手术排程问题：最大化手术室黄金时间利用率的仿真模型python具体代码

手术排程问题是一个经典的组合优化问题，可以用多种算法求解。以下是一个基于禁忌搜索算法的手术排程的仿真模型的Python代码示例：

import random

# 手术室数量
num_rooms = 5

# 手术室工作时间段
work_hours = (8, 18)

# 每个手术的时长
op_time = [random.randint(1, 4) for i in range(20)]

# 定义禁忌搜索参数
tabu_size = 10
max_iter = 100

# 初始化排程
schedule = [[0 for i in range(work_hours[1] - work_hours[0])] for j in range(num_rooms)]

# 计算黄金时间利用率
def calc_golden_time_util(schedule):
    total_golden_time = num_rooms * (work_hours[1] - work_hours[0])
    used_golden_time = sum([sum([1 for t in row if t > 0]) for row in schedule])
    return used_golden_time / total_golden_time

# 定义禁忌搜索函数
def tabu_search():
    # 初始化禁忌表
    tabu_list = []
    # 初始化当前最优解
    best_schedule = schedule
    best_util = calc_golden_time_util(best_schedule)
    # 迭代搜索
    for i in range(max_iter):
        # 生成邻居解集
        neighbors = []
        for room in range(num_rooms):
            for time in range(work_hours[1] - work_hours[0] - op_time[i]):
                neighbor = [row[:] for row in schedule]
                for t in range(op_time[i]):
                    neighbor[room][time+t] = i+1
                neighbors.append((neighbor, room, time))
        # 选择最好的非禁忌解
        best_neighbor = None
        best_neighbor_util = 0
        for neighbor in neighbors:
            neighbor_util = calc_golden_time_util(neighbor[0])
            if (neighbor not in tabu_list) and (neighbor_util > best_neighbor_util):
                best_neighbor = neighbor
                best_neighbor_util = neighbor_util
        # 更新当前解
        if best_neighbor_util > best_util:
            best_schedule = best_neighbor[0]
            best_util = best_neighbor_util
        # 更新禁忌表
        tabu_list.append(best_neighbor)
        if len(tabu_list) > tabu_size:
            tabu_list.pop(0)
    return best_schedule

# 输出最优解和黄金时间利用率
best_schedule = tabu_search()
best_util = calc_golden_time_util(best_schedule)
print("Best schedule:")
for i in range(len(best_schedule)):
    print("Room {}: {}".format(i+1, best_schedule[i]))
print("Golden time utilization: {:.2%}".format(best_util))

该代码实现了一个简单的手术排程问题的仿真模型，使用了禁忌搜索算法进行求解。在模型中，我们首先定义了手术室数量和工作时间段，并随机生成了每个手术的时长。然后，我们初始化了排程和禁忌表，并迭代搜索邻居解集，选择最好的非禁忌解进行更新。最后输出了最优解和黄金时间利用率。需要注意的是，该代码只是一个简单的示例，实际应用中需要根据具体问题进行修改和优化。