Data-driven Optimization Research Articles

Rightful Rewards: Refining Equity in Team Resource Allocation through a Data-Driven Optimization Approach

In group management, accurate assessment of individual performance is crucial for the fair allocation of resources such as bonuses. This paper explores the complexities of gauging each participant’s contribution in multi-participant projects, particularly through the lens of self-reporting—a method fraught with the challenges of under-reporting and over-reporting, which can skew resource allocation and undermine fairness. Addressing the limitations of current assessment methods, which often rely solely on self-reported data, this study proposes a novel equitable allocation policy that accounts for inherent biases in self-reporting. By developing a data-driven mathematical optimization model, we aim to more accurately align resource allocation with actual contributions, thus enhancing team efficiency and cohesion. Our computational experiments validate the proposed model’s effectiveness in achieving a more equitable allocation of resources, suggesting significant implications for management practices in team settings.

Metaheuristics-guided active learning for optimizing reaction conditions of high-performance methane conversion

Converting greenhouse gases into value-added chemical compounds has been widely studied in chemical science and engineering for sustainable industry. In particular, nonoxidative coupling of methane (NOCM) that transforms methane into useful chemical compounds has received great interests because methane is a naturally abundant greenhouse gas. NOCM has received significant attention in sustainable industry for addressing the climate crisis. However, there are complex optimization problems in maximizing the efficiency of the nonoxidative methane conversion. Although conventional data-driven methods have been successfully applied to various engineering problems, a data-driven optimization of NOCM remains a challenging problem because expensive chemical experiments should be performed to collect prior data for model training. To avoid the expensive costs of chemical experiments, we propose an active learning method that performs training data augmentation and model re-training without pre-defined unlabeled experimental data. To this end, we combine active learning with metaheuristic algorithms to perform active learning with statistically augmented data. We applied the proposed method to a high-throughput screening task to discover new reaction conditions of high-performance NOCM, and the high-throughput screening error was significantly reduced by 69.11%.

Empowering Capacitive Devices: Harnessing Transfer Learning for Enhanced Data-Driven Optimization.

Developing data-driven models has found successful applications in engineering tasks, such as material design, process modeling, and process monitoring. In capacitive devices like deionization and supercapacitors, there exists potential for applying this data-driven machine learning (ML) model in optimizing its potential use in energy-efficient separations or energy generation. However, these models are faced with limited datasets, and even in large quantities, the datasets are incomplete, limiting their potential use for successful data-driven modeling. Here, the success of transfer learning in resolving the challenges with limited datasets was exploited. A two-step data-driven ML modeling framework named ImputeNet involving training with ML-imputed datasets and then with clean datasets was explored. Through data imputation and transfer learning, it is possible to develop a data-driven model with acceptable metrics mirroring experimental measurements. By using the model, optimization studies using the genetic algorithm were implemented to analyze the solution under the Pareto optimality. This early insight can be used in the initial stage of experimental measurements to rapidly identify experimental conditions worthy of further investigation. Moreover, we expect that the insights from these results will drive accurate predictive modeling in other fields including healthcare, genomic data analysis, and environmental monitoring with incomplete datasets.

An intelligent data-driven investigation and optimization integrated with an eco-friendly thermal design approach for a marine diesel engine to study its waste-to-liquefied hydrogen generation potential

