137 lines
3.8 KiB
Python
137 lines
3.8 KiB
Python
# Copyright 2010-2024 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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"""Reallocate production to smooth it over years."""
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import collections
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from ortools.sat.python import cp_model
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def main():
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# Data
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data_0 = [
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[107, 107, 107, 0, 0], # pr1
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[0, 47, 47, 47, 0], # pr2
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[10, 10, 10, 0, 0], # pr3
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[0, 55, 55, 55, 55], # pr4
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]
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data_1 = [
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[119444030, 0, 0, 0],
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[34585586, 38358559, 31860661, 0],
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[19654655, 21798799, 18106106, 0],
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[298836792, 0, 0, 0],
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[3713428, 4118530, 4107277, 3072018],
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[6477273, 7183884, 5358471, 0],
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[1485371, 1647412, 1642911, 1228807]
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]
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data_2 = [
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[1194440, 0, 0, 0],
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[345855, 383585, 318606, 0],
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[196546, 217987, 181061, 0],
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[2988367, 0, 0, 0],
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[37134, 41185, 41072, 30720],
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[64772, 71838, 53584, 0],
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[14853, 16474, 16429, 12288]
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]
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pr = data_0
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num_pr = len(pr)
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num_years = len(pr[1])
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total = sum(pr[p][y] for p in range(num_pr) for y in range(num_years))
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avg = total // num_years
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# Model
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model = cp_model.CpModel()
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# Variables
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delta = model.NewIntVar(0, total, 'delta')
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contributions_per_years = collections.defaultdict(list)
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contributions_per_prs = collections.defaultdict(list)
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all_contribs = {}
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for p, inner_l in enumerate(pr):
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for y, item in enumerate(inner_l):
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if item != 0:
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contrib = model.NewIntVar(0, total, 'r%d c%d' % (p, y))
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contributions_per_years[y].append(contrib)
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contributions_per_prs[p].append(contrib)
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all_contribs[p, y] = contrib
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year_var = [
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model.NewIntVar(0, total, 'y[%i]' % i) for i in range(num_years)
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]
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# Constraints
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# Maintain year_var.
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for y in range(num_years):
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model.Add(year_var[y] == sum(contributions_per_years[y]))
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# Fixed contributions per pr.
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for p in range(num_pr):
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model.Add(sum(pr[p]) == sum(contributions_per_prs[p]))
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# Link delta with variables.
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for y in range(num_years):
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model.Add(year_var[y] >= avg - delta)
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for y in range(num_years):
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model.Add(year_var[y] <= avg + delta)
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# Solve and output
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model.Minimize(delta)
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# Solve model.
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solver = cp_model.CpSolver()
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status = solver.Solve(model)
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# Output solution.
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if status == cp_model.OPTIMAL:
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print('Data')
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print(' - total = ', total)
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print(' - year_average = ', avg)
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print(' - number of projects = ', num_pr)
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print(' - number of years = ', num_years)
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print(' - input production')
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for p in range(num_pr):
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for y in range(num_years):
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if pr[p][y] == 0:
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print(' ', end='')
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else:
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print('%10i' % pr[p][y], end='')
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print()
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print('Solution')
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for p in range(num_pr):
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for y in range(num_years):
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if pr[p][y] == 0:
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print(' ', end='')
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else:
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print('%10i' % solver.Value(all_contribs[p, y]), end='')
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print()
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for y in range(num_years):
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print('%10i' % solver.Value(year_var[y]), end='')
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print()
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if __name__ == '__main__':
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main()
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