Robust Optimization Technique Research Articles

Enhancing vehicle wheel suspension test equipment through Taguchi method for optimization

The study demonstrates a significant advancement in vehicle suspension testing by utilizing the Taguchi method for optimization. The suspension system determines a vehicle’s performance, directly affecting ride comfort, handling, and safety. The research presented in this study highlights a potentially effective method for enhancing suspension testing. The research systematically investigates the complex network of factors influencing suspension behavior using the Taguchi method, a robust optimization technique. The analysis includes examining road surface conditions, passenger weight variations, and tire pressure fluctuations. The objective is to design a suspension system that provides both comfort and stability without making any concessions, regardless of the obstacles encountered on the road. The car utilized for this research is an Altis sedan equipped with tires with a 205/55 R16 profile. The study’s findings indicate that factor A, which represents embankment height, significantly impacts 56 % of road irregularity management and the maintenance of a stable driving experience. The dynamic load factor (Factor D) contributes significantly to the vehicle’s overall stability and ride quality, accounting for 43 % in different scenarios. Based on the given framework, it can be observed that the variables B (tire pressure) and C (passenger weight) significantly influence suspension vibration, resulting in a reduction of below 0.1 %. While the research results presented here only cover a subset of automobiles, the methodology employed can be used to deal with similar problems in other vehicles.

Robust Operation Scheme of EV Charging Facility With Uncertain User Behavior

With the widespread of electric vehicles (EV), the necessity of installing EV charging facility has rapidly increased. Since operating EV charging facility are costly and complex, choosing a proper power operation scheme is critical in implementing a cost-effective charging service. Therefore, this paper proposes an efficient power operation scheme for EV charging facility by jointly analyzing not only monetary issues with various power rate policies and battery wear-out costs of the energy storage system (ESS) but also the uncertainty issues caused by EV users' behavior under different charging requirements. By considering the issues, this paper analyzes and formulates the problem with robust optimization techniques. By using the real-world EV operation datasets, the effectiveness of the proposed scheme is analyzed in terms of daily peak power and total cost. In addition, by applying the proposed scheme in an actual power system, it is shown that the proposed scheme is able to reduce the overall cost by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.5\%$</tex-math></inline-formula> and reduce peak power by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$17.3\%$</tex-math></inline-formula> compared to other benchmark models.

Improving Interfraction Robustness for IMPT Treatment Planning for Lung Cancer Using Multiple-CT Incorporated Robust Optimization

Dose deterioration due to motion-induced density variation is a major concern in intensity-modulated proton therapy (IMPT) for lung cancer. Robust optimization is capable to address the intrafraction motion issue but not the interfraction variation. This study aims to investigate the potential of a new robust optimization technique of IMPT in mitigating the interfraction variation of lung cancer patients. Two optimization techniques were used to create an IMPT plan for a lung cancer case, one was conventional robust optimization (ROcon) considering the perturbation of 3 mm setup uncertainty and 3.5% range uncertainty, and the other was multiple-CT incorporated robust optimization (ROmul) considering one more perturbation quantified using the end-of-inhalation-phase (T0) and end-of-exhalation-phase (T50) CTs. The ROcon plan was optimized on the average-intensity-projection (AIP) planning CT (pCT), and the ROmul plan was optimized on the AIP, T0, and T50 pCTs. The dose prescription was 40 Gy (RBE) in 5 fractions. The patient underwent a verification 4DCT (vCT) scan on six successive days. Both plans were recalculated on the T0 pCT, T50 pCT, and AIP vCT. The dose to the target and organ at risk of the ROcon and ROmul plans on pCT and vCT were compared. Compared with the ROcon plan, the ROmul plan reduced the deviation of target coverage by greater than 50% in presence of intrafraction motion (ROmul:0.38-0.88%, ROcon:1.90-2.23%) and interfraction variation (ROmul: 0.62-1.63% vs ROcon:0.50-2.75%) while meeting the dose criteria on the planning AIP CT. As for the dose to the organ at risk, the ROmul plan had a slightly high lung V20 (0.3%) than did the ROcon plan on the AIP pCT. The deviations in lung V20 of the ROmul plan (mean 0.15%) on the vCTs were similar to that of the ROcon plan (mean 0.17%). This study indicates that dose variation of an IMPT plan can be reduced in presence of interfraction variation along the treatment course by combining conventional robust optimization and novel multiple-CT optimization using only the planning CT.

