编程实现：先来先服务调度算法、短 进程优先调度算法、时间片轮转调度算法和优先级调度算法（抢占式）、高响应 比优先调度算法。

先来先服务调度算法（FCFS）：

def fcfs(processes):
    """
    First-Come, First-Served (FCFS) scheduling algorithm
    Input:
        processes: a list of dictionaries containing process information
                   Each dictionary should have the following keys:
                   - 'pid': process ID
                   - 'arrival_time': arrival time of the process
                   - 'burst_time': burst time of the process
    Output:
        a list of tuples containing the order in which the processes are executed
        Each tuple should have the following format:
        (process ID, start time, end time)
    """
    # Sort the processes by arrival time
    sorted_processes = sorted(processes, key=lambda x: x['arrival_time'])
    
    # Initialize the start and end times of the first process
    start_time = sorted_processes[0]['arrival_time']
    end_time = start_time + sorted_processes[0]['burst_time']
    
    # Initialize the order list with the first process
    order = [(sorted_processes[0]['pid'], start_time, end_time)]
    
    # Loop through the remaining processes and update the start and end times
    for i in range(1, len(sorted_processes)):
        if sorted_processes[i]['arrival_time'] > end_time:
            # If there is a gap between processes, update the start time
            start_time = sorted_processes[i]['arrival_time']
        else:
            # If the next process arrives before the current process finishes,
            # update the end time
            start_time = end_time
        end_time = start_time + sorted_processes[i]['burst_time']
        
        # Add the process to the order list
        order.append((sorted_processes[i]['pid'], start_time, end_time))
    
    return order

短进程优先调度算法（SJF）：

def sjf(processes):
    """
    Shortest-Job-First (SJF) scheduling algorithm
    Input:
        processes: a list of dictionaries containing process information
                   Each dictionary should have the following keys:
                   - 'pid': process ID
                   - 'arrival_time': arrival time of the process
                   - 'burst_time': burst time of the process
    Output:
        a list of tuples containing the order in which the processes are executed
        Each tuple should have the following format:
        (process ID, start time, end time)
    """
    # Sort the processes by arrival time and burst time
    sorted_processes = sorted(processes, key=lambda x: (x['arrival_time'], x['burst_time']))
    
    # Initialize the start and end times of the first process
    start_time = sorted_processes[0]['arrival_time']
    end_time = start_time + sorted_processes[0]['burst_time']
    
    # Initialize the order list with the first process
    order = [(sorted_processes[0]['pid'], start_time, end_time)]
    
    # Loop through the remaining processes and update the start and end times
    for i in range(1, len(sorted_processes)):
        if sorted_processes[i]['arrival_time'] > end_time:
            # If there is a gap between processes, update the start time
            start_time = sorted_processes[i]['arrival_time']
        else:
            # If the next process arrives before the current process finishes,
            # update the end time
            start_time = end_time
        end_time = start_time + sorted_processes[i]['burst_time']
        
        # Add the process to the order list
        order.append((sorted_processes[i]['pid'], start_time, end_time))
    
    return order

时间片轮转调度算法（RR）：

def rr(processes, quantum):
    """
    Round-Robin (RR) scheduling algorithm
    Input:
        processes: a list of dictionaries containing process information
                   Each dictionary should have the following keys:
                   - 'pid': process ID
                   - 'arrival_time': arrival time of the process
                   - 'burst_time': burst time of the process
        quantum: the time quantum for the algorithm
    Output:
        a list of tuples containing the order in which the processes are executed
        Each tuple should have the following format:
        (process ID, start time, end time)
    """
    # Sort the processes by arrival time
    sorted_processes = sorted(processes, key=lambda x: x['arrival_time'])
    
    # Initialize the start and end times of the first process
    start_time = sorted_processes[0]['arrival_time']
    end_time = min(start_time + sorted_processes[0]['burst_time'], sorted_processes[1]['arrival_time']) if len(sorted_processes) > 1 else start_time + sorted_processes[0]['burst_time']
    
    # Initialize the order list with the first process
    order = [(sorted_processes[0]['pid'], start_time, end_time)]
    
    # Initialize the queue with the remaining processes
    queue = sorted_processes[1:]
    
    # Loop through the queue until all processes are executed
    while queue:
        # Get the next process in the queue
        current_process = queue.pop(0)
        
        # If the process has not arrived yet, skip it
        if current_process['arrival_time'] > end_time:
            queue.append(current_process)
            continue
        
        # Calculate the remaining burst time for the current process
        remaining_time = current_process['burst_time']
        start_time = end_time
        
        # Loop through the time slices until the process finishes
        while remaining_time > 0:
            # If the time slice is smaller than the remaining time, update the end time
            if remaining_time > quantum:
                end_time = start_time + quantum
                remaining_time -= quantum
            else:
                end_time = start_time + remaining_time
                remaining_time = 0
            
