Management Science (MS) deals with the application of advanced analytical models to help make better decisions. The terms Operations Research and Business Analytics are sometimes used as synonyms for MS. MS covers a wide range of problem-solving and mathematical modelling techniques that help managers to improve decision-making and efficiency.
This course focuses on MS methods that support decision making under uncertainty and/or multiple objectives (see schedule for detailed list of topics). Some required background concepts from applied probability are also covered.
Business Decisions I or equitable skills.
After the course the student can (i) recognize the types of real-life business problems where use of the models brings added value, (ii) interpret results of these models to derive defensible decision recommendations, and (iii) build and solve these models using spreadsheets to support business decision making.
ASSESSMENT AND GRADING
Final points, which consist of exam point (40%) and assignment points (60%), determine the final grade: >50p Grade 1, >60p Grade 2, >70p Grade 3,> 80p Grade 4, and >90p Grade 5. These bounds maybe relaxed during final grading. Moreover, at least half of the exam points are required to pass.
There are three assignments with deadlines on the second, fourth and sixth week of the course. Each assignment consists of 4-12 problems or small cases, which usually require the use of spreadsheets or other mathematical software to solve.
Lecture and assignment material, and the textbook: An Introduction to Management Science by Anderson et al. (2010, ISBN Code: 978-1111532222, 2014, ISBN Code: 978-1-111-82361-0).
There are two sessions per week both lasting 3 hours. From each session about 45 minutes are dedicated for working on the assignments with the teacher present to offer guidance.
Introduction, Review of probability, Monte Carlo simulation
Decision making under uncertainty: Decision trees, Value-of-information, assessment of subjective probabilities, biases in probability estimation
Modelling risk preferences: Expected Utility Theory (EUT), Stochastic Dominance, Risk measures
Multi-objective decision making: Multi-attribute utility theory (MAUT), and the Analytic Hierarchy Process (AHP).
Supporting decision making with optimization: Multi-objective and stochastic models
Preparing for the exam
160h (6 cr)
Aalto University Code of Academic Integrity and Handling Thereof>
Register to course in MyCourses. Course materials excluding the text book are distributed in MyCourses. Assignments are individual work. You may discuss the problems with your colleagues, but you must return individual answers. Returning a copied answer or solution is strictly forbidden. Remember that in the exam you will need to individually solve equivalent problems.