Functions
def	bpp (s, B)

def	DiscreteUniform (n=10, LB=1, UB=99, B=100)

def	FFD (s, B)

def	solveBinPacking (s, B)

Variables
	B

def	bins = solveBinPacking(s, B)

def	ffd = FFD(s, B)

	s

Function Documentation

◆ bpp()

def bpp.bpp	(	s,
		B
	)

bpp: Martello and Toth's model to solve the bin packing problem.
Parameters:
    - s: list with item widths
    - B: bin capacity
Returns a model, ready to be solved.

Definition at line 38 of file bpp.py.

References FFD(), and pyscipopt.expr.quicksum().

◆ DiscreteUniform()

def bpp.DiscreteUniform	(	n = `10`,
		LB = `1`,
		UB = `99`,
		B = `100`
	)

DiscreteUniform: create random, uniform instance for the bin packing problem.

Definition at line 114 of file bpp.py.

◆ FFD()

def bpp.FFD	(	s,
		B
	)

First Fit Decreasing heuristics for the Bin Packing Problem.
Parameters:
    - s: list with item widths
    - B: bin capacity
Returns a list of lists with bin compositions.

Definition at line 17 of file bpp.py.

◆ solveBinPacking()

def bpp.solveBinPacking	(	s,
		B
	)

solveBinPacking: use an IP model to solve the in Packing Problem.

Parameters:
    - s: list with item widths
    - B: bin capacity

Returns a solution: list of lists, each of which with the items in a roll.

Definition at line 83 of file bpp.py.

References FFD().

Variable Documentation

◆ B

B

Definition at line 125 of file bpp.py.

◆ bins

def bins = solveBinPacking(s, B)

Definition at line 136 of file bpp.py.

◆ ffd

def ffd = FFD(s, B)

Definition at line 129 of file bpp.py.

◆ s

s