Sequential Optimization Approach Research Articles

Navigating Waste Oil Re-refinery Paths with Consideration of Uncertainty: A Sequential Optimization Approach

Navigating Waste Oil Re-refinery Paths with Consideration of Uncertainty: A Sequential Optimization Approach

Simultaneous Optimization of Launch Vehicle Stage and Trajectory Considering Flight-Requirement Constraints

AbstractA conceptual design of a launch vehicle encompasses various disciplines, such as structural design, aerodynamics, propulsion systems, flight control, and stage sizing. This paper focuses on optimizing trajectory and stage size. Traditional approaches used for the conceptual design of a launch vehicle conduct the stage and trajectory designs sequentially, often leading to high computational complexity and suboptimal results. This paper presents an optimization framework that addresses both trajectory optimization and staging in an integrated way. The proposed framework aims to minimize the lift-off mass while satisfying the constraints on flight requirements (e.g., maximum dynamic pressure and the impact points of separated parts). A case study demonstrates the advantage of the proposed framework compared to the traditional sequential optimization approach.

Completion Performance Evaluation in Multilateral Wells Incorporating Single and Multiple Types of Flow Control Devices Using Grey Wolf Optimizer

There has been a tendency in oil and gas industry towards the adoption of multilateral wells (MLWs) with completions that incorporate multiple types of flow control devices (FCDs). In this completion technique, passive inflow control devices (ICDs) or autonomous inflow control devices (AICDs) are positioned within the laterals, while interval control valves (ICVs) are installed at lateral junctions to regulate the overall flow from each lateral. While the outcomes observed in real field applications appear promising, the efficacy of this specific downhole completion combination has yet to undergo comparative testing against alternative completion methods that employ a singular flow control device type. Additionally, the design and current evaluations of such completions are predominantly based on analytical tools that overlook dynamic reservoir behavior, long-term production impacts, and the correlation effects among different devices. In this study, we explore the potential of integrating various types of flow control devices within multilateral wells, employing dynamic optimization process using numerical reservoir simulator while the Grey Wolf Optimizer (GWO) is used as optimization algorithm. The Egg benchmark reservoir model is utilized and developed with two dual-lateral wells. These wells serve as the foundation for implementing and testing 22 distinct completion cases considering single-type and multiple types of flow control devices under reactive and proactive management strategies. This comprehensive investigation aims to shed light on the advantages and limitations of these innovative completion methods in optimizing well and reservoir performance. Our findings revealed that the incorporation of multiple types of FCDs in multilateral well completions significantly enhance well performance and can surpass single-type completions including ICDs or AICDs. However, this enhancement depends on the type of the device implemented inside the lateral and the control strategy that is used to control the ICVs at the lateral junctions. The best performance of multiple-type FCD-based completion was achieved through combining AICDs with reactive ICVs which achieved around 75 million USD profit. This represents 42% and 22% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. The optimal settings for ICD and AICD in individual applications may significantly differ from the optimal settings when combined with ICVs. This highlights a strong correlation between the different devices (control variables), proving that using either a common, simplified analytical, or a standard sequential optimization approach that do not explore this inter-dependence between devices would result in sub-optimal solutions in such completion cases. Notably, the ICV-based completion, where only ICVs are installed with lateral completion, demonstrated superior performance, particularly when ICVs are reactively controlled, resulting in an impressive 80 million USD NPV which represents 53% and 30% increase in the objective function compared to single-type ICDs and AICDs installations, respectively.

First-Order Algorithms Without Lipschitz Gradient: A Sequential Local Optimization Approach

Most first-order methods rely on the global Lipschitz continuity of the objective gradient, which fails to hold in many problems. This paper develops a sequential local optimization (SLO) framework for first-order algorithms to optimize problems without Lipschitz gradient. Operating on the assumption that the gradient is locally Lipschitz continuous over any compact set, SLO develops a careful scheme to control the distance between successive iterates. The proposed framework can easily adapt to the existing first-order methods, such as projected gradient descent (PGD), truncated gradient descent (TGD), and a parameter-free variant of Armijo linesearch. We show that SLO requires [Formula: see text] gradient evaluations to find an ϵ-stationary point, where Y is certain compact set with [Formula: see text] radius, and [Formula: see text] denotes the Lipschitz constant of the i-th order derivatives in Y. It is worth noting that our analysis provides the first nonasymptotic convergence rate for the (slight variant of) Armijo linesearch algorithm without globally Lipschitz continuous gradient or convexity. As a generic framework, we also show that SLO can incorporate more complicated subroutines, such as a variant of the accelerated gradient descent (AGD) method that can harness the problem’s second-order smoothness without Hessian computation, which achieves an improved [Formula: see text] complexity. Funding: J. Zhang is supported by the MOE AcRF [Grant A-0009530-04-00], from Singapore Ministry of Education. M. Hong is supported by NSF [Grants CIF-1910385 and EPCN-2311007]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoo.2021.0029 .

