Optimal robust scheduling of energy-water nexus system using robust optimization technique

Abstract This paper presents a robust optimization technique for the reconfigurable measurement of sensory electronics for industry 4.0 to obtain a robust solution even in the presence of observer uncertainty using a cost-effective performance measurement method. The extrinsic evaluation of the proposed methodology is performed on an indirect current-feedback instrumentation amplifier (CFIA), which is a fundamental part of sensory systems. To reduce the CFIA device performance evaluation set-up cost, a low-cost test stimulus is applied to the circuit under test, and the output response of the circuit is examined to correlate with the device’s performance parameters. Due to the complexity of the smart sensory electronics search space, the meta-heuristic optimization algorithm is being selected as an optimizer. For objective space or observer uncertainty, the Gaussian process regression from the Bayesian statistical regression process is used to estimate the uncertainty level efficiently. Six different classical metrics have been used to evaluate the regression model accuracy. The highest achieved average expected error metrics value is 0.313, and the minimum value of correlation performance metrics is 0.908. The device is implemented using 0.35 μm austriamicrosystems technology.

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A robust optimization technique for analysis of multi-tracer experiments

Journal of Contaminant Hydrology ◽

10.1016/j.jconhyd.2019.04.004 ◽

2019 ◽

Vol 224 ◽

pp. 103481

Author(s):

Mehdi Gharasoo ◽

Luzie M. Wietzke ◽

Bastian Knorr ◽

Rani Bakkour ◽

Martin Elsner ◽

...

Keyword(s):

Robust Optimization ◽

Optimization Technique ◽

Tracer Experiments

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Robust Optimization-Based Commodity Portfolio Performance

International Journal of Financial Studies ◽

10.3390/ijfs8030054 ◽

2020 ◽

Vol 8 (3) ◽

pp. 54

Author(s):

Ramesh Adhikari ◽

Kyle J. Putnam ◽

Humnath Panta

Keyword(s):

At Risk ◽

Robust Optimization ◽

Value At Risk ◽

Optimization Technique ◽

Practical Importance ◽

Optimization Techniques ◽

Commodity Futures ◽

Conditional Value At Risk ◽

Holding Period ◽

Mean Variance

This paper examines the performance of a naïve equally weighted buy-and-hold portfolio and optimization-based commodity futures portfolios for various lookback and holding periods using data from January 1986 to December 2018. The application of Monte Carlo simulation-based mean-variance and conditional value-at-risk optimization techniques are used to construct the robust commodity futures portfolios. This paper documents the benefits of applying a sophisticated, robust optimization technique to construct commodity futures portfolios. We find that a 12-month lookback period contains the most useful information in constructing optimization-based portfolios, and a 1-month holding period yields the highest returns among all the holding periods examined in the paper. We also find that an optimized conditional value-at-risk portfolio using a 12-month lookback period outperforms an optimized mean-variance portfolio using the same lookback period. Our findings highlight the advantages of using robust optimization for portfolio formation in the presence of return uncertainty in the commodity futures markets. The results also highlight the practical importance of choosing the appropriate lookback and holding period when using robust optimization in the commodity portfolio formation process.

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A Robust Optimization Technique for Calculating Scaling Coefficients in an Energy-Based Fatigue Life Prediction Method

Volume 7: Structures and Dynamics, Parts A and B ◽

10.1115/gt2012-68905 ◽

2012 ◽

Author(s):

Todd Letcher ◽

John Wertz ◽

M.-H. Herman Shen

Keyword(s):

Fatigue Life ◽

Robust Optimization ◽

Life Prediction ◽

Stress Amplitude ◽

Optimization Technique ◽

Hysteresis Loops ◽

Prediction Method ◽

Interval Uncertainty ◽

Fatigue Life Prediction ◽

Robust Design Optimization

The energy-based lifing method is based on the theory that the cumulative energy in all hysteresis loops of a specimens’ lifetime is equal to the energy in a monotonic tension test. Based on this theory, fatigue life can be calculated by dividing monotonic tensile energy by a hysteresis energy model, which is a function of stress amplitude. Due to variations in the empirically measured hysteresis loops and monotonic fracture area, fatigue life prediction with the energy-based method shows some variation as well. In order to account for these variations, a robust design optimization technique is employed. The robust optimization procedure uses an interval uncertainty technique, eliminating the need to know an exact probability density function for the uncertain parameters. The robust optimization framework ensures that the difference between the predicted lifetime at a given stress amplitude and the corresponding experimental fatigue data point is minimized and within a specified tolerance range while accounting for variations in hysteresis loop energy and fracture diameter measurements. Accounting for these experimental variations will boost confidence in the energy-based fatigue life prediction method despite a limited number of test specimens.

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Robust Design Optimization Under Mixed Uncertainties With Stochastic Expansions

Journal of Mechanical Design ◽

10.1115/1.4024230 ◽

2013 ◽

Vol 135 (8) ◽

Cited By ~ 11

Author(s):

Yi Zhang ◽

Serhat Hosder

Keyword(s):

Monte Carlo ◽

Robust Optimization ◽

Objective Function ◽

Design Optimization ◽

Robust Design ◽

Optimization Technique ◽

Response Surfaces ◽

Robust Design Optimization ◽

Stochastic Expansions ◽

Mixed Uncertainties

The objective of this paper is to introduce a computationally efficient and accurate approach for robust optimization under mixed (aleatory and epistemic) uncertainties using stochastic expansions that are based on nonintrusive polynomial chaos (NIPC) method. This approach utilizes stochastic response surfaces obtained with NIPC methods to approximate the objective function and the constraints in the optimization formulation. The objective function includes a weighted sum of the stochastic measures, which are minimized simultaneously to ensure the robustness of the final design to both inherent and epistemic uncertainties. The optimization approach is demonstrated on two model problems with mixed uncertainties: (1) the robust design optimization of a slider-crank mechanism and (2) robust design optimization of a beam. The stochastic expansions are created with two different NIPC methods, Point-Collocation and Quadrature-Based NIPC. The optimization results are compared to the results of another robust optimization technique that utilizes double-loop Monte Carlo sampling (MCS) for the propagation of mixed uncertainties. The optimum designs obtained with two different optimization approaches agree well in both model problems; however, the number of function evaluations required for the stochastic expansion based approach is much less than the number required by the Monte Carlo based approach, indicating the computational efficiency of the optimization technique introduced.

