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Question. Consider a single-server system where there can be at most two customers in the system (including the one being served). In each hour, a new customer enters to the system with probability % unless there are already 2 customers in the system. Assume that new arrival occurs at the end of each hour.

Question. Consider a single-server system where there can be at most two customers in the system (including the one being served). In each hour, a new customer enters to the system with probability % unless there are already 2 customers in the system. Assume that new arrival occurs at the end of each hour.

A First Course in Probability (10th Edition)
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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**Question**: Consider a single-server system where there can be at most two customers in the system (including the one being served). In each hour, a new customer enters the system with a probability of ⅓ unless there are already 2 customers in the system. Assume that new arrivals occur at the end of each hour. At the beginning of each hour, the server can decide a configuration if there is a customer in the system. If the configuration is fast, with probability 0.7, one customer is served and leaves the system in a given hour. On the other hand, if the configuration is slow, this probability decreases to 0.4. 80 TL revenue is obtained for each customer whose service is completed. The costs of slow and fast configurations are 10 and 15 TL per hour, respectively. The hourly discount rate is β=0.8. **Objective**: We would like to maximize the total expected discounted profit over an infinite horizon. **Questions**: a. Formulate the problem as an MDP (Markov Decision Process) model by defining states, decision sets, transition probabilities, and expected rewards clearly. b. Find the optimal policy using Policy Iteration where the initial policy is to use the fast configuration whenever there is at least one customer in the system. c. Develop an LP (Linear Programming) model for the MDP. Assume that you have solved the LP optimally. What is the optimal solution, which constraints in your model are binding and why? (Do not solve the LP model; you can determine the binding constraints by observing the optimal solution found in part b).
Transcribed Image Text:**Question**: Consider a single-server system where there can be at most two customers in the system (including the one being served). In each hour, a new customer enters the system with a probability of ⅓ unless there are already 2 customers in the system. Assume that new arrivals occur at the end of each hour. At the beginning of each hour, the server can decide a configuration if there is a customer in the system. If the configuration is fast, with probability 0.7, one customer is served and leaves the system in a given hour. On the other hand, if the configuration is slow, this probability decreases to 0.4. 80 TL revenue is obtained for each customer whose service is completed. The costs of slow and fast configurations are 10 and 15 TL per hour, respectively. The hourly discount rate is β=0.8. **Objective**: We would like to maximize the total expected discounted profit over an infinite horizon. **Questions**: a. Formulate the problem as an MDP (Markov Decision Process) model by defining states, decision sets, transition probabilities, and expected rewards clearly. b. Find the optimal policy using Policy Iteration where the initial policy is to use the fast configuration whenever there is at least one customer in the system. c. Develop an LP (Linear Programming) model for the MDP. Assume that you have solved the LP optimally. What is the optimal solution, which constraints in your model are binding and why? (Do not solve the LP model; you can determine the binding constraints by observing the optimal solution found in part b).
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