MATH7232 Operations Research & Mathematical Planning 2022 Assignment 3 – Dynamic Programming 

This assignment is due by 3pm on Monday, May 30th and is worth 15% of your final grade. You can do this assignment in a group of up to three students, with a single submission. Your job with an Operations Research consulting company continues to go well. Your boss and client would like you to continue to work with Pacific Paradise to improve their strategy to manage this virus. Communications to you from their team will be provided through Blackboard. There will be five communications, with the first appearing at 4pm on Monday, May 9th, and the final communication appearing at 4pm on Monday, May 23rd. You will need to prepare a report for your boss and a presentation to the client: Section A – Report to your boss • A general formulation that describes the data, stages, states, actions and the transition and value functions used in your models. 6 marks • A single Python file with your implementations. This should be easy to relate back to the formulation. Your boss will attempt to execute this model. 6 marks Section B – Presentation to the client • The presentation should clearly and concisely address the needs of the client given through the communications. 4 marks • The presentation should explain the strategies for achieving the optimal values in the final three communications. 4 marks The presentation will be in the form of a recorded video. The video should be 7 minutes in length and feature each person in the group talking to camera (separately or together). We recommend using Zoom or Kaltura Capture – see the links on Blackboard for instructions. Submit your report, Python file and video via Blackboard, using PDF for the report (saved from Word or created in LaTeX) and uploading the video with Kaltura Media mashup tool. Only one submission per group is necessary but make sure all names are clearly shown on your report. Each student will receive separate data from the client but a group need only consider one data set in the report. Grading Criteria Section A Marks 0 1 2 Data Stages States Actions Value function Python code Execution Efficiency Utility Missing some or all descriptions of data Missing clear description of stages Incorrect or missing description of states Incorrect or missing description of actions Incorrect or missing description of value functions There is no relationship between Python code and mathematical formulation Python code fails to run Python implementation is slow to run Difficult to determine optimal strategy from Python implementation Correctly describes all data Correctly describes stages Correctly describes most states Correctly describes most actions Correctly describes value function for one communication Python code mostly matches mathematical formulation Python code runs but gives incorrect answer Python implementation is efficient Easy to determine optimal strategy from Python implementation Correctly describes value functions for all communications Python code clearly matches mathematical formulation Python code runs and gives correct answer Section B Marks 0 1 2 Slides Response to communications Presenters Length Poorly formatted and difficult to follow Fails to address any of the client questions Not all group members are introduced. Presentation too long or too short. Concisely addresses needs of client with few errors Correctly addresses one client question All group members introduce themselves on camera. Presentation between 6 and 8 minutes in length. Excellent proficiency in clearly and concisely addressing needs of client Correctly addresses all client questions Strategies Poor or missing description of optimal strategies for stochastic models Good description of optimal strategies for stochastic models Clear and insightful description of optimal strategies for stochastic models