A simulation based optimization method for multi-stage production-distribution systems subject to non-analytical constraints

2007 ◽  
Author(s):  
J. Li ◽  
A. Sava ◽  
X. Xie
Keyword(s):  
Distribution Systems ◽  
Optimization Method ◽  
Multi Stage ◽  
Simulation Based ◽  
Production Distribution ◽  
Simulation Based Optimization

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.

Deep Learning-based Surrogate Models for Water Distribution Systems

2020 ◽  
Author(s):  
Riccardo Taormina ◽  
Mohammad Ashrafi ◽  
Andres Murillo ◽  
Stefano Galelli
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Surrogate Models ◽  
Water Network ◽  
Control Logic ◽  
Simulation Based ◽  
Simulation Based Optimization

<p><span>Simulation-based optimization is widely used for designing and managing water distribution networks. The process involves the use of accurate computational models, such as EPANET, which represent the physical processes taking place in the water network and reproduce the control logic governing its operations. Unfortunately, running such models requires expensive computations, which, in turn, may hinder the application of simulation-based optimization to large and complex problems. This issue can be overcome by resorting to surrogate models, that is, simplified data-driven models that accurately mimic the behaviours of physical-based models at a fraction of the computational costs. In this work, we explore the potential of Deep Learning Neural Networks (DLNN) for building surrogate models for water distribution systems. Different DLNN architectures, including feed-forward and recurrent neural networks, are trained and validated on datasets generated through EPANET simulations. The DLNN models are then used in lieu of the original EPANET model to speed-up the evaluation of the objective function employed in a simulation-based optimization problem. The effectiveness of the proposed technique is assessed on a realistic case-study involving cyber-attacks on a water network. In particular, the DLNN surrogate models are employed by an evolutionary optimization algorithm that schedules the operations of hydraulic actuators in order to best respond to the attacks and facilitate the recovery process.</span></p>

Solder parameter sensitivity for CSP life-time prediction using simulation-based optimization method

2002 ◽  
Vol 25 (4) ◽  
pp. 318-325 ◽  
Author(s):  
B. Vandevelde ◽  
E. Beyne ◽  
K.G.Q. Zhang ◽  
J.F.J.M. Caers ◽  
D. Vandepitte ◽  
...  
Keyword(s):  
Life Time ◽  
Optimization Method ◽  
Parameter Sensitivity ◽  
Time Prediction ◽  
Simulation Based ◽  
Simulation Based Optimization
Sign in / Sign up

Export Citation Format

Share Document