def check_environment():
from docplex.mp.environment import Environment
env = Environment()
if not env.has_cplex:
print("CPLEX not installed.")
else:
print("CPLEX is installed.\n")
env.print_information()
def main(instances, verbose=False):
env = Environment()
env.print_information()
dir_path = os.path.dirname(os.path.realpath(__file__))
solutions = []
for index, instance in enumerate(instances):
data, h, w, problem_class = instance
data = sorted(data, key=itemgetter(0), reverse=True)
try:
mdl = build_model(data, w, h)
if verbose:
print(mdl.export_to_string())
mdl.print_information()
mdl.export_as_lp(dir_path + "/" + mdl.name + "_" + str(index) +
'.lp')
if mdl.solve(log_output=verbose):
if verbose:
mdl.float_precision = 3
print("* model solved as function:")
mdl.print_solution()
mdl.report_kpis()
# Save the CPLEX solution as "solution.json" program output
with get_environment().get_output_stream(
dir_path + "/" + mdl.name + "_" + str(index) +
"_solution.json") as fp:
mdl.solution.export(fp, "json")
else:
print("* model has no solution")
except Exception as e:
# Problem with more than 1000 variables
print(e)
#dd-markdown #dd-markdown Minimum demand is the product of duration (in hours) by the minimum required number of nurses. Thus, in number of #dd-markdown nurse-hours, this demand is stored in another new column `min_demand`. #dd-markdown #dd-markdown Finally, we display the updated shifts DataFrame with all calculated columns. #dd-cell # also compute minimum demand in nurse-hours df_shifts["min_demand"] = df_shifts.min_req * df_shifts.duration # finally check the modified shifts dataframe df_shifts #dd-markdown ### Step 5: Set up the prescriptive model #dd-cell from docplex.mp.environment import Environment env = Environment() env.print_information() #dd-markdown #### Create the DOcplex model #dd-markdown The model contains all the business constraints and defines the objective. #dd-markdown #dd-markdown We now use CPLEX Modeling for Python to build a Mixed Integer Programming (MIP) model for this problem. #dd-cell from docplex.mp.model import Model mdl = Model(name="nurses") #dd-markdown #### Define the decision variables #dd-markdown #dd-markdown For each (nurse, shift) pair, we create one binary variable that is equal to 1 when the nurse is assigned to the shift. #dd-markdown #dd-markdown We use the `binary_var_matrix` method of class `Model`, as each binary variable is indexed by _two_ objects: one nurse and one shift. #dd-cell # first global collections to iterate upon all_nurses = df_nurses.index.values
help='Try and find the "best" solution, rather than '
'just a feasible solution. Pass "min" to find '
'the least best solution.')
parser.add_argument('-C', '--check', action='store_true',
help='Check the result against the (forward) cascade.')
parser.add_argument('-l', '--lp-path',type=str, default=None,
help='File to write LP constraints to.')
args = parser.parse_args()
print "Loading cascade..."
cascade = Cascade.load(args.cascade)
docloud_context = DOcloudContext.make_default_context(DOCLOUD_URL)
docloud_context.print_information()
env = Environment()
env.print_information()
print "Solving..."
im = inverse_haar(cascade,
min_optimize=(args.optimize == "min"),
max_optimize=(args.optimize == "max"),
time_limit=args.time_limit,
docloud_context=docloud_context,
lp_path=args.lp_path)
cv2.imwrite(args.output, im)
print "Wrote {}".format(args.output)
if args.check:
print "Checking..."
ret = cascade.detect(im)