Increasing Distributed Generation Hosting Capacity Based on a Sequential Optimization Approach Using an Improved Salp Swarm Algorithm

In recent years, a pronounced transition to the exploitation of renewable energy sources has be observed worldwide, driven by current climate concerns and the scarcity of conventional fuels. However, this paradigm shift is accompanied by new challenges for existing power systems. Therefore, the hosting capacity must be exhaustively assessed in order to maximize the penetration of distributed generation while mitigating any adverse impact on the electrical grid in terms of voltage and the operational boundaries of the equipment. In this regard, multiple aspects must be addressed in order to maintain the proper functioning of the system following the new installations’ capacities. This paper introduces a sequential methodology designed to determine the maximum hosting capacity of a power system through the optimal allocation of both active and reactive power. To achieve this goal, an Improved Salp Swarm Algorithm is proposed, aiming to establish the appropriate operational planning of the power grid considering extensive distributed generation integration, while still ensuring a safe operation. The case study validates the relevance of the proposed model, demonstrating a successful enhancement of hosting capacity by 14.5% relative to standard models.

Improved production of Bacillus subtilis cholesterol oxidase by optimization of process parameters using response surface methodology

BackgroundCholesterol oxidase has numerous biomedical and industrial applications. In the current study, a new bacterial strain was isolated from sewage and was selected for its high potency for cholesterol degradation (%) and production of high cholesterol oxidase activity (U/OD600). ResultsBased on the sequence of 16S rRNA gene, the bacterium was identified as Bacillus subtilis. The fermentation conditions affecting cholesterol degradation (%) and the activity of cholesterol oxidase (U/OD600) of B. subtilis were optimized through fractional factorial design (FFD) and response surface methodology (RSM). According to this sequential optimization approach, 80.152% cholesterol degradation was achieved by setting the concentrations of cholesterol, inoculum size, and magnesium sulphate at 0.05 g/l, 6%, and 0.05 g/l, respectively. Moreover, 85.461 U of cholesterol oxidase/OD600 were attained by adjusting the fermentation conditions at initial pH, 6; volume of the fermentation medium, 15 ml/flask; and concentration of cholesterol, 0.05 g/l. The optimization process improved cholesterol degradation (%) and the activity of cholesterol oxidase (U/OD600) by 139% and 154%, respectively. No cholesterol was detected in the spectroscopic analysis of the optimized fermented medium via gas chromatography-mass spectroscopy (GC–MS). ConclusionThe current study provides principal information for the development of efficient production of cholesterol oxidase by B. subtilis that could be used in various applications.

Clearing payments in dynamic financial networks

This paper proposes a novel dynamical model for determining clearing payments in financial networks. We extend the classical Eisenberg–Noe model of financial contagion to multiple time periods, allowing financial operations to continue after initial pseudo defaults, thus permitting nodes to possibly recover and eventually fulfill their liabilities. Dynamic optimal clearing payments in our model are computed by solving a suitable linear program, both in the full matrix payments case and in the pro-rata constrained case. We prove that the proposed model obeys the priority of debt claims requirement, that is, each node at every step either pays its liabilities in full, or it pays out all its balance. In the pro-rata case, the optimal dynamic clearing payments are unique, and can be determined via a time-decoupled sequential optimization approach.