Parameter Estimation Techniques for Photovoltaic System Modeling

In improving PV system performance, the parameters associated with electrical photovoltaic equivalent models play a pivotal role. However, due to the increased mathematical complexities and non-linear traits of PV cells, the precise prediction of these parameters is a challenging task. To estimate the parameters associated with PV models, a reliable, robust, and accurate optimization technique is needed. This paper introduces a new algorithm, Rat Swarm Optimizer (RSO), for obtaining the optimum PV cell and module parameters. The proposed method maintains an adequate balance between the exploration and exploitation phases to overcome premature particle issues. The results obtained using RSO are compared with those of other algorithms, i.e., Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), and Grasshopper Optimization (GOA), in this work. The modified one-diode model (MODM) and modified two-diode model (MTDM) are used to analyze the parameters of the mono-crystalline PV cell using the suggested RSO. The obtained findings imply that the parameters estimated by the suggested RSO are more accurate than those calculated by the other algorithms taken into consideration in the paper. The statistical results are compared, and it is clear that RSO is a very accurate, fast, and dependable approach for the parameter estimation of PV cells.

A scalable problem to benchmark robust multidisciplinary design optimization techniques

A scalable problem to benchmark robust multidisciplinary design optimization techniques

On the estimation of the angle of attack for vertical axis wind turbines

Purpose This paper aims to investigate the problem of estimating the angle of attack (AoA) and relative velocity for vertical axis wind turbine (VAWT) blades from computational fluid dynamics data. Design/methodology/approach Two methods are implemented as function objects within the OpenFOAM framework for estimating the blade’s AoA and relative velocity. For the numerical analysis of the flow around and through the VAWT, 2 D unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are carried out and validated against experimental data. Findings To gain a better understanding of the complex flow features encountered by VAWT blades, the determination of the AoA is crucial. Relying on the geometrically-derived AoA may lead to wrong conclusions about blade aerodynamics. Practical implications This study can lead to the development of more robust optimization techniques for enhancing the variable-pitch control mechanism of VAWT blades and improving low-order models based on the blade element momentum theory. Originality/value Assessment of the reliability of AoA and relative velocity estimation methods for VAWT’ blades at low-Reynolds numbers using URANS turbulence models in the context of dynamic stall and blade–vortex interactions.

Convex Programming-Based Robust Optimization Procedure for Blast-Excited Structures with Bounded Uncertainty

Application of the robust optimization (RO) technique to ensure the least possible deviation of structural performance is gaining increasing attention in recent years. However, most RO studies have been conducted in the probabilistic domain, where sufficient uncertainty information is available to construct probability distribution functions of response parameters. But, in many cases, such as for structures under underground blast excitation, hazard parameters like charge weight and charge center distance do not have any definite probability density function. Only their ranges of variations are known to an engineer. Hence, it is more logical to model these parameters as uncertain-but-bounded (UBB) type, which requires only bounds of variations to characterize the uncertainty. The available RO studies in the UBB-domain mainly deal with static loads. Studies on time-variant dynamic loads, such as blast load, are scarce in the existing literature. Also, blast load time-histories show record-to-record variations in their signatures, even with the same setup of hazard parameters. This aspect cannot be considered by the conventional convex programming approaches of RO in the UBB domain. To address all these issues, a new RO procedure is proposed in the present study, which is based on the dual response surface method. The proposed RO procedure can transform the complex simulation-based RO procedure under dynamic blast load to an equivalent deterministic problem with explicit constraints. Thereby, the RO can be easily solved by a gradient-based optimizer. The effectiveness of the proposed approach is elucidated by the complex real-world problems of a multistory building and an underground reinforced concrete bunker. The results are compared with conventional RO approaches. The results show that the proposed approach consistently yields more economic as well as robust solutions in a computationally efficient way.

