Optimization Approach Research Articles

Recourse strategy for the routing problem of mobile parcel lockers with time windows under uncertain demands

It is expected that mobile parcel lockers (MPLs) will provide more responsive and flexible service to the increasing variation of demand than fixed parcel lockers. However, the service provided by MPLs following planned routes can be degraded and even fail due to the demand uncertainty, such as demand volume and pickup time. In this study, we propose an optimization approach integrated with the recourse strategies to overcome this barrier. Three types of uncertain demands and two types of service failures are addressed in this study, with consideration of uncertain demands and time window constraint. To optimally determine the a priori routes for a set of MPLs, we propose a tailored tabu search algorithm by taking into account the recourse cost. A series of numerical experiments were conducted to examine the effectiveness of the proposed approach in enhancing route reliability with a comparison to ordinary express vehicles. The results reveal that the service reliability can be significantly improved with only a marginal increase in the operational cost by the proposed approach.

Resilience-Oriented Coordination of Networked Microgrids: A Shapley Q-Value Learning Approach

High-impact and low-probability extreme events have occurred more frequently than before because of rapid climate change, which can seriously damage distribution systems. However, conventional distribution management can be dysfunctional after an event, destroying its centralized supervision towards resilience enhancement. In this context, networked microgrids (NMGs) with distributed energy resources provide a viable solution for the resilience enhancement of distribution systems. Existing literature tends to employ model-based optimization approaches for resilient operations of NMGs, which require complete system models and can be time-consuming. To address these challenges, this paper suggests a decentralized framework for resilience-oriented coordination of NMGs and proposes a novel multi-agent reinforcement learning (MARL) method to solve it. Specifically, the proposed MARL method develops an efficient credit assignment scheme for NMGs to learn their contributions to the distribution system resilience via the Shapley Q-value technique with more efficient resilience enhancement. Case studies based on two modified IEEE 15- and 69-bus distribution networks are conducted to validate the effectiveness of the proposed MARL method in enabling effective coordination among NMGs and providing a high resilience level.

A Hybrid Submodular Optimization Approach to Controlled Islanding With Post-Disturbance Stability Guarantees

A Hybrid Submodular Optimization Approach to Controlled Islanding With Post-Disturbance Stability Guarantees

Latency-Energy Aware Dynamic Application Placement for Edge Computing: A Vacation Queue Based Optimization Approach

Latency-Energy Aware Dynamic Application Placement for Edge Computing: A Vacation Queue Based Optimization Approach

A Distributionally Robust Fuzzy Optimization Approach for Resilient Manufacturing Supply Chain Network Design: An RCEP Perspective

A Distributionally Robust Fuzzy Optimization Approach for Resilient Manufacturing Supply Chain Network Design: An RCEP Perspective

Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity

Objective. Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain–machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Approach. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical SID method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and with spiking and local field potential population activity recorded during a naturalistic reach and grasp behavior. Main results. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson–Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior. Significance. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.

Stable Optimal Feedback Control for Landers Based on Machine Learning

Stability certification is critical before controllers are rolled out onto real systems. Despite recent progress in the development of neural network systems for feedback-optimal control, enforcement and assessment of the stability of the trained controllers remains an open problem. In this investigation, a comprehensive framework is developed to achieve certifiably stable fuel-optimal feedback control of pinpoint landers in four different formulations of varying complexity. By preconditioning a deep neural network policy and a deep neural network Lyapunov function, and then applying a constrained parameter optimization approach, we are able to address the shape mismatch problem posed by the standard sum-of-squares Lyapunov function and achieve feedback-optimal control. Phase-space plots of the Lyapunov derivative show the level of certificate enforcement achieved by the developed algorithms, and Monte Carlo simulations are performed to demonstrate the stable, optimal, real-time feedback control provided by the policy.

