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Showing content from https://scipopt.github.io/PySCIPOpt/docs/html/namespacepfs.html below:

PySCIPOpt: pfs Namespace Reference

  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   ◆ 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().

◆ f ◆ m ◆ model ◆ n ◆ p ◆ s ◆ 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

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