we will leverage an AI-driven neighborhood selection heuristic within the LNS framework to solve representative instances of the CSPP. The search process 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 problem instances. Its performance will be compared to a baseline vanilla LNS to a
Keywords
- Combinatorial Optimization
- Machine Learning
- Maritime applications
Status: accepted
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