Development of Scalable Algorithms for Production System Configuration in a Learning Factory

Institut

Institut für Werkzeugmaschinen und Betriebswissenschaften (TUM-ED)

Typ

Bachelorarbeit / Semesterarbeit / Masterarbeit /

Inhalt

 

Beschreibung

Initial Situation

The introduction of different products into existing production systems requires identifying suitable combinations of available manufacturing resources. In flexible production environments, multiple workstations may be capable of performing the same operations, resulting in numerous possible production system configurations. As the number of products, operations, and resources increases, the configuration search space grows rapidly. Existing approaches based on exhaustive enumeration therefore face computational limitations. A learning factory with different products, predefined assembly operations, and available workstations provides a suitable environment for investigating and evaluating alternative configuration algorithms.

Objective

The objective of this work is to develop and evaluate algorithms for the automated generation of feasible production system configurations using a learning factory as an application case. Initially, the available products, required assembly operations, workstation capabilities, and relevant constraints will be analyzed and represented in a formal configuration model. Based on this model, different approaches to combinatorial optimization, particularly constraint programming and heuristic search, will be investigated and prototypically implemented. The algorithms will generate feasible assignments of assembly operations to available workstations while considering capability restrictions, operation precedence, and relevant resource constraints. Their performance will be systematically evaluated using different products and problem instances of increasing complexity. The evaluation will focus on computational efficiency, scalability, and the feasibility and completeness of generated configurations, using exhaustive enumeration as a reference where computationally practical.

Voraussetzungen

Interest in production planning, mathematical optimization, and algorithm development. Good Python skills are required. Experience with constraint programming or operations research is advantageous. Independent and scientifically structured working skills are expected.

Möglicher Beginn

sofort

Kontakt

Jannik Stöver
Raum: 1325
jannik.stoeveriwb.tum.de