Python中的优化问题
我需要解决一个问题。我有5个设备。它们都有4种I / O类型。并且存在目标输入/输出组合。在第一步，我想找到设备之间的所有组合，以便所选设备的总I / O数量都等于或大于目标值。让我解释：
# Devices=[numberof_AI,numberof_AO,numberof_BI,numberof_BO,price]
Device1=[8,8,4,4,200]
Device1=[16,0,16,0,250]
Device1=[8,0,4,4,300]
Device1=[16,8,4,4,300]
Device1=[8,8,2,2,150]
Target=[24,12,16,8]
没有找到相关结果
已邀请:
3 个回复
先对冈蒲
import pulp prob = pulp.LpProblem("example", pulp.LpMinimize) # Variable represent number of times device i is used n1 = pulp.LpVariable("n1", 0, 5, cat="Integer") n2 = pulp.LpVariable("n2", 0, 5, cat="Integer") n3 = pulp.LpVariable("n3", 0, 5, cat="Integer") n4 = pulp.LpVariable("n4", 0, 5, cat="Integer") n5 = pulp.LpVariable("n5", 0, 5, cat="Integer") # Device params Device1=[8,8,4,4,200] Device2=[16,0,16,0,250] Device3=[8,0,4,4,300] Device4=[16,8,4,4,300] Device5=[8,8,2,2,150] # The objective function that we want to minimize: the total cost prob += n1 * Device1[-1] + n2 * Device2[-1] + n3 * Device3[-1] + n4 * Device4[-1] + n5 * Device5[-1] # Constraint that we use no more than 5 devices prob += n1 + n2 + n3 + n4 + n5 <= 5 Target = [24, 12, 16, 8] # Constraint that the total I/O for all devices exceeds the target for i in range(4): prob += n1 * Device1[i] + n2 * Device2[i] + n3 * Device3[i] + n4 * Device4[i] + n5 * Device5[i] >= Target[i] # Actually solve the problem, this calls GLPK so you need it installed pulp.GLPK().solve(prob) # Print out the results for v in prob.variables(): print v.name, "=", v.varValue桑娠贯涤
的可能性（因为五个位置中的每一个都可能未被使用，你必须使用）。然后针对每种可能性，检查它是否符合您的标准。我不明白为什么这需要花费这么多时间。 以下脚本在我的机器上花费不到一秒钟来计算这些东西。d1=[8,8,4,4,200] d2=[16,0,16,0,250] d3=[8,0,4,4,300] d4=[16,8,4,4,300] d5=[8,8,2,2,150] dummy=[0,0,0,0,0] t=[24,12,16,8] import itertools def computeit(devicelist, target): def check(d, t): for i in range(len(t)): if sum([dd[i] for dd in d]) < t[i]: return False return True results=[] for p in itertools.combinations_with_replacement(devicelist, 5): if check(p, t): results.append(p) return results print(computeit([d1,d2,d3,d4,d5,dummy],t))宠封钞轰
import constraint Device1=[8,8,4,4,200] Device2=[16,0,16,0,250] Device3=[8,0,4,4,300] Device4=[16,8,4,4,300] Device5=[8,8,2,2,150] Target=[24,12,16,8] devices = [Device1, Device2, Device3, Device4, Device5] vars_number_of_devices = range(len(devices)) max_number_of_devices = 5 problem = constraint.Problem() problem.addVariables(vars_number_of_devices, range(max_number_of_devices + 1)) problem.addConstraint(constraint.MaxSumConstraint(max_number_of_devices), vars_number_of_devices) for io_index, minimum_sum in enumerate(Target): problem.addConstraint(constraint.MinSumConstraint(minimum_sum, [device[io_index] for device in devices]), vars_number_of_devices) print min(problem.getSolutions(), key=lambda distribution: sum([how_many * devices[device][-1] for device, how_many in distribution.iteritems()])){0: 2, 1: 1, 2: 0, 3: 0, 4: 0}