Python调⽤SCIP求解器的选址模型代码⽰例
本⽂介绍 Python 语⾔调⽤ SCIP 求解器，求解选址模型的代码⽰例。
安装 SCIP 和 PySCIPOpt
安装 SCIP 求解器的教程：
.
笔者在 Windows 10 系统上安装了 SCIP v7.0.0，并通过清华镜像源安装了 PySCIPOpt v3.2.0，Python 版本为 3.7.8（UserWarning 显⽰，该版本的 PySCIPOpt 建议使⽤ SCIP v7.0.1）
pip install PySCIPOpt==3.2.0 -i pypi.tuna.tsinghua.edu/simple
选址模型代码⽰例
模型模块：
from typing import List, Dict
from pyscipopt import Model, quicksum
def location(num_node:int, num_centre:int, mat_distance: List[List[float]])-> Dict[int, List[int]]:
"""
location model
:param num_node:  number of nodes
:param num_centre:  number of centres
:param mat_distance:  distance matrix
:return: dict_res:  {centre node: [node] list} dictionary
"""
model = Model("location")
x ={(i, j): model.addVar(vtype="B", name="x_[{},{}]".format(i, j))for i in range(num_node)
for j in range(num_node)}
# basic constraint
for j in range(num_node):
model.addCons(quicksum(x[i, j]for i in range(num_node))==1, name="basic_cover_[{}]".format(j))
for i in range(num_node):
model.addCons(x[i, j]<= x[i, i], name="basic_centre_[{},{}]".format(i, j))
# number of centres
model.addCons(quicksum(x[i, i]for i in range(num_node))== num_centre, name="num_centre")
# objective: min, total distancejava调用python模型
model.setObjective(quicksum(mat_distance[i][j]* x[i, j]for i in range(num_node)for j in range(num_node)),
sense="minimize")
# time limit
model.setRealParam("limits/time",1000)
# solve
model.optimize()
model.optimize()
print()
# status
status = Status()
print("model status： {}".format(status),'\n')
if status !="optimal":
return{}
# optimal objective value
obj_value = ObjVal()
print("optimal objective value： {}".format(obj_value),'\n')
# optimal solution
x_ ={(i, j): Val(x[i, j])for i in range(num_node)for j in range(num_node)}
# result process
dict_res ={}
for i in range(num_node):
if x_[i, i]>0.9:
list_tmp =[]
for j in range(num_node):
if x_[i, j]>0.9:
list_tmp.append(j)
dict_res[i]= list_tmp
print("centre code {} serves nodes:  {}".format(i, list_tmp))
return dict_res
主程序：
import random
from model import location
num_node =100
ran_coo =(0,100)
random.seed(a=1024)
list_coo =[(random.randint(ran_coo[0], ran_coo[1]),
random.randint(ran_coo[0], ran_coo[1]))for _ in range(num_node)]
mat_dist =[[((list_coo[i][0]- list_coo[j][0])**2+(list_coo[i][1]- list_coo[j][1])**2)**0.5 for j in range(num_node)]for i in range(num_node)]
num_amount =10
dict_res = location(num_node=num_node, num_centre=num_amount, mat_distance=mat_dist)求解⽇志：

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