不要62_打表

杭州人称那些傻乎乎粘嗒嗒的人为62(音:laoer)。
杭州交通管理局经常会扩充一些的士车牌照,新近出来一个好消息,以后上牌照,不再含有不吉利的数字了,这样一来,就可以消除个别的士司机和乘客的心理障碍,更安全地服务大众。
不吉利的数字为所有含有4或62的号码。例如:
62315 73418 88914
都属于不吉利号码。但是,61152虽然含有6和2,但不是62连号,所以不属于不吉利数字之列。
你的任务是,对于每次给出的一个牌照区间号,推断出交管局今次又要实际上给多少辆新的士车上牌照了。

Input 输入的都是整数对n、m(0<n≤m<1000000),如果遇到都是0的整数对,则输入结束。
Output 对于每个整数对,输出一个不含有不吉利数字的统计个数,该数值占一行位置。
Sample Input
1 100
0 0
Sample Output
80


#include<bits/stdc++.h>
using namespace std;

const int N=1e6+10;

int a[N];

int main(){
    ios_base::sync_with_stdio(0);
    cin.tie(0); cout.tie(0);
    //freopen("in.txt","r",stdin);
    for(int i=0;i<N;i++){
        int x=i;
        a[i]=1;
        while(x){
            if(x%10==4||x%100==62){
                a[i]=0;break;
            }
            x/=10;
        }
        a[i]+=a[i-1];
    }
    int r,l;
    while(cin>>l>>r,l||r){
        cout<<a[r]-a[l-1]<<endl;
    }
}

untitled16 最优成本:8022.4132 最优成本:8022.4132 ========== 全局配送方案 ========== 总运营成本: 1.10 万元最适度函数最优成本:8022.4132没有加车辆固定成本%% 基础数据加载 node_coords = [ 12039.846772,3908.892133 12051.704390,3899.422153 12038.547752,3904.837542 ]; % 配送节点坐标 customer_data = xlsread('新建 Microsoft Excel 工作表.xlsx', 'Sheet1'); customer_coords = customer_data(:, 1:2); % 客户坐标 delivery_demand = customer_data(:,3); % 正向配送需求 recycle_demand = customer_data(:,4); % 逆向回收需求 %% 参数设置 max_distance = 400; % 单程最大距离(km) vehicle_speed = 50; % 车速(km/h) vehicle_capacity = 10; % 单车运载量 depot_capacity = 10000; % 节点服务能力 operating_cost = 400; % 节点运营成本 cr1 = 300; % 车辆租赁成本 c0 = 200; % 车辆启动成本 c1 = 0.64; % 载重运输成本系数(元/吨·公里) c2 = 1.96; % 空车行驶成本系数(元/公里) %% 距离矩阵生成 all_points = [node_coords; customer_coords]; distance_matrix = squareform(pdist(all_points, 'euclidean')); known_solution.node1 = [145,70,156,109,73,37,24,117,41,63,15,52,86,3,115,80,5,68,23,100,54,106,67,8,11,42,92,101,153,28,20,94,62,130,128,79,158,40,119,55,43,103,7,104,59,87,95,110,163,48,46,88,75,144,139,140,151,142,91,90,124,58,72,112]; known_solution.node2 = [136,137,147,121,135,157,96,22,1,35,125,74,120,25,89,107,13,71,98,132,36,116,17,97,76,160,126,114,32,77,56,154,111,85,83,61,31,141,149,148,143,138,113,150,16,66,30,122,69,155,57,33,12,162,60,38,108,65,26,2]; known_solution.node3 = [19,146,131,84,47,29,18,78,10,99,64,51,127,82,21,105,6,134,53,14,159,9,44,93,50,161,118,45,102,123,49,27,39,34,129,133,4,81,152]; %% 遗传算法参数 pop_size = 1000; % 种群数量 max_gen = 1000; % 最大迭代次数 cross_rate = 0.9; % 交叉概率 mutate_rate = 0.25; % 变异概率 elite_percentage = 0.10; % 新增 elite_size = max(2, round(pop_size * elite_percentage)); % 新增 %% 染色体编码函数 function chrom = createChrom(nodes_num, customers_num, known_solution) if nargin > 2 && ~isempty(known_solution) % 从已知方案构建染色体 alloc_vec = zeros(1, customers_num); seq_vec = zeros(1, customers_num); % 分配部分编码 alloc_vec(known_solution.node1) = 1; alloc_vec(known_solution.node2) = 2; alloc_vec(known_solution.node3) = 3; % 顺序部分编码 (保持客户在原始列表中的顺序) seq_vec = [known_solution.node1, known_solution.node2, known_solution.node3]; chrom = [alloc_vec, seq_vec]; else % 随机初始化 allocation = randi(nodes_num, 1, customers_num); sequence = randperm(customers_num); chrom = [allocation, sequence]; end end %% 适应度函数 