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

An Artificial Hormone-based Algorithm for Production Scheduling

Artificial hormone systems are inspired by the natural endocrine system that adjusts the metabolism of tissue cells in our body. By connecting decisions and actions in a system to the production and evaporation of artificial hormones, it is possible to create a bio-inspired self-organizing algorithm.

Application areas for such algorithms are problems with many agents to be coordinated, where existing optimization approaches come to their limit. An example of such a problem is the production of logic and power integrated circuits (ICs) in the semiconductor industry. Unlike the high-volume production of memory ICs, wafer production in the logic and power sector has a large product mix. This involves many processing steps and dynamic changes of involved machines.

Weekly workloads can involve around 100 000 operations on thousands of machines. Optimizing such a system for work in progress and flow factor is an NP-hard problem. At this size, existing dispatching rules and linear optimization methods cannot cope with the NP-hard search space, thus not optimize the entire system.

To address this issue, we have modeled a production plant as a self-organizing system of agents that interact with each other in a non-linear way. As it is common in the semiconductor industry, wafers are combined in groups of 25 pieces forming a so-called lot. In our approach, an artificial hormone systems is used to express a lot's urgency and the need for new lots at a machine type, thus providing a system using local information for optimization. The algorithm builds upon five principles, which are 

• (i) machines produce hormone to attract lots, 
• (ii) hormone diffuses process-upstream, 
• (iii) incoming lots diffuse hormone, 
• (iv) lots are prioritized by their timing, and 
• (v) lots are attracted by hormone. 

Via these mechanisms, machines can balance their workload by pulling required lots towards them. The algorithm has been implemented and evaluated in a NetLogo simulation model. Simulation results indicate that the artificial hormone system improves around 5% for overall production time and flow factor compared to a baseline algorithm. Future work will investigate if the hormone algorithm can be used on top of existing production systems. In a productive system an improvement of 5% would be highly notable.

More information can be found on the SWILT project webpage and in the paper

Wilfried Elmenreich, Alexander Schnabl, and Melanie Schranz. An artificial hormone-based algorithm for productionscheduling from the bottom-up. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence. SciTePress, February 2021.

Click triangle for Bibtex entry

@inproceedings{elmenreich:Hormone:21,
  author = {Elmenreich, Wilfried and Schnabl, 
    Alexander and Schranz, Melanie},
  title = {An artificial hormone-based algorithm
    for production scheduling from the bottom-up},
  booktitle = {Proceedings of the 13th International 
    Conference on Agents and Artificial Intelligence},
  year = {2021},
  month = feb,
  publisher = {SciTePress}
}  
  

SEAHORSE: A Middleware for Search and Delivery of Information Units based on an Artificial Hormone System Algorithm

SEAHORSE structure

With the rise of networked smart devices and the so called Internet of Things, services require more scalability and robustness to handle the complexity of the underlying ecosystem. In some situations (e.g., disaster areas, large-area sports events, battle fields, etc.), a traditional network infrastructure does not exist, or is expensive to set up. In this context content is also consumed in a more dynamic way than in traditional environments. By looking at principles found in nature we can see that it is possible to handle complexity and dynamics by relying exclusively on simple, local decisions (bio-inspired self-organizing systems). As an example, ants are exploring the surrounding area to find food, and if found they go back to their home base leaving pheromones to guide other ants.
We introduce SEAHORSE, a middleware showing by example how an existing self-organizing algorithm can be generalized. SEAHORSE is a first step for bringing self-organizing algorithms towards real-world applications.
By specifying interfaces to the application the middleware transparently handles the distribution of content. We show two use cases from different technical fields and performed a parameter analysis to reduce the configuration effort.

A. Sobe, W. Elmenreich, T. Skalicky, and L. Böszörmenyi. SEAHORSE: Generalizing an artificial hormone system algorithm to a middleware for search and delivery of information units. Elsevier Journal of Computer Networks, 2015.

A self-organizing algorithm for video distribution networks

The way how users consume videos has changed with the availability of large repositories with a high number of more or less related videos. Many users are only interested in tiny fractions of a video and not even necessarily in the original temporal order. Moreover, they might wish to dynamically compose portions of di erent videos into one presentation.
For example, take a video recording of a ski-jumping competition. Some users might be interested in watching it sequentially. A trainer might be interested in studying the jumping-off technique of athletes in parallel. Another user might be interested in the performance of several jumpers from one country.
In order to keep up with this emergent access patterns, we invented a self-organizing video delivery network that is based on artificial hormones which are spread throughout the network when a particular video is requested. The hormone spreading is affected by the bandwidth and delay parameters of the network edges, thus indirectly help in searching for the (currently) best path to transmit a video.
The interactions between nodes like spreading/evaporating hormone or moving a video according to the neighbor with highest hormone gradient are all local within a node's neighborhood. Still, the system is able
to guide the overall transportation and placement of units in the system up to near optimum.

• A. Sobe, W. Elmenreich, and L. Böszörmenyi. Towards a Self-Organizing Replication Model for Non-Sequential Media Access ACM Multimedia 2010 Workshop - Social, Adaptive and Personalized Multimedia Interaction and Access, SAPMIA 2010
  
• videonetwork - A simulator for self-organizing video replication and delivery @ Google Code
  
• Self Organizing Multimedia Architecture - A Lakeside Labs Research Project