GRA 4154 Supply Chain Optimization with Mathematical Programming
GRA 4154 Supply Chain Optimization with Mathematical Programming
Close to all 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 difficult decision-making problems encountered in the supply chain context.
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 implement such methods.
- Differentiate between different types of supply chain uncertainty and how these can be handled in an analytical manner.
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 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.
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.
- Overview of supply chain decisions and strategies
- Mathematical modelling via Linear Programming and Integer Linear Programming
- Introduction of some optimization methods (Branch and Bound, heuristics, metaheuristics) 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
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, Python (PuLP), or similar mathematical modelling software.
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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.
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 beneficial, but not needed.
| Assessments |
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Exam category: Submission Form of assessment: Written submission Weight: 30 Grouping: Group (1 - 3) Duration: 7 Week(s) Exam code: GRA 41541 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 70 Grouping: Individual Duration: 3 Hour(s) Comment: 25/01/2023 The form of the exam has changed from individual school exams to individual home exam in spring 2023. Exam code: GRA 41542 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.