1.0.572

examples/cp/basic/golomb_ruler_all_solutions.py

@@ -0,0 +1,83 @@
+# --------------------------------------------------------------------------
+# Source file provided under Apache License, Version 2.0, January 2004,
+# http://www.apache.org/licenses/
+# (c) Copyright IBM Corp. 2015, 2016
+# --------------------------------------------------------------------------
+
+"""
+In mathematics, a Golomb ruler is a set of marks at integer positions along
+an imaginary ruler such that no two pairs of marks are the same distance apart.
+The number of marks on the ruler is its order, and the largest distance
+between two of its marks is its length.
+
+This implementation differs from the 'basic' implementation, given in the
+examp;e module golomb_ruler.py, because it calls the solver twice:
+ * First time to know the minimal size of the ruler for the required order,
+ * A second time to list all possible rulers for this optimal size
+
+See https://en.wikipedia.org/wiki/Golomb_ruler for more information.
+
+For order 5: 2 solutions: 0 1 4 9 11
+                          0 2 7 8 11
+For order 7: 6 solutions: 0 1 4 10 18 23 25
+                          0 1 7 11 20 23 25
+                          0 1 11 16 19 23 25
+                          0 2 3 10 16 21 25
+                          0 2 7 13 21 22 25
+
+Please refer to documentation for appropriate setup of solving configuration.
+"""
+
+
+from docplex.cp.model import *
+from sys import stdout
+
+# Set model parameters
+ORDER = 7                      # Number of marks
+MAX_LENGTH = (ORDER - 1) ** 2  # Max rule length
+
+# Create model
+mdl = CpoModel()
+
+# Create array of variables corresponding to position rule marks
+marks = integer_var_list(ORDER, 0, MAX_LENGTH, "M")
+
+# Create marks distances that should be all different
+dist = [marks[i] - marks[j] for i in range(1, ORDER) for j in range(0, i)]
+mdl.add(all_diff(dist))
+
+# Avoid symmetric solutions by ordering marks
+mdl.add(marks[0] == 0)
+for i in range(1, ORDER):
+    mdl.add(marks[i] > marks[i - 1])
+
+# Avoid mirror solution
+mdl.add((marks[1] - marks[0]) < (marks[ORDER - 1] - marks[ORDER - 2]))
+
+# Minimize ruler size (position of the last mark)
+minexpr = minimize(marks[ORDER - 1])
+mdl.add(minexpr)
+
+# First solve the model to find the smallest ruler length
+msol = mdl.solve()
+if not msol:
+    print("No Golomb ruler available for order " + str(ORDER))
+else:
+    rsize = msol[marks[ORDER - 1]]
+    print("Shortest ruler for order " + str(ORDER) + " has length " + str(rsize))
+    # Remove minimization from the model
+    mdl.remove(minexpr)
+    # Force position of last mark
+    mdl.add(marks[ORDER - 1] == rsize)
+
+    # Request all solutions
+    print("List of all possible rulers for length {}:".format(rsize))
+    solver = CpoSolver(mdl, SearchType='DepthFirst', Workers=1) # Parameters needed to avoid duplicate solutions
+    try:
+        for i, msol in enumerate(solver):
+            stdout.write(str(i + 1) + ": ")
+            for v in marks:
+                stdout.write(" " + str(msol[v]))
+            stdout.write("\n")
+    except CpoNotSupportedException:
+        print("This instance of the solver does not support solution iteration.")

