Online parameter estimation for cyber-physical production systems based on mixed integer nonlinear programming, process mining and black-box optimization techniques

2018 ◽  
Vol 66 (4) ◽  
pp. 331-343 ◽  
Author(s):  
Jens Otto ◽  
Birgit Vogel-Heuser ◽  
Oliver Niggemann
Keyword(s):  
Production Systems ◽  
Process Mining ◽  
Black Box ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Software Components ◽  
Integer Nonlinear Programming ◽  
Engineering Effort ◽  
Free Parameters ◽  
Cyber Physical Production Systems

AbstractCyber-Physical Production Systems (CPPS) should adapt to new products or product variants efficiently and without extensive manual engineering effort. In comparison to rewriting the automation software for each adaption, manual engineering effort can be reduced by reusable software components with free parameters, which must be adjusted to individual production scenarios. This paper introduces CyberOpt Online, a novel online parameter estimation approach for reusable automation software components. In contrast to classic mathematical modeling approaches, such as Mixed Integer Nonlinear Programming (MINLP), our approach requires no predefined models that represent the system. Models, e. g., of the energy consumption of CPPS, are learned automatically from data observed during the operation of the production system. Therefore, the manual engineering effort is minimized as postulated by the paradigm of CPPS. The presented approach combines MINLP, process mining and black-box optimization techniques for calculating optimal timing parameter configurations for automation software components with free parameters in the domain of discrete manufacturing.

scholarly journals Applying optimization techniques to identify multistate collaborations for improving biopower energy policy

2017 ◽  
Author(s):  
◽  
Bayram Dundar
Keyword(s):  
Nonlinear Programming ◽  
Energy Policy ◽  
Emission Reduction ◽  
Minimum Cost ◽  
Optimization Techniques ◽  
Mixed Integer Nonlinear Programming ◽  
Mixed Integer ◽  
Integer Nonlinear Programming ◽  
Milp Model ◽  
The Impact

The US Environmental Protection Agency (EPA) proposed a rule that aims to reduce carbon emissions from US coal-fired power plants. The proposed "Clean Power Plan" specifies state-specific rate-based goals to achieve a total US carbon emission reduction of 32% below 2005 levels by 2030. An increase in the co-firing of woody biomass with coal to generate biopower is one of the potential approaches that electricity providers could take to comply with EPA's proposed rules. We develop a mixed integer linear programming (MILP) model to identify minimum-cost approaches for reducing CO[subscript 2] emissions via co-firing biomass subject to spatially-explicit biomass availability constraints. An important feature of the EPA recommendations is an allowance for states to participate in multi-state compliance strategies. We extend the MILP model to optimize within a larger geographical framework that allows states to identify minimum-cost partnerships that meet aggregated emission reduction goals. We apply the MILP model to data for five Midwestern US states to determine the role that co-firing biomass could play in achieving their EPA-proposed emission reduction targets, and find that some states can meet their renewable energy generation targets through co-firing, although co-firing alone is not sufficient to achieve any state's emission reduction targets. This MILP is extended to robust MILP model, addressing the uncertainties in power plant boiler installation cost, coal electricity generation cost, as well as the emission rate. We apply this robust model to a set of 18 states in the northern US to identify optimal sets of multi-state collaborations. Finally, we investigate the impact of energy policy-related regulations on biomass demand and procurement cost using econometric models. We develop a demand response model and then incorporated this into a robust mixed-integer nonlinear programming (MINLP) model. We utilize a two-stage approach to solve the resultant robust mixed integer nonlinear programming model. This model is then applied the same set of 18 states in the northern US to identify optimal sets of multi-state collaborations for different feasibility rates and emission levels.

scholarly journals MINLP formulations for continuous piecewise linear function fitting

2021 ◽  
Author(s):  
Noam Goldberg ◽  
Steffen Rebennack ◽  
Youngdae Kim ◽  
Vitaliy Krasko ◽  
Sven Leyffer
Keyword(s):  
Linear Function ◽  
Piecewise Linear ◽  
Piecewise Linear Function ◽  
Mixed Integer ◽  
Function Fitting ◽  
Computational Performance ◽  
Integer Nonlinear Programming ◽  
Minlp Model ◽  
Necessary Constraints ◽  
Continuous Piecewise Linear Function

AbstractWe consider a nonconvex mixed-integer nonlinear programming (MINLP) model proposed by Goldberg et al. (Comput Optim Appl 58:523–541, 2014. 10.1007/s10589-014-9647-y) for piecewise linear function fitting. We show that this MINLP model is incomplete and can result in a piecewise linear curve that is not the graph of a function, because it misses a set of necessary constraints. We provide two counterexamples to illustrate this effect, and propose three alternative models that correct this behavior. We investigate the theoretical relationship between these models and evaluate their computational performance.

scholarly journals Context-aware heatstroke relief station placement and route optimization for large outdoor events

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Yan Wu ◽  
Tianqi Xia ◽  
Adam Jatowt ◽  
Haoran Zhang ◽  
Xiao Feng ◽  
...  
Keyword(s):  
Physical Environment ◽  
Emergency Service ◽  
Programming Model ◽  
Heterogeneous Data ◽  
Route Optimization ◽  
Mixed Integer ◽  
Context Aware ◽  
Outdoor Activities ◽  
Integer Nonlinear Programming ◽  
Nonlinear Programming Model

Abstract Background Heatstroke is becoming an increasingly serious threat to outdoor activities, especially, at the time of large events organized during summer, including the Olympic Games or various types of happenings in amusement parks like Disneyland or other popular venues. The risk of heatstroke is naturally affected by a high temperature, but it is also dependent on various other contextual factors such as the presence of shaded areas along traveling routes or the distribution of relief stations. The purpose of the study is to develop a method to reduce the heatstroke risk of pedestrians for large outdoor events by optimizing relief station placement, volume scheduling and route. Results Our experiments conducted on the planned site of the Tokyo Olympics and simulated during the two weeks of the Olympics schedule indicate that planning routes and setting relief stations with our proposed optimization model could effectively reduce heatstroke risk. Besides, the results show that supply volume scheduling optimization can further reduce the risk of heatstroke. The route with the shortest length may not be the route with the least risk, relief station and physical environment need to be considered and the proposed method can balance these factors. Conclusions This study proposed a novel emergency service problem that can be applied in large outdoor event scenarios with multiple walking flows. To solve the problem, an effective method is developed and evaluates the heatstroke risk in outdoor space by utilizing context-aware indicators which are determined by large and heterogeneous data including facilities, road networks and street view images. We propose a Mixed Integer Nonlinear Programming model for optimizing routes of pedestrians, determining the location of relief stations and the supply volume in each relief station. The proposed method can help organizers better prepare for the event and pedestrians participate in the event more safely.

scholarly journals Development of a Decision Support System for 3D Printing Processes based on Cyber Physical Production Systems

Procedia CIRP ◽  
2021 ◽  
Vol 98 ◽  
pp. 348-353
Author(s):  
Rishi Kumar ◽  
Christopher Rogall ◽  
Sebastian Thiede ◽  
Christoph Herrmann ◽  
Kuldip Singh Sangwan
Keyword(s):  
Decision Support ◽  
3D Printing ◽  
Decision Support System ◽  
Support System ◽  
Production Systems ◽  
Cyber Physical Production Systems ◽  
Printing Processes
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