EURO 2025 Leeds
Abstract Submission

2524. Simulating pick-and-place operations for optimized robotic operations in reconfigurable manufacturing environments

Invited abstract in session WC-15: Topics in Combinatorial Optimization 1, stream Combinatorial Optimization.

Wednesday, 12:30-14:00
Room: Esther Simpson 1.08

Authors (first author is the speaker)

1. Konstantinos Giannakos
University of Piraeus Research Center
2. Dimitrios Tsakoumis
Industrial Management and Technology, University of Piraeus
3. Stathis Plitsos
Industrial Management and Technology, University of Piraeus
4. Gregory Koronakos
Department of Informatics, University of Piraeus
5. Giulio Vivo
Centro Ricerche Fiat
6. Pavlos Eirinakis
Industrial Management and Technology, University of Piraeus

Abstract

Reconfigurable Manufacturing Systems (RMS) require adaptable solutions for dynamic production demands. In robotic pick-and-place tasks, optimizing the picking sequence boosts efficiency. This study models the problem as a Bipartite Traveling Salesman Problem (BTSP), with a robot alternating between two sets: Gravity Racks (GR), storing components, and Kit Holders (KH), receiving assembled kits. We compare three optimization approaches: Heuristic Methods (e.g., Nearest Neighbor, 2-opt), a baseline Linear Method, and an Exact Method using Integer Programming. The Exact Method excels, offering up to 20% improvement over the linear approach. Additionally, we introduce a simulation service extends beyond sequence optimization by testing various GR configurations against multiple KH sequences. By simulating the full sequence of KH setups as a continuous tour, reveals that optimized GR layouts can yield 20–30% efficiency gains beyond sequence optimization alone. The system is modular, processing structured data inputs, enabling rapid evaluation of different layouts and their impact on total picking costs. Experimental results confirm that combining layout optimization with sequence planning enhances performance, reducing operational costs and time. This integrated approach paves the way for intelligent, scalable warehouse automation, with potential in integrating machine learning to predict optimal configurations or enabling multi-agent coordination for multi-robot environments.

Keywords

Status: accepted

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