gbp4d_solver_dpp {gbp}

gbp4d_solver_dpp

Description

solve gbp4d via extreme point heuristic and best information score fit strategy.

Usage

gbp4d_solver_dpp(p, ldhw, m)

Arguments

p

p profit of it fit into bn <vector> - cluster w via gbp1d, cluster max(l,d,h) and area via gbp4d_solver_dpp_main_create_p()

ldhw

it scales <matrix> - l, d, h, w it scale along x, y, z and w (weight on separate single dimension) <numeric>

m

bn scales <vector> - l, d, h, w bn scale along x, y, z and w (weight on separate single dimension) <numeric>

Details

gbp4d init a profit vector p, a length l, a depth d, a height h, and a weight w, along with associate constraints ml, md, mh and mw. gbp4d should fit it (l, d, h, w) into bn (ml, md, mh, mw) with w on weight limit constraint and l, d, h on geometry intepretation. gbp4d solver would solve

maximize sum_j=1^n p_j k_j

subject to sum_j=1^n w_j k_j leq mw and

fit (l_j, d_j, h_j) at coordinate (x_j, y_j, z_j) such that no overlap in ml x md x mh cuboid, j = 1, ......, n

and instantiate a gbp4d object with a x-axis coordinate vector x, a y-axis coordinate vector y, a z-axis coordinate vector z, a selection vector k, and an objective o.

Value

gbp4d a gbp4d instantiate with p profit, it item (x, y, z, w, l, d, h, w) position scale matrix, bn bin (l, d, h, w) scale vector, k selection, o objective, and ok an indicator of all fit or not.

See Also

Other gbp4d: gbp4d_checkr, gbp4d