Potential of data-driven simulation-based optimization for adaptive scheduling and control of dynamic manufacturing systems

2016 ◽  
Author(s):  
Mirko Kuck ◽  
Jens Ehm ◽  
Torsten Hildebrandt ◽  
Michael Freitag ◽  
Enzo M. Frazzon
Keyword(s):  
Manufacturing Systems ◽  
Adaptive Scheduling ◽  
Data Driven ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
And Control

Simulationsbasierte Optimierung von Fabrikabläufen*/Simulation-based optimization of factory processes

2018 ◽  
Vol 108 (04) ◽  
pp. 221-227
Author(s):  
T. Donhauser ◽  
L. Baier ◽  
T. Ebersbach ◽  
J. Franke ◽  
P. Schuderer
Keyword(s):  
Calcium Silicate ◽  
Optimization Approach ◽  
Process Technology ◽  
High Quality ◽  
Decomposition Approach ◽  
Planning And Control ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
And Control

Die Kalksandsteinherstellung weist aufgrund prozesstechnisch und zeitlich divergierender Teilprozesse einen hohen Planungs- sowie Steuerungsaufwand auf. Durch Einsatz eines simulationsgestützten Optimierungsverfahrens kann diese Komplexität bewältigt werden. Um bei hoher Lösungsqualität eine Laufzeit zu erreichen, die einen operativen Einsatz des Verfahrens gestattet, wird auf Basis einer vorangegangenen Studie ein Dekompositionsansatz implementiert und dessen Eignung durch Testläufe validiert.   Calcium silicate masonry production requires a great deal of planning and control due to the fact that subprocesses vary in terms of process technology and time. To overcome this complexity, a simulation-based optimization approach is applied. As a short runtime that allows the method to be used operationally and yet still offers a high quality of solution is crucial, a decomposition approach is implemented on the basis of a previous study and its suitability is validated by means of test runs.

Simulation-Based Optimization of Electricity Demand Response for Sustainable Manufacturing Systems

2014 ◽  
Author(s):  
Leah Cuyler ◽  
Zeyi Sun ◽  
Lin Li
Keyword(s):  
Demand Response ◽  
Manufacturing Systems ◽  
Manufacturing Sector ◽  
Sustainable Manufacturing ◽  
Optimization Method ◽  
Electricity Demand ◽  
System Throughput ◽  
Commercial Building ◽  
Simulation Based ◽  
Simulation Based Optimization

Electricity demand response is considered a promising tool to balance the electricity demand and supply during peak periods. It can effectively reduce the cost of building and operating those peaking power generators that are only run a few hundred hours per year to satisfy the peak demand. The research on the electricity demand response implementation for residential and commercial building sectors has been very mature. Recently, it has also been extended to the manufacturing sector. In this paper, a simulation-based optimization method is developed to identify the optimal demand response decisions for the typical manufacturing systems with multiple machines and buffers. Different objectives, i.e. minimizing the power consumption under the constraint of system throughput, and maximize the overall earnings considering the tradeoff between power demand reduction and potential production loss, are considered. Different energy control decisions are analyzed and compared regarding the potential influence on the throughput of manufacturing system due to the different control actions adopted by throughput bottleneck machine.

scholarly journals Managing uncertainty in data-driven simulation-based optimization

2020 ◽  
Vol 136 ◽  
pp. 106519 ◽  
Author(s):  
Gordon Hüllen ◽  
Jianyuan Zhai ◽  
Sun Hye Kim ◽  
Anshuman Sinha ◽  
Matthew J. Realff ◽  
...  
Keyword(s):  
Data Driven ◽  
Simulation Based ◽  
Simulation Based Optimization

scholarly journals Simulation-based analysis of shared manufacturing systems

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
Gustavo Furtado da Silva ◽  
Nelson Casarotto Filho ◽  
Enzo Morosini Frazzon
Keyword(s):  
Information And Communication Technologies ◽  
Optimization Model ◽  
Manufacturing Systems ◽  
Ad Hoc ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Information And Communication ◽  
Traditional Manufacturing ◽  
The One ◽  
Resources Sharing

Abstract Advancements in information and communication technologies are encouraging researches in shared manufacturing systems, especially on current high-competitiveness and low-resources scenarios. This paper aims to compare productive resources sharing with traditional manufacturing systems by using a simulation-based optimization model. The model is based on the One Product Integrated Manufacturing paradigm in which the efficiency optimization is pursued by designing ad-hoc virtual factories allocating the best resources available on an existing network. The proposed simulation-based optimization model is capable of identifying the best production path and plan for different distances between network members. Along with a better overall efficiency, it is also possible to argue that dedicated virtual factories ease the identification of problems and allow for improvements without negatively affecting other resources.

Simulation-Based Optimization as a Service for Dynamic Data-Driven Applications Systems

2018 ◽  
pp. 589-614 ◽  
Author(s):  
Yi Li ◽  
Shashank Shekhar ◽  
Yevgeniy Vorobeychik ◽  
Xenofon Koutsoukos ◽  
Aniruddha Gokhale
Keyword(s):  
Data Driven ◽  
Dynamic Data ◽  
Simulation Based ◽  
Simulation Based Optimization

Aspects of Parameter Sensitivity for Series Hydraulic Hybrid Light-Weight Duty Vehicle Design

2016 ◽  
Author(s):  
Katharina Baer ◽  
Liselott Ericson ◽  
Petter Krus
Keyword(s):  
Design Parameters ◽  
Drive Cycle ◽  
Hydraulic Hybrid ◽  
Component Sizing ◽  
Alternative Approach ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Hydraulic Hybrid Vehicle ◽  
Definition Of ◽  
And Control

Hybridization of a vehicle’s drivetrain can in principle help to improve its energy efficiency by allowing for recuperation of kinetic energy and modulating the engine’s load. How well this can be realized depends on appropriate sizing and control of the additional components. The system is typically designed sequentially, with the hardware setup preceding the development and tuning of advanced controller architectures. Taking an alternative approach, component sizing and controller tuning can be addressed simultaneously through simulation-based optimization. The results of such optimizations, especially with standard algorithms with continuous design variable ranges, can however be difficult to realize, considering for example limitations in available components. Furthermore, drive-cycle based optimizations are prone to cycle-beating. This paper examines the results of such simulation-based optimization for a series hydraulic hybrid vehicle in terms of sensitivity to variations in design parameters, system parameters and drive cycle variations. Additional relevant aspects concerning the definition of the optimization problem are pointed out.

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