Estimating the robust optimal parameters of tuned mass damper

The tuned mass damper (TMD) is one of the oldest and most extensively used passive vibration control systems in dynamically excited buildings. However, the uncertainties or randomness in the loading and the primary structure parameters (i.e., stiffness and damping coefficient) significantly affected the TMD performance. In this framework, Robust Design Optimization (RDO) technique is advantageous, aiming to simultaneously minimize the performance function’s mean and variance. Simulation-based RDO techniques are particularly effective at solving difficult nonconvex and multimodal issues. Therefore, in the present study, a stochastic simulation-based approach is proposed for the RDO of TMD. The performance function’s mean and variance are considered robustness measures. The TMD’s frequency and damping ratios are considered design parameters. TMD attached to a Multiple-Degree-of-Freedom (MDOF) structural system is analyzed to demonstrate the efficacy of the proposed approach. The top floor displacement variance ratio of the protected structural system to that of the unprotected structural system is adopted as the performance function. The simulation results show that the proposed strategy successfully identifies the optimal design for all levels of uncertainty. The necessity of considering the uncertainties is well highlighted. In conclusion, the proposed approach is found to be effective and offers the designer more information for an optimal design decision.

Analytical Study for Optimization Techniques to Prolong WSNs Life

Wireless Sensor Networks (WSNs) have become the most cost-effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often operate in some of the world's most unusual or dangerous conditions. Networking errors, memory and processor limitations, and energy constraints all pose problems for WSN developers. Many problems in WSNs are expressed as multivariate optimization problems that are solved using biologically inspired techniques. Particle swarm optimization (PSO) is an easy, algorithmically sound, and robust optimization technique. It has been used to address problems like Clustering, data routing, Cluster Head (CH) collection, and data collecting in WSNs. This paper presents a brief analysis of WSN studies in which the PSO algorithm was used as the primary or secondary algorithm for enhancing lifespan of WSNs, focusing on results that show energy efficiency in the sensors, extending the network's life. Index Term— Cluster Head selection, Clustering, PSO, Routing, Wireless Sensor Networks.

Robust homecare service capacity planning

We study a home healthcare planning problem under the demand uncertainty, where the service type (authorization) and capacity are the first-stage decision and the homecare resource allocation is the second-stage decision which adapts to the demand realizations. We model the problem using an adaptive robust optimization technique where we construct a budget uncertainty set of demand using the well-known Mahalanobis Distance. We analyze the impact of the authorization, capacity decisions as well as the budget (robustness level) of the Mahalanobis uncertainty set onto the worst-case revenue. To solve the model, we develop a Benders decomposition algorithm that solves a pair of a mixed-integer second-order cone program (MISOCP) and a mixed integer linear program (MILP) in each iteration, both can be handled by off-the-shelf MIP solvers, with finite-step convergence. We also develop an affine approximation approach that directly solves one instance of MISOCP. Finally, sufficient numerical studies demonstrate the effectiveness of our model and the solution approaches.

Robust function deployment against uncertain recovery time in different protection types with workload-dependent failure probability

