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Q1: Airline yield management (Python dynamic programming) Consider the following simplified model of airline yield management: your airline has a flight that has 50 unfilled coach seats and is due to depart in 10 days. There are two fares full fare for $500 and discount for $250. Each day, you must decide how many seats to release for discount sales. Assume you are using the following simplified demand model: Day 1 2 3 4 5 6 7 8 9 10 Ft 1.3 1.4 1.9 2.0 2.2 2.8 2.2 2.4 1.8 3.7 Dt 10 10 10 10 10 5 5 5 5 5 On day t, you model demand for full fare seats to be a Poisson random variable with mean Ft. On this flight, your experience is that all seats released for the discount fare will be sold the day they are released, but you have a corporate policy limiting the number of discount seats released on day t to at most D+ (this policy is to prevent business flyers from "gaming the system"). The incremental cost of having a seat occupied as opposed to empty is $8. (Note that the real situation is more complicated in several ways, but fundamentally similar.) Create a Python program that computes pattern of discount seat releases that maximizes the total expected profit from the flight. Its output should indicate how many seats should be released each day, as a function of how many seats remain unsold. To condense your output, you need not print anything in situations in which zero seats are released at the discount fare.

Q1: Airline yield management (Python dynamic programming) Consider the following simplified model of airline yield management: your airline has a flight that has 50 unfilled coach seats and is due to depart in 10 days. There are two fares full fare for $500 and discount for $250. Each day, you must decide how many seats to release for discount sales. Assume you are using the following simplified demand model: Day 1 2 3 4 5 6 7 8 9 10 Ft 1.3 1.4 1.9 2.0 2.2 2.8 2.2 2.4 1.8 3.7 Dt 10 10 10 10 10 5 5 5 5 5 On day t, you model demand for full fare seats to be a Poisson random variable with mean Ft. On this flight, your experience is that all seats released for the discount fare will be sold the day they are released, but you have a corporate policy limiting the number of discount seats released on day t to at most D+ (this policy is to prevent business flyers from "gaming the system"). The incremental cost of having a seat occupied as opposed to empty is $8. (Note that the real situation is more complicated in several ways, but fundamentally similar.) Create a Python program that computes pattern of discount seat releases that maximizes the total expected profit from the flight. Its output should indicate how many seats should be released each day, as a function of how many seats remain unsold. To condense your output, you need not print anything in situations in which zero seats are released at the discount fare.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
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Q1: Airline yield management (Python dynamic programming) Consider the following simplified model of airline yield management: your airline has a flight that has 50 unfilled coach seats and is due to depart in 10 days. There are two fares: full fare for $500 and discount for $250. Each day, you must decide how many seats to release for discount sales. Assume you are using the following simplified demand model: Day 1 2 3 4 5 6 7 8 9 10 Ft 1.3 1.4 1.9 2.0 2.2 2.8 2.2 2.4 1.8 3.7 Dt 10 10 10 10 10 5 5 5 5 5 On day t, you model demand for full fare seats to be a Poisson random variable with mean Ft. On this flight, your experience is that all seats released for the discount fare will be sold the day they are released, but you have a corporate policy limiting the number of discount seats released on day t to at most D+ (this policy is to prevent business flyers from "gaming the system"). The incremental cost of having a seat occupied as opposed to empty is $8. (Note that the real situation is more complicated in several ways, but fundamentally similar.) Create a Python program that computes pattern of discount seat releases that maximizes the total expected profit from the flight. Its output should indicate how many seats should be released each day, as a function of how many seats remain unsold. To condense your output, you need not print anything in situations in which zero seats are released at the discount fare.
Transcribed Image Text:Q1: Airline yield management (Python dynamic programming) Consider the following simplified model of airline yield management: your airline has a flight that has 50 unfilled coach seats and is due to depart in 10 days. There are two fares: full fare for $500 and discount for $250. Each day, you must decide how many seats to release for discount sales. Assume you are using the following simplified demand model: Day 1 2 3 4 5 6 7 8 9 10 Ft 1.3 1.4 1.9 2.0 2.2 2.8 2.2 2.4 1.8 3.7 Dt 10 10 10 10 10 5 5 5 5 5 On day t, you model demand for full fare seats to be a Poisson random variable with mean Ft. On this flight, your experience is that all seats released for the discount fare will be sold the day they are released, but you have a corporate policy limiting the number of discount seats released on day t to at most D+ (this policy is to prevent business flyers from "gaming the system"). The incremental cost of having a seat occupied as opposed to empty is $8. (Note that the real situation is more complicated in several ways, but fundamentally similar.) Create a Python program that computes pattern of discount seat releases that maximizes the total expected profit from the flight. Its output should indicate how many seats should be released each day, as a function of how many seats remain unsold. To condense your output, you need not print anything in situations in which zero seats are released at the discount fare.
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