function [total_cost] = fitnessFunc(chrom, distance_matrix, params) nodes_num = size(params.node_coords,1); customers_num = length(params.customer_coords); allocation = chrom(1:customers_num); global_sequence = chrom(customers_num+1:end); total_cost = 0; for n = 1:nodes_num % 获取当前节点的所有客户 node_customers = find(allocation == n); % 无客户分配到当前节点时的处理 if isempty(node_customers) continue; end % 从全局序列中提取分配给当前节点的客户(保持原始顺序) route_order = global_sequence(ismember(global_sequence, node_customers)); % 节点容量检查 node_delivery = sum(params.delivery_demand(route_order)); node_recycle = sum(params.recycle_demand(route_order)); if node_delivery > params.depot_capacity total_cost = total_cost + 1e6; % 大容量惩罚 continue; end % 路径分割与成本计算 vehicle_routes = baseSplitRoutes(route_order, n, params, distance_matrix); vehicle_count = length(vehicle_routes); % 计算运输成本 for r = 1:vehicle_count route = vehicle_routes{r}; transport_cost = calculateSegmentCost(route, n, params, distance_matrix); total_cost = total_cost + transport_cost; end % 累加固定成本 total_cost = total_cost + params.operating_cost + ... vehicle_count * (params.cr1 + params.c0); end end figure('Name','适应度进化曲线','NumberTitle','off'); h_plot = plot(0, 0, 'b-', 0, 0, 'r--'); title('适应度进化过程'); xlabel('迭代代数'); ylabel('适应度值'); legend('最佳适应度', '平均适应度'); grid on; hold on; % 初始化存储数组 best_history = zeros(max_gen,1); avg_history = zeros(max_gen,1); %% 遗传算法主循环 population = arrayfun(@(x) createChrom(size(node_coords,1), size(customer_coords,1)),... 1:pop_size, 'UniformOutput', false); best_cost = Inf; adaptive_params = struct(... 'cross_rate', 0.85,... % 初始交叉率 'mutate_rate', 0.15,... % 初始变异率 'stagnation', 0); % 停滞计数器 for gen = 1:max_gen % 计算适应度 %% 修改后的适应度函数调用(添加max_distance字段) costs = cellfun(@(x) fitnessFunc(x, distance_matrix, struct(... 'node_coords', node_coords,... 'customer_coords', customer_coords,... 'delivery_demand', delivery_demand,... 'recycle_demand', recycle_demand,... 'depot_capacity', depot_capacity,... 'vehicle_capacity', vehicle_capacity,... 'max_distance', max_distance,... % 新增字段 'operating_cost', operating_cost,... 'cr1', cr1,... 'c0', c0,... 'c1', c1,... 'c2', c2)),... population); [min_cost, idx] = min(costs); current_avg = mean(costs); best_history(gen) = min_cost; avg_history(gen) = current_avg; % 更新可视化曲线 set(h_plot(1), 'XData', 1:gen, 'YData', best_history(1:gen)); set(h_plot(2), 'XData', 1:gen, 'YData', avg_history(1:gen)); xlim([1 max_gen]); drawnow; if min_cost < best_cost best_solution = population{idx}; best_cost = min_cost; end [sorted_costs, sort_idx] = sort(costs); elite_pop = population(sort_idx(1:elite_size)); % 选择操作 select_size = pop_size - elite_size; selected_pop = tournamentSelection(population, costs, 3, select_size); % 交叉操作 new_population = cell(1, select_size); num_pairs = floor(select_size/2); % 计算完整配对数 % 处理成对交叉 for i = 1:num_pairs idx1 = 2*i-1; % 第一个个体索引 idx2 = 2*i; % 第二个个体索引 parent1 = selected_pop{idx1}; parent2 = selected_pop{idx2}; if rand() < cross_rate [child1, child2] = depotCrossover(parent1, parent2, size(customer_coords,1)); new_population{idx1} = child1; new_population{idx2} = child2; else