智能排课系统

# 排课系统核心算法示例（Python）

class Course:

def __init__(self, name, teacher, time, room):

self.name = name

self.teacher = teacher

self.time = time

self.room = room

class Scheduler:

def __init__(self, courses, rooms, teachers):

self.courses = courses

self.rooms = rooms

self.teachers = teachers

def schedule(self):

scheduled = []

for course in self.courses:

for room in self.rooms:

if self.is_room_available(room, course.time) and self.is_teacher_available(course.teacher, course.time):

course.room = room

scheduled.append(course)

break

return scheduled

def is_room_available(self, room, time):

# 检查教室是否在该时间段可用

return True # 简化逻辑，实际应查询数据库

def is_teacher_available(self, teacher, time):

# 检查教师是否在该时间段有其他课程

return True # 简化逻辑，实际应查询数据库

# 示例数据

courses = [

Course("数学", "张老师", "周一10:00", None),

Course("英语", "李老师", "周二14:00", None),

Course("物理", "王老师", "周三15:00", None)

]

rooms = ["A101", "B202", "C303"]

teachers = ["张老师", "李老师", "王老师"]

scheduler = Scheduler(courses, rooms, teachers)

result = scheduler.schedule()

for course in result:

print(f"课程：{course.name}，教师：{course.teacher}，时间：{course.time}，教室：{course.room}")

import random

# 遗传算法参数

POPULATION_SIZE = 100

GENERATIONS = 100

MUTATION_RATE = 0.1

class Chromosome:

def __init__(self, genes):

self.genes = genes # 基因表示课程安排

self.fitness = 0

def calculate_fitness(self, constraints):

# 计算适应度，例如避免时间冲突、教师冲突等

self.fitness = 0

for i in range(len(self.genes)):

for j in range(i + 1, len(self.genes)):

if self.genes[i].time == self.genes[j].time:

self.fitness -= 1

return self.fitness

class GeneticScheduler:

def __init__(self, courses, rooms, teachers):

self.courses = courses

self.rooms = rooms

self.teachers = teachers

def run(self):

population = [Chromosome([random.choice(rooms) for _ in self.courses]) for _ in range(POPULATION_SIZE)]

for generation in range(GENERATIONS):

# 评估适应度

for chrom in population:

chrom.calculate_fitness(None)

# 选择

sorted_pop = sorted(population, key=lambda x: x.fitness, reverse=True)

selected = sorted_pop[:int(POPULATION_SIZE * 0.2)]

# 交叉

offspring = []

while len(offspring) < POPULATION_SIZE - len(selected):

parent1 = random.choice(selected)

parent2 = random.choice(selected)

child_genes = [parent1.genes[i] if random.random() < 0.5 else parent2.genes[i] for i in range(len(parent1.genes))]

offspring.append(Chromosome(child_genes))

# 变异

for chrom in offspring:

for i in range(len(chrom.genes)):

if random.random() < MUTATION_RATE:

chrom.genes[i] = random.choice(self.rooms)

# 合并

population = selected + offspring

best_chrom = max(population, key=lambda x: x.fitness)

return best_chrom.genes

# 示例数据

courses = ["数学", "英语", "物理"]

rooms = ["A101", "B202", "C303"]

teachers = ["张老师", "李老师", "王老师"]

scheduler = GeneticScheduler(courses, rooms, teachers)

result = scheduler.run()

print("最终排课方案：", result)

CREATE TABLE course (

id INT PRIMARY KEY AUTO_INCREMENT,

name VARCHAR(100) NOT NULL,

teacher_id INT NOT NULL,

time VARCHAR(50) NOT NULL,

room_id INT NOT NULL,

FOREIGN KEY (teacher_id) REFERENCES teacher(id),

FOREIGN KEY (room_id) REFERENCES room(id)

);