pfs Namespace Reference

Functions
def	example ()
def	make_data (n, m)
def	permutation_flow_shop (n, m, p)

Variables
	f
int	m = 10
def	model = permutation_flow_shop(n, m, p)
int	n = 15
def	p = make_data(n, m)
	s
list	seq = [j for (k, j) in sorted([(k, j) for (j, k) in x if model.getVal(x[j, k]) > 0.5])]
	for (j,k) in sorted(x): if x[j,k].X > 0.5: print(x[j,k].VarName,x[j,k].X More...
	x

Function Documentation

example()

def pfs.example

(

)

creates example data set

Definition at line 64 of file pfs.py.

make_data()

def pfs.make_data	(		n,
			m
	)

make_data: prepare matrix of m times n random processing times

Definition at line 55 of file pfs.py.

permutation_flow_shop()

def pfs.permutation_flow_shop	(		n,
			m,
			p
	)

gpp -- model for the graph partitioning problem
Parameters:
    - n: number of jobs
    - m: number of machines
    - p[i,j]: processing time of job i on machine j
Returns a model, ready to be solved.

Definition at line 15 of file pfs.py.

References pyscipopt.expr.quicksum().

Variable Documentation

f

f

Definition at line 92 of file pfs.py.

m

int m = 10

Definition at line 77 of file pfs.py.

model

def model = permutation_flow_shop(n, m, p)

Definition at line 89 of file pfs.py.

n

int n = 15

Definition at line 76 of file pfs.py.

p

def p = make_data(n, m)

Definition at line 78 of file pfs.py.

s

s

Definition at line 92 of file pfs.py.

seq

list seq = [j for (k, j) in sorted([(k, j) for (j, k) in x if model.getVal(x[j, k]) > 0.5])]

for (j,k) in sorted(x): if x[j,k].X > 0.5: print(x[j,k].VarName,x[j,k].X

for i in sorted(s): print(s[i].VarName,s[i].X

for i in sorted(f): print(f[i].VarName,f[i].X

Definition at line 106 of file pfs.py.

x

x

Definition at line 92 of file pfs.py.