Bottom-Up Bio-Inspired Algorithms for Optimizing Industrial Plants

Scheduling in a production plant with a high product diversity is an NP-hard problem. In large plants, traditional optimization methods reach their limits regarding computational time. In this paper, we use inspiration from two bio-inspired optimization algorithms, namely, the artificial bee colony (ABC) algorithm and the bat algorithm, and apply them to the job shop scheduling problem. Unlike previous work using these algorithms for global optimization, we do not apply them to solutions in the solution space, though, but rather choose a bottom-up approach and apply them as literal swarm intelligence algorithms. We use the example of a semiconductor production plant and map the bees and bats to actual entities in the plant (lots, machines) using agent-based modeling using the NetLogo simulation platform. These agents then interact with each other and the environment using local rules from which the global behavior – the optimization of the industrial plant – emerges. We measure performance compared to a baseline algorithm using engineered heuristics (FIFO, fill fullest batches first). Our results show that these types of algorithms, employed bottom-up, show promise of performance improvements using only low-effort local calculations.

Our newest research paper builds upon the simulation framework presented in http://demesos.blogspot.com/2022/08/swarmfabsim-simulation-framework-for.html

Asking Question at ICAART Panel Session

As in this earlier paper, algorithm performance was compared against a reference baseline algorithm using the key performance indicators of makespan, flow factor, delay, and machine utilization. Results show that using these swarm intelligence algorithms in a bottom-up manner with only low-effort local calculations can lead to performance improvements. The paper details the application of two algorithms to factory optimization, an artificial bee algorithm (ABC) and a bat-inspired algorithm. Both algorithms are used in a bottom-up approach, not as a global optimizer. In this type of approach, the swarm members do not represent solutions in the solution space of the problem but actual lots and/or machines in the factory. In conclusion, swarm intelligence algorithms can be a powerful tool for production plant scheduling optimization. Although the case study is based on a semiconductor production plant, the same approach applies well to other production plants as well.

Paper presentation at ICAART'23

Further information can be found in the paper

M. Umlauft, M. Gojkovic, K. Harshina and M. Schranz: Bottom-Up Bio-Inspired Algorithms for Optimizing Industrial Plants, Proceedings of ICAART 2023, ISBN: 978-989-758-623-1, doi:10.5220/0011693400003393

SwarmFabSim: A simulation framework for bottom-up optimization in flexible job-shop scheduling using Netlogo

It was great to be in presence at a conference again. At the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, aka SIMULTECH, we presented our paper 

Martina Umlauft, Melanie Schranz, and Wilfried Elmenreich. SwarmFabSim: A simulation framework for bottom-up optimization in flexible job-shop scheduling using Netlogo. In Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH. SciTePress, July 2022. (doi:10.5220/0011274700003274) 

Click triangle for Bibtex entry

@inproceedings{umlauft:swarmfabsim:22,
  author = {Umlauft, Martina and Schranz, Melanie and Elmenreich, Wilfried},
  title = {{SwarmFabSim}: {A} Simulation Framework for Bottom-up Optimization
in Flexible Job-Shop Scheduling Using {N}etLogo},
  booktitle = {Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
  year = {2022},
  month = jul,
  publisher = {SciTePress},
  doi = {10.5220/0011274700003274},
}
  

The paper shows how to the programming language NetLogo to model and simulate a factory producing according to the job-shop manufacturing principle. The main contribution is a modular simulation framework that can apply various algorithms to optimize a make-to-order manufacturing system and supports multiple configurable scenarios. The evaluation framework was used to assess the effectiveness of an artificial hormone algorithm compared to a naïve basic implementation and a reference baseline algorithm. The evaluation was based on three key performance indicators: Flow factor, delay, and utilization. The simulations show promising results of the artificial hormone algorithm in three reference scenarios with significant improvements over the reference algorithms. The implementation of the simulation environment is published as open source in the Git repository https://swarmfabsim.github.io. Readers are welcome to contribute with their ideas and developments.

Screenshot of the SwarmFabSim application