LOG904-131 Applied Dynamic and Stochastic Programming for Logistics (Autumn 2023)

About the course

The course focuses on building of selected optimization models and on key ideas of selected solution methods that are suitable for applied problems in logistics. Most real-world applications must involve quantitative techniques. Therefore, the course should help students with computational parts of their master theses and with quantitative challenges in their jobs in the future.

Step-by-step, we will follow and develop principal techniques for the cases where uncertainty (e.g. in demand) and time (e.g. planning horizon) must be considered. General outline:

Day 1: separability,

Day 2: decomposability,

Day 3: more decision stages,

Day 4: uncertainty by scenarios.

We will study fundamental concepts by using explanatory educational examples to get insight. Motivating real-world industrial applications will be discussed, focus will be on these recently solved with participation of the lecturer and his colleagues (also from Molde University College). The lecturer utilizes (and reviews, if necessary) basic concepts from introductory Calculus course. Course participants will work in groups and they will discuss submitted results of solved assignments on Day 5 during an oral presentation.

The course is connected to the following study programs

• Master of Science in Logistics
• Experience-based Master in Logistics
• Master of Science in Sustainable Energy Logistics
• Exchange programme - Master's level
• Master of Science in Sustainable Transport and Urban Mobility

Recommended requirements

Basic Calculus knowledge (by 1st year of bachelor study)

The student's learning outcomes after completing the course

Knowledge:

During the course, students will learn and understand fundamental ideas about separability, dynamic programming and stochastic programming approaches suitable for real-world applications of logistics.

Skills:

After the course, students will be able to build applicable stochastic programming models (like wait-and-see, here-and-now, scenario based two-stage linear), utilize separability ideas for optimal decision making, and apply algorithmic principles of dynamic programming (by graphs, tables and formulas) in production, transport, supply chain management and logistics in general.

General competence:

Students will improve their general skills for model building in logistics and they will learn how to make their knowledge about theoretical concepts useful in practice. They will improve in addressing best questions to the users when they develop quantitative models in logistics. They will get the feedback about their ability to work in the team under pressure of deadlines and challenges that are typical for those who collaborate with industry on real world problems.

Forms of teaching and learning

teaching consists of lectures and group work on assignments

Examination

• Form of assessment: Home assessment is based on 4 assignments (1 each day and deadline on Friday at 11 a.m.) with oral presentation (on Friday at 1 p.m.). All 5 parts are eqully marked.

• Proportion: 100 %

• Duration: 4 assignments are given to students every day till 2 p.m. (on Monday, Tuesday, Wednesday, Thursday), deadline for all submissions is on Friday 11 a.m. and oral presentation of achieved results is on Friday at 1 p.m. till 3 p.m.

• Grouping: group work

• Grading scale: Letter (A - F)

• Support material: Students will get references (e.g. to books available in MUC library) and main supporting readings at the beginning of the course and optional potentionally helpful resources will be delivered during the course (together with specification of assignments).

Syllabus

TBA