公交车排班问题matlab

公交车调度排班优化是城市公交运营中一个重要的问题，它涉及到如何合理安排公交车辆的运营时间表和司机的工作班次，以最大程度地满足乘客需求、提高运营效率和降低成本。MATLAB是一种强大的数学计算软件，可以用于解决各种优化问题，包括公交车调度排班优化问题。以下是一个使用MATLAB和遗传算法来解决公交车调度排班优化问题的示例代码：

% 设置参数
numBuses = 5; % 公交车数量
numDrivers = 10; % 司机数量
numShifts = 3; % 班次数量
numDays = 7; % 运营天数
popSize = 50; % 种群大小
numGen = 100; % 迭代次数

% 初始化种群
pop = zeros(numBuses*numShifts*numDays,numDrivers);
for i = 1:popSize
    for j = 1:numBuses*numShifts*numDays
        pop(j,randperm(numDrivers,1)) = 1;
    end
end

% 计算适应度
fitness = zeros(popSize,1);
for i = 1:popSize
    schedule = reshape(pop(i,:),[numBuses*numShifts,numDays,numDrivers]);
    fitness(i) = calculateFitness(schedule);
end

% 进化
for i = 1:numGen
    % 选择
    parents = selectParents(pop,fitness);
    
    % 交叉
    offspring = crossover(parents);
    
    % 变异
    offspring = mutate(offspring);
    
    % 计算适应度
    offspringFitness = zeros(size(offspring,1),1);
    for j = 1:size(offspring,1)
        schedule = reshape(offspring(j,:),[numBuses*numShifts,numDays,numDrivers]);
        offspringFitness(j) = calculateFitness(schedule);
    end
    
    % 合并种群
    pop = [pop;offspring];
    fitness = [fitness;offspringFitness];
    
    % 选择下一代
    [fitness,idx] = sort(fitness,'descend');
    pop = pop(idx(1:popSize),:);
end

% 输出最优解
bestSchedule = reshape(pop(1,:),[numBuses*numShifts,numDays,numDrivers]);
disp(bestSchedule);

% 计算适应度函数
function fitness = calculateFitness(schedule)
    % 计算每个司机的工作时间
    driverTime = sum(sum(schedule,1),2);
    driverTime = squeeze(driverTime);
    
    % 计算每个公交车的工作时间
    busTime = sum(sum(schedule,2),3);
    busTime = squeeze(busTime);
    
    % 计算适应度
    fitness = sum(driverTime>8) + sum(busTime>10);
end

% 选择函数
function parents = selectParents(pop,fitness)
    % 轮盘赌选择
    prob = fitness/sum(fitness);
    cumProb = cumsum(prob);
    parents = zeros(size(pop));
    for i = 1:size(pop,1)
        r = rand();
        idx = find(cumProb>=r,1);
        parents(i,:) = pop(idx,:);
    end
end

% 交叉函数
function offspring = crossover(parents)
    % 单点交叉
    offspring = zeros(size(parents));
    for i = 1:2:size(parents,1)
        r = randi(size(parents,2)-1);
        offspring(i,:) = [parents(i,1:r),parents(i+1,r+1:end)];
        offspring(i+1,:) = [parents(i+1,1:r),parents(i,r+1:end)];
    end
end

% 变异函数
function offspring = mutate(parents)
    % 随机变异
    offspring = parents;
    for i = 1:size(offspring,1)
        if rand()<0.01
            j = randi(size(offspring,2));
            offspring(i,j) = 1 - offspring(i,j);
        end
    end
end