An ensemble learning with sequential model-based optimization approach for pavement roughness estimation using smartphone sensor data

Pavement roughness monitoring has always been a concern in the field of road asset management. However, the utilization of laser profilometers to measure pavement roughness is costly and inconvenient. To address these challenges, this study developed a low-cost, lightweight, and rapid approach for pavement roughness estimation using smartphone sensor data. Firstly, the feasibility of the chosen smartphone in vibration acceleration acquisition was examined through micro-scale shaking table tests. Then, the Categorical Boosting (CatBoost) model with sequential model-based optimization (SMBO) approach was developed for smartphone-based pavement roughness estimation. Finally, the feature importance and feature interaction effects in the pavement roughness estimation task were interpreted using Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP), respectively. The results show that the chosen smartphone has great potential in vibration data acquisition, and the recorded data are highly consistent with the results obtained by the professional accelerometer. The CatBoost model outperforms the other reference models in terms of pavement roughness estimation accuracy, with coefficient of determination (R2) of 0.807, root mean square error (RMSE) of 0.553, and mean absolute error (MAE) of 0.433. Furthermore, the feature interpretation results indicate that the smartphone-based pavement roughness estimation approach relies on the coupling effects of vibration and velocity features. This study provides a novel insight into the lightweight pavement roughness estimation approach, which has potential implications for assisting pavement maintenance decision-making in road asset management.

Optimal sequencing using a scheduling heuristic

The problem of finding the optimal appointment schedule has received significant consideration in the literature on healthcare optimization. Due to randomness, determining when clients should be scheduled in order to strike a balance in terms of idle times, waiting times, and overtime is a non-trivial task. Furthermore, it is often solely studied in the dimension of scheduling, whereas the sequencing decision in which heterogeneous clients (e.g., new and return clients) are to arrive is at least of equal importance. The approach presented integrates an approximation method with a scheduling heuristic that tackles this joint objective of sequencing and scheduling.A moment-iteration method is employed to construct appointment schedules. This method permits analytical solutions and thereby is incredibly fast to solve and optimize. Next, a sequential optimization approach is applied which aligns well with the moment-iteration method and permits per-client control of costs. Moreover, within this approach, the classical surgery scheduling problem of minimizing earliness and tardiness by determining due dates is equivalent to the appointment scheduling problem, i.e., in a sequentially optimal schedule the inter-arrival time equals the previous client’s due date.Finally, for the case of clients having heterogeneous service-time distributions, rigorous sequencing rules are derived in terms of the first two moments. The focus lies on non-identical exponential service times and log-normal service times, as commonly found in healthcare modelling. The smallest-mean/variance-first rule is sequentially optimal. For cases in which the mean and variance move in opposite directions contour plots are provided to aid practitioners in sequencing clients.

Picking winners: Diversification through portfolio optimization

We develop a general framework for selecting a small pool of candidate solutions to maximize the chances that one will be optimal for a combinatorial optimization problem, under a linear and additive random payoff function. We formulate this problem using a two‐stage distributionally robust model, with a mixed 0–1 semidefinite program. This approach allows us to exploit the “diversification” effect inherent in the problem to address how different candidate solutions can be selected to improve the chances that one will attain a high ex post payoff. More interestingly, using this distributionally robust optimization approach, our model recovers the “evil twin” strategy, well known in the field of football pool betting, under appropriate settings. We also address the computational challenges of scaling up our approach to construct a moderate number of candidate solutions to increase the chances of finding one that performs well. To this end, we develop a sequential optimization approach based on a compact semidefinite programming reformulation of the problem. Extensive numerical results show the superiority of our approach over existing methods.

Optimizing public transport transfers by integrating timetable coordination and vehicle scheduling

Transfer optimization in public transport (PT) networks can be achieved through coordinated timetabling and vehicle scheduling. Traditionally, the coordinated timetabling problem is solved first before proceeding to the vehicle scheduling problem. The integration of these two problems can help further reduce the total operation cost and improve the level of service, especially when timetables of different PT lines are well-coordinated at transfer stations. This work addresses the integrated PT timetable coordination and vehicle scheduling problem while ensuring that each PT line is dispatched with an even headway. We first separately formulate two integer linear programming models for the timetable coordination and vehicle scheduling problems. Next, the two models are integrated into a bi-objective integer linear programming model for the integrated timetable coordination and vehicle scheduling problem. For small size PT networks, the model can be solved by using an ɛ-constraint method, together with off-the-shelfoptimization solvers. For large-size problems, two constraint-reduction procedures are developed to reduce the number of redundant constraints so as to reduce the computation complexity and improve the solution process. Finally, the models and solution method are applied to a numerical example and a real-world bus rapid transit (BRT) network in Chengdu, China. Computation results show that the solution generated by the sequential optimization approach is usually dominated by the Pareto-optimal solutions generated by the integrated optimization approach. Our findings suggest that it is not a wise decision to use the solution generated by the sequential optimization approach or the solution with the minimum fleet size generated by the integrated optimization approach. For practical implementation, it is recommended to choose the solution that has a fleet size of one more vehicle than the minimum fleet size.