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A Robust Optimization of Capacity Allocation Policies in the Third-Party Warehouse

Mathematical Problems in Engineering ◽

10.1155/2015/810798 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Xu Xian-hao ◽

Dong Wei-hong ◽

Peng Hongxia

Keyword(s):

Robust Optimization ◽

Revenue Management ◽

Optimization Technique ◽

Capacity Allocation ◽

Third Party ◽

Management Problem ◽

Robust Methods ◽

Numerical Examples ◽

The Third ◽

Management Perspective

We study the capacity allocation policies of a third-party warehouse center, which supplies several different level services on different prices with fixed capacity, on revenue management perspective. For the single period situation, we use three different robust methods, absolute robust, deviation robust, and relative robust method, to maximize the whole revenue. Then we give some numerical examples to verify the practical applicability. For the multiperiod situation, as the demand is uncertain, we propose a stochastic model for the multiperiod revenue management problem of the warehouse. A novel robust optimization technique is applied in this model to maximize the whole revenue. Then we give some numerical examples to verify the practical applicability of our method.

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An Approach to Flexible-Robust Optimization of Large-Scale Systems

Volume 2B: 43rd Design Automation Conference ◽

10.1115/detc2017-67221 ◽

2017 ◽

Author(s):

Arpan Biswas ◽

Yong Chen ◽

Christopher Hoyle

Keyword(s):

Decision Making ◽

Robust Optimization ◽

Large Scale ◽

Real Option ◽

Optimization Technique ◽

Integrated Model ◽

Engineering System ◽

Optimization Result ◽

Future Value ◽

Net Revenue

Though Robust Optimization has proven useful in solving many design problems with uncertainties, it is not suitable for certain problems which have sequential options in the decision making process. In this work, an integration of a Real Option model with the Robust Optimization technique is presented. This approach aims to eliminate the shortcomings of robust optimization for sequential decision making problems. We provide an example of applying this new integrated model to the operational control of a single reservoir of the Oregon-Washington Columbia River system by optimizing the flexibility of the system. Flexibility for an engineering system is the ease with which the system can respond to uncertainty in a manner to sustain or increase its value delivery through decision-making. In this paper, we define flexibility as the amount of water left in the storage reservoir to produce electricity after meeting demand. Real Option analysis is an economic tool which helps to value the multiple courses of actions in a decision: that is to either sell the flexibility or hold it for future use based upon the future value of flexibility. Selling flexibility causes one to lose some future value because one may be forced to repurchase that flexibility from the market at higher prices later due to shortages; Real Options analysis values future purchases to support decision-making. Robust optimization focuses on for selling the flexibility in a daily market and gives an optimal result by maximizing net revenue, considering all the physical and operational constraints of the reservoir to avoid floods or other environmental calamities. Net revenue is defined as cost of selling and cost of future purchase of the flexibility. We provide an optimization result of 27 random inflow scenarios which gives high, medium and low flexibility to allocate using the integrated model. We compare the optimal solutions given by the integrated model with that given by robust optimization. The integrated real option-robust optimization model improves the revenue from allocating flexibility as much as 40 percent over the robust optimization result.

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Energy management of wind-PV-storage-grid based large electricity consumer using robust optimization technique

Journal of Energy Storage ◽

10.1016/j.est.2019.101054 ◽

2020 ◽

Vol 27 ◽

pp. 101054 ◽

Cited By ~ 8

Author(s):

Dongmin Yu ◽

Tao Zhang ◽

Guixiong He ◽

Sayyad Nojavan ◽

Kittisak Jermsittiparsert ◽

...

Keyword(s):

Robust Optimization ◽

Energy Management ◽

Optimization Technique ◽

Grid Based

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Robust Scheduling for Plural Lot Size Manufacturing

ASME/ISCIE 2012 International Symposium on Flexible Automation ◽

10.1115/isfa2012-7244 ◽

2012 ◽

Author(s):

Saki Kusumoto ◽

Akira Kitamura ◽

Masahiro Nakamura

Keyword(s):

Numerical Simulation ◽

Lead Time ◽

Optimization Technique ◽

Setup Time ◽

Traditional Scheduling ◽

Manufacturing Processes ◽

Multi Objective Optimization ◽

Lot Size ◽

Robust Scheduling ◽

Scheduling System

This paper describes a robust scheduling system for manufacturing processes. In this system, an adequate balance between large-lot and small-lot manufacturing can be achieved by a multi objective optimization technique, and the lead time for manufacturing large-lot products and appropriation rate for small-lot products can be improved in comparison with those used in traditional scheduling methods. In small-lot manufacturing, the change in setup time is considered by its static distribution, and thus, the productivity of small-lot products can be maximized. These effects of the robust scheduling system have been examined quantitatively by means of a numerical simulation.

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