Current Term  Winter 2021/22
Column Generation und BranchandPrice
Infos:
 Lecturer: Marco Lübbecke
 Typ:lectures
 Languagegerman or english
Content:
Prerequisites
Indispensible: Solid knowledge in linear/integer optimization from the courses "quantitative methods" and "operations research 1" (BWL) or "efficient algorithms" (CS) or "integer linear optimization" (maths), i.e., in particular you master duality, branchandbound, and modeling with integer programs.
Contents
State of the art in models and algorithms to solve extremely largescale and complex optimization problems, in particular column generation and branchandprice: structued integer programs, DantzigWolfe reformulation, Lagrange relaxation, cutting planes in connection with column generation, branching rules, stabilization technqiues, implementation tricks, practical applications
Learning Goals
The students aquire basic and advanced skills in modelling very largescale and practical optimization problems, as well as the algorithmic thinking to solve such models using decomposition approaches. By working on practical implementation exercises, these algorithms shall be actually applied and the basic implementation of a column generation approach will be learned. Students will be enabled to read scientific publications at the current level of scientific knowledge and transfer this knowledge to practical modeling situations.
Operations Research 1
Infos:
 Lecturer: Marco Lübbecke

 Hermann von Westerholt
 Typ:lectures
 Languageenglish
 Contact:or1@or.rwthaachen.de
Content:
Prerequisites
Basic knowledge in linear optimization and graph algorithms, in particular modeling with networks (minimum cost flows, shortest paths) and linear programs. If missing, this background must be acquired before or simultaneously to the course.
Contents
1. Modeling with integer linear programs: assignment problems, knapsack problem, faclity location, machine scheduling, traveling salesperson problem, vehicle routing, set partitioning, bin packing, cutting stock, logical constraints; 2. algorithms for solving integer programs: branchandbound, cutting planes, branchandcut, dynamic programming; 3. basics of heuristics and meta heuristics (construction and improvement heuristics, greedy algorithm, local search, simulated annealing, tabu search, evolutionary and genetic algorithms); 4. particular modeling situations like soft constraints, nonlinearities, heuristic modeling; introduction to computational complexity theory
Learnig Goals
Students learn modeling techniques and methods of integer optimization, in particular their use cases and limitations. Students aquire the ability to identify the mathematical core of a practical optmization task and to exploit problem structure when selecting/developing a model or selecting an algorithm. The theoretical knowledge is deepened using practical exercises with standard software (currently Gurobi/Python). We generally sharpen abstraction capabilities.
Videos from winter term WS17/18 are here.
OR Praktikum
Infos:
 Lecturer: Marco Lübbecke

 Elisabeth RodriguezHeck
 Typ:Practice Project
 Languagegerman
Content:
Seminar
Infos:
 Lecturer: Marco Lübbecke
 Typ:seminar
 Languagegerman
Content:
Next Term  Summer 2022
Operations Research 2 every summer term
Infos:
 Lecturer: Marco Lübbecke
 Typ:lectures
 Languagegerman or english
 Turnus: every summer term
Content:
OR Praktikum every term
Infos:
 Lecturer: Marco Lübbecke
 Typ:Practice Project
 Languagegerman
 Turnus: every term
Content:
Praktische Optimierung mit Modellierungssprachen every summer term
Infos:
 Lecturer: Marco Lübbecke
 Typ:lectures and exercises
 Languagegerman or english
 Turnus: every summer term
Content:
Quantitative Methoden every summer term
Infos:
 Lecturer: Marco Lübbecke
 Typ:lectures
 Languagegerman
 Turnus: every summer term
Content:
Entire List of Courses we offer
Approximationsalgorithmen irregularly
Infos:
 Lecturer: Marco Lübbecke
 Typ:lectures and exercises
 Languagegerman or english
Content:
Column Generation und BranchandPrice every winter term
Infos:
 Lecturer: Marco Lübbecke
 Typ:lectures
 Languagegerman or english
Content:
Prerequisites
Indispensible: Solid knowledge in linear/integer optimization from the courses "quantitative methods" and "operations research 1" (BWL) or "efficient algorithms" (CS) or "integer linear optimization" (maths), i.e., in particular you master duality, branchandbound, and modeling with integer programs.
Contents
State of the art in models and algorithms to solve extremely largescale and complex optimization problems, in particular column generation and branchandprice: structued integer programs, DantzigWolfe reformulation, Lagrange relaxation, cutting planes in connection with column generation, branching rules, stabilization technqiues, implementation tricks, practical applications
Learning Goals
The students aquire basic and advanced skills in modelling very largescale and practical optimization problems, as well as the algorithmic thinking to solve such models using decomposition approaches. By working on practical implementation exercises, these algorithms shall be actually applied and the basic implementation of a column generation approach will be learned. Students will be enabled to read scientific publications at the current level of scientific knowledge and transfer this knowledge to practical modeling situations.
Operations Research 1 every winter term
Infos:
 Lecturer: Marco Lübbecke
 Typ:lectures
 Languageenglish
Content:
Prerequisites
Basic knowledge in linear optimization and graph algorithms, in particular modeling with networks (minimum cost flows, shortest paths) and linear programs. If missing, this background must be acquired before or simultaneously to the course.
Contents
1. Modeling with integer linear programs: assignment problems, knapsack problem, faclity location, machine scheduling, traveling salesperson problem, vehicle routing, set partitioning, bin packing, cutting stock, logical constraints; 2. algorithms for solving integer programs: branchandbound, cutting planes, branchandcut, dynamic programming; 3. basics of heuristics and meta heuristics (construction and improvement heuristics, greedy algorithm, local search, simulated annealing, tabu search, evolutionary and genetic algorithms); 4. particular modeling situations like soft constraints, nonlinearities, heuristic modeling; introduction to computational complexity theory
Learnig Goals
Students learn modeling techniques and methods of integer optimization, in particular their use cases and limitations. Students aquire the ability to identify the mathematical core of a practical optmization task and to exploit problem structure when selecting/developing a model or selecting an algorithm. The theoretical knowledge is deepened using practical exercises with standard software (currently Gurobi/Python). We generally sharpen abstraction capabilities.
Videos from winter term WS17/18 are here.
Operations Research 2 every summer term
Infos:
 Lecturer: Marco Lübbecke
 Typ:lectures
 Languagegerman or english
Content:
OR Praktikum every term
Infos:
 Lecturer: Marco Lübbecke
 Typ:Practice Project
 Languagegerman
Content:
Praktische Optimierung mit Modellierungssprachen every summer term
Infos:
 Lecturer: Marco Lübbecke
 Typ:lectures and exercises
 Languagegerman or english
Content:
Polyedrische Kombinatorik currently not offered
Infos:
 Lecturer: Matthias Walter
 Typ:lectures
 Languagegerman
Content:
Programmieren, Algorithmen, Datenstrukturen currently not offered
Proseminar irregularly
Infos:
 Lecturer: Marco Lübbecke
 Typ:proseminar
 Languagegerman or english
Content:
Quantitative Methoden every summer term
Infos:
 Lecturer: Marco Lübbecke
 Typ:lectures
 Languagegerman
Content:
Seminar every term
Infos:
 Lecturer: Marco Lübbecke
 Typ:seminar
 Languagegerman