Robust joint transmit and receive beamforming by sequential optimization for bistatic radar system

AbstractThe conventional joint transmit and receive beamforming algorithms maximise the output signal‐to‐interference‐plus‐noise ratio (SINR) by designing the weight coefficients simultaneously at the transmitter and receiver. However, their performance degrades significantly when the model mismatch exists. In this paper, the robust version of the joint transmit and receive beamforming method is considered in the context of the bistatic radar system. In particular, both the transmit array and the receive array suffer model mismatch. The designed beamformer aims to maximise its output SINR subjected to a robustness constraint by simultaneously designing the transmit beamforming vector and the receive beamforming vector. Two types of robustness constraints, namely the worst‐case constraint and the probability constraint, are considered in this paper. The proposed formulations are non‐convex, and the sub‐optimal solutions are obtained by a sequential optimization approach. In addition, a novel method to estimate the interference‐plus‐noise covariance matrices at the transmitter and receiver is proposed, which makes the proposed robust joint beamforming algorithms adapt to the variation of the surrounding environment. Numerical simulations verify the effectiveness of the proposed algorithms.

Towards Optimal Triage and Mitigation of Context-Sensitive Cyber Vulnerabilities

Cyber vulnerabilities are security deficiencies in computer and network systems of organizations, which can be exploited by an adversary to cause significant damage. The technology and security personnel resources currently available in organizations to mitigate the vulnerabilities are highly inadequate. As a result, systems routinely remain unpatched, thus making them vulnerable to security breaches from the adversaries. The potential consequences of an exploited vulnerability depend upon the context as well as the severity of the vulnerability, which may differ among networks and organizations. Furthermore, security personnel tend to have varying levels of expertise and technical proficiencies associated with different computer and network devices. There exists a critical need to develop a resource-constrained approach for effectively identifying and mitigating important context-sensitive cyber vulnerabilities. In this article, we develop an advanced analytics and optimization framework to address this need and compare our approach with rule-based methods employed in real-world cybersecurity operations centers, as well as a vulnerability prioritization method from recent literature. First, we propose a machine learning-based vulnerability priority scoring system (VPSS) to calculate the priority scores for each of the vulnerabilities found in an organization’s network and quantify organizational context-based vulnerability exposure. Next, we propose a decision-support system, which consists of a two-step sequential optimization approach. The first model selects the high priority vulnerability instances from the dense report subject to resource constraints, and the second model then optimally allocates them to the security personnel with matching skill types for mitigation. Experiment results conducted using a real-world vulnerability data set show that our approach 1) outperforms both the rule-based methods and the vulnerability prioritization method from literature in prioritizing context-sensitive vulnerabilities, which are found across highly susceptible organizationally relevant host machines, and 2) maximizes the pairs of vulnerability instance type and the respective security analyst skill type for optimal mitigation.

Sequential Model-based Optimization Approach Deep Learning Model for Classification of Multi-class Traffic Sign Images

Sequential Model-based Optimization Approach Deep Learning Model for Classification of Multi-class Traffic Sign Images

Effect of magnetic resonance imaging pre-processing on the performance of model-based prostate tumor probability mapping

