FCFS with Product Restrictions¶

In this example, we add product restrictions to the simulation. These restrictions will cause some passengers to "buy up" to a fare product other than the lowest price.

In [1]:

import passengersim as pax

pax.versions()

import passengersim as pax pax.versions()

passengersim 0.51.dev11+g921f2a8
passengersim.core 0.51.dev2+g02d3ce20

This example adds the network/02-buyup.yaml configuration file, to enable the product restrictions.

In [2]:

cfg = pax.Config.from_yaml(["network/01-base.yaml", "network/02-buyup.yaml"])

cfg = pax.Config.from_yaml(["network/01-base.yaml", "network/02-buyup.yaml"])

The configuration can be manipulated in Python after loading. This allows for a more interactive experience, where individual input values can readily be altered for a given analysis.

In [3]:

cfg.simulation_controls.num_trials = 4

cfg.simulation_controls.num_trials = 4

After all the desired changes have been completed, we use the Config to initialize the Simulation.

In [4]:

sim = pax.Simulation(cfg)

sim = pax.Simulation(cfg)

In [5]:

summary = sim.run()

summary = sim.run()

Task Completed after 11.12 seconds

In [6]:

summary.fig_carrier_revenues()

summary.fig_carrier_revenues()

Out[6]:

In [7]:

summary.fig_carrier_load_factors()

summary.fig_carrier_load_factors()

Out[7]:

In [8]:

summary.fig_fare_class_mix()

summary.fig_fare_class_mix()

Out[8]:

In [9]:

summary.fig_bookings_by_timeframe()

summary.fig_bookings_by_timeframe()

Out[9]:

In [10]:

summary.to_xlsx("outputs/3mkt-02.xlsx")

summary.to_xlsx("outputs/3mkt-02.xlsx")

Download 3mkt-02.xlsx

Comparing against Targets¶

In [11]:

import targets

target = targets.load(2, cfg)

import targets target = targets.load(2, cfg)

In [12]:

from passengersim import contrast

comps = contrast.Contrast(
    {
        "simulation": summary,
        "target": target,
    }
)

from passengersim import contrast comps = contrast.Contrast( { "simulation": summary, "target": target, } )

In [13]:

comps.fig_bookings_by_timeframe(by_carrier="AL1")

comps.fig_bookings_by_timeframe(by_carrier="AL1")

Out[13]:

In [14]:

comps.fig_carrier_revenues()

comps.fig_carrier_revenues()

Out[14]:

In [15]:

comps.fig_fare_class_mix()

comps.fig_fare_class_mix()

Out[15]:

In [16]:

comps.fig_segmentation_by_timeframe("bookings", by_carrier="AL1", by_class=True)

comps.fig_segmentation_by_timeframe("bookings", by_carrier="AL1", by_class=True)

Out[16]:

In [17]:

comps.fig_leg_forecasts(of=["mu", "sigma"], by_leg_id=111)

comps.fig_leg_forecasts(of=["mu", "sigma"], by_leg_id=111)

Out[17]:

In [18]:

comps.fig_leg_forecasts(of=["mu", "sigma"], by_leg_id=111, agg_booking_classes=True)

comps.fig_leg_forecasts(of=["mu", "sigma"], by_leg_id=111, agg_booking_classes=True)

Out[18]:

In [19]:

comps.fig_leg_forecasts(of=["mu", "sigma"], by_leg_id=101)

comps.fig_leg_forecasts(of=["mu", "sigma"], by_leg_id=101)

Out[19]: