餐馆程序员用 Python 优化排班表节省成本18个绝招曝光

# 员工信息列表，包括姓名、可用时间段  
employees = [  
    {"name": "张三", "available": [(9, 17), (20, 23)]},  
    {"name": "李四", "available": [(10, 18), (21, 24)]},  
    # 更多员工...  
]  
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def get_available_times(employee):  
    return employee["available"]  

print(get_available_times(employees[0]))  # 输出: [(9, 17), (20, 23)]  
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peak_hours = [(11, 14), (18, 21)]  
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def greedy_scheduling(employees, peak_hours):  
    schedule = []  
    for start, end in peak_hours:  
        for emp in employees:  
            if any(peak_start <= t[0] < peak_end <= t[1] for t in emp["available"]):  
                schedule.append((emp["name"], start, end))  
                emp["available"] = [t for t in emp["available"] if not (peak_start <= t[0] < peak_end <= t[1])]  
                break  
    return schedule  

print(greedy_scheduling(employees, peak_hours))  
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def consider_work_hours(schedule, employee, max_hours=8):  
    current_hours = sum((end - start) for _, start, end in schedule if _ == employee["name"])  
    return current_hours < max_hours  

def optimized_greedy_scheduling(employees, peak_hours, max_hours=8):  
    schedule = []  
    for start, end in peak_hours:  
        for emp in employees:  
            if consider_work_hours(schedule, emp, max_hours) and any(peak_start <= t[0] < peak_end <= t[1] for t in emp["available"]):  
                schedule.append((emp["name"], start, end))  
                emp["available"] = [t for t in emp["available"] if not (peak_start <= t[0] < peak_end <= t[1])]  
                break  
    return schedule  

print(optimized_greedy_scheduling(employees, peak_hours))  
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import random  

# 定义遗传算法的基本组件  
def create_individual(employees, peak_hours):  
    # 随机选择员工覆盖高峰时段  
    individual = []  
    for start, end in peak_hours:  
        emp = random.choice([emp for emp in employees if any(peak_start <= t[0] < peak_end <= t[1] for t in emp["available"])])  
        individual.append((emp["name"], start, end))  
        emp["available"] = [t for t in emp["available"] if not (peak_start <= t[0] < peak_end <= t[1])]  
    return individual  

def fitness(individual):  
    # 定义一个简单的适应度函数，比如覆盖的高峰时段越多，适应度越高  
    covered_hours = sum(end - start for _, start, end in individual)  
    return covered_hours  

def select(population, fitnesses):  
    # 轮盘赌选择  
    total_fitness = sum(fitnesses)  
    probabilities = [f / total_fitness for f in fitnesses]  
    selected_indices = random.choices(range(len(population)), weights=probabilities, k=len(population))  
    return [population[i] for i in selected_indices]  

def crossover(parent1, parent2):  
    # 单点交叉  
    point = random.randint(1, len(parent1) - 1)  
    child1 = parent1[:point] + [t for t in parent2 if t not in parent1[:point]]  
    child2 = parent2[:point] + [t for t in parent1 if t not in parent2[:point]]  
    return child1, child2  

def mutate(individual, mutation_rate=0.1):  
    # 随机变异  
    if random.random() < mutation_rate:  
        idx = random.randint(0, len(individual) - 1)  
        individual[idx] = (random.choice([emp for emp in employees if emp["available"]]), *individual[idx][1:])  
    return individual  

# 遗传算法主流程  
def genetic_algorithm(employees, peak_hours, generations=100, population_size=10, mutation_rate=0.1):  
    population = [create_individual(employees.copy(), peak_hours) for _ in range(population_size)]  
    for _ in range(generations):  
        fitnesses = [fitness(ind) for ind in population]  
        population = select(population, fitnesses)  
        new_population = []  
        for i in range(0, len(population), 2):  
            parent1, parent2 = population[i], population[i + 1]  
            child1, child2 = crossover(parent1, parent2)  
            new_population.extend([mutate(child1, mutation_rate), mutate(child2, mutation_rate)])  
        population = new_population  
    return max(population, key=fitness)  

best_schedule = genetic_algorithm(employees, peak_hours)  
print(best_schedule)  
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# 员工信息  
employees = [  
    {"name": "张三", "available": [(9, 17), (20, 23)]},  
    {"name": "李四", "available": [(10, 18), (21, 24)]},  
    {"name": "王五", "available": [(11, 19), (22, 24)]},  
    {"name": "赵六", "available": [(9, 16), (20, 23)]},  
    {"name": "孙七", "available": [(10, 18), (21, 24)]},  
]  

# 用餐高峰时段  
peak_hours = [(11, 14), (18, 21)]  

# 使用遗传算法优化排班  
best_schedule = genetic_algorithm(employees, peak_hours, generations=200, population_size=20, mutation_rate=0.05)  
print("优化后的排班表：")  
for emp, start, end in best_schedule:  
    print(f"{emp} 从 {start} 到 {end}")  
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