Session: DAC-15-01-Multidisciplinary Design Optimization, Multiobjective Optimization, and Sensitivity Analysis

Paper Number: 89797

89797 - Ship Deck Object Placement Optimization Using a Many-Objective Bilevel Approach 

The placement of objects on a ship is critical to many facets of the performance of a ship. Most notably, the mass distribution properties of objects in a ship affect the ship’s stability, trim, and structural loading. Information gathered from object placement optimization can allow naval architects to further optimize the design of the whole ship by potentially reducing the structural weight of the vessel, and adjusting the shape of the hull or the general arrangements based on available space in the ship. This paper presents a novel, many-objective bin packing problem for object placement across multiple decks on a ship. This problem is also highly constrained to avoid object intersection and protrusion. The problem was optimized with the NSGA-II algorithm, utilizing a heuristic population initialization and by separating the objectives into a bilevel optimization scheme. The bilevel scheme decouples certain objectives and design variables from the rest of the problem and sequences the evaluation for the objectives in a two-stage process. The hypervolume of the final population measured the performance of the optimization test. The results indicate that sequencing the objectives with a bilevel scheme produces an 80.3% larger hypervolume than an all-in-one optimization for the same problem. The findings from this study provide a systematic way by combining concepts from many-objective optimization, bin packing heuristics, and bilevel optimization to sequence the optimization of many-objective, object placement problems.

Presenting Author: Noah Bagazinski Massachusetts Institute of Technology

Presenting Author Biography: Noah Bagazinski is a first year doctoral student at the Massachusetts Institute of Technology, studying Mechanical Engineering. He is a member of MIT's Design Computation and Digital Engineering (DeCoDE) Lab, researching in machine learning applications for marine vessel design optimization. Prior to beginning studies at MIT, Noah worked as an engineer at the Ford Motor Company for two years in the Ford College Graduate rotational program. He attended the University of Michigan, earning a B.S.E. and M.S.E. in Naval Architecture and Marine Engineering in 2018 and 2019, respectively. Noah is also a recipient of the National Defense Science and Engineering Graduate (NDSEG)Fellowship.

Authors:

Noah Bagazinski Massachusetts Institute of Technology
Faez Ahmed Massachusetts Institute of Technology