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combinatorial_auction2

<table align="left"> <td> <a href="https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/contrib/combinatorial_auction2.ipynb">Run in Google Colab</a> </td> <td> <a href="https://github.com/google/or-tools/blob/main/examples/contrib/combinatorial_auction2.py">View source on GitHub</a> </td> </table>

First, you must install ortools package in this colab.

python

%pip install ortools

Combinatorial auction in Google CP Solver.

This is a more general model for the combinatorial example in the Numberjack Tutorial, pages 9 and 24 (slides 19/175 and 51/175).

The original and more talkative model is here: http://www.hakank.org/numberjack/combinatorial_auction.py

Compare with the following models:

MiniZinc: http://hakank.org/minizinc/combinatorial_auction.mzn
Gecode: http://hakank.org/gecode/combinatorial_auction.cpp

This model was created by Hakan Kjellerstrand ([email protected]) Also see my other Google CP Solver models: http://www.hakank.org/google_or_tools/

python

import sys
from collections import *
from ortools.constraint_solver import pywrapcp


def main():

  # Create the solver.
  solver = pywrapcp.Solver("Problem")

  #
  # data
  #
  N = 5

  # the items for each bid
  items = [
      [0, 1],  # A,B
      [0, 2],  # A, C
      [1, 3],  # B,D
      [1, 2, 3],  # B,C,D
      [0]  # A
  ]
  # collect the bids for each item
  items_t = defaultdict(list)

  # [items_t.setdefault(j,[]).append(i) for i in range(N) for j in items[i] ]
  # nicer:
  [items_t[j].append(i) for i in range(N) for j in items[i]]

  bid_amount = [10, 20, 30, 40, 14]

  #
  # declare variables
  #
  X = [solver.BoolVar("x%i" % i) for i in range(N)]
  obj = solver.IntVar(0, 100, "obj")

  #
  # constraints
  #
  solver.Add(obj == solver.ScalProd(X, bid_amount))
  for item in items_t:
    solver.Add(solver.Sum([X[bid] for bid in items_t[item]]) <= 1)

  # objective
  objective = solver.Maximize(obj, 1)

  #
  # solution and search
  #
  solution = solver.Assignment()
  solution.Add(X)
  solution.Add(obj)

  # db: DecisionBuilder
  db = solver.Phase(X, solver.CHOOSE_FIRST_UNBOUND, solver.ASSIGN_MIN_VALUE)

  solver.NewSearch(db, [objective])
  num_solutions = 0
  while solver.NextSolution():
    print("X:", [X[i].Value() for i in range(N)])
    print("obj:", obj.Value())
    print()
    num_solutions += 1

  solver.EndSearch()

  print()
  print("num_solutions:", num_solutions)
  print("failures:", solver.Failures())
  print("branches:", solver.Branches())
  print("WallTime:", solver.WallTime())


main()