ppe-match 0.1.3

Intuitive framework that allows researchers to implement and test matching methodologies

Framework for PPE Matching

Table of Contents

• Overview
  • What is the PPE matching problem?
  • Who needs to solve the PPE matching problem?
  • What does this software package do?
• Installation
• TestingFramework Class
  • Parameters
  • Methods

Overview

What is the PPE matching problem?

The PPE Matching Problem consists of optimally matching a set of requests, D, made by donors interested in donating Personal Protective Equipment (or PPE, such as masks, gowns, gloves, etc) with a set of requests, R, made by recipients interested in receieving PPE. Requests are characterized by a timestamp (date), a type and quantity of PPE to donate or request, and a donor or recipient id. The input of the problem also includes a matrix M of distances between donors and recipients. The objectives are multiple, and include maximizing the recipients' fill rate, minimizing the total shipping distance, minimizing the holding time of PPE, and minimizing the number of shipments of each donor.

Who needs to solve the PPE matching problem?

During health crises like the Covid-19 pandemic, organizations such as GetUsPPE.org provide a platform that aims at connecting prospective donors of PPE to prospective recipients of PPE. Requests by donors and recipients are collected over time. Every delta days, the organization solves the PPE Matching Problem, in order to direct each donor to ship a certain quantity of PPE to a given recipient.

What does this software package do?

Our package provides an open-source framework for researchers interested in developing and testing methodologies to solve the PPE matching problem.

The user only needs to implement a function ppestrategy(D,R,M), which solves the PPE matching problem. Our testing framework evaluates the performance of that user-defined solution method on real-world requests received by GetUsPPE.org in the early months of the Covid-19 pandemic (April-July 2020).

Installation

In a virtual environment with Python 3.6+, ppe_match can be installed via pip

pip install ppe_match

Import the package using

from ppe_match import TestingFramework

Test the installation with the code snippet below

from ppe_match import TestingFramework

# Initiate the testing framework with default parameters
s = TestingFramework()

# Run the testing procedure
s.run()

# Retrieve the decisions made throughout the simulation
s.get_decisions() # Pandas dataframe that can be stored

# Retrieve the performance metrics
s.get_metrics() # Pandas dataframe that can be stored

Visualize the five summary metrics

The last five metrics are the average holding time, the average number of shipments per donor, the average unit-miles, the average fill rate, and the average fill rate excluding zeros.

res = s.get_metrics()
res.tail(5)

Obtain a single metric

By defining a weight for each of the five summary metrics, the user can easily obtain a single metric.

import numpy as np

# set weights of different objectives
avg_holding_time_weight = 1
avg_shipments_weight = 10
avg_unit_miles_weight = 1
fill_rate_weight = 100
fill_rate_0_weight = 100

weights = np.array([avg_holding_time_weight,avg_shipments_weight,avg_unit_miles_weight,fill_rate_weight,fill_rate_0_weight])

# retrieve the five metrics above
metric_values = res['value'].tail(5).values

# compute dot product
metric_values.dot(weights)

User-defined matching solution methods

To test a new matching solution method, start by defining a function that takes as input the current date (date, a datetime object), the current donor and recipient requests (Dt and Rt), and the distance matrix between donors and recipients, M. Dt is a DataFrame with columns (don_id,date,ppe,qty), Rt is a DataFrame with columns (rec_id,date,ppe,qty), M is a DataFrame with columns (don_id,rec_id,distance). The function must return the DataFrame Xt of matching decisions (don_id, rec_id, ppe, qty).

For example, a proximity match strategy that matches the ech donor's request with the closest recipient's request is implemented as follows:

import pandas as pd
def proximity_match_strategy(date,Dt,Rt,M):
    # prepare the result DataFrame (X^t)
    Xt = pd.DataFrame(columns=['don_id','rec_id','ppe','qty'])
    ppes_to_consider = set(Dt.ppe.unique())
    ppes_to_consider = ppes_to_consider.intersection(set(Rt.ppe.unique()))

    # for each ppe to consider, match each donor request with the closest recipient request
    for ppe in ppes_to_consider:
	donors_ppe = Dt[Dt.ppe == ppe].copy()
	recipients_ppe = Rt[Rt.ppe == ppe].copy()

	for _, drow in donors_ppe.iterrows():
	    if len(recipients_ppe) == 0:
		break # if we don't have any more recipient with this ppe, consider the next ppe