new_population{idx1} = parent1; new_population{idx2} = parent2; end end % 处理奇数情况的剩余个体 if mod(select_size, 2) == 1 last_idx = select_size; if rand() < mutate_rate % 当种群为奇数时,最后一个个体变异概率增加 new_population{last_idx} = depotMutate(selected_pop{last_idx},... size(node_coords,1),... size(customer_coords,1)); else new_population{last_idx} = selected_pop{last_idx}; end end % 变异操作 for i = 1:select_size if rand() < mutate_rate new_population{i} = depotMutate(new_population{i},... size(node_coords,1),... size(customer_coords,1)); end end if gen > 20 && std(costs) < 0.1*mean(costs) adaptive_params.cross_rate = min(0.95, adaptive_params.cross_rate + 0.05); adaptive_params.mutate_rate = min(0.3, adaptive_params.mutate_rate + 0.02); else adaptive_params.cross_rate = 0.85; adaptive_params.mutate_rate = 0.15; end % 使用调整后的参数 cross_rate = adaptive_params.cross_rate; mutate_rate = adaptive_params.mutate_rate; population = [elite_pop, new_population]; end %% 结果显示 disp(['最优成本:' num2str(best_cost)]); visualizeRoutes(best_solution, node_coords, customer_coords); %% 修正后的可视化函数(分拆为两个独立函数) function visualizeRoutes(chrom, depot_coords, customer_coords) % 整体路径可视化函数 num_customers = length(customer_coords); num_depots = size(depot_coords,1); figure; hold on; % 绘制仓库 scatter(depot_coords(:,1), depot_coords(:,2), 100, 'k^', 'filled'); % 绘制客户点 scatter(customer_coords(:,1), customer_coords(:,2), 50, 'bo'); % 解析路径 for d = 1:num_depots depot_customers = find(chrom(1:num_customers) == d); if ~isempty(depot_customers) [~, seq_pos] = ismember(depot_customers, chrom(num_customers+1:end)); valid_seq = seq_pos(seq_pos > 0); [~, order] = sort(valid_seq); sorted_customers = depot_customers(order); route = [depot_coords(d,:); customer_coords(sorted_customers,:); depot_coords(d,:)]; plot(route(:,1), route(:,2), 'LineWidth', 1.5); end end hold off; title('全局配送路径'); xlabel('X坐标'); ylabel('Y坐标'); legend('仓库', '客户点'); end function visualizeDepotRoutes(chrom, depot_coords, customer_coords) % 各节点独立路径可视化函数 num_customers = length(customer_coords); num_depots = size(depot_coords,1); for d = 1:num_depots figure('Position', [200+(d-1)*50, 200+(d-1)*50, 600, 400]); hold on; title(['配送中心' num2str(d) '路径规划']); % 绘制当前配送中心 scatter(depot_coords(d,1), depot_coords(d,2), 150, 'r^', 'filled'); depot_customers = find(chrom(1:num_customers) == d); if ~isempty(depot_customers) [~, seq_pos] = ismember(depot_customers, chrom(num_customers+1:end)); valid_seq = seq_pos(seq_pos > 0 & seq_pos <= length(chrom)-num_customers); [~, order] = sort(valid_seq); sorted_customers = depot_customers(order); % 客户点标注 scatter(customer_coords(sorted_customers,1),... customer_coords(sorted_customers,2),... 80, 'bo', 'filled'); text_offset = 0.1 * max(range(customer_coords)); text(customer_coords(sorted_customers,1)+text_offset,... customer_coords(sorted_customers,2)+text_offset,... cellstr(num2str(sorted_customers')),... 'FontSize',8); % 路径绘制 route = [depot_coords(d,:); customer_coords(sorted_customers,:); depot_coords(d,:)]; plot(route(:,1), route(:,2), 'b--o',... 'LineWidth',1.5,... 'MarkerSize',6,... 'MarkerFaceColor','w'); else text(mean(depot_coords(d,1)), mean(depot_coords(d,2)),... '无服务客户',... 