Optimizing dynamic keystroke pattern recognition with hybrid deep learning technique and multiple soft biometric factors

In this work, we propose an optimization approach for dynamic keystroke pattern recognition by leveraging a hybrid deep learning technique and multiple soft biometric factors. Our methodology begins with the introduction of a novel algorithm called dynamic drone squadron optimization (DDSO) to optimize the selection of optimal features from a pool of multiple keystroke features. We then present an enhanced version of the improved sperm swarm optimization (ISSO) algorithm, which effectively combines the optimal weight features derived from multiple biometric responses. Furthermore, we introduce the multi-stage recurrent neural network (MS-RNN) classifier to accurately recognize and classify keystroke patterns. The performance of our proposed ISSO+MS-RNN technique is evaluated using the benchmark KBOC dataset to validate its effectiveness. Comparative analysis is conducted against existing state-of-the-art techniques, employing various evaluation measures, to demonstrate the superior performance of proposed approach.`

N-BIR: A NUMERIC OPTIMIZATION APPROACH FOR POWER ELECTRONIC CONVERTER BURN-IN TESTING TIME REDUCTION

Burn-in testing is an effective method for detecting early faults in electronic products before they reach the market. This test has a high cost due to lengthy test time on a test bench. In this paper, we propose N-BIR (Numeric optimization approach for power electronic Burn-In testing time Reduction), an algorithm capable of predicting the burn-in (BI) test temperature of power electronic converters, intending to shorten the duration of such tests. This algorithm optimizes by least squares a theoretical model of the system, using as data a fraction of the total burn-in test. Moreover, not only is it capable of making accurate predictions, but it also accompanies them with a prediction interval (PI), so that the algorithm itself can quantify how confident it is of its predictions. We show that using 40% of a conventional rolling test total, our algorithm outperforms several of today's most common Machine Learning (ML) algorithms. Furthermore, we show that it can reduce burning time by 50% to 60% by making accurate predictions, which makes it possible to identify a significant portion of converters that don't require full testing, ultimately lowering costs and boosting productivity. Keywords: Burn-in temperature prediction, Burn-in time reduction, Levelized Cost of Energy, Machine Learning, Electronic Power Converter, Reliability.

Heterogeneous tunable III-V-on-silicon-nitride mode-locked laser emitting wide optical spectra

We demonstrate a III-V-on-silicon-nitride mode-locked laser through the heterogeneous integration of a semiconductor optical amplifier on a passive silicon-nitride cavity using the technique of micro-transfer printing. In the initial phase of our study, we focus on optimizing the lasing wavelength to be centered at 1550 nm. This optimization is achieved by conducting experiments with 27 mode-locked lasers, each incorporating optical amplifiers featuring distinct multiple-quantum-well photoluminescence values. Subsequently we present a comprehensive study investigating the behavior of the mode-locking regime when the electrical driving parameters are varied. Specifically, we explore the impact of the gain voltage and saturable absorber current on the locking stability of a tunable mode-locked laser. By manipulating these parameters, we demonstrate the precise control of the optical spectrum across a wide range of wavelengths spanning from 1530 to 1580 nm. Furthermore, we implement an optimization approach based on a Monte Carlo analysis aimed at enhancing the mode overlap within the gain region. This adjustment enables the achievement of a laser emitting a 23-nm-wide spectrum while maintaining a defined 10 dB bandwidth for a pulse repetition rate of 3 GHz.

Analysis of the efficacy of sleeve gastrectomy, one-anastomosis gastric bypass, and single-anastomosis sleeve ileal bypass in the treatment of metabolic syndrome

The objective of this study was to evaluate and compare the effectiveness of three different types of bariatric surgeries, namely, sleeve gastrectomy (SG), one-anastomotic gastric bypass (OAGB), and single anastomosis sleeve ileal (SASI) bypass, in the treatment of metabolic syndrome (MS). The optimal approach for managing MS remains uncertain, and thus this study aimed to provide a recent analysis of the efficacy of these surgical procedures. This retrospective study evaluated data of individuals who underwent SG, OAGB, and SASI bypass. The primary outcome measures included weight, body mass index (BMI), glucolipid metabolic index, and the occurrence of treatment-related complications within 6 to 12 months post-surgery. A total of 324 patients were included in this study. Of these, 264 patients underwent SG, 30 underwent OAGB, and 30 underwent SASI bypass. A significant decrease in weight was observed at the 6-month and 12-month marks following all three surgical procedures. Of these, patients who underwent SASI bypass exhibited the greatest reduction in weight and BMI post-surgery. Furthermore, the SASI bypass was associated with a significantly higher percentage of total weight loss (%TWL) and excess body mass index loss (%EBMIL) compared to SG and OAGB. Patients who underwent OAGB and SASI bypass demonstrated notable improvements in type 2 diabetes mellitus (T2DM). Patients who underwent SASI bypass and OAGB experienced greater postoperative comfort and reported fewer complaints of discomfort compared to the other procedure. Based on the retrospective analysis of the data, SASI bypass was associated with greater reductions in weight and BMI, higher percentages of %TWL and %EBMIL, and better improvement in T2DM compared to SG and OAGB. Therefore, both SASI bypass and OAGB were found to be more effective than SG in the treatment of MS.