The waste heat generated by vehicles, especially heavy-duty engines, has motivated the current research to propose a novel thermal design for a ship's engine. Hence, the suggested system comprises an organic-flash power plant, a bi-evaporator cooling subsystem, a thermal desalination cycle, a water electrolysis-based hydrogen generation unit, and a Claude unit for liquefying hydrogen. The innovative aspects associated with the proposed design include utilizing a novel cascade heat recovery process aimed at reduced irreversibility for the engine's waste and the integration of bi-evaporator technology and Claude unit to facilitate the process of waste-to-liquefied hydrogen generation for a marine engine for improved handling. In addition, the research includes an intelligent study that comprehensively analyzes and optimizes the proposed system in terms of long-term sustainability and economic feasibility. For this purpose, a data-driven optimization technique is implemented using artificial neural networks in combination with multi-objective grey wolf optimization. The aims of the research encompass the optimization of the sustainability index and unit cost of liquefied hydrogen. The research findings indicate that the vapor generator’s terminal temperature difference exerts the most substantial influence on performance metrics, as evidenced by a mean sensitivity index of 0.368. Also, the mentioned objective functions exhibit values of 1.139 and 7.60 $/kg, respectively. Besides, the optimum exergy destruction rate is 124.3 kW, the total investment cost rate is 4.94 $/year, the specific cost of products is 74.87 /GJ, the payback period is 2.2 years, and the net present value is 7.48 M$.

A data-driven optimization model for renewable electricity supply chain design

The rising electricity demand, driven by technological progress and population growth, has made the design of the electricity supply chain a major challenge. The transition to renewable energy sources has led to a substantial reduction in the costs and greenhouse gases emissions of electricity production. This study proposes a hybrid approach consisting of a recurrent neural network and an optimization model for designing a resilient electricity supply chain based on wind and solar energies. First, the electricity demand is forecast using the long short-term memory algorithm. Second, based on the forecast demand, a two-stage stochastic programming model is developed for designing an electricity supply chain under disruption risks. Partial and complete disruptions in power lines and facilities, including power plants, distribution and subtransmission substations, and low-power solar panel farms are considered. The objective of the proposed model is to minimize the total cost of the supply chain by making optimal decisions on facility location; technology selection for power plants; setting up transmission lines; and the quantity of power generation, power transmission, and power shortage in low- and high-voltage energy consumption systems. Three strategies are adopted to enhance the supply chain's resilience: multiple renewable energy sources, multiple power transmission lines, and lateral power lines. Lastly, the Sistan and Baluchestan Electric Power Distribution Company in Iran is used as a case study for this work to demonstrate the applicability of the proposed approach, analyze the findings, and derive relevant managerial insights.

Performance Analysis of Energy-Efficient Path Planning for Sustainable Transportation

Optimizing path planning for energy efficiency is critical for achieving sustainable vehicular transportation. This paper presents a novel framework for evaluating the impact of path planning algorithms (PPAs) on energy consumption within a simulated environment. We leverage the CARLA simulator to conduct a comparative analysis between the widely used A* and a Hybrid Genetic Algorithm (HGA) across diverse vehicular scenarios. This investigation aims to quantify the influence of PPA selection on vehicle energy expenditure, enabling data-driven optimization for energy minimization. We leverage an offline energy estimation model to further streamline the comparison of the two PPAs. Extensive simulations are employed to demonstrate the efficiency and adaptability of the proposed framework. The findings contribute to the development of energy-efficient path-planning strategies, promoting advancements toward sustainable transportation systems.

A hybrid approach based on multi-criteria decision making and data-driven optimization in solving portfolio selection problem

A hybrid approach based on multi-criteria decision making and data-driven optimization in solving portfolio selection problem

Data-driven optimization and machine learning analysis of compatible molecules for halide perovskite material

Optoelectronic stability of halide perovskite material in hostile conditions such as water is rather limited, preventing them from further industrial deployment. Here, we optimize and perform machine learning analysis on CH3NH3PbI3 materials with additives, solvents and post-treatment molecules using combined experimental and data-driven methods. A champion system consisting of a compatible tertiary molecular combination ‘calcein+PbBr2 + DMSO’ active at diverse surfaces is identified, delivering a large aqueous photoelectrochemical (PEC) photocurrent of 10-5 A/cm2 and an improved aqueous stability of 92.5%. Subsequently, machine interpretation is provided to decouple the multi-molecule contributions with the assistance of genetic programming (GP) and extra-trees (ET) machine learning models, highlighting the intricate molecular features for the target outputs. The post-hoc density functional theory (DFT) calculation suggests the presence of multiple hydrogen bond and anion··π surface interactions to stabilize the interfacial structures. The present ‘PEC + GP + ET + DFT’ approach is suggested to be an effective approach to design and comprehensively evaluate molecule-modified materials.