'HorizontalAlignment','center',... 'FontSize',12); end xlabel('X坐标 (米)'); ylabel('Y坐标 (米)'); grid on; axis equal; hold off; end end %% 交叉操作函数实现 function [child1, child2] = depotCrossover(parent1, parent2, num_customers) % 分配部分交叉(均匀交叉) alloc_part1 = parent1(1:num_customers); alloc_part2 = parent2(1:num_customers); mask = randi([0 1], 1, num_customers); child1_alloc = alloc_part1.*mask + alloc_part2.*(~mask); child2_alloc = alloc_part1.*(~mask) + alloc_part2.*mask; % 路径顺序交叉(OX交叉) seq_part1 = parent1(num_customers+1:end); seq_part2 = parent2(num_customers+1:end); [child1_seq, child2_seq] = oxCrossover(seq_part1, seq_part2); child1 = [child1_alloc, child1_seq]; child2 = [child2_alloc, child2_seq]; end %% 修正后的OX交叉辅助函数 function [child1, child2] = oxCrossover(parent1, parent2) n = length(parent1); cp = sort(randi(n-1,1,2)); % 确保交叉点有效 if cp(1) == cp(2), cp(2) = cp(2)+1; end % 防止相同切点 % 子代1生成 segment = parent1(cp(1):cp(2)); remaining = parent2(~ismember(parent2, segment)); child1 = [remaining(1:cp(1)-1), segment, remaining(cp(1):end)]; % 子代2生成(修正索引错误) segment = parent2(cp(1):cp(2)); remaining = parent1(~ismember(parent1, segment)); % 确保索引不越界 if (cp(1)-1) <= length(remaining) part1 = remaining(1:cp(1)-1); else part1 = remaining(1:end); end child2 = [part1, segment, remaining(cp(1):end)]; end %% 变异操作函数实现 function mutated = depotMutate(chrom, num_depots, num_customers) if rand() < 0.5 % 分配变异:随机改变一个客户的分配 pos = randi(num_customers); new_depot = randi(num_depots); mutated = chrom; mutated(pos) = new_depot; else % 路径顺序变异:交换两个位置 seq = chrom(num_customers+1:end); swap_pos = randperm(num_customers, 2); seq(swap_pos) = seq(fliplr(swap_pos)); mutated = [chrom(1:num_customers), seq]; end end %% 历史最优成本可视化 % 生成累积最优成本数组 cumulative_min = cummin(best_history); figure('Color','w'); plot(cumulative_min, 'b-o',... 'LineWidth',1.2,... 'MarkerSize',4,... 'MarkerFaceColor','w'); % 设置坐标轴标签 xlabel('迭代代数'); ylabel('历史最优成本 (元)'); title('全局最优成本进化过程'); % 自动标注最终最优值 [final_min, final_gen] = min(cumulative_min); text(final_gen, final_min,... sprintf(' %.2f万 @%d代', final_min/10000, final_gen),... 'VerticalAlignment','bottom',... 'FontSize',9); % 智能坐标轴设置 ax = gca; ax.YAxis.Exponent = floor(log10(final_min)) - 1; % 自动确定指数 grid on; %% 新增的运输成本计算函数 function cost = calculateRouteCost(route, params, distance_matrix) num_nodes = size(params.node_coords,1); depot_id = mode(params.chrom(route)); % 获取所属配送中心 % 正向运输成本 forward_cost = 0; current_load = sum(params.delivery_demand(route)); % 配送中心到第一个客户 from = depot_id; to = route(1) + num_nodes; distance = distance_matrix(from, to); forward_cost = forward_cost + (params.c1*current_load + params.c2)*distance; % 客户间运输 for k = 2:length(route) from = route(k-1) + num_nodes; to = route(k) + num_nodes; distance = distance_matrix(from, to); current_load = current_load - params.delivery_demand(route(k-1)); forward_cost = forward_cost + (params.c1*current_load + params.c2)*distance; end % 逆向运输成本 recycle_load = sum(params.recycle_demand(route)); from = route(end) + num_nodes; to = depot_id; distance = distance_matrix(from, to); recycle_cost = (params.c1*recycle_load + params.c2)*distance; cost = forward_cost + recycle_cost; end %% 配送方案输出函数 function printDeliveryPlan(best_solution, params, distance_matrix) num_depots = size(params.node_coords,1); num_customers = size(params.customer_coords,1); % 解析染色体 allocation = best_solution(1:num_customers); global_sequence = best_solution(num_customers+1:end); % 创建结果结构体 depot_info = struct(... 