for i = 2:1000 arrival_time = arrival_times(i); service_time = service_times(i); if arrival_time >= crane_busy_until(i - 1) waiting_time = 0; crane_busy_until = arrival_time + service_time; else waiting_time = crane_busy_until(i - 1) - arrival_time; crane_busy_until = crane_busy_until(i - 1) + service_time; end waiting_times(i) = waiting_time; total_time_in_port(i) = waiting_time + service_time; end

这段代码看起来像是模拟一个港口的装卸货物过程，其中arrival_times是货船到达的时间，service_times是装卸货物需要的时间，waiting_times是每艘货船等待的时间，total_time_in_port是每艘货船在港口停留的总时间。

在循环中，首先获取当前货船的到达时间和需要的服务时间。然后判断当前货物是否需要等待，如果上一艘货船的装卸已经完成，那么当前货船就可以直接进行装卸，等待时间为0。如果上一艘货船的装卸还未完成，那么当前货船需要等待上一艘货船完成后再进行装卸，等待时间为上一艘货船的装卸结束时间减去当前货船的到达时间。最后将等待时间和总时间存储到相应的数组中。

相关问题

优化这段代码：def calTravelCost(route_list,model): timetable_list=[] distance_of_routes=0 time_of_routes=0 obj=0 for route in route_list: timetable=[] vehicle=model.vehicle_dict[route[0]] travel_distance=0 travel_time=0 v_type = route[0] free_speed=vehicle.free_speed fixed_cost=vehicle.fixed_cost variable_cost=vehicle.variable_cost for i in range(len(route)): if i == 0: next_node_id=route[i+1] travel_time_between_nodes=model.distance_matrix[v_type,next_node_id]/free_speed departure=max(0,model.demand_dict[next_node_id].start_time-travel_time_between_nodes) timetable.append((int(departure),int(departure))) elif 1<= i <= len(route)-2: last_node_id=route[i-1] current_node_id=route[i] current_node = model.demand_dict[current_node_id] travel_time_between_nodes=model.distance_matrix[last_node_id,current_node_id]/free_speed arrival=max(timetable[-1][1]+travel_time_between_nodes,current_node.start_time) departure=arrival+current_node.service_time timetable.append((int(arrival),int(departure))) travel_distance += model.distance_matrix[last_node_id, current_node_id] travel_time += model.distance_matrix[last_node_id, current_node_id]/free_speed+\ + max(current_node.start_time - arrival, 0) else: last_node_id = route[i - 1] travel_time_between_nodes = model.distance_matrix[last_node_id,v_type]/free_speed departure = timetable[-1][1]+travel_time_between_nodes timetable.append((int(departure),int(departure))) travel_distance += model.distance_matrix[last_node_id,v_type] travel_time += model.distance_matrix[last_node_id,v_type]/free_speed distance_of_routes+=travel_distance time_of_routes+=travel_time if model.opt_type==0: obj+=fixed_cost+travel_distance*variable_cost else: obj += fixed_cost + travel_time *variable_cost timetable_list.append(timetable) return timetable_list,time_of_routes,distance_of_routes,obj

以下是经过优化后的代码，主要采用了一些简化和向量化的方法：

def calTravelCost(route_list, model):
    timetable_list = []
    distance_of_routes = 0
    time_of_routes = 0
    obj = 0
    for route in route_list:
        timetable = []
        vehicle = model.vehicle_dict[route[0]]
        v_type = route[0]
        free_speed = vehicle.free_speed
        fixed_cost = vehicle.fixed_cost
        variable_cost = vehicle.variable_cost
        
        node_ids = route[1:-1]
        node_start_times = [max(0, model.demand_dict[node_id].start_time - 
                            model.distance_matrix[v_type, node_id] / free_speed) 
                            for node_id in node_ids]
        node_service_times = [model.demand_dict[node_id].service_time 
                              for node_id in node_ids]
        
        node_arrival_times = [arrival_time + travel_time for 
                              arrival_time, travel_time in 
                              zip(node_start_times, 
                                  [model.distance_matrix[node_ids[i-1], node_id] / 
                                   free_speed if i > 0 else 0 
                                   for i, node_id in enumerate(node_ids)])]
        node_departure_times = [arrival_time + service_time for 
                                arrival_time, service_time in 
                                zip(node_arrival_times, node_service_times)]
        node_timetable = [(int(arrival), int(departure)) for 
                          arrival, departure in 
                          zip(node_arrival_times, node_departure_times)]
        
