A new approach to generating, evaluating, and optimizing general arrangements of naval surface ships is presented. Beginning from a user editable database of spaces, the algorithms return an optimized arrangement. The user drives the design by quantitatively defining spaces' goals and constraints for location, proximity, and shape. The arrangements task is undertaken in two parts: Allocation and Arrangement.

Allocation is the assignment of a space to a region of a ship. The unit region used is dubbed a Zone-deck. The Zone-deck is the intersection of one deck and one watertight subdivision. The allocation solution evaluates Zone-deck area utilization and spaces' relative and global position goals. Adjacency and separation distance between spaces is measured in increments of deck and subdivision. Global position is assessed by deck and subdivision. Each discrete distance and position has an editable default fuzzy preference value. Allocation is essentially a very large scale combinatorial bin packing problem. With the added complexity of relative location constraints, this also becomes a type of quadratic assignment problem. The independent variable vector is an ordered listing by space index number of each space's assigned Zone-deck index number. A customized Genetic Algorithm optimizes the integer-coded chromosome.

The second part arranges one Zone-deck at a time. Arrangement is done in two iterative steps: topology and geometry. Topology gives the relative longitudinal and transverse position of each space's seed location. These locations are translated onto an orthogonal grid. In the second step, spaces are expanded to have size and shape filling the available area in the stochastic growth loop. Spaces are defined by up to three contiguous boxes allowing for L, T, C and Z shapes. The arrangement cost function evaluates each space's required area satisfaction, aspect ratio, minimum overall dimension, minimum segment dimension, perimeter, connectivity to access, and proximity constraints to other spaces. Editable piecewise linear fuzzy utility functions translate each criteria measure to a fuzzy utility. The best of a modest number of geometry solutions returns joiner bulkhead locations and a cost function value to a Genetic Algorithm optimization of the topology chromosome.