Objective. Multi-parametric magnetic resonance imaging (mpMRI) has become an important tool for the detection of prostate cancer in the past two decades. Despite the high sensitivity of MRI for tissue characterization, it often suffers from a lack of specificity. Several well-established pre-processing tools are publicly available for improving image quality and removing both intra- and inter-patient variability in order to increase the diagnostic accuracy of MRI. To date, most of these pre-processing tools have largely been assessed individually. In this study we present a systematic evaluation of a multi-step mpMRI pre-processing pipeline to automate tumor localization within the prostate using a previously trained model. Approach. The study was conducted on 31 treatment-naïve prostate cancer patients with a PI-RADS-v2 compliant mpMRI examination. Multiple methods were compared for each pre-processing step: (1) bias field correction, (2) normalization, and (3) deformable multi-modal registration. Optimal parameter values were estimated for each step on the basis of relevant individual metrics. Tumor localization was then carried out via a model-based approach that takes both mpMRI and prior clinical knowledge features as input. A sequential optimization approach was adopted for determining the optimal parameters and techniques in each step of the pipeline. Main results. The application of bias field correction alone increased the accuracy of tumor localization (area under the curve (AUC) = 0.77; p-value = 0.004) over unprocessed data (AUC = 0.74). Adding normalization to the pre-processing pipeline further improved diagnostic accuracy of the model to an AUC of 0.85 (p-value = 0.000 12). Multi-modal registration of apparent diffusion coefficient images to T2-weighted images improved the alignment of tumor locations in all but one patient, resulting in a slight decrease in accuracy (AUC = 0.84; p-value = 0.30). Significance. Overall, our findings suggest that the combined effect of multiple pre-processing steps with optimal values has the ability to improve the quantitative classification of prostate cancer using mpMRI. Clinical trials: NCT03378856 and NCT03367702.

Bioprocess development for biosurfactant production by Natrialba sp. M6 with effective direct virucidal and anti-replicative potential against HCV and HSV

Halophilic archaea is considered an promising natural source of many important metabolites. This study focused on one of the surface-active biomolecules named biosurfactants produced by haloarchaeon Natrialba sp. M6. The production trend was optimized and the product was partially purified and identified using GC–Mass spectrometry. Sequential optimization approaches, Plackett–Burman (PB) and Box–Behnken Designs (BBD) were applied to maximize the biosurfactants production from M6 strain by using 14 factors; pH, NaCl, agitation and glycerol; the most significant factors that influenced the biosurfactant production were used for Response Surface Methodology (RSM). The final optimal production conditions were agitation (150 rpm), glycerol (3%), NaCl (20.8%), pH (12) and cultivation temperature (37°C). GC–Mass spectrometry for the recovered extract revealed the presence of a diverse group of bipolar nature, hydrophobic hydrocarbon chain and charged function group. The majority of these compounds are fatty acids. Based on results of GC–MS, compositional analysis content and Zetasizer, it was proposed that the extracted biosurfactant produced by haloarchaeon Natrialba sp. M6 could be a cationic lipoprotein. The antiviral activity of such biosurfactant was investigated against hepatitis C (HCV) and herpes simplex (HSV1) viruses at its maximum safe doses (20 μg/mL and 8 μg/mL, respectively). Its mode of antiviral action was declared to be primarily via deactivating viral envelopes thus preventing viral entry. Moreover, this biosurfactant inhibited RNA polymerase- and DNA polymerase-mediated viral replication at IC50 of 2.28 and 4.39 μg/mL, respectively also. Molecular docking studies showed that surfactin resided well and was bound to the specified motif with low and accepted binding energies (ΔG = − 5.629, − 6.997 kcal/mol) respectively. Therefore, such biosurfactant could be presented as a natural safe and effective novel antiviral agent.

Integrated stochastic optimization of stope design and long-term underground mine production scheduling

In the commonly used underground mine planning framework, mine design is first established and is the main input for the subsequent long-term mine production scheduling optimization. This sequential optimization approach cannot, therefore, capture the synergies between the involved planning steps, generating solutions that depart substantially from a global optimum. In addition, traditional underground mine planning methods for stope design and life-of-mine production scheduling are deterministic and are based on a single estimated orebody model. As a result, the uncertainty and variability in grades and material types are not incorporated into the optimization process, resulting in designs that misrepresent all high-, medium- and low-grade stoping volumes and production schedules with misleading forecasts. A two-stage stochastic integer program (SIP) for integrated optimization of stope and development network designs and an underground mine production scheduling are proposed for the sublevel open stoping mining method under grade uncertainty and variability, quantified by a set of geostatistical simulations of the mineral deposit considered. Assuming a mine is accessed through a shaft, the model defines a schedule of levels and stopes, which aims to maximize the discounted revenues, minimize development costs, and manage the risk of not meeting production targets, while satisfying geotechnical constraints. The practical aspects of the proposed method are presented through an application at an underground gold mine. A comparison with the stepwise framework, where the stope design is input to a subsequent optimization of the production schedule, shows that the proposed approach provides a physically different design and production schedule with an 11% higher net present value (NPV) and a life-of-mine that is two years shorter, affirming the advantages of the integrated optimization process.

