updated to 2.9.141

Author: vlkong

examples/cp/basic/plant_location_with_kpis.py

@@ -0,0 +1,120 @@
+# --------------------------------------------------------------------------
+# Source file provided under Apache License, Version 2.0, January 2004,
+# http://www.apache.org/licenses/
+# (c) Copyright IBM Corp. 2015, 2016, 2018
+# --------------------------------------------------------------------------
+
+"""
+A ship-building company has a certain number of customers. Each customer is supplied
+by exactly one plant. In turn, a plant can supply several customers. The problem is
+to decide where to set up the plants in order to supply every customer while minimizing
+the cost of building each plant and the transportation cost of supplying the customers.
+
+For each possible plant location there is a fixed cost and a production capacity.
+Both take into account the country and the geographical conditions.
+
+For every customer, there is a demand and a transportation cost with respect to
+each plant location.
+
+While a first solution of this problem can be found easily by CP Optimizer, it can take
+quite some time to improve it to a very good one. We illustrate the warm start capabilities
+of CP Optimizer by giving a good starting point solution that CP Optimizer will try to improve.
+This solution could be one from an expert or the result of another optimization engine
+applied to the problem.
+
+In the solution we only give a value to the variables that determine which plant delivers
+a customer. This is sufficient to define a complete solution on all model variables.
+CP Optimizer first extends the solution to all variables and then starts to improve it.
+
+The model has been enriched by the addition of KPIs (key performance indicators), operational with a
+version of COS greater or equal to 12.9.0.0.
+These are named expressions which are of interest to help get an idea of the performance of the model.
+Here, we are interested in two indicators:
+ - the first is the `occupancy'' defined as the total demand divided by the total plant capacity.
+ - the second indicator is the occupancy which is the lowest of all the plants.
+
+The KPIs are displayed in the log whenever an improving solution is found and at the end of the search.
+"""
+
+from docplex.cp.model import CpoModel
+from docplex.cp.config import context
+from docplex.cp.utils import compare_natural
+from collections import deque
+import os
+
+#-----------------------------------------------------------------------------
+# Initialize the problem data
+#-----------------------------------------------------------------------------
+
+# Read problem data from a file and convert it as a list of integers
+filename = os.path.dirname(os.path.abspath(__file__)) + "/data/plant_location.data"
+data = deque()
+with open(filename, "r") as file:
+    for val in file.read().split():
+        data.append(int(val))
+
+# Read number of customers and locations
+nbCustomer = data.popleft()
+nbLocation = data.popleft()
+
+# Initialize cost. cost[c][p] = cost to deliver customer c from plant p
+cost = list([list([data.popleft() for l in range(nbLocation)]) for c in range(nbCustomer)])
+
+# Initialize demand of each customer
+demand = list([data.popleft() for c in range(nbCustomer)])
+
+# Initialize fixed cost of each location
+fixedCost = list([data.popleft() for p in range(nbLocation)])
+
+# Initialize capacity of each location
+capacity = list([data.popleft() for p in range(nbLocation)])
+
+
+#-----------------------------------------------------------------------------
+# Build the model
+#-----------------------------------------------------------------------------
+
+mdl = CpoModel()
+
+# Create variables identifying which location serves each customer
+cust = mdl.integer_var_list(nbCustomer, 0, nbLocation - 1, "CustomerLocation")
+
+# Create variables indicating which plant location is open
+open = mdl.integer_var_list(nbLocation, 0, 1, "OpenLocation")
+
+# Create variables indicating load of each plant
+load = [mdl.integer_var(0, capacity[p], "PlantLoad_" + str(p)) for p in range(nbLocation)]
+
+# Associate plant openness to its load
+for p in range(nbLocation):
+      mdl.add(open[p] == (load[p] > 0))
+
+# Add constraints
+mdl.add(mdl.pack(load, cust, demand))
+
+# Add objective
+obj = mdl.scal_prod(fixedCost, open)
+for c in range(nbCustomer):
+    obj += mdl.element(cust[c], cost[c])
+mdl.add(mdl.minimize(obj))
+
+# Add KPIs
+if compare_natural(context.model.version, '12.9') >= 0:
+    mdl.add_kpi(mdl.sum(demand) / mdl.scal_prod(open, capacity), "Occupancy")
+    mdl.add_kpi(mdl.min([load[l] / capacity[l] + (1 - open[l]) for l in range(nbLocation)]), "Min occupancy")
+
+
+#-----------------------------------------------------------------------------
+# Solve the model and display the result
+#-----------------------------------------------------------------------------
+
+# Solve the model
+print("Solve the model")
+msol = mdl.solve(TimeLimit=10, trace_log=False)  # Set trace_log=True to have a real-time view of the KPIs
+if msol:
+    print("   Objective value: {}".format(msol.get_objective_values()[0]))
+    if context.model.version >= '12.9':
+        print("   KPIs: {}".format(msol.get_kpis()))
+else:
+    print("   No solution")
+