智能排课系统与培训机构的融合：从代码实现到实际应用

# 智能排课基础示例

import itertools

# 定义课程

courses = ['数学', '英语', '物理']

teachers = ['王老师', '李老师', '张老师']

classrooms = ['101', '102', '103']

time_slots = ['09:00-10:30', '10:40-12:10', '14:00-15:30']

# 每个课程需要分配的资源

course_teacher = {course: teacher for course, teacher in zip(courses, teachers)}

course_classroom = {course: classroom for course, classroom in zip(courses, classrooms)}

# 尝试为所有课程分配时间

def assign_time(courses, time_slots):

for times in itertools.product(time_slots, repeat=len(courses)):

# 检查是否有时间冲突

if all(times[i] != times[j] for i, j in itertools.combinations(range(len(courses)), 2)):

return dict(zip(courses, times))

return None

# 执行排课

schedule = assign_time(courses, time_slots)

print("排课结果：", schedule)

from ortools.constraint_solver import pywrapcp

# 创建求解器

solver = pywrapcp.Solver('CourseScheduling')

# 定义变量

courses = ['数学', '英语', '物理']

time_slots = ['09:00-10:30', '10:40-12:10', '14:00-15:30']

classrooms = ['101', '102', '103']

teachers = ['王老师', '李老师', '张老师']

# 为每个课程分配时间、教室、老师

course_vars = {}

for course in courses:

course_vars[course] = solver.IntVar(0, len(time_slots) - 1, f'course_{course}_time')

course_vars[f'{course}_class'] = solver.IntVar(0, len(classrooms) - 1, f'course_{course}_class')

course_vars[f'{course}_teacher'] = solver.IntVar(0, len(teachers) - 1, f'course_{course}_teacher')

# 添加约束：同一时间不同课程不能在同一个教室

for t in range(len(time_slots)):

for c in range(len(classrooms)):

solver.Add(solver.Sum([course_vars[course] == t and course_vars[f'{course}_class'] == c for course in courses]) <= 1)

# 添加约束：老师不能同时上两门课

for t in range(len(time_slots)):

for teacher_id in range(len(teachers)):

solver.Add(solver.Sum([course_vars[course] == t and course_vars[f'{course}_teacher'] == teacher_id for course in courses]) <= 1)

# 解决问题

solution = solver.Solve()

if solution:

for course in courses:

time_idx = course_vars[course].Value()

class_idx = course_vars[f'{course}_class'].Value()

teacher_idx = course_vars[f'{course}_teacher'].Value()

print(f"{course} -> 时间: {time_slots[time_idx]}, 教室: {classrooms[class_idx]}, 老师: {teachers[teacher_idx]}")

else:

print("没有找到可行的排课方案。")

from flask import Flask, request, jsonify

from ortools.constraint_solver import pywrapcp

app = Flask(__name__)

@app.route('/schedule', methods=['POST'])

def schedule_courses():

data = request.json

courses = data.get('courses', [])

time_slots = data.get('time_slots', [])

classrooms = data.get('classrooms', [])

teachers = data.get('teachers', [])

solver = pywrapcp.Solver('CourseScheduling')

course_vars = {}

for course in courses:

course_vars[course] = solver.IntVar(0, len(time_slots) - 1, f'course_{course}_time')

course_vars[f'{course}_class'] = solver.IntVar(0, len(classrooms) - 1, f'course_{course}_class')

course_vars[f'{course}_teacher'] = solver.IntVar(0, len(teachers) - 1, f'course_{course}_teacher')

for t in range(len(time_slots)):

for c in range(len(classrooms)):

solver.Add(solver.Sum([course_vars[course] == t and course_vars[f'{course}_class'] == c for course in courses]) <= 1)

for t in range(len(time_slots)):

for teacher_id in range(len(teachers)):

solver.Add(solver.Sum([course_vars[course] == t and course_vars[f'{course}_teacher'] == teacher_id for course in courses]) <= 1)

solution = solver.Solve()

result = {}

if solution:

for course in courses:

time_idx = course_vars[course].Value()

class_idx = course_vars[f'{course}_class'].Value()

teacher_idx = course_vars[f'{course}_teacher'].Value()

result[course] = {

'time': time_slots[time_idx],

'classroom': classrooms[class_idx],

'teacher': teachers[teacher_idx]

}

else:

result['error'] = '无法生成有效排课方案'

return jsonify(result)

if __name__ == '__main__':

app.run(debug=True)