176 lines
5.0 KiB
Python
176 lines
5.0 KiB
Python
# Copyright 2010 Hakan Kjellerstrand hakank@gmail.com
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""
|
|
|
|
Moving furnitures (scheduling) problem in Google CP Solver.
|
|
|
|
Marriott & Stukey: 'Programming with constraints', page 112f
|
|
|
|
The model implements an experimental decomposition of the
|
|
global constraint cumulative.
|
|
|
|
Compare with the following models:
|
|
* ECLiPSE: http://www.hakank.org/eclipse/furniture_moving.ecl
|
|
* MiniZinc: http://www.hakank.org/minizinc/furniture_moving.mzn
|
|
* Comet: http://www.hakank.org/comet/furniture_moving.co
|
|
* Choco: http://www.hakank.org/choco/FurnitureMoving.java
|
|
* Gecode: http://www.hakank.org/gecode/furniture_moving.cpp
|
|
* JaCoP: http://www.hakank.org/JaCoP/FurnitureMoving.java
|
|
* SICStus: http://hakank.org/sicstus/furniture_moving.pl
|
|
* Zinc: http://hakank.org/minizinc/furniture_moving.zinc
|
|
|
|
|
|
This model was created by Hakan Kjellerstrand (hakank@gmail.com)
|
|
Also see my other Google CP Solver models:
|
|
http://www.hakank.org/google_or_tools/
|
|
"""
|
|
import sys
|
|
from ortools.constraint_solver import pywrapcp
|
|
|
|
|
|
#
|
|
# Decompositon of cumulative.
|
|
#
|
|
# Inspired by the MiniZinc implementation:
|
|
# http://www.g12.csse.unimelb.edu.au/wiki/doku.php?id=g12:zinc:lib:minizinc:std:cumulative.mzn&s[]=cumulative
|
|
# The MiniZinc decomposition is discussed in the paper:
|
|
# A. Schutt, T. Feydy, P.J. Stuckey, and M. G. Wallace.
|
|
# 'Why cumulative decomposition is not as bad as it sounds.'
|
|
# Download:
|
|
# http://www.cs.mu.oz.au/%7Epjs/rcpsp/papers/cp09-cu.pdf
|
|
# http://www.cs.mu.oz.au/%7Epjs/rcpsp/cumu_lazyfd.pdf
|
|
#
|
|
#
|
|
# Parameters:
|
|
#
|
|
# s: start_times assumption: array of IntVar
|
|
# d: durations assumption: array of int
|
|
# r: resources assumption: array of int
|
|
# b: resource limit assumption: IntVar or int
|
|
#
|
|
def my_cumulative(solver, s, d, r, b):
|
|
|
|
# tasks = [i for i in range(len(s))]
|
|
tasks = [i for i in range(len(s)) if r[i] > 0 and d[i] > 0]
|
|
times_min = min([s[i].Min() for i in tasks])
|
|
times_max = max([s[i].Max() + max(d) for i in tasks])
|
|
for t in range(times_min, times_max + 1):
|
|
bb = []
|
|
for i in tasks:
|
|
c1 = solver.IsLessOrEqualCstVar(s[i], t) # s[i] <= t
|
|
c2 = solver.IsGreaterCstVar(s[i] + d[i], t) # t < s[i] + d[i]
|
|
bb.append(c1 * c2 * r[i])
|
|
solver.Add(solver.Sum(bb) <= b)
|
|
|
|
# Somewhat experimental:
|
|
# This constraint is needed to contrain the upper limit of b.
|
|
if not isinstance(b, int):
|
|
solver.Add(b <= sum(r))
|
|
|
|
|
|
def main():
|
|
|
|
# Create the solver.
|
|
solver = pywrapcp.Solver("Furniture moving")
|
|
|
|
#
|
|
# data
|
|
#
|
|
n = 4
|
|
duration = [30, 10, 15, 15]
|
|
demand = [3, 1, 3, 2]
|
|
upper_limit = 160
|
|
|
|
#
|
|
# declare variables
|
|
#
|
|
start_times = [
|
|
solver.IntVar(0, upper_limit, "start_times[%i]" % i) for i in range(n)
|
|
]
|
|
end_times = [
|
|
solver.IntVar(0, upper_limit * 2, "end_times[%i]" % i) for i in range(n)
|
|
]
|
|
end_time = solver.IntVar(0, upper_limit * 2, "end_time")
|
|
|
|
# number of needed resources, to be minimized
|
|
num_resources = solver.IntVar(0, 10, "num_resources")
|
|
|
|
#
|
|
# constraints
|
|
#
|
|
for i in range(n):
|
|
solver.Add(end_times[i] == start_times[i] + duration[i])
|
|
|
|
solver.Add(end_time == solver.Max(end_times))
|
|
|
|
my_cumulative(solver, start_times, duration, demand, num_resources)
|
|
|
|
#
|
|
# Some extra constraints to play with
|
|
#
|
|
|
|
# all tasks must end within an hour
|
|
# solver.Add(end_time <= 60)
|
|
|
|
# All tasks should start at time 0
|
|
# for i in range(n):
|
|
# solver.Add(start_times[i] == 0)
|
|
|
|
# limitation of the number of people
|
|
# solver.Add(num_resources <= 3)
|
|
|
|
#
|
|
# objective
|
|
#
|
|
# objective = solver.Minimize(end_time, 1)
|
|
objective = solver.Minimize(num_resources, 1)
|
|
|
|
#
|
|
# solution and search
|
|
#
|
|
solution = solver.Assignment()
|
|
solution.Add(start_times)
|
|
solution.Add(end_times)
|
|
solution.Add(end_time)
|
|
solution.Add(num_resources)
|
|
|
|
db = solver.Phase(start_times, solver.CHOOSE_FIRST_UNBOUND,
|
|
solver.ASSIGN_MIN_VALUE)
|
|
|
|
#
|
|
# result
|
|
#
|
|
solver.NewSearch(db, [objective])
|
|
num_solutions = 0
|
|
while solver.NextSolution():
|
|
num_solutions += 1
|
|
print("num_resources:", num_resources.Value())
|
|
print("start_times :", [start_times[i].Value() for i in range(n)])
|
|
print("duration :", [duration[i] for i in range(n)])
|
|
print("end_times :", [end_times[i].Value() for i in range(n)])
|
|
print("end_time :", end_time.Value())
|
|
print()
|
|
|
|
solver.EndSearch()
|
|
|
|
print()
|
|
print("num_solutions:", num_solutions)
|
|
print("failures:", solver.Failures())
|
|
print("branches:", solver.Branches())
|
|
print("WallTime:", solver.WallTime())
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|