examples/cp/basic/plant_location_with_starting_point.py

@@ -0,0 +1,117 @@
+# --------------------------------------------------------------------------
+# Source file provided under Apache License, Version 2.0, January 2004,
+# http://www.apache.org/licenses/
+# (c) Copyright IBM Corp. 2015, 2016
+# --------------------------------------------------------------------------
+
+"""
+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.
+
+Please refer to documentation for appropriate setup of solving configuration.
+"""
+
+from docplex.cp.model import *
+from collections import deque
+
+##############################################################################
+# Initialize model data
+##############################################################################
+
+# Read data from file
+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))
+
+# Initialize main dimensions
+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)])
+
+
+##############################################################################
+# Create the model
+##############################################################################
+
+mdl = CpoModel()
+
+# Create variables identifying which location serves each customer
+cust = integer_var_list(nbCustomer, 0, nbLocation - 1, "CustomerLocation")
+
+# Create variables indicating which plant location is open
+open = integer_var_list(nbLocation, 0, 1, "OpenLocation")
+
+# Create variables indicating load of each plant
+load = [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(pack(load, cust, demand))
+
+# Add objective
+obj = scal_prod(fixedCost, open)
+for c in range(nbCustomer):
+    obj += element(cust[c], cost[c])
+mdl.add(minimize(obj))
+
+
+##############################################################################
+# Solve the model
+##############################################################################
+
+# Solve without starting point
+print("Solve the model with no starting point")
+msol = mdl.solve(TimeLimit=10)
+print("   Objective value: " + str(msol.get_objective_values()[0]))
+
+# Solve with starting point
+print("Solve the model with starting point")
+custValues = [19,  0, 11,  8, 29,  9, 29, 28, 17, 15,  7,  9, 18, 15,  1, 17, 25, 18, 17, 27,
+              22,  1, 26,  3, 22,  2, 20, 27,  2, 16,  1, 16, 12, 28, 19,  2, 20, 14, 13, 27,
+               3,  9, 18,  0, 13, 19, 27, 14, 12,  1, 15, 14, 17,  0,  7, 12, 11,  0, 25, 16,
+              22, 13, 16,  8, 18, 27, 19, 23, 26, 13, 11, 11, 19, 22, 28, 26, 23,  3, 18, 23,
+              26, 14, 29, 18,  9,  7, 12, 27,  8, 20]
+sp = CpoModelSolution()
+for c in range(nbCustomer):
+    sp.add_integer_var_solution(cust[c], custValues[c])
+mdl.set_starting_point(sp)
+
+try:
+    msol = mdl.solve(TimeLimit=10)
+    print("   Objective value: " + str(msol.get_objective_values()[0]))
+except:
+    print("   Starting point is available only with CPO 13 and later.")

examples/cp/jupyter/_utils_visu.py

@@ -26,6 +26,7 @@
     LIBRARIES_PRESENT = False
 
 from docplex.cp.function import CpoFunction
+from docplex.cp.expression import CpoTransitionMatrix
 from docplex.cp.solution import *
 import docplex.cp.config as config
 
@@ -1588,7 +1589,7 @@ def show(object=None, name=None, origin=None, horizon=None):
     """
     if config.context.visu_enabled:
         if object is not None:
-            if isinstance(object, CpoModelSolution):
+            if isinstance(object, CpoSolveResult):
                 # use default display for an instance of CPOSolution
                 _define_solution(object, name)
             elif isinstance(object, CpoTransitionMatrix):

examples/cp/visu/_utils_visu.py

@@ -26,6 +26,7 @@
     LIBRARIES_PRESENT = False
 
 from docplex.cp.function import CpoFunction
+from docplex.cp.expression import CpoTransitionMatrix
 from docplex.cp.solution import *
 import docplex.cp.config as config
 
@@ -970,11 +971,11 @@ def show(self):
 
 _visu = _Visu()
 
-"""
-    This section depends on CPO classes and defines the default display of some classes
-    like CpoSolution and CpoTransitionMatrix
-"""
 
+##############################################################################
+# This section depends on CPO classes and defines the default display of some
+# classes like CpoSolution and CpoTransitionMatrix
+##############################################################################
 
 def _canonical_interval(*args):
     """
@@ -1588,7 +1589,7 @@ def show(object=None, name=None, origin=None, horizon=None):
     """
     if config.context.visu_enabled:
         if object is not None:
-            if isinstance(object, CpoModelSolution):
+            if isinstance(object, CpoSolveResult):
                 # use default display for an instance of CPOSolution
                 _define_solution(object, name)
             elif isinstance(object, CpoTransitionMatrix):

examples/cp/visu/sched_calendar.py

@@ -10,9 +10,9 @@
 place before others and these requirements are expressed through
 precedence constraints.
 
-There are two workers and each task requires a specific worker.  The
-worker has a calendar of days off that must be taken into account. The
-objective is to minimize the overall completion date.
+There are two workers and each task requires a specific worker.
+The worker has a calendar of days off that must be taken into account.
+The objective is to minimize the overall completion date.
 
 Please refer to documentation for appropriate setup of solving configuration.
 """
@@ -154,7 +154,7 @@ def make_house(loc):
 ##############################################################################
 
