基础知识

创建模型：model=Model(name)#name是模型名字

创建变量：model.addVar(vtype,name,lb=0,ub=1)#vtype是变量类型，有I（Integer）表示离散变量，B（Binary ）表示0/1变量

创建目标函数： model.setObjective(coeffs,sense)#前者是目标函数，后者是"minisize"或者"maxsize"

创建约束条件：model.addCons(cons)#cons是约束条件

求解：model.optimize()#求解
model.getBestSol()#得到决策变量数值
model.getObjVal()#得到目标函数值

导入库

from pyscipopt import Model,quicksum

栗子1 单个变量+简单模型

def prumen1():#创建模型model=Model("remen")#创建变量x=model.addVar(vtype="I",name="x",lb=0)y = model.addVar(vtype="I", name="y", lb=0)z = model.addVar(vtype="I", name="z", lb=0)w = model.addVar(vtype="I", name="w", lb=0)#创建目标函数model.setObjective(5 * x + 6 * y + 7 * z + 8 * w , "minimize")#创建约束条件model.addCons(x + y + z + w == 100)model.addCons(5 * x + 4 * y + 5 * z + 6 * w >= 530)model.addCons(2 * x + y + z + 2 * w <= 160)#求解model.optimize()sol = model.getBestSol()print("x: {}".format(sol[x]))print("y: {}".format(sol[y]))print("z: {}".format(sol[z]))print("w: {}".format(sol[w]))print(model.getObjVal())

栗子2 一维变量+二维变量+复杂模型

模型感谢Cathy友情提供

def prumen2():n = 200  # residential areas -变量i居民区数量m = 40  # shelters-变量j庇护所数量areas = range(n)shelters = range(m)path = 'Pb2_areas.csv'areas_matrix = np.genfromtxt(path, dtype=float, delimiter=',', encoding='utf-8-sig')path = 'Pb2_shelters.csv'shelters_matrix = np.genfromtxt(path, dtype=float, delimiter=',', encoding='utf-8-sig')R = areas_matrix[:, 2]  # 第三列的居民区居民数量-变量R_iC = shelters_matrix[:, 2]  # 第三列的庇护所能庇护的容量-变量C_j# 计算居民到庇护所的距离D = np.zeros((n, m))  # -变量D_ij距离for i in areas:for j in shelters:D[i, j] = abs(areas_matrix[i, 0] - shelters_matrix[j, 0]) + abs(areas_matrix[i, 1] - shelters_matrix[j, 1])# 定义问题model=Model("Cathy_exp")# 定义变量for i in range(n):x = model.addVar(vtype="B", name="x")#一维变量for i in range(n):for j in range(m):y_ij=model.addVar(vtype="B", name="y")#二维变量# 目标函数ansa=0.0for j in range(m):for i in range(n):ansa+=(D[i, j] * y_ij[i,j])model.setObjective(ansa, "minimize")# 约束条件model.addCons(quicksum(x[j] for j in shelters)==10)for i in areas:model.addCons(quicksum(y_ij[i,j] for j in shelters)==1)for j in shelters:model.addCons(quicksum(R[i]*y_ij[i,j] for i in areas)<=C[j]*x[j])model.optimize()print(model.getObjVal())# print(m)

不完整栗子 三维变量

这个的完整代码就不透露了~~~

    # 创建决策变量x, y, h = {}, {}, {}for i in range(n):for j in range(m):x[i, j] = model.addVar(vtype="B", name="x(%s,%s)" % (i, j)) #二维变量定义y = [[[model.addVar(vtype="I", name="y(%s,%s,%s)" % (i, j, k), lb=0) for k in range(t)] for j in range(m)] for i inrange(n)] #三维变量定义#输出sol = model.getBestSol()      for i in range(n):for j in range(m):print(x[i, j], "=", sol[x[i, j]])#二维变量输出for k in range(t):print(y[i, j, k], "=", sol[y[i, j, k]])#三维变量输出