随着高校教学规模的不断扩大，课程安排问题日益复杂。传统的手动排课方式已难以满足现代大学对高效、合理课程安排的需求。因此，开发一款智能排课表软件成为高校信息化建设的重要方向。

数据管理系统

在理工大学中，课程安排涉及多个维度，包括教师资源、教室容量、学生选课偏好等。为提高排课效率和准确性，本文提出一种基于遗传算法的课程优化系统。该系统通过模拟自然选择过程，寻找最优的课程分配方案，从而减少冲突并提升资源利用率。

下面是该系统的部分核心代码示例：

    import random

    class Course:
        def __init__(self, name, teacher, time, room):
            self.name = name
            self.teacher = teacher
            self.time = time
            self.room = room

    class Schedule:
        def __init__(self, courses):
            self.courses = courses

        def fitness(self):
            # 计算当前排课方案的适应度
            conflicts = 0
            for i in range(len(self.courses)):
                for j in range(i + 1, len(self.courses)):
                    if self.courses[i].time == self.courses[j].time and self.courses[i].room == self.courses[j].room:
                        conflicts += 1
            return 1 / (conflicts + 1)

    def genetic_algorithm(courses, population_size=50, generations=100):
        population = [Schedule(random.sample(courses, len(courses))) for _ in range(population_size)]
        for _ in range(generations):
            population.sort(key=lambda x: x.fitness(), reverse=True)
            next_generation = population[:int(population_size * 0.2)]
            for _ in range(population_size - len(next_generation)):
                parent1 = random.choice(population[:int(population_size * 0.5)])
                parent2 = random.choice(population[:int(population_size * 0.5)])
                child = Schedule(parent1.courses[:len(courses)//2] + parent2.courses[len(courses)//2:])
                next_generation.append(child)
            population = next_generation
        return max(population, key=lambda x: x.fitness())

    # 示例数据
    courses = [
        Course("数学分析", "张教授", "周一9-11", "A101"),
        Course("计算机基础", "李老师", "周二13-15", "B202"),
        Course("物理实验", "王教授", "周三14-16", "C303")
    ]

    best_schedule = genetic_algorithm(courses)
    print("最佳排课方案：", best_schedule.courses)
    

本系统已在某理工大学的试点中取得良好效果，显著提升了课程安排的合理性与自动化水平，为高校信息化管理提供了有力支持。