Pareto clustering search applied for 3D container ship loading plan problem

• Pareto Clustering Search (PCS) is a hybrid method to solve multi-objective problems.
• PCS detects promising areas and applies local search heuristics only in these areas.
• We apply the PCS to solve the 3D Container ship Loading Plan Problem (CLPP).
• The PCS provides better solutions for the CLPP than mono-objective methods.
• Decision maker chooses the solution that best meets their interests in a situation.

The 3D Container ship Loading Plan Problem (CLPP) is an important problem that appears in seaport container terminal operations. This problem consists of determining how to organize the containers in a ship in order to minimize the number of movements necessary to load and unload the container ship and the instability of the ship in each port. The CLPP is well known to be NP-hard. In this paper, the hybrid method Pareto Clustering Search (PCS) is proposed to solve the CLPP and obtain a good approximation to the Pareto Front. The PCS aims to combine metaheuristics and local search heuristics, and the intensification is performed only in promising regions. Computational results considering instances available in the literature are presented to show that PCS provides better solutions for the CLPP than a mono-objective Simulated Annealing.