R2ogs5: Calibration of Numerical Groundwater Flow Models with Bayesian Optimization in R.

Large-scale and high-resolution groundwater models are currently becoming increasingly important in order to clarify the extent to which climate trends and extreme weather affect the groundwater balance regionally. As a result, the parameterization of groundwater models is becoming more detailed and more complex, making conventional calibration methods too time-consuming. Moderating the computational demand to find optimal solutions for the resulting potentially multi-modal objective function requires intelligent and efficient global optimization methods. Moreover, the increasing use of modern scripting languages R and Python to craft environmental analysis workflows calls to integrate groundwater flow simulators in such. Here we introduce and exemplify r2ogs5, a tool that integrates version 5 of the open-source simulation software OpenGeoSys into the programming language R. r2ogs5 allows for calibration of numerical groundwater flow models with a sequential model-based optimization approach that combines Bayesian optimization (BO) with surrogate modeling. Here, we describe the structure and function of r2ogs5 as well as the implemented calibration method. We then demonstrate the calibration method by calibrating 4 and 12 parameters of two simple groundwater flow models. The results indicate that this method needs fewer runs of the groundwater flow model than conventional gradient search and Latin hypercube sampling in case of the 12 parameter model. We believe that our method offers the potential to calibrate computationally expensive groundwater flow models. r2ogs5 supports groundwater flow modelers to access the statistical analysis and visualization capabilities of the R language and is a valuable tool for geoscientists already using R.

Largest small polygons: a sequential convex optimization approach

A small polygon is a polygon of unit diameter. The maximal area of a small polygon with $n=2m$ vertices is not known when $m\ge 7$. Finding the largest small $n$-gon for a given number $n\ge 3$ can be formulated as a nonconvex quadratically constrained quadratic optimization problem. We propose to solve this problem with a sequential convex optimization approach, which is an ascent algorithm guaranteeing convergence to a locally optimal solution. Numerical experiments on polygons with up to $n=128$ sides suggest that the optimal solutions obtained are near-global. Indeed, for even $6 \le n \le 12$, the algorithm proposed in this work converges to known global optimal solutions found in the literature.

Integrated line planning and train timetabling through price-based cross-resolution feedback mechanism

Railway line planning and train timetabling are two key planning steps that determine the operating cost and passenger service quality of a railway operator under the infrastructure capacity limitations. Traditionally, the line planning and train timetabling problems are solved sequentially at the strategic and tactical level, respectively. In this study, by introducing two types of binary decision variables, we first propose a unified integer linear programming (ILP) model for the integrated optimization of line planning and train timetabling. The line planning problem is modeled using ILP to satisfy passenger demand, whereas the cyclic train timetabling problem is formulated as a multi-commodity network flow model with a side track capacity constraint. The two types of binary decision variables are coupled by a cross-resolution consistency constraint, which ensures the conformity of the line planning and train timetabling decisions. Furthermore, a dual decomposition mechanism based on the Alternating Direction Method of Multipliers (ADMM) is developed to dualize the cross-resolution consistency and track capacity constraints, such that the original ILP model is decomposed into a line planning sub-problem and a set of train-specific sub-problems. After the linearization of the quadratic penalty terms in the ADMM, each sub-problem contains the Lagrangian relaxation price information based on the cross-resolution consistency constraint. Moreover, the primal and dual solutions are obtained by iteratively and efficiently solving the line planning sub-problem using a commercial solver, and each train-specific sub-problem through a tailored forward dynamic programming algorithm. Furthermore, a real-life case study is conducted based on the Beijing–Shanghai high-speed railway corridor to verify the efficiency and effectiveness of the proposed model and algorithm. The results of the numerical experiments demonstrate that the ADMM can achieve significantly smaller optimality gaps than Lagrangian relaxation, and the integrated optimization approach can improve the objective value by 5.78% on average compared with the sequential optimization approach.