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sharing_clustering.py
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sharing_clustering.py
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# Copyright 2022 Recruit Co., Ltd.
#
# 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.
import random
from codableopt import Problem, Objective, CategoryVariable, OptSolver, PenaltyAdjustmentMethod
# 問題のパラメータ設定
CUSTOMER_NUM = 30
LIMIT_NUM_PER_taxi = 4
TAXI_NUM = 10
customer_names = [f'CUS_{i}' for i in range(CUSTOMER_NUM)]
taxi_names = [f'taxi_{i}' for i in range(TAXI_NUM)]
# 年齢・性別のマッチング
customers_age = [random.choice(['20-30', '30-60', '60-']) for _ in customer_names]
customers_sex = [random.choice(['m', 'f']) for _ in customer_names]
# 顧客の車割り当て変数を作成
x = [CategoryVariable(name=x, categories=taxi_names) for x in customer_names]
# 問題を設定
problem = Problem(is_max_problem=True)
# 目的関数として、距離を計算する関数を定義
def calc_matching_score(var_x, para_taxi_names, para_customers_age, para_customers_sex):
score = 0
for para_taxi_name in para_taxi_names:
customers_in_taxi = [(age, sex) for var_bit_x, age, sex
in zip(var_x, para_customers_age, para_customers_sex)
if var_bit_x == para_taxi_name]
num_in_taxi = len(customers_in_taxi)
if num_in_taxi > 1:
score += num_in_taxi - len(set([age for age, _ in customers_in_taxi]))
score += num_in_taxi - len(set([sex for _, sex in customers_in_taxi]))
return score
# 目的関数を定義
problem += Objective(objective=calc_matching_score,
args_map={'var_x': x,
'para_taxi_names': taxi_names,
'para_customers_age': customers_age,
'para_customers_sex': customers_sex})
# 必ず1度以上、全てのポイントに到達する制約式を追加
for taxi_name in taxi_names:
problem += sum([(bit_x == taxi_name) for bit_x in x]) <= LIMIT_NUM_PER_taxi
# 最適化実施
solver = OptSolver(round_times=4, debug=True, debug_unit_step=1000)
method = PenaltyAdjustmentMethod(steps=10000)
answer, is_feasible = solver.solve(problem, method, n_jobs=-1)
print(f'answer_is_feasible:{is_feasible}')
print(f'answer: {answer}')