 # Trace model
-#  mdl.export_as_cpo()
+# mdl.export_as_cpo()
 
 # Solve model
 print("Solving model....")

examples/mp/jupyter/mining_pandas.ipynb

@@ -107,17 +107,17 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
     "import pip\n",
     "REQUIRED_MINIMUM_PANDAS_VERSION = '0.17.1'\n",
-    "d = {pkg.key : pkg.version for pkg in pip.get_installed_distributions()}\n",
     "try:\n",
-    "    assert d['pandas'] >= REQUIRED_MINIMUM_PANDAS_VERSION\n",
+    "    import pandas as pd\n",
+    "    assert pd.__version__ >= REQUIRED_MINIMUM_PANDAS_VERSION\n",
     "except:\n",
-    "    raise Exception(\"Version \" + REQUIRED_MINIMUM_PANDAS_VERSION + \" or above of Pandas is required to run this notebook\")"
+    "    raise Exception(\"Version %s or above of Pandas is required to run this notebook\" % REQUIRED_MINIMUM_PANDAS_VERSION)"
    ]
   },
   {
@@ -942,7 +942,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.11"
   }
  },
  "nbformat": 4,

examples/mp/jupyter/nurses_pandas.ipynb

@@ -91,11 +91,11 @@
    "source": [
     "import pip\n",
     "REQUIRED_MINIMUM_PANDAS_VERSION = '0.17.1'\n",
-    "d = {pkg.key : pkg.version for pkg in pip.get_installed_distributions()}\n",
     "try:\n",
-    "    assert d['pandas'] >= REQUIRED_MINIMUM_PANDAS_VERSION\n",
+    "    import pandas as pd\n",
+    "    assert pd.__version__ >= REQUIRED_MINIMUM_PANDAS_VERSION\n",
     "except:\n",
-    "    raise Exception(\"Version \" + REQUIRED_MINIMUM_PANDAS_VERSION + \" or above of Pandas is required to run this notebook\")"
+    "    raise Exception(\"Version %s or above of Pandas is required to run this notebook\" % REQUIRED_MINIMUM_PANDAS_VERSION)"
    ]
   },
   {
@@ -1353,7 +1353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.11"
   }
  },
  "nbformat": 4,

examples/mp/modeling/diet.py

@@ -11,6 +11,7 @@
 from collections import namedtuple
 
 from docplex.mp.model import Model
+from docplex.util.environment import get_environment
 
 FOODS = [
     ("Roasted Chicken", 0.84, 0, 10),
@@ -47,7 +48,7 @@
 ]
 
 
-def build_diet_model(context=None):
+def build_diet_model(**kwargs):
     # Create tuples with named fields for foods and nutrients
     Food = namedtuple("Food", ["name", "unit_cost", "qmin", "qmax"])
     food = [Food(*f) for f in FOODS]
@@ -59,7 +60,7 @@ def build_diet_model(context=None):
                       fn[1 + n] for fn in FOOD_NUTRIENTS for n in range(len(NUTRIENTS))}
 
     # Model
-    mdl = Model("diet", context=context)
+    mdl = Model("diet", **kwargs)
 
     # Decision variables, limited to be >= Food.qmin and <= Food.qmax
     qty = dict((f, mdl.continuous_var(f.qmin, f.qmax, f.name)) for f in food)
@@ -107,3 +108,7 @@ def build_diet_model(context=None):
         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("solution.json") as fp:
+            mdl.solution.export(fp, "json")
+

examples/mp/modeling/nurses.py

@@ -7,6 +7,8 @@
 from collections import namedtuple
 
 from docplex.mp.model import Model
+from docplex.util.environment import get_environment
+
 
 
 # utility to convert a weekday string to an index in 0..6
@@ -243,13 +245,13 @@ def setup_constraints(model):
                 ctname = 'medium_ct_nurse_incompat_{0!s}_{1!s}_{2:d}'.format(nurse_id1, nurse_id2, c)
                 model.add_constraint(nurse_assigned[nurse1, s] + nurse_assigned[nurse2, s] <= 1, ctname)
 
-    model.total_number_of_assignments = model.sum(nurse_assigned[n,s] for n in all_nurses for s in all_shifts)
-    #model.total_salary_cost = model.sum(nurse_work_time[n] * n.pay_rate for n in all_nurses)
+    model.total_number_of_assignments = model.sum(nurse_assigned[n, s] for n in all_nurses for s in all_shifts)
     model.nurse_costs = [model.nurse_assignment_vars[n, s] * n.pay_rate * model.shift_activities[s].duration for n in
                          model.nurses
                          for s in model.shifts]
     model.total_salary_cost = model.sum(model.nurse_costs)
 
+
 def setup_objective(model):
     model.add_kpi(model.total_salary_cost, "Total salary cost")
     model.add_kpi(model.total_number_of_assignments, "Total number of assignments")
@@ -518,4 +520,8 @@ def build(context=None, **kwargs):
 
     # Solve the model. If a key has been specified above, the solve
     # will use IBM Decision Optimization on cloud.
-    status = solve(model, url=url, key=key)
+    solve(model, url=url, key=key)
+
+    # Save the CPLEX solution as "solution.json" program output
+    with get_environment().get_output_stream("solution.json") as fp:
+        model.solution.export(fp, "json")