LineLab
| Developer(s) | Advanced Analytics |
|---|---|
| Initial release | 2021 |
| Engine | |
| Operating system | Microsoft Windows, macOS |
| Type | Simulation, Optimization |
| License | Proprietary |
| Website | www.linelab.io |
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LineLab is a software product for modeling, simulating and optimizing systems characterized by complex, stochastic flow behavior.[1][2] The software is used to help with trade studies, exploring feasible throughput and required investment in complex, high-mix production settings, and to validate new business cases.[1] It is utilized in different industries including aerospace, semiconductor manufacturing, and green tech.[1][3]
Functionality
LineLab was developed to model innovative production approaches, including scale-up and modular, automated production.[1][3] It is used to explore complex, high-dimensional design spaces with numerous unknowns. The software can handle different phases of production ramp-up, from initial shared facilities to high-investment permanent setups, and different products that share equipment.[3]
Model creation
LineLab models may be created from spreadsheets of process flow and station information.[1]
Optimization
LineLab optimizes production system configurations with many unknowns to meet target production rates or minimize unit costs for a given station count.[1] In comparison to most approaches to simulation-based optimization, where the simulation is treated like a black box by the optimizer,[4][5] LineLab uses optimization to simulate dynamic behavior, and to determine values for different free variables (such as inventory, station count or capacity) that characterize a system.[6]
Cost models
The software applies program accounting and calculates various costs, including variable costs, carrying cost associated with holding some amount of work-in-process inventory, and amortized capital costs, as it optimizes unknowns subject to minimizing average total unit cost.[3] LineLab models may include costs for scope 1 & 2 carbon emissions.[3]
Hybrid simulation
Internally, LineLab uses a set of mathematical models that are partly based on queueing theory, and additionally cover more practical but complex configurations including reentrant flow, feeder lines, product mix, and batching.[7] It does this instead of using Monte-Carlo methods like discrete-event simulation to capture dynamic behavior. Commonly in dynamic simulation, repeated Monte Carlo simulation runs are used, since the nonlinear relationships that arise from dynamic behavior are very difficult to capture in a steady-state simulation.[8] LineLab's implementation bridges steady-state and dynamic simulation. Based on the algorithms used, models cover the impact of discrete events (e.g. a reactor breaking down) and of continuous processes (e.g. in a chemical plant or refinery). LineLab is comparable in accuracy to discrete-event simulation of queueing behavior.[1][9][10]
Trade study features
The software has features built-in to identify favorable production rates[2] and examine relationships between variables across a broad design space.[3] LineLab's sensitivity analysis automatically exposes and ranks cost and performance drivers. It can quantify how a proposed new component would impact a required production system and costs.[3]
Uncertainty quantification
LineLab accepts uncertain inputs, such as equipment costs that are not yet determined[1] (epistemic uncertainty), and stochastic inputs, such as variation of process time[3] (stochastic or aleatoric uncertainty). The software uses uncertain input information as part of a risk analysis that includes financial metrics.[11]
Usage
Published references exist for the following industries:
- Aerospace: LineLab is used in aerospace and defense and has been validated in modeling and optimizing production systems for aerospace manufacturing.[3]
- Semiconductor manufacturing: LineLab has been used to model semiconductor fabs, optimizing tool count, batch sizes, and overall production rates for products with shared equipment.[1]
- Green tech: LineLab has been used to model and optimize conceptual production systems, such as for mass timber building components.[3] It has supported factory dimensioning, capacity planning, and cost estimation, assisting in validating the business case for new production approaches.
See also
- List of optimization software
- List of computer simulation software
- Computer simulation
- Simulation-based optimization
- Multidisciplinary design optimization
- OptQuest
References
- ↑ 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 Nietner, Larissa; Gould, Parker; Nill, Scott. "A case study for modeling the economics of foundry operations" (PDF). Proceedings of the 2023 Winter Simulation Conference (WSC '23).
- ↑ 2.0 2.1 "Trades & Comparisons | LineLab". Retrieved 2024-07-27.
- ↑ 3.00 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 Nill, Scott; Nietner, Larissa. "Optimizing production system configurations across a broad design space: a case study" (PDF). Proceedings of the 2023 Winter Simulation Conference (WSC '23).
- ↑ Jerbi, Abdessalem; Ammar, Achraf; Krid, Mohamed; Salah, Bashir (November 2019). "Performance optimization of a flexible manufacturing system using simulation: the Taguchi method versus OptQuest". Simulation. 95 (11): 1085–1096. doi:10.1177/0037549718819804. ISSN 0037-5497.
- ↑ Kleijnen, Jack P.C.; Wan, Jie (March 2007). "Optimization of simulated systems: OptQuest and alternatives". Simulation Modelling Practice and Theory. 15 (3): 354–362. doi:10.1016/j.simpat.2006.11.001.
- ↑ "Multi-Variable Mathematical Optimization | LineLab". Retrieved 2024-07-27.
- ↑ "Detailed Factory Dynamics | LineLab". Retrieved 2024-07-27.
- ↑ Simulation of Dynamic Systems with MATLAB® and Simulink®, Third Edition. CRC Press. 2018-02-02. doi:10.1201/9781315154176. ISBN 978-1-315-15417-6. Search this book on
- ↑ Buss, Arnold; Al Rowaei, Ahmed (December 2010). "A comparison of the accuracy of discrete event and discrete time". Proceedings of the 2010 Winter Simulation Conference. IEEE. pp. 1468–1477. doi:10.1109/wsc.2010.5679045. hdl:10945/38586. ISBN 978-1-4244-9866-6. Search this book on
- ↑ Eskandari, Hamidreza; Mahmoodi, Ehsan; Fallah, Hamed; Geiger, Christopher D. (December 2011). "Performance analysis of comercial simulation-based optimization packages: OptQuest and Witness Optimizer". Proceedings of the 2011 Winter Simulation Conference (WSC). IEEE. pp. 2358–2368. doi:10.1109/wsc.2011.6147946. ISBN 978-1-4577-2109-0. Search this book on
- ↑ "Input Uncertainty & Risk Profiles | LineLab". Retrieved 2024-07-27.
External links
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