Operations Research 1
About the course
The course can be taken as a single course.
The course will provide students with knowledge in operations research and decision analysis. The main contents include mathematical optimization and computer-based simulation as well as their applications for solving various real-life industrial problems.
Optimization:
Optimization is prescriptive analytics that concerns the application of mathematical modeling to solve optimization/decision-making problems in the management and operation of complex systems, which may consist of human beings, machines, materials, and capital. The purpose is to help determine the best policy and actions for the allocation of resources. The main techniques are linear programming, integer programming, stochastic programming, non-linear programming, as well as the exact and heuristic methods for solving these mathematical models.
Simulation:
Simulation is descriptive analytics and models real-life systems in a detailed manner, which allows comprehensive scenario analysis, performance measurement, and sometimes, a nice visualization of the system. The purpose is to compare different setups in a more comprehensive and close-to-real-life manner in order to obtain robust decisions and different performance indicators. The main techniques are Monte Carlo simulation, discrete-event simulation, agent-based simulation, and system dynamics.
Applications:
The application areas of optimization and simulation include production planning, product-mix decision-making, facility location, network analysis, project management, routing and scheduling of vehicles and crews, logistics and supply chain management, and blending problems. Furthermore, the historical development, the practical significance, and the limitations of mathematical modeling and simulation are also discussed.
Software:
The course is practice-focused, and students will learn the most powerful and state-of-the-art optimization and simulation software tools including Gurobi, Cplex, AMPL, and Anylogic. These tools have been widely used by world leading companies and organizations to improve their operational effectiveness, efficiency, and sustainability, including NASA, IBM, Google, Microsoft, Apple, Siemens, Hp, ABB, Pwc, Accenture, Boeing, Airbus, DHL, Pfizer, Volvo, BMW, to name a few.
Admission requirements
Bachelor degree in Engineering program in mechanical, electrical power, electronics, mechatronics, material science, industrial engineering, process engineering or other equivalent majors.
In addition, the following requirements must be met:
-minimum 25 ECTS in mathematics (equivalent to Mathematical Methods 1, 2 og 3), 5 ECTS in statistics and 7,5 ECTS in physics on a higher level is required.
Prerequisite(s) Knowledge about linear algebra, classical calculus (maximum/minimum for multi-variable functions) and computer programming at bachelor level.
Application code: 9371
Objectives of the course
Objectives of the course
Knowledge:
This course provides an introduction to operations research, equipping students with foundational knowledge in mathematical modeling of complex industrial, economics, and management problems. Students will learn about various basic optimization and simulation techniques that are applicable to these models and what software tools can be used to implement these models.
The course is practice-focused, and students will learn the most powerful and state-of-the-art optimization and simulation software tools including Gurobi, Cplex, AMPL, and Anylogic. These tools have been widely used by world leading companies and organizations to improve their operational effectiveness, efficiency, and sustainability, including NASA, IBM, Google, Microsoft, Apple, Siemens, Hp, ABB, Pwc, Accenture, Boeing, Airbus, DHL, Pfizer, Volvo, BMW, to name a few.
Skills:
- Problem Formulation: Students will develop the ability to develop optimization/simulation models from real-life challenges or textual descriptions, facilitating optimal decision-making in different fields.
- Software Proficiency: Students will learn to use diverse state-of-the-art software tools for solving optimization problems and conducting complex simulations, enhancing their technical capabilities.
- Analytical Techniques: Students will gain proficiency in applying different analytical techniques and interpret the results obtained from the analytical models into practical actions and implications.
- AI Competency: Students will gain knowledge to work with Generative AI in an ethical and responsible way to develop optimization/simulation solutions to solve industrial problems.
General (overall) Qualifications:
Upon completing this course, candidates will possess the qualifications to effectively communicate and solve complex industrial, economic, and management problems using their knowledge and skills in optimization and simulation. This foundation will not only support their further education but also enhance their professional capabilities, preparing them for successful careers in various industries. This course aims to develop problem-solvers who can innovate and improve operational efficiencies in their future educational pursuits and working lives. In addition, this course will also equip students with the competence to work with Generative AI in an ethical and responsible manner.
Credit reduction
If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:
- SMN6196 Operations Research 1 5 ects
Teaching methods
The lectures for the course are divided into two weeks in the autumn. It is not possible to take the exam in the spring semester.
The course can be taken physically at Narvik, or it can be fully taken online.
Language of instruction and examination
EnglishSchedule
The schedules are normally finalized and published well in advance of the start of the semester, often a few weeks beforehand. This gives students the opportunity to organize their studies and prepare for upcoming activities.
It is recommended to check the schedule regularly, as changes may occur.
Information to incoming exchange students
This course is open to incoming exchange students.
Study Level: Master's
Admission prerequisites:
To take this course, you must first meet the requirements listed in the “Admission requirements” section above.
For details on how to apply for exchange, course selection guidelines, or to contact the Incoming Admissions Team, please visit: Admissions for Student Exchange.
Examination
| Assignment | Hand in: 07.10.2026 14:00 |
Grade: A–E, fail F |
To take an examination, the student must have passed the following coursework requirements
| Mandatory assignments | Grade: Approved – not approved |
Everything you need to know about before, during, and after the exam; registration, absence, appeals, and diplomas: UiT Exams homepage
More info about the coursework requirements
The candidate needs to complete 6 mandatory assignments within the due before the submission of the project report.More info about the assignment
The course will be evaluated based on a group project to solve real industry-related challenges using optimization and/or simulation methods.
A project report with all the codes/simulation models developed needs to be submitted for evaluation.