	    # find the closest recipient to drow.don_id
	    dr = M[(M.don_id == drow.don_id)].merge(recipients_ppe,on='rec_id').sort_values('distance').iloc[0]
	    dqty = drow.qty # donor's qty
	    rqty = recipients_ppe.loc[recipients_ppe.rec_id == dr.rec_id,'qty'].values[0] #recipient's qty
	    qty = min(dqty,rqty) #qty to ship
	    if qty == 0:
		logger.info('qty is zero')
	    if qty == rqty:
		recipients_ppe = recipients_ppe[recipients_ppe.rec_id !=  dr.rec_id] # remove recipient
	    else:
		recipients_ppe.loc[recipients_ppe.rec_id == dr.rec_id,'qty'] -= qty #update recipient's qty
	    Xt.loc[len(Xt),:] = [dr.don_id, dr.rec_id,ppe,qty]

    return Xt

On the other hand, a first-come-first-matched (FCFM) strategy that matches the i-th donor's request with the i-th recipient's request is implemented as follows:

import pandas as pd
def FCFM_strategy(date,Dt,Rt,M):
    # prepare the result DataFrame (X^t)
    Xt = pd.DataFrame(columns=['don_id','rec_id','ppe','qty'])

    # the ppe to consider are the intersection of the PPEs in the table of current donors Dt (D^t) and the table of current recipients Rt (R^t)
    ppes_to_consider = set(Dt.ppe.unique())
    ppes_to_consider = ppes_to_consider.intersection(set(Rt.ppe.unique()))

    # for each ppe to consider, match the i-th donor request with the i-th recipient request
    for ppe in ppes_to_consider:
        donors_ppe = Dt[Dt.ppe == ppe]
        recipients_ppe = Rt[Rt.ppe == ppe]

        n = min(len(donors_ppe),len(recipients_ppe))
        for i in range(n):
            don = donors_ppe.iloc[i]
            rec = recipients_ppe.iloc[i]
            qty = min(don.qty,rec.qty)

            # add
            Xt.loc[len(Xt)] = [don.don_id,rec.rec_id,ppe,qty]
    return Xt

Once you have implemented your own matching strategy (let us call it my_strategy), run the test on the GetUsPPE.org data set by passing the function to the TestingFramework constructor:

s = TestingFramework(strategy=my_strategy)

The ppe_match package contains the implementation of two strategies illustrated above: the first-come-first-matched strategy (strategies.FCFM_strategy) and the "proximity matching" strategy tested by Bala et al. (2021) (strategies.proximity_match_strategy).

TestingFramework Class

Parameters

donor_path

Path to the data set containing the donors' requests. See expected format in the data folder.

Expected input type: csv

Default: anon_donors.csv (which is the anonymized table of donors' requests from GetUsPPE.org)

---

recipient_path

Path to the data set containing the recipients' requests. See expected format in the data folder.

Expected input type: csv

Default: anon_recipients.csv (which is the anonymized table of recipients' requests from GetUsPPE.org)

---

distance_matrix_path

Path to distance matrix between donors and recipients. See expected format in the data folder.

Expected input type: csv

Default: anon_distance_matrix.csv (which is the anonymized distance matrix from GetUsPPE.org)

---

strategy

User defined strategy to allocate PPE The function must have the following arguments:

ppestrategy(date, Dt,Rt,M)

where,

• date is a datetime with the current date
• Dt is a pandas.DataFrame object whose rows contain the current donor requests
• Rt is a pandas.DataFrame object whose rows contain the current recipient requests
• M is a pandas.DataFrameobject that reports the distance between each donor and each recipient.

Default: proximity_match_strategy

Returns:

pd.dataframe of decisions with columns (don_id, rec_id, ppe, qty). Each row represents the decision of shipping from donor don_id to recipient rec_id qty units of PPE of type ppe.

---

interval

Day Interval set for framework to iterate over. Default: 7 (days)

---

max_donation_qty

Maximum quantity limit for donor to donate (helps filter out dummy entries or test entries) Default: 1000 (ppe units)

---

writeFiles

Boolean flag to save intermediate input objects (donors, recipients, and distances) and outputs (decisions) as csv Default: False

If set to True intermediate data will be saved for every iteration as follows:

output
├── 2020-04-09
	 ├── decisions.csv
	 ├── distance_matrix.csv
	 ├── donors.csv
	 └── recipients.csv
├── 2020-04-16
	 ├── decisions.csv
	 ├── distance_matrix.csv
	 ├── donors.csv
	 └── recipients.csv
├── ...

---

output_directory

Sets the directory where the intermediate files and results will be saved Default: output/

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Methods

run()

Tests a strategy function by simulating the arrival of the requests given in the input data

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get_decisions()

Returns the list of all matching decisions made during the test.

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get_metrics()

Returns the performance metrics described in Section 4 of the research article. The metrics are reported at the PPE level, the recipient level, and the "overall" level (see Section 4). The "overall" metrics are at the bottom of the DataFrame.

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debug(bool_flag)

Sets the logging level to DEBUG if True Default: False (Loglevel sets to WARN)

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