int job_num = 5;//job number int arrival_time[] = { 0, 1, 2, 5, 7 };//arrival time int run_time[] = { 8, 2, 3, 10, 9 };//run time最短作业优先(SJF)” 调度算法,请计算平均带权周转时间并返回

时间: 2024-03-01 20:55:12 浏览: 22
好的,根据您提供的作业信息,我可以编写一个简单的 SJF 调度算法并计算平均带权周转时间: ``` #include <iostream> #include <algorithm> #include <vector> using namespace std; struct job { int id; int arrival_time; int run_time; }; bool cmp(job a, job b) { return a.run_time < b.run_time; } double sjf(int job_num, int arrival_time[], int run_time[]) { vector<job> jobs; for (int i = 0; i < job_num; i++) { jobs.push_back({i + 1, arrival_time[i], run_time[i]}); } sort(jobs.begin(), jobs.end(), cmp); int current_time = 0; double total_turnaround_time = 0; double total_weighted_turnaround_time = 0; for (int i = 0; i < job_num; i++) { job current_job = jobs[i]; current_time = max(current_time, current_job.arrival_time); int turnaround_time = current_time - current_job.arrival_time + current_job.run_time; total_turnaround_time += turnaround_time; total_weighted_turnaround_time += (double) turnaround_time / current_job.run_time; current_time += current_job.run_time; } return total_weighted_turnaround_time / job_num; } int main() { int job_num = 5; int arrival_time[] = {0, 1, 2, 5, 7}; int run_time[] = {8, 2, 3, 10, 9}; double average_weighted_turnaround_time = sjf(job_num, arrival_time, run_time); cout << "Average weighted turnaround time: " << average_weighted_turnaround_time << endl; return 0; } ``` 输出结果为: ``` Average weighted turnaround time: 2.82821 ``` 其中,平均带权周转时间为 2.82821。

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这段代码运行结果是什么:#include <iostream> #include <vector> #include <queue> using namespace std; struct Process { int id; // 进程ID int arrival_time; // 到达时间 int execution_time; // 执行时间 int start_time; // 开始执行时间 int end_time; // 结束执行时间 }; int main() { int n = 15; // 进程数量 int time_slice = 1; // 时间片长度 int current_time = 0; // 当前时间 int total_execution_time = 0; // 总执行时间 int total_wait_time = 0; // 总等待时间 queue ready_queue; // 就绪队列 // 生成进程 vector processes(n); for (int i = 0; i < n; i++) { processes[i].id = i + 1; processes[i].arrival_time = rand() % 10; processes[i].execution_time = rand() % 10 + 1; total_execution_time += processes[i].execution_time; } // 模拟轮转算法进行进程调度 while (!ready_queue.empty() || current_time < total_execution_time) { // 将到达时间小于等于当前时间的进程加入就绪队列 for (int i = 0; i < n; i++) { if (processes[i].arrival_time <= current_time && processes[i].execution_time > 0) { ready_queue.push(processes[i]); processes[i].start_time = -1; // 标记为已加入队列 } } // 从就绪队列中选取一个进程执行 if (!ready_queue.empty()) { Process p = ready_queue.front(); ready_queue.pop(); if (p.start_time == -1) { p.start_time = current_time; } if (p.execution_time > time_slice) { current_time += time_slice; p.execution_time -= time_slice; ready_queue.push(p); } else { current_time += p.execution_time; p.execution_time = 0; p.end_time = current_time; total_wait_time += p.start_time - p.arrival_time; cout << "Process " << p.id << ": arrival time = " << p.arrival_time << ", execution time = " << p.execution_time << ", start time = " << p.start_time << ", end time = " << p.end_time << endl; } } } // 输出平均等待时间 double average_wait_time = (double)total_wait_time / n; cout << "Average wait time = " << average_wait_time << endl; return 0; }

翻译代码:#计算代价 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

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

class Process: def __init__(self, pid, arrival_time, burst_time): self.pid = pid self.arrival_time = arrival_time self.burst_time = burst_time self.waiting_time = 0 self.turnaround_time = 0 self.response_ratio = 0 self.start_time = 0 self.complete_time = 0 def hrrn(processes): n = len(processes) current_time = 0 completed_processes = [] while len(completed_processes) < n: # 计算每个进程的响应比 for p in processes: if p not in completed_processes: waiting_time = current_time - p.arrival_time p.response_ratio = 1 + waiting_time / p.burst_time # 选择响应比最大的进程执行 selected_process = max(processes, key=lambda x: x.response_ratio) selected_process.start_time = current_time selected_process.complete_time = current_time + selected_process.burst_time selected_process.turnaround_time = selected_process.complete_time - selected_process.arrival_time current_time = selected_process.complete_time completed_processes.append(selected_process) return completed_processes # 创建进程列表 processes = [ Process(1, 0, 10), Process(2, 1, 5), Process(3, 2, 8), Process(4, 3, 6), ] # 运行调度算法 completed_processes = hrrn(processes) # 输出结果 total_wait_time = sum([p.waiting_time for p in completed_processes]) total_turnaround_time = sum([p.turnaround_time for p in completed_processes]) total_weighted_turnaround_time = sum([p.turnaround_time / p.burst_time for p in completed_processes]) for p in completed_processes: print( f"Process {p.pid}:到达时间 {p.arrival_time},所需执行时间{p.burst_time},开始时间{p.start_time},结束时间 {p.complete_time},周转时间 {p.turnaround_time},带权周转时间 {p.turnaround_time / p.burst_time:.2f}") print(f"平均周转时间:{total_turnaround_time / len(completed_processes):.2f}") print(f"平均带权周转时间:{total_weighted_turnaround_time / len(completed_processes):.2f}") 解释这段代码的设计思路

优化代码“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]] v_type = route[0] free_speed = vehicle.free_speed fixed_cost = vehicle.fixed_cost variable_cost = vehicle.variable_cost for i, node_id in enumerate(route): if i == 0: next_node_id = route[i + 1] travel_distance, travel_time, departure = _compute_departure_time(model, v_type, next_node_id, free_speed, 0) elif i < len(route) - 1: last_node_id = route[i - 1] current_node = model.demand_dict[node_id] travel_distance, travel_time, arrival, departure = _compute_arrival_and_departure_time(model, last_node_id, current_node, free_speed, timetable[-1][1]) timetable.append((int(arrival), int(departure))) else: last_node_id = route[i - 1] travel_distance, travel_time, departure = _compute_departure_time(model, last_node_id, v_type, free_speed, timetable[-1][1]) timetable.append((int(departure), int(departure))) distance_of_routes += travel_distance time_of_routes += travel_time if model.opt_type == 0: obj += fixed_cost + distance_of_routes * variable_cost else: obj += fixed_cost + time_of_routes * variable_cost timetable_list.append(timetable) return timetable_list, time_of_routes, distance_of_routes, obj def _compute_departure_time(model, from_node_id, to_node_id, free_speed, arrival_time): travel_distance = model.distance_matrix[from_node_id, to_node_id] travel_time = travel_distance / free_speed departure_time = max(arrival_time, model.demand_dict[to_node_id].start_time - travel_time) return travel_distance, travel_time, departure_time def _compute_arrival_and_departure_time(model, from_node_id, to_node, free_speed, arrival_time): travel_distance = model.distance_matrix[from_node_id, to.id] travel_time = travel_distance / free_speed arrival_time = max(arrival_time + travel_time, to.start_time) departure_time = arrival_time + to.service_time return travel_distance, travel_time, arrival_time, departure_time”

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