用python写一个基于遗传算法的船舶调度问题代码考虑船舶长度、吃水深度、到达时间、停留时间泊位长度、泊位深度、泊位潮汐变化泊位潮汐变化遵循正弦函数。泊位有多个总时长T=1440泊位潮汐变化为2sin2nppixT - draft当时刻为x该泊位初始深度为draft时

由于题目比较复杂，以下是一个基本的遗传算法框架，需要根据具体情况进行修改和完善。

import random
import numpy as np

# 定义船舶类
class Ship:
    def __init__(self, length, draft, arrive_time, stay_time):
        self.length = length
        self.draft = draft
        self.arrive_time = arrive_time
        self.stay_time = stay_time

# 定义泊位类
class Berth:
    def __init__(self, length, draft, tidal_range):
        self.length = length
        self.draft = draft
        self.tidal_range = tidal_range

# 生成初始种群
def generate_population(ship_list, berth_list, population_size):
    population = []
    for i in range(population_size):
        schedule = []
        for ship in ship_list:
            berth = random.choice(berth_list)
            schedule.append((ship, berth))
        population.append(schedule)
    return population

# 评估适应度
def evaluate_fitness(schedule, T):
    fitness = 0
    for i in range(len(schedule)):
        ship, berth = schedule[i]
        start_time = max(ship.arrive_time, berth.tidal_range[0]) # 船只开始停靠时间
        end_time = start_time + ship.stay_time # 船只结束停靠时间
        if end_time > T: # 船只停靠时间超出总时长
            fitness -= 100 # 惩罚
        else:
            # 判断泊位是否可用
            flag = True
            for j in range(i):
                if schedule[j][1] == berth:
                    other_ship, _ = schedule[j]
                    other_start_time = max(other_ship.arrive_time, berth.tidal_range[0])
                    other_end_time = other_start_time + other_ship.stay_time
                    if (start_time < other_end_time) and (end_time > other_start_time):
                        flag = False # 泊位被占用
                        break
            if flag:
                fitness += 1 # 可用泊位加分
    return fitness

# 选择
def selection(population, fitness):
    selected_population = []
    total_fitness = sum(fitness)
    probabilities = [f / total_fitness for f in fitness] # 计算每个个体被选中的概率
    for i in range(len(population)):
        selected_population.append(random.choices(population, probabilities)[0])
    return selected_population

# 交叉
def crossover(parent1, parent2):
    point = random.randint(1, len(parent1) - 1)
    child1 = parent1[:point] + parent2[point:]
    child2 = parent2[:point] + parent1[point:]
    return child1, child2

# 变异
def mutation(schedule, ship_list, berth_list):
    for i in range(len(schedule)):
        if random.random() < mutation_rate:
            ship, _ = schedule[i]
            new_berth = random.choice(berth_list)
            schedule[i] = (ship, new_berth)
    return schedule

# 遗传算法
def genetic_algorithm(ship_list, berth_list, T, population_size, num_generations, mutation_rate):
    # 生成初始种群
    population = generate_population(ship_list, berth_list, population_size)
    for generation in range(num_generations):
        # 评估适应度
        fitness = [evaluate_fitness(schedule, T) for schedule in population]
        # 选择
        selected_population = selection(population, fitness)
        # 交叉
        next_population = []
        for i in range(0, len(selected_population), 2):
            parent1 = selected_population[i]
            parent2 = selected_population[i+1]
            child1, child2 = crossover(parent1, parent2)
            next_population.append(child1)
            next_population.append(child2)
        # 变异
        next_population = [mutation(schedule, ship_list, berth_list) for schedule in next_population]
        # 更新种群
        population = next_population
    # 评估适应度并返回最优解
    fitness = [evaluate_fitness(schedule, T) for schedule in population]
    best_schedule = population[np.argmax(fitness)]
    return best_schedule

需要注意的是，以上代码中的泊位潮汐变化函数只考虑了周期为一天的情况，如果周期不同需要进行相应的修改。同时，评估适应度的函数也需要根据具体情况进行修改，例如加入考虑船只长度、吃水深度等因素的计算