# GRA 4154 Supply Chain Optimization with Mathematical Programming

## GRA 4154 Supply Chain Optimization with Mathematical Programming

Course code:
GRA 4154
Department:
Accounting and Operations Management
Credits:
6
Course coordinator:
Atle Nordli
Course name in Norwegian:
Supply Chain Optimization with Mathematical Programming
Product category:
Master
Portfolio:
MSc in Data Science for Business
Semester:
2025 Spring
Active status:
Active
Level of study:
Master
Teaching language:
English
Course type:
One semester
Introduction

Modern business are exposed to complex supply chains to produce and sell their products.

To enhance business profitability and customer satisfaction, as well as cater for sustainable operations, competitive advantage can be obtained by businesses that rely on data-driven decision-making tools when designing and managing their supply chains.

In this course, you will learn how to create optimization models to support decision making at the strategic (e.g., location of facilities), tactical (production and inventory planning), and operational (production and transportation) levels when designing and managing supply chains. Additionally, given the importance of coordination across these different levels, you will learn integrated models for coordinated decision making across multiple stages of the supply chain.

In terms of methodology, you will learn about linear, mixed integer and nonlinear programming models, as well as heuristic solution methods to solve some of the decision-making problems encountered in the supply chain context.

Learning outcomes - Knowledge

By the end of the course, the student can:

• Explain and reflect upon how quantitative modelling support decision making in supply chain design, planning and control.
• Identify when heuristic methods are relevant in the supply chain context, and explain how to use these methods.
• Differentiate between different types of supply chain uncertainty and how these can be handled in an analytical manner.
Learning outcomes - Skills

By the end of the course, the student:

• Can create, solve, and analyze mathematical programming models for various types of business optimization problems.
• Is able to design and implement heuristic approaches for some basic problems in the supply chain context.
• Knows how different functions and stages in a supply chain can be coordinated using optimization models for integrated decision making.
General Competence

By the end of the course, the student:

• Has obtained an overview of challenges and solutions for supply chain analytics both from a theoretical and practical perspective.
• Can apply various optimization models to support decision making at all levels in supply chain management.
Course content
• Overview of supply chain decisions and strategies
• Mathematical modelling via Linear Programming and Integer Linear Programming
• Introduction of some optimization methods (Branch and Bound, simple heuristics) with applications to various supply chain decisions.
• Planning and coordinating demand and supply (Demand forecasting, lot sizing models)
• Inventory management and safety stock analysis
• Transportation planning (Vehicle routing problems)
• Operations scheduling
• Integrated decision making in supply chains
Teaching and learning activities

The learning activities will combine lectures and case discussions. Students are expected to prepare the lectures by reading assigned materials and participating actively in the discussion of the lecture topics.

Software: AMPL or similar mathematical modelling software.

Software tools
Software defined under the section "Teaching and learning activities".

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

All parts of the assessment must be passed in order to get a grade in the course.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

### Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

Students are expected to have completed all prior courses in the program. Prior exposure to Supply Chain Optimization and mathematical programming topics or courses is not needed.

Assessments
Assessments
Exam category:
Submission
Form of assessment:
Submission PDF
Exam/hand-in semester:
First Semester
Weight:
30
Grouping:
Group/Individual (1 - 3)
Duration:
5 Week(s)
Exam code:
GRA 41541
ECTS
Resit:
Examination when next scheduled course
Exam category:
School Exam
Form of assessment:
Written School Exam - pen and paper
Exam/hand-in semester:
First Semester
Weight:
70
Grouping:
Individual
Support materials:
• All printed and handwritten support materials
• BI-approved exam calculator
• Simple calculator
• Bilingual dictionary
Duration:
3 Hour(s)
Exam code:
GRA 41542