Physical insights into trapping effects on vertical GaN-on-Si trench MOSFETs from TCAD

Vertical GaN power MOSFET is a novel technology that offers great potential for power switching applications. Being still in an early development phase, vertical GaN devices are yet to be fully optimized and require careful studies to foster their development. In this work, we report on the physical insights into device performance improvements obtained during the development of vertical GaN-on-Si trench MOSFETs (TMOS’s) provided by TCAD simulations, enhancing the dependability of the adopted process optimization approaches. Specifically, two different TMOS devices are compared in terms of transfer-curve hysteresis (H) and subthreshold slope (SS), showing a ≈ 75% H reduction along with a ≈ 30% SS decrease. Simulations allow attributing the achieved improvements to a decrease in the border and interface traps, respectively. A sensitivity analysis is also carried out, allowing to quantify the additional trap density reduction required to minimize both figures of merit.

Multirobot Cooperative Path Optimization Approach for Multiobjective Coverage in a Congestion Risk Environment

Multirobot Cooperative Path Optimization Approach for Multiobjective Coverage in a Congestion Risk Environment

Adaptive Bayesian contextual hyperband: A novel hyperparameter optimization approach

<p>Hyperparameter tuning plays a significant role when building a machine learning or a deep learning model. The tuning process aims to find the optimal hyperparameter setting for a model or algorithm from a pre-defined search space of the hyperparameters configurations. Several tuning algorithms have been proposed in recent years and there is scope for improvement in achieving a better exploration-exploitation tradeoff of the search space. In this paper, we present a novel hyperparameter tuning algorithm named adaptive Bayesian contextual hyperband (Adaptive BCHB) that incorporates a new sampling approach to identify best regions of the search space and exploit those configurations that produce minimum validation loss by dynamically updating the threshold in every iteration. The proposed algorithm is assessed using benchmark models and datasets on traditional machine learning tasks. The proposed Adaptive BCHB algorithm shows a significant improvement in terms of accuracy and computational time for different types of hyperparameters when compared with state-of-the-art tuning algorithms.</p>

Economic enhancement and grid‐frequency control of a wind farm‐CAES‐fuel cell‐based hybrid system

AbstractIn the present era, the electrical network faces difficulty owing to the restricted supply of conventional energy resources. Solar and wind power are supremely acceptable renewable energy around the globe due to their cheap availability, zero fuel costs, and environmental safety features. Maintaining power balance in a renewable combined day‐ahead power network becomes challenging owing to the uncertainty of renewable sources, resulting in economic instability. An energy‐storing device is essential for combining renewable sources with existing thermal power plants to control the energy supply and maintain system stability. Any government agency in the deregulated market does not set up energy pricing. The ISO (independent system operator) is the primary organization in the day‐ahead power network that is responsible for collecting energy bids from DISCOs (distribution companies), TRANSCOs (transmission companies), and GENCOs (generation companies). The energy pricing is set by the market regulator at a rate known as Location‐based marginal pricing (LBMP), which provides benefits to GENCOs, and DISCOs as well as the customers. In a deregulated market, the unpredictability of renewable sources affects system profit owing to the creation of instability cost (ISC), induced by mismatching the bidding power production from the renewable plant. To lessen these concerns, the presented approach has proposed an innovative model that employs ideal scheduling of the WF (wind‐farm)‐CAES (compressed‐air‐energy‐storage)‐fuel cell (FC) hybrid systems to enhance system economic profitability while keeping the grid frequency (GF) in the safe zone. The fuel cell acts as the energy‐storing device that can be utilized during high‐demand times on the grid, with the ultimate goal of maximizing profits for the overall system. The CAES system's energy level is separated into four dissimilar points, and this study presents an optimum approach for efficiently using it to maintain grid frequency. The new strategy outperformed the previous methods in all categories. The study was performed using the IEEE 30 bus test system.

Brain Re-Irradiation Or Chemotherapy: a phase II randomised trial of re-irradiation and chemotherapy in patients with recurrent glioblastoma (BRIOChe) – protocol for a multi-centre open-label randomised trial

IntroductionGlioblastoma (GBM) is the most common adult primary malignant brain tumour. The condition is incurable and, despite aggressive treatment at first presentation, almost all tumours recur after a median of...

