手术排程问题：最大化手术室黄金时间利用率并且最小化手术间用时标准差的启发式算法python具体代码

手术排程问题是一个NP难问题，因此常用启发式算法来求解。以下是一个基于模拟退火算法的python代码来解决这个问题：

import random
import math
import copy

def objective_function(schedule):
    # 计算黄金时间利用率和用时标准差
    total_time = 0
    total_golden_time = 0
    for room in schedule:
        for time in room:
            total_time += time
            if time >= 120 and time <= 240:
                total_golden_time += time
    golden_ratio = total_golden_time / total_time
    standard_dev = math.sqrt(sum([(time - total_time/len(schedule)**2)**2 for room in schedule for time in room])/(len(schedule)**2))
    # 将黄金时间利用率最大化并且用时标准差最小化作为目标函数
    return golden_ratio - standard_dev

def generate_initial_solution(num_rooms, num_surgeries):
    # 随机生成初始解
    return [[random.randint(60, 300) for j in range(num_surgeries)] for i in range(num_rooms)]

def get_neighbors(schedule):
    # 获取所有邻居解
    neighbors = []
    for i in range(len(schedule)):
        for j in range(len(schedule[0])):
            for k in range(-15, 16):
                if k != 0 and schedule[i][j] + k >= 60 and schedule[i][j] + k <= 300:
                    neighbor = copy.deepcopy(schedule)
                    neighbor[i][j] += k
                    neighbors.append(neighbor)
    return neighbors

def simulated_annealing(num_rooms, num_surgeries, initial_temperature, cooling_rate):
    # 模拟退火算法
    current_solution = generate_initial_solution(num_rooms, num_surgeries)
    current_objective = objective_function(current_solution)
    best_solution = current_solution
    best_objective = current_objective
    temperature = initial_temperature
    while temperature > 1:
        neighbors = get_neighbors(current_solution)
        neighbor = random.choice(neighbors)
        neighbor_objective = objective_function(neighbor)
        delta = neighbor_objective - current_objective
        if delta > 0 or math.exp(delta/temperature) > random.random():
            current_solution = neighbor
            current_objective = neighbor_objective
            if current_objective > best_objective:
                best_solution = current_solution
                best_objective = current_objective
        temperature *= cooling_rate
    return best_solution

# 示例
num_rooms = 3
num_surgeries = 5
initial_temperature = 1000
cooling_rate = 0.99
best_solution = simulated_annealing(num_rooms, num_surgeries, initial_temperature, cooling_rate)
print(best_solution)

该代码中使用了模拟退火算法，通过随机生成初始解和获取邻居解来搜索最优解。其中，`objective_function`函数用于计算目标函数，`generate_initial_solution`函数用于生成初始解，`get_neighbors`函数用于获取所有邻居解，`simulated_annealing`函数用于实现模拟退火算法。最后，我们可以使用示例数据来测试该算法的效果。