Modern network services and functions, such as firewalls, load balancers, and network address translators, are virtualized to virtual network functions (VNFs) and run in virtual machines and containers. This paper proposes a robust VNF placement model against uncertain recovery time with satisfying an expected recovery time guarantee in a cost-efficient manner. We consider that each node fails with a workload-dependent failure probability, which is a non-decreasing function that reveals the empirical relationship between the workload and the failure probability. The preventively deployed backup resources can recover an unavailable function hosted by a failed node in a period of time, which is related to the backup strategies and protection types. Multiple functions protected by a node can share the backup resources to reduce the number of active nodes to save cost, which also affects the recovery time if the number of unavailable functions is so large that the remaining capacity cannot recover them and causes a waiting procedure of an unavailable function before being recovered. We introduce an uncertainty set that considers the upper and lower bounds of the recovery time of a function protected by each node and the upper bound of the average recovery time among nodes. We consider the expected remaining capacity of a node and the expected number of unavailable functions hosted by the node to approximate the waiting time in the shared protection. The robust optimization technique is applied to obtain the worst-case expected recovery time under an uncertain recovery time set. With this technique, the model is formulated as a mixed integer linear programming problem. To solve the problem in a practical time, a heuristic algorithm is developed. It reduces the number of active nodes while decreasing the worst-case expected recovery time within the uncertainty set by converting the linear programming problem to a graph problem. The numerical results reveal the superiority of the proposed model by considering the recovery time guarantee, uncertainty set, and shared protection; we investigate the dependency on the bounds of recovery time.

A holistic, integrated supply-production–distribution problem in the dairy industry under uncertain supply and demand

Although the simultaneous planning of several relevant operations in the form of an integrated model has been gaining prestige in recent years on miscellaneous topics, the dairy industry, due to multifaceted and complex planning aspects of each level, has been less investigated. Hence, this study introduces an unprecedented, integrated supply-production–distribution problem under uncertain supply and demand in the dairy industry. In this regard, a novel mathematical model is developed to minimize the total cost of the supply chain, wherein a broad range of decisions, including assignment, routing, and scheduling, are reflected. More importantly, each stage encompasses a wide range of realistic concerns, to name a few, a comprehensive supply network, the blending process, various time limitations, and coordination among means of transportation, such as lorries and different multi-compartment vehicles, are considered in the supply phase. Additionally, a multi-period, multi-product, multi-stage production system with various production capacities and perishability requirements is considered in the production phase. The considerations of multi-compartment vehicles alongside the assignment of product-oriented time windows and products' early and late delivery are also reflected in the distribution phase. Furthermore, a set-induced robust optimization technique with the interval-ellipsoidal-polyhedral uncertainty set is applied to handle supply and demand uncertainties. Finally, a real case study is investigated to demonstrate the applicability and validation of the proposed model and the uncertainty approach. The obtained results revealed that considering external cooling units and the blending process can diminish the system’s total costs by approximately 16% and 11%, respectively.

A bi-level decision-making system to optimize a robust-resilient-sustainable aggregate production planning problem

This study introduces a sustainable-robust aggregate production planning (APP) problem taking into account workforce productivity, outsourcing option and supplier resilience. In this regard, a bi-level decision-making system is designed using multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models, respectively. In the MADM section, a hybrid method called BWM-WASPAS-Neutrosophic based on best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) under type-2 neutrosophic number (T2NN) is utilized to investigate resilient supplier selection. To design the MODM section, a multi-objective mixed-integer linear programming (MILP) model is suggested which is then treated with the help of weighted goal programming (WGP) method. The multi-objective model honors sustainable development goals as it aims not only to minimize total cost and maximize weighted purchasing from suppliers, but also to minimize negative environmental impacts simultaneously. Robust optimization (RO) technique is then implemented to the MODM model to address the demand uncertainty. In order to validate the suggested methodology, a real case study from the literature is examined. The obtained results reveal the efficiency of our decision-making system in finding the optimal policy in less than 1 s where sensitivity analyses also contribute to practical managerial implications. Finally, it is revealed that the total weighted purchase from suppliers has the highest sensitivity to the conservatism levels, which determine the extent to which our uncertain demand parameter can deviate from its nominal value.

Development of a genetic algorithm-based graph model for conflict resolution for optimizing resolutions in environmental conflicts

Abstract Graph model for conflict resolution (GMCR) is a robust tool for resolving disagreements among parties with contradictory interests in a potential conflict. In GMCR, decision-makers (DMs) and their preferences are determined. The DMs are defined as people, parties, or groups having the authority to make decisions and the power to get these decisions approved. This definition excludes some potential stakeholders with no ability to make and exert decisions, like the natural environment. Therefore, this study aims to find an impartial viewpoint representing the natural environment's interests. A new GMCR based on genetic algorithm (GA) optimization is proposed to modify and optimize the final resolution of the GMCR regarding natural environment benefits. Having been applied to a real-world case study, this methodology showed competence in satisfying the fundamental interests of the natural environment to an acceptable extent. This case study is about an endangered seasonal lake, where there is contention between the governmental and agricultural sectors. The results revealed that the disagreement between two conflicting groups could be resolved by modifying the current agreement to consider both groups' demands. Finally, GA, incorporated in GMCR, proved to be a robust optimization technique in complex environmental conflicts.

