pfs.py File Reference

model for the permutation flow shop problem More...

Go to the source code of this file.

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

Variables
int	n = 15
int	m = 10
	p = make_data(n,m)
	model = permutation_flow_shop(n,m,p)
	x
	s
	f
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...

Detailed Description

model for the permutation flow shop problem

Definition in file pfs.py.

Function Documentation

def pfs.example

(

)

creates example data set

Definition at line 63 of file pfs.py.

def pfs.make_data	(		n,
			m
	)

make_data: prepare matrix of m times n random processing times

Definition at line 54 of file pfs.py.

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 14 of file pfs.py.

Variable Documentation

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 105 of file pfs.py.