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Diffstat (limited to 'ant.py')
| -rw-r--r-- | ant.py | 250 |
1 files changed, 250 insertions, 0 deletions
@@ -0,0 +1,250 @@ +import numpy as np +import copy +import random +from vrptw_base import VrptwGraph + + +class Ant: + def __init__(self, graph: VrptwGraph, start_index=0): + super() + self.graph = graph + self.current_index = 0 + self.vehicle_load = 0 + self.vehicle_travel_time = 0 + self.travel_path = [start_index] + self.arrival_time = [0] + + self.index_to_visit = list(range(graph.node_num)) + self.index_to_visit.remove(start_index) + + self.total_travel_distance = 0 + + def move_to_next_index(self, next_index): + # 更新蚂蚁路径 + self.travel_path.append(next_index) + self.total_travel_distance += self.graph.node_dist_mat[self.current_index][next_index] + + dist = self.graph.node_dist_mat[self.current_index][next_index] + self.arrival_time.append(self.vehicle_travel_time + dist) + + if self.graph.nodes[next_index].is_depot: + # 如果一下个位置为服务器点，则要将车辆负载等清空 + self.vehicle_load = 0 + self.vehicle_travel_time = 0 + + else: + # 更新车辆负载、行驶距离、时间 + self.vehicle_load += self.graph.nodes[next_index].demand + # 如果早于客户要求的时间窗(ready_time)，则需要等待 + + self.vehicle_travel_time += dist + max(self.graph.nodes[next_index].ready_time - self.vehicle_travel_time - dist, 0) + self.graph.nodes[next_index].service_time + self.index_to_visit.remove(next_index) + + self.current_index = next_index + + def index_to_visit_empty(self): + return len(self.index_to_visit) == 0 + + def get_active_vehicles_num(self): + return self.travel_path.count(0)-1 + + def check_condition(self, next_index) -> bool: + """ + 检查移动到下一个点是否满足约束条件 + :param next_index: + :return: + """ + if self.vehicle_load + self.graph.nodes[next_index].demand > self.graph.vehicle_capacity: + return False + + dist = self.graph.node_dist_mat[self.current_index][next_index] + wait_time = max(self.graph.nodes[next_index].ready_time - self.vehicle_travel_time - dist, 0) + service_time = self.graph.nodes[next_index].service_time + + # 检查访问某一个旅客之后，能否回到服务店 + if self.vehicle_travel_time + dist + wait_time + service_time + self.graph.node_dist_mat[next_index][0] > self.graph.nodes[0].due_time: + return False + + # 不可以服务due time之外的旅客 + if self.vehicle_travel_time + dist > self.graph.nodes[next_index].due_time: + return False + + return True + + def cal_next_index_meet_constrains(self): + """ + 找出所有从当前位置（ant.current_index）可达的customer + :return: + """ + next_index_meet_constrains = [] + for next_ind in self.index_to_visit: + if self.check_condition(next_ind): + next_index_meet_constrains.append(next_ind) + return next_index_meet_constrains + + def cal_nearest_next_index(self, next_index_list): + """ + 从待选的customers中选择，离当前位置（ant.current_index）最近的customer + + :param next_index_list: + :return: + """ + current_ind = self.current_index + + nearest_ind = next_index_list[0] + min_dist = self.graph.node_dist_mat[current_ind][next_index_list[0]] + + for next_ind in next_index_list[1:]: + dist = self.graph.node_dist_mat[current_ind][next_ind] + if dist < min_dist: + min_dist = dist + nearest_ind = next_ind + + return nearest_ind + + def cal_total_travel_distance(self, travel_path): + distance = 0 + current_ind = travel_path[0] + for next_ind in travel_path[1:]: + distance += self.graph.node_dist_mat[current_ind][next_ind] + current_ind = next_ind + return distance + + def try_insert_on_path(self, node_id): + """ + 尝试性地将node_id插入当前的travel_path中 + 插入的位置不能违反载重，时间，行驶距离的限制 + 如果有多个位置，则找出最优的位置 + :param node_id: + :return: + """ + feasible_insert_index = [] + feasible_distance = [] + + path = copy.deepcopy(self.travel_path) + + for insert_index in range(len(path)): + if self.graph.nodes[path[insert_index]].is_depot: + continue + + front_depot_index = insert_index + while front_depot_index >= 0 and not self.graph.nodes[self.travel_path[front_depot_index]].is_depot: + front_depot_index -= 1 + front_depot_index = max(front_depot_index, 0) + + check_ant = Ant(self.graph, path[0]) + + # 让check_ant 走过 path中下标从front_depot_index开始到insert_index-1的点 + for i in range(front_depot_index, insert_index): + check_ant.move_to_next_index(path[i]) + + # 开始尝试性地对排序后的index_to_visit中的结点进行访问 + if check_ant.check_condition(node_id): + check_ant.move_to_next_index(node_id) + + # 如果可以到node_id，则要保证vehicle可以行驶回到depot + for next_ind in path[insert_index:]: + if check_ant.check_condition(next_ind): + check_ant.move_to_next_index(next_ind) + if self.graph.nodes[next_ind].is_depot: + feasible_insert_index.append(insert_index) + path.insert(insert_index, node_id) + feasible_distance.append(self.cal_total_travel_distance(path)) + # 如果不可以回到depot，则返回上一层 + else: + break + + if len(feasible_distance) == 0: + return None + else: + feasible_distance = np.array(feasible_distance) + min_insert_ind = np.argmin(feasible_distance) + best_ind = feasible_insert_index[int(min_insert_ind)] + return best_ind + + def insertion_procedure(self): + """ + 为每个未访问的结点尝试性地找到一个合适的位置，插入到当前的travel_path + 插入的位置不能违反载重，时间，行驶距离的限制 + :return: + """ + if self.index_to_visit_empty(): + return + + ind_to_visit = copy.deepcopy(self.index_to_visit) + + demand = np.zeros(len(ind_to_visit)) + for i in range(len(ind_to_visit)): + demand[i] = self.graph.nodes[i].demand + + sorted_ind = np.argsort(demand) + ind_to_visit = ind_to_visit[sorted_ind] + + for node_id in ind_to_visit: + best_insert_index = self.try_insert_on_path(node_id) + if best_insert_index is not None: + self.travel_path.insert(best_insert_index, node_id) + self.index_to_visit.remove(node_id) + + self.total_travel_distance = self.cal_total_travel_distance(self.travel_path) + + def local_search_procedure(self): + """ + 对当前的已经访问完graph中所有节点的travel_path使用cross进行局部搜索 + :return: + """ + # 找出path中所有的depot的位置 + depot_ind = [] + for ind in range(len(self.travel_path)): + if self.graph.nodes[self.travel_path[ind]].is_depot: + depot_ind.append(ind) + + new_path_travel_distance = [] + new_path = [] + # 将self.travel_path分成多段，每段以depot开始，以depot结束，称为route + for i in range(1, len(depot_ind)): + for j in range(i+1, len(depot_ind)): + + # 随机在两段route，各随机选择一段customer id，交换这两段customer id + start_a = random.randint(depot_ind[i-1]+1, depot_ind[i]-1) + end_a = random.randint(depot_ind[i-1]+1, depot_ind[i]-1) + if end_a < start_a: + start_a, end_a = end_a, start_a + + start_b = random.randint(depot_ind[j-1]+1, depot_ind[j]-1) + end_b = random.randint(depot_ind[j - 1] + 1, depot_ind[j] - 1) + if end_b < start_b: + start_b, end_b = end_b, start_b + + path = [] + path.extend(self.travel_path[:start_a]) + path.extend(self.travel_path[start_b:end_b+1]) + path.extend(self.travel_path[end_a:start_b]) + path.extend(self.travel_path[start_a:end_a+1]) + path.extend(self.travel_path[end_b+1:]) + + if len(path) != self.travel_path: + raise RuntimeError('error') + + # 判断新生成的path是否是可行的 + check_ant = Ant(self.graph, path[0]) + for ind in path[1:]: + if check_ant.check_condition(ind): + check_ant.move_to_next_index(ind) + else: + break + # 如果新生成的path是可行的 + if check_ant.index_to_visit_empty(): + # print('success to search') + new_path_travel_distance.append(check_ant.total_travel_distance) + new_path.append(path) + + # 找出新生成的path中，路程最小的 + new_path_travel_distance = np.array(new_path_travel_distance) + min_distance_ind = np.argmin(new_path_travel_distance) + min_distance = new_path_travel_distance[min_distance_ind] + + if min_distance < self.total_travel_distance: + return new_path[int(min_distance_ind)] + else: + return None
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