Credits: 6

Schedule: 28.10.2019 - 11.12.2019

Teacher in charge (valid 01.08.2018-31.07.2020): 

Juuso Liesiö

Contact information for the course (applies in this implementation): 

Management Science (MS) deals with the application of analytical models to help make better decisions. The terms Business Analytics and Operations Research 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 based on mathematical optimization such as Linear Programming, Mixed-Integer Linear Programming and Non-Linear Programming. These methods are introduced through applications in, for instance, production planning & scheduling, logistics, marketing and finance. The course also introduces approaches for modelling uncertainties and multiple decision objectives in optimization models.

Teaching Period (valid 01.08.2018-31.07.2020): 

Period II (2018-2019) Otaniemi campus

Period II (2019-2020) Otaniemi campus

Learning Outcomes (valid 01.08.2018-31.07.2020): 

Management Science deals with the use of analytical models to help make better business decisions. This course focuses on optimization models that are commonly used in business applications. After the course the student can (i) recognize the types of real-life business decision 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.

Content (valid 01.08.2018-31.07.2020): 

Linear programming, network and distribution models, integer linear programming, mixed-integer linear programming, non-linear programming.

Details on the course content (applies in this implementation): 

See the schedule

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Assignments 50%, exam 50%.

Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation): 

Final points consist of exam point (50%) and assignment points (50%). The final points determine the course grade as follows:  >50p->1, >60p->2, >70p->3, >80p->4, and >90p->5, with the exception that at least half of the exam points are required to pass. These bounds maybe relaxed during final grading.

There are three assignments with deadlines on roughly the second, fourth and sixth week of the course. Each assignment consists of several problems or cases, which usually require the use of spreadsheets or other mathematical software to solve. The total points are not equal for all three assignments.

Workload (valid 01.08.2018-31.07.2020): 

Contact teaching 36h, individual work 121h, exam 3h. Total 160h (ECTS).

Details on calculating the workload (applies in this implementation): 

Classroom hours 36h
Class preparation 12h
Assignments 102h
Preparing for the exam 7h
Exam 3h

Study Material (valid 01.08.2018-31.07.2020): 

Lecture slides, articles, assignments, computer implementations of mathematical models, and the textbook (An Introduction to Management Science by Anderson et al., 2014, ISBN Code: 978-1-111-82361-0).

Details on the course materials (applies in this implementation): 

All material except for the textbook will be available at MyCourses.

Course Homepage (valid 01.08.2018-31.07.2020):

Prerequisites (valid 01.08.2018-31.07.2020): 

Basic knowledge on multivariate equations and functions.

Grading Scale (valid 01.08.2018-31.07.2020): 


Registration for Courses (valid 01.08.2018-31.07.2020): 

Via WebOodi

Further Information (valid 01.08.2018-31.07.2020): 

A maximum of 100 students will be admitted to the course. Priority will be given to Aalto ISM students.

Details on the schedule (applies in this implementation): 

Preliminary schedule:

1 Introduction; Linear programming (LP)
2 LP sensitivity analysis; Applications to distribution and network problems
3 Integer and Mixed-Integer linear programming (MILP) and applications
4 Non-linear programming (NLP); Modelling uncertainties
5 Multi-objective programming
6 Course summary Guest lecturer from Business


Registration and further information