Enhanced transient search optimization algorithm-based optimal reactive power dispatch including electric vehicles

Optimal reactive power dispatch (ORPD) in electrical networks is essential for the secure and stable operation of the entire power system; it also significantly impacts the economic situation. However, the solution of ORPD is highly complex while considering the multiple variables, time-varying characteristics, dynamic loads such as electric vehicles, and operational constraints. The ORPD problem is classified as a non-linear, non-convex complex problem. Thus, a robust optimization technique should be utilized to solve the ORPD. This paper presents a novel Enhanced Transient Search Optimization (ETSO) technique for the optimal solution of the ORPD problem by integrating electric vehicles (EVs). The proposed ETSO implemented for the optimal solution of ORPD consequently minimize the active power loss and voltage deviation at the load buses. The proposed method is tested and verified on the IEEE 30-bus, the IEEE-57 bus, and IEEE 118-bus systems. The simulation results show the robustness and efficiency of the proposed ETSO optimization in solving the ORPD problem by evaluating and comparing it with other well-established metaheuristic optimization methods under the same system data, control variables, and constraints. Statistical features of the proposed ETSO algorithm and the results obtained led to an improvement in the performance of the power systems.

Phase stability analysis and phase equilibrium calculations in reactive and nonreactive systems using new hybrids of Pelican and Gorilla troops algorithms

Phase stability and equilibrium calculations are very significant in the process industries, ranging from process simulation, equipment design, operation and design. These problems involve several systems of complex and nonlinear equations, which are mathematically challenging. An alternative solution is formulating the problems into a global optimisation problem. A robust and reliable optimisation technique is required to solve these global optimisation problems. In this study, two recently developed stochastic global optimisation algorithms, POA and GTO were applied to phase stability and equilibrium problems. The need to enhance their performance necessitated the development of two modified algorithms, PGOA1 and PGOA2, which are the hybridisation of POA and GTO. All the algorithms' abilities were explored for solving computationally challenging and multidimensional phase stability and equilibrium problems, and their performances were compared. The implementation of POA and GTO was good in solving most problems, though GTO slightly outperformed POA. The hybridisation of both to develop PGOA1 and PGOA2 was worthwhile as both outperformed the POA and GTO. PGOA2 is more robust and reliable in solving phase stability and equilibrium, and its performance superseded the performances of the other algorithms.

A Deep Learning‐to‐learning Based Control system for renewable microgrids

AbstractIn terms of microgrids (MGs) operation, optimal control and management are vital issues that must be addressed carefully. This paper proposes a practical framework for the optimal energy management and control of renewable MGs considering energy storage (ES) devices, wind turbines, and microturbines. Due to the non‐linearity and complexity of operation problems in MGs, it is vital to use an accurate and robust optimization technique to control the power flow of units efficiently. To this end, in the proposed framework, teacher learning‐based optimization (TLBO) is utilized to solve the power flow dispatch in the system efficiently. Moreover, a novel hybrid deep learning model based on principal component analysis (PCA), convolutional neural networks (CNN), and bidirectional long short‐term memory (BLSTM) is proposed to address the short‐term wind power forecasting problem. The feasibility and performance of the proposed framework and the effect of wind power forecasting on operation efficiency are examined using the IEEE 33‐bus test system. Also, the Australian Woolnorth wind site data is utilized as a real‐world dataset to evaluate the performance of the forecasting model. The results show that the proposed framework can be used to schedule MGs in the best way possible.