Subregional Differentiated Safety Factors Design Based on Nonprobabilistic Structural Reliability

The structural safety factor is an essential parameter in aircraft design, representing the ratio of the design load to the operating load. Traditional design methods rely on subjective determination of safety factor values based on experience, lacking objectivity in quantifying uncertainty. However, with advancements in aircraft design technology and increasing competition in the commercial space market, new-generation hypersonic aircraft with complex load environments require a more optimal approach. Applying a uniform safety factor to each component within subregions of the aircraft leads to overly conservative results and impacts flight performance. To address this limitation, a design scheme that incorporates subregional, differentiated safety factors is necessary. This approach allows for better material utilization and ensures compliance with safety requirements. This paper utilizes reliability-based design optimization theory to consider uncertainty in structural systems. It establishes a mapping relationship between structural reliability and differentiated safety factors, providing safety under uncertainty while guaranteeing weight reduction. Additionally, this paper develops a subregional, differentiated safety factors distribution program to determine the safety factors of different subregions of the structure. Consequently, a refined subregional differentiated safety factors scheme that balances safety and economy is derived.

SISS-MCO: large scale sparsity-induced spot selection for fast and fully-automated robust multi-criteria optimisation of proton plans

Objective. Intensity modulated proton therapy (IMPT) is an emerging treatment modality for cancer. However, treatment planning for IMPT is labour-intensive and time-consuming. We have developed a novel approach for multi-criteria optimisation (MCO) of robust IMPT plans (SISS-MCO) that is fully automated and fast, and we compare it for head and neck, cervix, and prostate tumours to a previously published method for automated robust MCO (IPBR-MCO, van de Water 2013). Approach. In both auto-planning approaches, the applied automated MCO of spot weights was performed with wish-list driven prioritised optimisation (Breedveld 2012). In SISS-MCO, spot weight MCO was applied once for every patient after sparsity-induced spot selection (SISS) for pre-selection of the most relevant spots from a large input set of candidate spots. IPBR-MCO had several iterations of spot re-sampling, each followed by MCO of the weights of the current spots. Main results. Compared to the published IPBR-MCO, the novel SISS-MCO resulted in similar or slightly superior plan quality. Optimisation times were reduced by a factor of 6 i.e. from 287 to 47 min. Numbers of spots and energy layers in the final plans were similar. Significance. The novel SISS-MCO automatically generated high-quality robust IMPT plans. Compared to a published algorithm for automated robust IMPT planning, optimisation times were reduced on average by a factor of 6. Moreover, SISS-MCO is a large scale approach; this enables optimisation of more complex wish-lists, and novel research opportunities in proton therapy.

One-shot procedures for efficient minimum compliance topology optimization

In this paper, a one-shot approach for minimum compliance topology optimization is investigated. In the convex case of variable thickness sheet optimization, an optimality criteria scheme with a single step of an iterative state solver converges to the same solutions as an accurate procedure. This remarkable behavior is explained by the crucial role of the geometric multigrid preconditioner that generates accurate design sensitivities on a coarse scale. When applied to SIMP-based topology optimization, a one-shot procedure yields designs with the same primary load-transferring features as an accurate procedure. The difference is in the absence and presence of thin features that have a relatively minor impact on the objective. Several practical remedies are suggested that can close the small performance gaps between the one-shot procedure and an accurate one. The solution scheme is directly applicable to large-scale problems executed on parallel computers, allowing for significant computational savings.

Improving the hydraulic performance of a high-speed submersible axial flow pump based on CFD technology

The hydraulic performance of a high-speed submersible axial flow pump is investigated to reduce its energy consumption. A more efficient and stable optimization method that combines parametric design, computational fluid dynamics, and a computer algorithm is proposed. The main aim is to broaden the high-efficiency operating zone, so the average efficiency under multiple conditions is optimized while considering rotor–stator matching. The design-of-experiments method and a radial-basis-function neural network are combined to form the optimization platform, and automatic optimization of the pump design is realized through repeated execution of design and simulation. The flow loss mechanism inside the pump is studied in depth via the entropy generation rate, and regression analysis shows that the pump efficiency is influenced mainly by the blade angles. After optimization, the target efficiency is increased by 8.34%, and the flow field distribution shows that the channel vortex and hydraulic loss are controlled effectively. Finally, the results are validated by experiment. The proposed optimization approach has advantages in saving manpower and obtaining globally optimal solutions.