Study points:
2.5
Responsible department:
Faculty of Logistics
Homayoun Shaabani
Lecture Semester:
Autumn
Teaching language:
English
Duration:
½ year

# LOG904-119 Scheduling Models and Algorithms (Autumn 2019)

Course content (abstract): Scheduling and sequencing are decision-making processes that are used on a regular basis in many manufacturing and service industries. Mathematically, most of the scheduling models belong to the area of combinatorial optimisation, where the traditional techniques include enumerative schemes, local search procedures as well as purpose-built algorithms. The main purpose of this course is to introduce the students to the variety of scheduling models, to help them to choose a right model for a practical problem and to select an appropriate tool, analytical or software, to find a suitable solution.

Topics covered: Single-stage and multi-stage scheduling systems. Problems on parallel machines. Shop scheduling problems. Preemtion and precedence constraints. Local search and approximation algorithms. Branch-and-bound.

## Recommended requirements

Basic Linear Algebra, Operations ResearchScheduling and sequencing are decision-making processes that are used on a regular basis in many manufacturing and service industries. Mathematically, most of the scheduling models belong to the area of combinatorial optimisation, where the traditional techniques include enumerative schemes, local search procedures as well as purpose-built algorithms. The main purpose of this course is to introduce the students to the variety of scheduling models, to help them to choose a right model for a practical problem and to select an appropriate tool, analytical or software, to find a suitable solution.

## The student's learning outcomes after completing the course

By the end of the course students will be able to: formulate practical problems in terms of standard scheduling models and their extensions; find exact and approximate solutions to scheduling problems, design and run branch and bound and local search algorithms

## Forms of teaching and learning

3 hours of lectures + 2 hours of supervised lab work

## Examination

• Form of assessment: Home assessment
• Proportion: 30%
• Duration: -
• Grouping: Group
• Grading scale: Letter (A - F)
• Support material: All printed and written supporting material
• Form of assessment: Home assessment
• Proportion: 70%
• Duration: -
• Grouping: Individual
• Grading scale: Letter (A - F)
• Support material: All printed and written supporting material
Last updated from FS (Common Student System) Aug. 12, 2020 11:20:06 AM