用python解决巡检线路排班问题代码

巡检线路排班问题可以使用Python中的遗传算法、模拟退火等优化算法来解决。以下是一个基于遗传算法的排班问题解决方案的代码示例：

import random
import copy

class Schedule:
    def __init__(self, n_days, n_shifts, n_staff):
        self.n_days = n_days
        self.n_shifts = n_shifts
        self.n_staff = n_staff
        self.shifts = []
        for i in range(n_days):
            day_shifts = []
            for j in range(n_shifts):
                day_shifts.append(-1)
            self.shifts.append(day_shifts)

    def assign_shift(self, day, shift, staff):
        self.shifts[day][shift] = staff

    def get_shift(self, day, shift):
        return self.shifts[day][shift]

    def __str__(self):
        res = ""
        for i in range(self.n_days):
            res += "Day " + str(i) + "\n"
            for j in range(self.n_shifts):
                res += "\tShift " + str(j) + ": "
                if self.shifts[i][j] == -1:
                    res += "Unassigned\n"
                else:
                    res += "Staff " + str(self.shifts[i][j]) + "\n"
        return res

class GeneticAlgorithm:
    def __init__(self, n_days, n_shifts, n_staff, population_size, num_generations, crossover_rate, mutation_rate):
        self.n_days = n_days
        self.n_shifts = n_shifts
        self.n_staff = n_staff
        self.population_size = population_size
        self.num_generations = num_generations
        self.crossover_rate = crossover_rate
        self.mutation_rate = mutation_rate

    def run(self):
        population = self.initialize_population()
        for i in range(self.num_generations):
            population = self.evolve_population(population)
            fittest_schedule = self.get_fittest_schedule(population)
            print("Generation " + str(i+1) + ":")
            print(fittest_schedule)
            if self.is_optimal(fittest_schedule):
                break

    def initialize_population(self):
        population = []
        for i in range(self.population_size):
            schedule = self.create_random_schedule()
            population.append(schedule)
        return population

    def create_random_schedule(self):
        schedule = Schedule(self.n_days, self.n_shifts, self.n_staff)
        for i in range(self.n_days):
            for j in range(self.n_shifts):
                staff = random.randint(0, self.n_staff-1)
                schedule.assign_shift(i, j, staff)
        return schedule

    def evolve_population(self, population):
        new_population = []
        for i in range(self.population_size):
            parent1 = self.select_parent(population)
            parent2 = self.select_parent(population)
            child = self.crossover(parent1, parent2)
            if random.random() < self.mutation_rate:
                child = self.mutate(child)
            new_population.append(child)
        return new_population

    def select_parent(self, population):
        tournament_size = 5
        tournament = random.sample(population, tournament_size)
        fittest_schedule = None
        fittest_fitness = float('inf')
        for schedule in tournament:
            fitness = self.calculate_fitness(schedule)
            if fitness < fittest_fitness:
                fittest_schedule = schedule
                fittest_fitness = fitness
        return fittest_schedule

    def crossover(self, parent1, parent2):
        child = copy.deepcopy(parent1)
        for i in range(self.n_days):
            for j in range(self.n_shifts):
                if random.random() < self.crossover_rate:
                    child.assign_shift(i, j, parent2.get_shift(i, j))
        return child

    def mutate(self, schedule):
        mutated_schedule = copy.deepcopy(schedule)
        day = random.randint(0, self.n_days-1)
        shift = random.randint(0, self.n_shifts-1)
        staff = random.randint(0, self.n_staff-1)
        mutated_schedule.assign_shift(day, shift, staff)
        return mutated_schedule

    def calculate_fitness(self, schedule):
        fitness = 0
        for i in range(self.n_days):
            for j in range(self.n_shifts):
                staff = schedule.get_shift(i, j)
                for k in range(j+1, self.n_shifts):
                    if schedule.get_shift(i, k) == staff:
                        fitness += 1
                for k in range(i+1, self.n_days):
                    for l in range(self.n_shifts):
                        if schedule.get_shift(k, l) == staff:
                            fitness += 1
        return fitness

    def get_fittest_schedule(self, population):
        fittest_schedule = None
        fittest_fitness = float('inf')
        for schedule in population:
            fitness = self.calculate_fitness(schedule)
            if fitness < fittest_fitness:
                fittest_schedule = schedule
                fittest_fitness = fitness
        return fittest_schedule

    def is_optimal(self, schedule):
        return self.calculate_fitness(schedule) == 0


if __name__ == "__main__":
    n_days = 7
    n_shifts = 3
    n_staff = 5
    population_size = 100
    num_generations = 1000
    crossover_rate = 0.8
    mutation_rate = 0.1

    ga = GeneticAlgorithm(n_days, n_shifts, n_staff, population_size, num_generations, crossover_rate, mutation_rate)
    ga.run()

该代码实现了 `Schedule` 类和 `GeneticAlgorithm` 类。其中，`Schedule` 类表示一个排班方案，可以通过 `assign_shift` 方法将员工分配到某个班次中。`GeneticAlgorithm` 类实现了遗传算法的主要步骤，包括初始化种群、交叉、突变、选择等操作。其 `calculate_fitness` 方法用于评估一个排班方案的适应度，即计算其中存在的冲突数量。在运行过程中，程序将输出每一代中最优的排班方案，直到找到最优解或达到最大迭代次数。

需要注意的是，该代码仅为示例，实际应用中可能需要对其进行修改和调整以适应具体的巡检线路排班问题。