89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
# Copyright 2010-2018 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# We are trying to group items in equal sized groups.
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# Each item has a color and a value. We want the sum of values of each group to
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# be as close to the average as possible.
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# Furthermore, if one color is an a group, at least k items with this color must
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# be in that group.
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from __future__ import print_function
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from __future__ import division
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from ortools.sat.python import cp_model
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import math
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# Data
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max_quantities = [["N_Total", 1944], ["P2O5", 1166.4], ["K2O", 1822.5],
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["CaO", 1458], ["MgO", 486], ["Fe", 9.7], ["B", 2.4]]
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chemical_set = [["A", 0, 0, 510, 540, 0, 0, 0], ["B", 110, 0, 0, 0, 160, 0, 0],
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["C", 61, 149, 384, 0, 30, 1, 0.2],
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["D", 148, 70, 245, 0, 15, 1, 0.2],
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["E", 160, 158, 161, 0, 10, 1, 0.2]]
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num_products = len(max_quantities)
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all_products = range(num_products)
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num_sets = len(chemical_set)
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all_sets = range(num_sets)
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# Model
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model = cp_model.CpModel()
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# Scale quantities by 100.
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max_set = [
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int(
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math.ceil(
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min(max_quantities[q][1] * 1000 / chemical_set[s][q + 1]
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for q in all_products
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if chemical_set[s][q + 1] != 0)))
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for s in all_sets
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]
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set_vars = [model.NewIntVar(0, max_set[s], "set_%i" % s) for s in all_sets]
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epsilon = model.NewIntVar(0, 10000000, "epsilon")
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for p in all_products:
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model.Add(
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sum(int(chemical_set[s][p + 1] * 10) * set_vars[s]
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for s in all_sets) <= int(max_quantities[p][1] * 10000))
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model.Add(
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sum(int(chemical_set[s][p + 1] * 10) * set_vars[s]
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for s in all_sets) >= int(max_quantities[p][1] * 10000) - epsilon)
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model.Minimize(epsilon)
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# Creates a solver and solves.
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solver = cp_model.CpSolver()
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status = solver.Solve(model)
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print("Status = %s" % solver.StatusName(status))
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# The objective value of the solution.
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print("Optimal objective value = %f" % (solver.ObjectiveValue() / 10000.0))
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for s in all_sets:
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print(
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" %s = %f" % (chemical_set[s][0], solver.Value(set_vars[s]) / 1000.0),
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end=" ")
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print()
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for p in all_products:
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name = max_quantities[p][0]
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max_quantity = max_quantities[p][1]
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quantity = sum(
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solver.Value(set_vars[s]) / 1000.0 * chemical_set[s][p + 1]
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for s in all_sets)
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print("%s: %f out of %f" % (name, quantity, max_quantity))
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