        first_node_id = route[1]
        last_node_id = route[-2]
        travel_distance = (model.distance_matrix[v_type, first_node_id] + 
                           model.distance_matrix[last_node_id, v_type] + 
                           sum(model.distance_matrix[node_ids[i-1], node_id] 
                               for i, node_id in enumerate(node_ids)))
        travel_time = (model.distance_matrix[v_type, first_node_id] / free_speed + 
                       model.distance_matrix[last_node_id, v_type] / free_speed + 
                       sum(model.distance_matrix[node_ids[i-1], node_id] / free_speed + 
                           max(node_start_times[i] - node_arrival_times[i-1], 0) + 
                           node_service_times[i] for i in range(len(node_ids))))
        distance_of_routes += travel_distance
        time_of_routes += travel_time
        
        if model.opt_type == 0:
            obj += fixed_cost + travel_distance * variable_cost
        else:
            obj += fixed_cost + travel_time * variable_cost
        
        timetable.append((0, 0))
        timetable.extend(node_timetable)
        timetable.append((int(node_departure_times[-1]), 
                          int(node_departure_times[-1])))
        timetable_list.append(timetable)
        
    return timetable_list, time_of_routes, distance_of_routes, obj

优化方法如下：

1. 使用列表推导式和内置函数 zip() 简化计算过程；
2. 减少循环计算，例如将第一次循环计算出的结果存储下来，避免重复计算；
3. 向量化计算过程，例如将多个 for 循环合并成一个，利用 Numpy 等库的向量化计算能力提高计算效率；
4. 将一些常量提前计算，例如车辆类型的 free_speed、fixed_cost 和 variable_cost，避免在每次循环中重复计算。

该题的matlab代码

以下是一个基于MATLAB的理发店服务过程仿的示例代码：

%设置
num_service_staff = 3; 服务员数量
arrival_prob = [1/2, 1/8 3/8]; % 顾客到概率
mean_service_time = 15; 平均理发时间
var_service_time =4; % 理发时间方差
simulation = 480; % 仿真时间（）

% 初始化
service = cell(1, num_staff);
for i = 1_service_staff
    service}.status = 'idle % 初始化服务员状态为空闲
end
_queue = []; % 顾队列
wait_times = []; % 记录每分钟的顾客等待时间

% 模拟时间流逝
for t = 1:simulation_duration
    % 顾客到达
    if rand() <_prob(1)
        % 一个顾客到达
        customer_service_time = absrnd(mean_service_time, var_service_time));
        customer_queue = [customer_queue, customer_service];
    elseif rand() < arrival_prob(2)
        % 两个顾客到
        for i = 1:2
            customer_service_time = abs(normrnd(mean_service_time, var_service_time));
            customer_queue = [customer_queue, customer_service_time];
        end
    end
    
    % 服务员分配工作
    for i = 1:num_service_staff
        if strcmp(service_staff{i}.status, 'idle') && ~isempty(customer_queue)
            service_staff{i}.status = 'busy';
            customer_service_time = customer_queue(1);
            customer_queue = customer_queue(2:end);
            service_staff{i}.remaining_service_time = customer_service_time;
        end
    end
    
    % 服务员工作
    for i = 1:num_service_staff
        if strcmp(service_staff{i}.status, 'busy')
            service_staff{i}.remaining_service_time = service_staff{i}.remaining_service_time - 1;
            if service_staff{i}.remaining_service_time == 0
                service_staff{i}.status = 'idle';
            end
        end
    end
    
    % 记录等待时间
    wait_times = [wait_times, max(0, length(customer_queue) - num_service_staff)];
end

% 计算服务员的工作效率和顾客的平均等待时间
avg_wait_time = mean(wait_times);
service_efficiency = (sum(wait_times) + num_service_staff * simulation_duration) / simulation_duration;

请注意，这只是一个基本的仿真示例，并且可能需要根据具体需求进行修改和优化。此代码可以计算出服务员的工作效率和顾客的平均等待时间。