'DepotID', {},... 'Vehicles', {},... 'TotalCost', {},... 'Details', {}); % 遍历所有配送中心 for depot_id = 1:num_depots % 获取当前配送中心分配的客户 customers = find(allocation == depot_id); if isempty(customers) continue; end % 获取路径顺序 [~, seq_pos] = ismember(customers, global_sequence); valid_seq = seq_pos(seq_pos > 0); [~, sort_idx] = sort(valid_seq); route_order = customers(sort_idx); % 路径分割 vehicle_routes = baseSplitRoutes(route_order, depot_id, params, distance_matrix); % 计算成本和详细信息 depot_cost = 0; vehicle_details = cell(length(vehicle_routes),1); for v = 1:length(vehicle_routes) route = vehicle_routes{v}; [cost, detail] = calculateVehicleCost(route, depot_id, params, distance_matrix); vehicle_details{v} = detail; depot_cost = depot_cost + cost; end % 添加固定成本 depot_cost = depot_cost + params.operating_cost + ... length(vehicle_routes)*(params.cr1 + params.c0); % 存储结果 depot_info(end+1) = struct(... 'DepotID', depot_id,... 'Vehicles', length(vehicle_routes),... 'TotalCost', depot_cost,... 'Details', {vehicle_details}); end %% 打印结果 fprintf('========== 全局配送方案 ==========\n'); total_cost = sum([depot_info.TotalCost]); fprintf('总运营成本: %.2f 万元\n', total_cost/10000); for d = 1:length(depot_info) fprintf('\n=== 配送中心%d ===\n', depot_info(d).DepotID); fprintf('派出车辆: %d\n', depot_info(d).Vehicles); fprintf('中心总成本: %.2f 万元\n', depot_info(d).TotalCost/10000); % 打印车辆明细 fprintf('\n车辆明细:\n'); fprintf('%-8s%-12s%-12s%-10s%-10s%-12s%-15s\n',... '车辆ID','正向载货量','逆向载载量','里程(km)','运输成本','总成本','服务客户顺序'); for v = 1:length(depot_info(d).Details) detail = depot_info(d).Details{v}; total = detail.transport_cost + params.cr1 + params.c0; % 生成客户顺序字符串 customer_str = strjoin(arrayfun(@(x) sprintf('%d',x), detail.customers, 'UniformOutput', false),'->'); fprintf('%-8d%-12.2f%-12.2f%-10.2f%-10.2f%-12.2f%-15s\n',... v,... detail.delivery_load,... detail.recycle_load,... detail.distance,... detail.transport_cost,... total,... customer_str); % 新增客户顺序输出 end end end %% 非递归路径分割函数 function vehicle_routes = baseSplitRoutes(route_order, depot_id, params, distance_matrix) vehicle_routes = {}; num_nodes = size(params.node_coords,1); i = 1; % 预计算所有客户到仓库的距离 depot_distances = distance_matrix(depot_id, num_nodes+1:end); while i <= length(route_order) current_load = 0; current_distance = 0; segment = []; % 初始仓库到第一个客户的检查 first_cust = route_order(i); start_distance = depot_distances(first_cust); return_distance = distance_matrix(num_nodes+first_cust, depot_id); for j = i:length(route_order) cust = route_order(j); % 新增客户后的载重检查 new_load = current_load + params.delivery_demand(cust) + params.recycle_demand(cust); % 计算新增客户后的总距离 if isempty(segment) new_dist = start_distance + return_distance; else last_cust = segment(end); leg1 = distance_matrix(num_nodes+last_cust, num_nodes+cust); leg2 = distance_matrix(num_nodes+cust, depot_id); new_dist = current_distance - distance_matrix(num_nodes+last_cust, depot_id)... + leg1 + leg2; end % 约束条件验证 if new_load > params.vehicle_capacity || new_dist > params.max_distance break; end % 更新当前路径段 segment = [segment, cust]; current_load = new_load; current_distance = new_dist; end if ~isempty(segment) vehicle_routes{end+1} = segment; i = i + length(segment); else % 处理无法服务的客户 warning('客户%d无法满足约束条件', route_order(i)); i = i + 1; end end end %% 修正后的车辆成本计算函数 function [total_cost, detail] = calculateVehicleCost(route, depot_id, params, distance_matrix) num_nodes = size(params.node_coords,1); % 运输成本计算 transport_cost = 0; total_distance = 0; % 仓库到第一个客户 from = depot_id; to = route(1) + num_nodes; distance = distance_matrix(from, to); transport_cost = transport_cost + (params.c1*sum(params.delivery_demand(route)) + params.c2)*distance; total_distance = total_distance + distance; % 客户间移动 for k = 2:length(route) from = route(k-1) + num_nodes; to = route(k) + num_nodes; distance = distance_matrix(from, to); remaining_delivery = sum(params.delivery_demand(route(k:end))); transport_cost = transport_cost + (params.c1*remaining_delivery + params.c2)*distance; total_distance = total_distance + distance; end % 返回仓库 from = route(end) + num_nodes; to = depot_id; distance = distance_matrix(from, to); transport_cost = transport_cost + (params.c1*sum(params.recycle_demand(route)) + params.c2)*distance; total_distance = total_distance + distance; % 修正后的结构体定义(合并字段定义) detail = struct(... 'customers', route,... % 客户顺序 'delivery_load', sum(params.delivery_demand(route)),... 'recycle_load', sum(params.recycle_demand(route)),... 'distance', total_distance,... 'transport_cost',transport_cost); total_cost = transport_cost + params.cr1 + params.c0; end %% 在主循环后调用输出函数(添加在结果显示部分) % 结果显示 disp(['最优成本:' num2str(best_cost)]); visualizeRoutes(best_solution, node_coords, customer_coords); visualizeDepotRoutes(best_solution, node_coords, customer_coords); % 分节点视图 %% 修改后的printDeliveryPlan调用 printDeliveryPlan(best_solution, struct(... 'node_coords', node_coords,... 'customer_coords', customer_coords,... 'delivery_demand', delivery_demand,... 'recycle_demand', recycle_demand,... 'depot_capacity', depot_capacity,... 'vehicle_capacity', vehicle_capacity,... 'max_distance', max_distance,... % 新增字段 'operating_cost', operating_cost,... 'cr1', cr1,... 'c0', c0,... 'c1', c1,... 'c2', c2), distance_matrix); function optimized_route = twoOptOptimization(route, distance_matrix, params) num_nodes = length(route); improved = true; best_route = route; best_cost = calculateRouteCost(route, params, distance_matrix); while improved improved = false; for i = 1:num_nodes-1 for j = i+2:num_nodes new_route = best_route; new_route(i+1:j) = new_route(j:-1:i+1); new_cost = calculateRouteCost(new_route, params, distance_matrix); if new_cost < best_cost best_route = new_route; best_cost = new_cost; improved = true; end end end end optimized_route = best_route; end function selected = tournamentSelection(population, fitness, tournament_size, select_size) pop_size = length(population); selected = cell(1, select_size); for i = 1:select_size candidates = randperm(pop_size, tournament_size); [~, idx] = min(fitness(candidates)); % 选择适应度最优的个体 selected{i} = population{candidates(idx)}; end end function optimized_routes = optimizedSplitRoutes(route_order, depot_id, params, distance_matrix) % 调用基础函数 raw_routes = baseSplitRoutes(route_order, depot_id, params, distance_matrix); % 2-opt优化处理 optimized_routes = cellfun(@(x) twoOptOptimization(x, distance_matrix, params),... raw_routes, 'UniformOutput', false); end function transport_cost = calculateSegmentCost(segment, depot_id, params, distance_matrix) num_nodes = size(params.node_coords,1); transport_cost = 0; % 正向运输成本 current_delivery = sum(params.delivery_demand(segment)); % 配送中心到第一个客户 from = depot_id; to = segment(1) + num_nodes; distance = distance_matrix(from, to); transport_cost = transport_cost + (params.c1*current_delivery + params.c2)*distance; % 客户间运输 for k = 2:length(segment) from = segment(k-1) + num_nodes; to = segment(k) + num_nodes; distance = distance_matrix(from, to); remaining_delivery = current_delivery - sum(params.delivery_demand(segment(1:k-1))); transport_cost = transport_cost + (params.c1*remaining_delivery + params.c2)*distance; end % 逆向运输成本 recycle_load = sum(params.recycle_demand(segment)); from = segment(end) + num_nodes; to = depot_id; distance = distance_matrix(from, to); transport_cost = transport_cost + (params.c1*recycle_load + params.c2)*distance; end function [vehicle_routes, extra_vehicles] = enhancedSplitRoutes(route_order, depot_id, params, distance_matrix) vehicle_routes = {}; extra_vehicles = struct('customers', {}, 'count', {}, 'cost', {}); i = 1; while i <= length(route_order) current_load = 0; segment = []; extra_count = 0; % 逐个客户装载直到容量上限 for j = i:length(route_order) customer = route_order(j); new_load = current_load + params.delivery_demand(customer) + params.recycle_demand(customer); % 分载条件检查 if new_load > params.vehicle_capacity % 处理单个客户超容的情况 if isempty(segment) extra_count = extra_count + 1; extra_vehicles(end+1) = struct(... 'customers', customer,... 'count', 1,... 'cost', params.c0 + params.cr1); i = i + 1; % 跳过当前客户 break; end % 正常分载处理 break; end segment = [segment, customer]; current_load = new_load; end if ~isempty(segment) vehicle_routes{end+1} = segment; i = i + length(segment); end % 记录额外车辆数 if extra_count > 0 extra_vehicles(end).count = extra_count; end end end %% 修改后的成本计算函数 function [node_cost, extra_info] = calculateNodeCost(route_order, depot_id, params, distance_matrix) [vehicle_routes, extra_vehicles] = enhancedSplitRoutes(route_order, depot_id, params, distance_matrix); transport_cost = 0; % 常规运输成本 for v = 1:length(vehicle_routes) segment = vehicle_routes{v}; transport_cost = transport_cost + calculateSegmentCost(segment, depot_id, params, distance_matrix); end % 额外车辆成本 extra_cost = sum([extra_vehicles.cost]) .* sum([extra_vehicles.count]); % 总成本计算 node_cost = transport_cost + ... (length(vehicle_routes) * (params.cr1 + params.c0)) + ... extra_cost; % 返回额外车辆信息 extra_info = extra_vehicles; end
06-04
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