Package: ppe-match
Framework for PPE MatchingTable of ContentsOverviewWhat is the PPE matching problem?Who needs to solve the PPE matching problem?What does this software package do?InstallationTestingFramework ClassParametersMethodsOverviewWhat 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 receiving 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. Everydeltadays, 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 functionppestrategy(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).InstallationIn a virtual environment with Python 3.6+, ppe_match can be installed via pippip install ppe_matchImport the package usingfrom ppe_match import TestingFrameworkTest the installation with the code snippet belowfrom 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 storedVisualize the five summary metricsThe 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 metricBy 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 methodsTo 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 each 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 XtOn 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 XtOnce you have implemented your own matching strategy (let us call itmy_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 ClassParametersdonor_pathPath to the data set containing the donors' requests. See expected format in the data folder.Expected input type: csvDefault: anon_donors.csv (which is the anonymized table of donors' requests from GetUsPPE.org)recipient_pathPath to the data set containing the recipients' requests. See expected format in the data folder.Expected input type: csvDefault: anon_recipients.csv (which is the anonymized table of recipients' requests from GetUsPPE.org)distance_matrix_pathPath to distance matrix between donors and recipients. See expected format in the data folder.Expected input type: csvDefault: anon_distance_matrix.csv (which is the anonymized distance matrix from GetUsPPE.org)strategyUser defined strategy to allocate PPE
The function must have the following arguments:ppestrategy(date, Dt,Rt,M)where,dateis a datetime with the current dateDtis a pandas.DataFrame object whose rows contain the current donor requestsRtis a pandas.DataFrame object whose rows contain the current recipient requestsMis a pandas.DataFrameobject that reports the distance between each donor and each recipient.Default: proximity_match_strategyReturns:pd.dataframe of decisions with columns (don_id, rec_id, ppe, qty). Each row represents the decision of shipping from donordon_idto recipientrec_idqtyunits of PPE of typeppe.intervalDay Interval set for framework to iterate over.Default: 7 (days)max_donation_qtyMaximum quantity limit for donor to donate (helps filter out dummy entries or test entries)Default: 1000 (ppe units)writeFilesBoolean flag to save intermediate input objects (donors, recipients, and distances) and outputs (decisions) as csvDefault: FalseIf set toTrueintermediate 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_directorySets the directory where the intermediate files and results will be savedDefault: output/Methodsrun()Tests a strategy function by simulating the arrival of the requests given in the input dataget_decisions()Returns the list of all matching decisions made during the test.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.debug(bool_flag)Sets the logging level to DEBUG ifTrueDefault: False (Loglevel sets to WARN)