623.622 (23W) Constraint-based Product Configuration

Wintersemester 2023/24

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Erster Termin der LV
20.10.2023 10:00 - 12:00 online Off Campus
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Überblick

Lehrende/r
LV-Titel englisch Constraint-based Product Configuration
LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
LV-Modell Onlinelehrveranstaltung
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 3.0
Anmeldungen 14 (30 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 20.10.2023
eLearning zum Moodle-Kurs

Zeit und Ort

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LV-Beschreibung

Intendierte Lernergebnisse

At the end of the course, the students shall understand the benefits of product configuration in the context of Product Lifecycle Management and be able to use appropriate tools (mainly constraint-based) to find solutions for configuration requirements arising in practice.

Lehrmethodik inkl. Einsatz von eLearning-Tools

Organization

  • Online course (in BigBlueButton), starting on 2023-10-20
  • 9 synchronous sessions on Fridays 10:00 - 12:00 (incl. 15 min break)
  • Between the sessions, students watch pre-recorded lectures and solve exercises (homework)
  • During the sessions, students present their solutions and we discuss lecture contents and questions
  • Grading (exam) comprises
    • 50% - Exercises solved in homework (individually or in pairs)
    • 15% - Presentations and other contributions during the course (individually or in pairs)
    • 35% - Multiple-choice test with CheckR at the end of the last lesson (individually)

Inhalt/e

Overview

  • Who needs configuration (i.e. individualization of products and services) and what for?
  • Typical examples from long-time experience with configuring complex technical systems
  • Key technologies (especially constraint solving) for tackling product configuration problems

Topics

  • What is product configuration? Real-word examples from mass customization to engineer-to-order
  • Modelling of knowledge bases (product variability, constraints, logic), debugging and testing
  • User interaction and use cases (check, solve, optimize, reconfigure)
  • Constraint Satisfaction Problem and variants (GCSP), Constraint Optimization Problem
  • Tackling performance issues: e.g., pre-compilation, local search, symmetry breaking
  • Diagnosis of inconsistent configurations and knowledge bases
  • Advanced topics, e.g., solution and systems configuration, configuration and data analytics, product and production configuration, sustainability challenges
  • Practical work with open-source tools, mainly MiniZinc (alternatively other constraint solvers)

Erwartete Vorkenntnisse

Basic knowledge in logics and in data modeling

Prüfungsinformationen

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Prüfungsmethode/n

Grading comprises:

  • 50% - Exercises solved in homework (individually or in pairs)
  • 15% - Presentations and other contributions during the course (individually or in pairs)
  • 35% - Multiple-choice test with CheckR at the end of the last lesson (individually)

Prüfungsinhalt/e

  • Product configuration and its benefits and context
  • Configuration technologies
  • Variant tables
  • Decision diagrams
  • Feature models
  • Propositional logic and ASP
  • Constraint satisfaction
  • Constraint optimization
  • Object-oriented constraint satisfaction
  • Quality
  • Conflict detection and diagnosis
  • Performance tuning
  • System configuration
  • Production configuration

Beurteilungskriterien/-maßstäbe

  • 50% - Exercises solved in homework (individually or in pairs)
    • Defined maximum points per exercise (50 in total)
    • Submitted solutions will be reviewed; points will be awarded corresponding to solution quality
  • 15% - Presentations and other contributions during the course (individually or in pairs)
    • Up to 10 points for successful presentation (presenters chosen randomly)
    • In case of pairs, both contributors need to contribute to presentation
    • Up to 5 points for other significant contributions in lessons
  • 35% - Multiple-choice test with CheckR at the end of the last lesson (individually)
    • Duration: 45 minutes (45 questions)
    • Each question offers several answers which may be true or false
    • You earn one point for each question where you select the correct combination of all true answers (could be any number, incl. zero or all)

Grade derived from sum: 

  • >85% → 1

  • >70% → 2

  • >60% → 3

  • >50% → 4

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 19W.2)
    • Fach: Artificial Intelligence and Natural Language Processing (Wahlfach)
      • 8.1 Artificial Intelligence and Natural Language Processing ( 0.0h XX / 12.0 ECTS)
        • 623.622 Constraint-based Product Configuration (2.0h VC / 3.0 ECTS)
          Absolvierung im 4., 5., 6. Semester empfohlen
  • Bachelorstudium Robotics and Artificial Intelligence (SKZ: 295, Version: 22W.1)
    • Fach: Robotics & AI Applications (Wahlfach)
      • 8.1 Robotics & AI Applications ( 0.0h VO, VC, UE, KS / 12.0 ECTS)
        • 623.622 Constraint-based Product Configuration (2.0h VC / 3.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Wintersemester 2022/23
  • 623.622 VC Constraint-based Product Configuration (2.0h / 4.0ECTS)
Wintersemester 2021/22
  • 623.622 VC Constraint-based Product Configuration (2.0h / 4.0ECTS)
Wintersemester 2020/21
  • 623.622 VC Constraint-based Product Configuration (2.0h / 4.0ECTS)
Wintersemester 2019/20
  • 623.622 VC Constraint-based Product Configuration (2.0h / 4.0ECTS)