Synthetic simulated environment for discrete manufacturing systems: A demonstrator through a computational modeling approach
Paper i proceeding, 2024

In light of the challenges posed by the often unavailability of coherent data in manufacturing for operational Artificial Intelligence (AI) decision support systems, the generation and utilization of synthetic datasets have become essential. This study introduces a simple numerical Synthetic Simulated Environment (SSE) using timed and parametrizable Petri Net (PN) modules, embedded in a Directed Acyclic Graph (DAG) structure described by an adjacency matrix to represent material flow. Implemented in PyTorch for seamless integration with AI components, our simulation framework simplifies manufacturing systems, yet remains expandable for diverse use cases. The simulation model was demonstrated displaying its capability of generating synthetic data. This approach explores the practicality and applicability of generated data. It could serve as an ideal environment to benchmark Artificial Intelligence (AI) algorithms in comparative experiments, investigating operational problems featured in the dynamic interactions of discrete manufacturing systems.

Författare

Silvan Marti

Chalmers, Industri- och materialvetenskap, Produktionssystem

Paulo Victor Lopes

Chalmers, Industri- och materialvetenskap, Produktionssystem

Instituto Tecnológico de Aeronáutica (ITA)

Siyuan Chen

Chalmers, Industri- och materialvetenskap, Produktionssystem

Mohan Rajashekarappa

Chalmers, Industri- och materialvetenskap, Produktionssystem

Elham Rekabi Bana

Chalmers, Industri- och materialvetenskap, Produktutveckling

Amon Göppert

RWTH Aachen University

Mélanie Despeisse

Chalmers, Industri- och materialvetenskap, Produktionssystem

Johan Stahre

Chalmers, Industri- och materialvetenskap, Produktionssystem

Björn Johansson

Chalmers, Industri- och materialvetenskap, Produktionssystem

Proceedings - Winter Simulation Conference

08917736 (ISSN)

1716-1727
9798331534202 (ISBN)

2024 Winter Simulation Conference, WSC 2024
Orlando, USA,

Ämneskategorier (SSIF 2025)

Datavetenskap (datalogi)

DOI

10.1109/WSC63780.2024.10838939

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Senast uppdaterat

2025-02-21