e179c8b847
* remove python script * remove RTE actions * fix test_xpress_interface.cc * remove callback_xpress.py * revert writing colnames and rownames * accept suggestion from Mizux * clean * change cmake/README.md * try fix build bazel * try fix build bazel add MPSWriteError.h * xpress tests gracefully exit if Xpress not found * add integer and linear programming test for dotnet python and java * remove MPSWriteError * try fix Window build * remove useless line from CMakeLists.txt * try fix test under windows * reformat * use XPRESS_LP instead of XPRESS for linear programming examples * tools: add --platform arg when possible make script more resilient/cross-platform * [CP-SAT] convert to PEP8 convention * use XPRSmipoptimize and XPRSlpoptimize instead of XPRSminim and XPRSmaxim (#114) * use XPRSmipoptimize and XPRSlpoptimize instead of XPRSminim and XPRSmaxim * clean xpress/environment files * accept changes: empty char* parameter for XPRS*optimize * Add test on number iterations with LP basis * fix gtests flags * refactor * suggestions by @flomnes * remove unwanted files --------- Co-authored-by: Andrea Sgattoni <andrea.sgattoni@rte-france.com> Co-authored-by: Laurent Perron <lperron@google.com> Co-authored-by: Corentin Le Molgat <corentinl@google.com> Co-authored-by: Andrea Sgattoni <andrea.sgattoni@gmail.com>
120 lines
3.4 KiB
Python
120 lines
3.4 KiB
Python
#!/usr/bin/env python3
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# Copyright 2010-2022 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 be
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as close to the average as possible. Furthermore, if one color is an a group, at
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least k items with this color must be in that group.
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"""
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import math
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from typing import Sequence
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from absl import app
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from ortools.sat.python import cp_model
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def chemical_balance():
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"""Solves the chemical balance problem."""
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# Data
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max_quantities = [
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["N_Total", 1944],
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["P2O5", 1166.4],
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["K2O", 1822.5],
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["CaO", 1458],
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["MgO", 486],
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["Fe", 9.7],
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["B", 2.4],
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]
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chemical_set = [
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["A", 0, 0, 510, 540, 0, 0, 0],
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["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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]
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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(
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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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)
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)
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)
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for s in all_sets
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]
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set_vars = [model.new_int_var(0, max_set[s], f"set_{s}") for s in all_sets]
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epsilon = model.new_int_var(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] for s in all_sets)
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<= int(max_quantities[p][1] * 10000)
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)
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model.add(
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sum(int(chemical_set[s][p + 1] * 10) * set_vars[s] for s in all_sets)
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>= int(max_quantities[p][1] * 10000) - epsilon
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)
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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(f"Status = {solver.status_name(status)}")
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# The objective value of the solution.
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print(f"Optimal objective value = {solver.objective_value / 10000.0}")
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for s in all_sets:
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print(
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f" {chemical_set[s][0]} = {solver.value(set_vars[s]) / 1000.0}",
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end=" ",
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)
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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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)
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print(f"{name}: {quantity} out of {max_quantity}")
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def main(argv: Sequence[str]) -> None:
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if len(argv) > 1:
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raise app.UsageError("Too many command-line arguments.")
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chemical_balance()
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if __name__ == "__main__":
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app.run(main)
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