Multi-Objective Optimisation of a Novel Bypass Graft with a Spiral Ridge.

The low long-term patency of bypass grafts is a major concern for cardiovascular treatments. Unfavourable haemodynamic conditions in the proximity of distal anastomosis are closely related to thrombus creation and lumen lesions. Modern graft designs address this unfavourable haemodynamic environment with the introduction of a helical component in the flow field, either by means of out-of-plane helicity graft geometry or a spiral ridge. While the latter has been found to lack in performance when compared to the out-of-plane helicity designs, recent findings support the idea that the existing spiral ridge grafts can be further improved in performance through optimising relevant design parameters. In the current study, robust multi-objective optimisation techniques are implemented, covering a wide range of possible designs coupled with proven and well validated computational fluid dynamics (CFD) algorithms. It is shown that the final set of suggested design parameters could significantly improve haemodynamic performance and therefore could be used to enhance the design of spiral ridge bypass grafts.

A new optimized demand management system for smart grid-based residential buildings adopting renewable and storage energies

Demand Side Management (DSM) implies intelligently managing load appliances in a Smart Grid (SG). DSM programs help customers save money by reducing their electricity bills, minimizing the utility’s peak demand, and improving load factor. To achieve these goals, this paper proposes a new load shifting-based optimal DSM model for scheduling residential users’ appliances. The proposed system effectively handles the challenges raised in the literature regarding the absence of using recent, easy, and more robust optimization techniques, a comparison procedure with well-established ones, using Renewable Energy Resources (RERs), Renewable Energy Storage (RES), and adopting consumer comfort. This system uses recent algorithms called Virulence Optimization Algorithm (VOA) and Earth Worm Optimization Algorithm (EWOA) for optimally shifting the time slots of shiftable appliances. The system adopts RERs, RES, as well as utility grid energy for supplying load appliances. This system takes into account user preferences, timing factors for each appliance, and a pricing signal for relocating shiftable appliances to flatten the energy demand profile. In order to figure out how much electricity users will have to pay, a Time Of Use (TOU) dynamic pricing scheme has been used. Using MATLAB simulation environment, we have made effectiveness-based comparisons of the adopted optimization algorithms with the well-established meta-heuristics and evolutionary algorithms (Genetic Algorithm (GA), Cuckoo Search Optimization (CSO), and Binary Particle Swarm Optimization (BPSO) in order to determine the most efficient one. Without adopting RES, the results indicate that VOA outperforms the other algorithms. The VOA enables 59% minimization in Peak-to-Average Ratio (PAR) of consumption energy and is more robust than other competitors. By incorporating RES, the EWOA, alongside the VOA, provides less deviation and a lower PAR. The VOA saves 76.19% of PAR, and the EWOA saves 73.8%, followed by the BPSO, GA, and CSO, respectively. The electricity consumption using VOA and EWOA-based DSM cost 217 and 210 USD cents, respectively, whereas non-scheduled consumption costs 273 USD cents and scheduling based on BPSO, GA, and CSO costs 219, 220, and 222 USD cents.

Comparative study of optimization methods for optimal coordination of directional overcurrent relays with distributed generators

&lt;span lang="EN-US"&gt;Due to the growing penetration of distributed generators (DGs), that are based on renewable energy, into the distribution network, it is necessary to address the coordination of directional overcurrent relays (DOCR) in the presence of these generators. This problem has been solved by many metaheuristic optimization techniques to obtain the optimal relay parameters and to have an optimal coordination of the protection relays by considering the coordination constraints. In this article, a comparative study of the optimization techniques proposed in the literature addresses the optimal coordination problem using digital DOCRs with standard properties according to IEC60-255. For this purpose, the three most efficient and robust optimization techniques, which are particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are considered. Simulations were performed using MATLAB R2021a by applying the optimization methods to an interconnected 9-bus and 15-bus power distribution systems. The obtained simulation results show that, in case of distributed generation, the best optimization method to solve the relay protection coordination problem is the differential evolution DE. &lt;/span&gt;