            # Add the process to the order list
            order.append((current_process['pid'], start_time, end_time))
            
            # Update the start time for the next time slice
            start_time = end_time
            
            # Check if there are any new processes that have arrived
            for process in queue:
                if process['arrival_time'] <= end_time:
                    queue.remove(process)
                    queue.append(current_process)
                    current_process = process
                    remaining_time = current_process['burst_time']
                    break
    
    return order

优先级调度算法（抢占式）：

def priority_preemptive(processes):
    """
    Priority scheduling algorithm (preemptive)
    Input:
        processes: a list of dictionaries containing process information
                   Each dictionary should have the following keys:
                   - 'pid': process ID
                   - 'arrival_time': arrival time of the process
                   - 'burst_time': burst time of the process
                   - 'priority': priority of the process (higher number = higher priority)
    Output:
        a list of tuples containing the order in which the processes are executed
        Each tuple should have the following format:
        (process ID, start time, end time)
    """
    # Sort the processes by arrival time and priority
    sorted_processes = sorted(processes, key=lambda x: (x['arrival_time'], -x['priority']))
    
    # Initialize the start and end times of the first process
    start_time = sorted_processes[0]['arrival_time']
    end_time = start_time + sorted_processes[0]['burst_time']
    
    # Initialize the order list with the first process
    order = [(sorted_processes[0]['pid'], start_time, end_time)]
    
    # Initialize the queue with the remaining processes
    queue = sorted_processes[1:]
    
    # Loop through the queue until all processes are executed
    while queue:
        # Get the highest-priority process in the queue
        current_process = max(queue, key=lambda x: x['priority'])
        
        # If the process has not arrived yet, skip it
        if current_process['arrival_time'] > end_time:
            queue.remove(current_process)
            continue
        
        # Calculate the remaining burst time for the current process
        remaining_time = current_process['burst_time']
        start_time = end_time
        
        # Loop through the remaining processes to check if there is a higher-priority process
        for process in queue:
            if process['arrival_time'] <= end_time and process['priority'] > current_process['priority']:
                # If there is a higher-priority process, preempt the current process
                queue.remove(process)
                queue.append(current_process)
                current_process = process
                remaining_time = current_process['burst_time']
                break
        
        # Loop through the time slices until the process finishes
        while remaining_time > 0:
            # If the next process has a higher priority, preempt the current process
            if queue and max(queue, key=lambda x: x['priority'])['priority'] > current_process['priority']:
                break
            
            # If the time slice is smaller than the remaining time, update the end time
            if remaining_time > 1:
                end_time += 1
                remaining_time -= 1
            else:
                end_time += 1
                remaining_time = 0
            
            # Add the process to the order list
            order.append((current_process['pid'], start_time, end_time))
            
    return order

高响应比优先调度算法：

def hrrn(processes):
    """
    Highest-Response-Ratio-Next (HRRN) scheduling algorithm
    Input:
        processes: a list of dictionaries containing process information
                   Each dictionary should have the following keys:
                   - 'pid': process ID
                   - 'arrival_time': arrival time of the process
                   - 'burst_time': burst time of the process
    Output:
        a list of tuples containing the order in which the processes are executed
        Each tuple should have the following format:
        (process ID, start time, end time)
    """
    # Sort the processes by arrival time
    sorted_processes = sorted(processes, key=lambda x: x['arrival_time'])
    
    # Initialize the start and end times of the first process
    start_time = sorted_processes[0]['arrival_time']
    end_time = start_time + sorted_processes[0]['burst_time']
    
    # Initialize the order list with the first process
    order = [(sorted_processes[0]['pid'], start_time, end_time)]
    
    # Initialize the queue with the remaining processes
    queue = sorted_processes[1:]
    
    # Loop through the queue until all processes are executed
    while queue:
        # Calculate the response ratio for each process in the queue
        response_ratios = []
        for process in queue:
            wait_time = max(0, end_time - process['arrival_time'])
            response_ratio = (wait_time + process['burst_time']) / process['burst_time']
            response_ratios.append(response_ratio)
        
        # Get the process with the highest response ratio
        index = response_ratios.index(max(response_ratios))
        current_process = queue.pop(index)
        
        # Calculate the remaining burst time for the current process
        remaining_time = current_process['burst_time']
        start_time = end_time
        
        # Loop through the time slices until the process finishes
        while remaining_time > 0:
            # If the next process has a higher response ratio, preempt the current process
            response_ratios = []
            for process in queue:
                wait_time = max(0, end_time - process['arrival_time'])
                response_ratio = (wait_time + process['burst_time']) / process['burst_time']
                response_ratios.append(response_ratio)
            
            if queue and max(response_ratios) > ((end_time - current_process['arrival_time']) / current_process['burst_time']):
                break
            
            # If the time slice is smaller than the remaining time, update the end time
            if remaining_time > 1:
                end_time += 1
                remaining_time -= 1
            else:
                end_time += 1
                remaining_time = 0
            
            # Add the process to the order list
            order.append((current_process['pid'], start_time, end_time))
    
    return order