Best Route in a Grid(dp)

本文介绍了一个矩阵寻路问题,目标是从左上角到右下角,避开值为0的方格,寻找使途经数值乘积末尾0最少的路径。通过动态规划方法,将问题转化为寻找含有最少2和5因子的路径。

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Description

Given an n*n grid with non-negative integers, you start from the upper left corner (1,1) and can only move right or down.

Your task is to find the best route to the lower right corner (n,n) without reaching the grid marked with 0.

The best route is when you multiply all the integers you reach on the route, the number of trailing zeros of the product is minimized.

Input

First line with an integer n.

Then n lines, each line with n integers. The numbers in the grid are <= 1,000,000.

The data guarantee that there is at least one legal route.

Output

One line with an integer indicating the answer.

Sample Input

4
1 3 0 0
0 8 2 25
6 5 0 3
0 15 7 4

Sample Output

2


题意大概是一个矩阵,从(1,1)走到右下角,只能向下或向右走,且不能到达值为0的方格,求最优路径,最佳路径是指途经的数的乘积的末尾连续的0最少。

dp自然是不能用末尾0的个数做状态的,那么怎么求最少的0呢?10分解成5和2,所以问题就变成求最少的5或最少的2的路径。

upd:今天有人问我这题,他觉得这解法有点问题,其实可以用反证法证明,假设路径A min2最小(比最小的min5还小!!),假如有比它更少的0路径,则一定存在一条路径min5更小,这与A min2 最小矛盾。

状态转移方程:dp[i][j]=min(dp[i][j],min(dp[i][j-1],dp[i-1][j])+w[i][j])

初始条件:dp[1][0]=0,其他为较大值

#include <bits/stdc++.h>
using namespace std;
int ma[1100][1100],w[1100][1100],dp[1100][1100];
int inf=1e8;
int main()
{
    int n;
    cin>>n;
    for(int i=1;i<=n;i++)
        for(int j=1;j<=n;j++)
        cin>>ma[i][j];
    for(int i=1;i<=n;i++)
        for(int j=1;j<=n;j++)
         if(!ma[i][j])//不能到达为0的方格
           w[i][j]= inf;
         else
         {
             int t=ma[i][j];
             w[i][j]=0;
             while(t%2==0)
             {
                 w[i][j]++;
                 t/=2;
             }
         }
         memset(dp,0x3f3f3f,sizeof dp);
         dp[1][0]=0;
         for(int i=1;i<=n;i++)
            for(int j=1;j<=n;j++)
             dp[i][j]=min(dp[i][j],min(dp[i][j-1],dp[i-1][j])+w[i][j]);//只能向下或向右
             int ans=dp[n][n];
     for(int i=1;i<=n;i++)
        for(int j=1;j<=n;j++)
         if(!ma[i][j])
           w[i][j]= inf;
         else
         {
             int t=ma[i][j];
             w[i][j]=0;
             while(t%5==0)
             {
                 w[i][j]++;
                 t/=5;
             }
         }
         memset(dp,0x3f3f3f,sizeof dp);
         dp[1][0]=0;
         for(int i=1;i<=n;i++)
            for(int j=1;j<=n;j++)
             dp[i][j]=min(dp[i][j],min(dp[i][j-1],dp[i-1][j])+w[i][j]);
             ans=min(dp[n][n],ans);
             cout<<ans<<endl;
             return 0;
}
题后感想:觉得这种拆分的方法还蛮多地方用的。越来越觉得dp只是一个解题的工具,并不能算一种方法。找到有序的整体(即最优子结构)其实就解了一半,计算的对象要是有序的,要有明显的维度,很多时候一道线性dp题感到棘手,就是感觉题目多了点东西,或者一个对象太大无从下手,如何把多的东西有效的装进一个整体或把一个大的对象拆开往往是解题的突破口。
得到最优结果得到速度太慢优化一下%% 基础数据加载 node_coords = [ 12041.7252, 3892.7009 12051.7044, 3899.4222 12051.4094, 3897.3103 12046.3461, 3904.73 ]; % 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 = [4, 6, 10, 14, 21, 27, 34, 39, 49, 51, 53, 59, 76, 78, 82, 99, 105, 123, 126, 127,129, 133, 134, 160]; known_solution.node2 = [8, 11, 17, 23, 31, 32, 36, 42, 43, 45, 54, 55, 58, 61, 67, 74, 83, 84, 85, 87,90, 91, 100, 103, 106, 113, 116, 118, 124, 136, 137, 138, 139, 140, 141, 142, 143, 144, 149, 150,151, 161]; known_solution.node3 = [3, 7, 9, 16, 18, 20, 28, 29, 30, 33, 40, 44, 47, 50, 56, 57, 62, 64, 66, 69,79, 81, 92, 93, 94, 97, 101, 102, 104, 111, 114, 115, 119, 122, 128, 130, 132, 152, 153, 154,155, 158, 159]; known_solution.node4 = [1, 2, 5, 12, 13, 15, 19, 22, 24, 25, 26, 35, 37, 38, 41, 46, 48, 52, 60, 63,65, 68, 70, 71, 72, 73, 75, 77, 80, 86, 88, 89, 95, 96, 98, 107, 108, 109, 110, 112,117, 120, 121, 125, 131, 135, 145, 146, 147, 148, 156, 157, 162, 163]; %% 遗传算法参数 pop_size = 50; % 种群数量 max_gen = 100; % 最大迭代次数 cross_rate = 0.85; % 交叉概率 mutate_rate = 0.1; % 变异概率 elite_percentage = 0.05; % 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; alloc_vec(known_solution.node4) = 4; % 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, known_solution.node4]; 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 ()'); ylabel('Y坐标 ()'); 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-',... '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 (万元)'); 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
最新发布
06-08
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