如何为Poisson到达生成器的客户添加随机位置属性？

Got it, let's integrate the random position generation into your existing code. Here's a straightforward approach that includes defining a Customer class to hold arrival details (including coordinates) and modifying the arrival generator to create these customer instances with randomized positions.

Step 1: Define a Customer Class #

First, let's create a simple Customer class to encapsulate all relevant data for each arriving customer—this includes their ID, arrival time, and (x,y) coordinates. If you already have a Customer class, you can just add the x and y attributes to it.

class Customer:
    def __init__(self, customer_id, arrival_time, x, y):
        self.id = customer_id
        self.arrival_time = arrival_time
        # Random position coordinates
        self.x = x
        self.y = y

Step 2: Modify the Arrival Class #

Next, update your Arrival class to generate the random (x,y) values for each customer. We'll use scipy's uniform distribution to get values in your specified ranges (x: 0-50, y: 0-60) each time a customer arrives. We'll also pass the completed customer object to your referrer (assuming it has a method to handle incoming customers, like process_customer).

Here's the updated Arrival class:

import scipy.stats as spstats

class Arrival(object):
    def __init__(self, stream_defin, referrer):
        self.service = stream_defin["service"]
        self.arrival_rate = stream_defin["rate"]
        self.referrer = referrer
        # Define position ranges (you can make these configurable via stream_defin if needed)
        self.x_min = 0
        self.x_max = 50
        self.y_min = 0
        self.y_max = 60

    def sim_init(self, env, sim_stats):
        self.sim_stats = sim_stats
        self.env = env
        self.env.process(self.sim_arrival_generator())

    def sim_arrival_generator(self):
        i = 0
        while True:
            # Generate inter-arrival time (Poisson arrival process)
            interarrival = spstats.expon(scale=1/self.arrival_rate).rvs()
            yield self.env.timeout(interarrival)
            
            # Generate random (x,y) coordinates
            x = spstats.uniform(loc=self.x_min, scale=self.x_max - self.x_min).rvs()
            y = spstats.uniform(loc=self.y_min, scale=self.y_max - self.y_min).rvs()
            
            # Create customer instance with arrival details
            customer = Customer(
                customer_id=i,
                arrival_time=self.env.now,
                x=x,
                y=y
            )
            
            # Pass the customer to the referrer (adjust method name if your referrer uses something else)
            self.referrer.process_customer(customer)
            
            i += 1

Key Explanations: #

• Position Generation: We use spstats.uniform to generate values in your desired ranges. The loc parameter sets the minimum value, and scale sets the range (max - min). For x, that's 0 to 50, so scale=50; for y, 0 to 60, so scale=60.
• Customer Encapsulation: The Customer class keeps all customer-related data organized, making it easy to pass to other parts of your simulation (like the referrer).
• Configurable Ranges: If you want to make the x/y ranges configurable per arrival stream, you can add them to your stream_defin dictionary and initialize them in __init__ (e.g., self.x_max = stream_defin["x_range"]).

Just make sure your referrer class has a process_customer method (or whatever method name you use) to receive and handle the customer object with its coordinates.

内容的提问来源于stack exchange，提问作者shirin elahi