Functions | |
| def | make_1r () |
| def | make_2r () |
| def | rcs (J, P, R, T, p, c, a, RUB) |
Variables | |
| a | |
| c | |
| J | |
| def | model = rcs(J, P, R, T, p, c, a, RUB) |
| P | |
| p | |
| R | |
| RUB | |
| s | |
| T | |
| x | |
| def rcs.make_1r | ( | ) |
creates example data set 1
Definition at line 55 of file rcs.py.
References pyscipopt.Multidict.multidict().
| def rcs.make_2r | ( | ) |
creates example data set 2
Definition at line 80 of file rcs.py.
References pyscipopt.Multidict.multidict().
| def rcs.rcs | ( | J, | |
| P, | |||
| R, | |||
| T, | |||
| p, | |||
| c, | |||
| a, | |||
| RUB | |||
| ) |
rcs -- model for the resource constrained scheduling problem
Parameters:
- J: set of jobs
- P: set of precedence constraints between jobs
- R: set of resources
- T: number of periods
- p[j]: processing time of job j
- c[j,t]: cost incurred when job j starts processing on period t.
- a[j,r,t]: resource r usage for job j on period t (after job starts)
- RUB[r,t]: upper bound for resource r on period t
Returns a model, ready to be solved.
Definition at line 9 of file rcs.py.
References pyscipopt.expr.quicksum().