IMPACT OF ONLINE ADVERTISING IN MARKETING OF THE PRODUCT

In recent years, the landscape of marketing has undergone a profound transformation, largely propelled by the exponential growth of the internet and the emergence of various digital platforms and technologies. Online advertising, encompassing a diverse array of formats such as display ads, social media ads, search engine marketing, video ads, and influencer partnerships, has emerged as a cornerstone of modern marketing strategies. One of the primary drivers behind the rise of online advertising is its unparalleled ability to reach highly targeted audiences with precision and efficiency. Unlike traditional advertising mediums like print or television, online advertising offers sophisticated targeting capabilities based on factors such as demographics, interests, behavior, and geographic location. This granular targeting allows advertisers to tailor their messages to specific audience segments, thereby maximizing relevance and resonance. Moreover, the real-time nature of online advertising enables businesses to measure and track the performance of their campaigns with unprecedented granularity. Through metrics such as click-through rates, conversion rates, impressions, and engagement metrics, advertisers can gain valuable insights into the effectiveness of their campaigns and make data-driven optimizations in real-time. This iterative approach to advertising not only improves campaign performance but also enhances return on investment (ROI) by ensuring that marketing budgets are allocated efficiently. Furthermore, online advertising offers unparalleled flexibility and scalability, allowing businesses of all sizes to launch and manage campaigns with varying budgets and objectives. Whether it's a small startup looking to build brand awareness or a multinational corporation aiming to drive sales and conversions, online advertising offers a range of solutions to suit diverse marketing goals and budgets. In addition to its targeting capabilities and measurability, online advertising also plays a crucial role in enhancing brand visibility and awareness in today's crowded marketplace.

Data-driven optimization for microgrid control under distributed energy resource variability

The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

Model based data-driven optimisation of automated fibre placement set up

The composites manufacturing industry relies heavily on manual hand layup, unsuitable for increasing demands. Automated Fiber Placement (AFP) offers a solution, but setup complexity and extensive trials inflate costs. Digital tools promise to expedite development, but CPU-intensive simulations limit large-scale parameter optimisation. This paper introduces Gaussian Process (GP) models for understanding AFP parameters’ relationship with nip-point temperature. The GP emulator streamlines optimisation, offering accurate power curves with minimal simulations. An efficient method to enhance emulator performance through sequential design is also proposed, providing a cost-effective decision tool for AFP optimisation.

Data-Driven Heuristic Optimization for Complex Large-Scale Crude Oil Operation Scheduling

This paper addresses the challenging scheduling of crude oil operations (SCOO) problem, characterized by the intricate sequencing of activities involving discrete events and continuous variables. Given the NP-Hard nature of scheduling problems due to their combinatorial complexity, this study employs a data-driven optimization approach. Initially, historical operational data relevant to the SCOO are scrutinized; however, due to data limitations, small-scale instances are solved using a mathematical programming model to generate data. Subsequently, operational solution data are processed using the Apriori algorithm, a renowned data mining technique. The insights gained are translated into heuristic rules, laying the groundwork for a novel data-driven heuristic algorithm tailored for the SCOO problem. This algorithm is then applied to a 45-day scheduling scenario, demonstrating the efficacy of the proposed approach.

Multi-fidelity deep learning for aerodynamic shape optimization using convolutional neural network

Aerodynamic shape design is essential for improving aircraft performance and efficiency. First, this study introduces a data-driven optimization framework utilizing a multi-fidelity convolutional neural network (MFCNN) for aerodynamic shape optimization. To achieve better optimization results with reduced computational cost, the framework dynamically incorporates new data in each optimization cycle. Specifically, it constantly involves the optimal solution from previous cycle as a new high-fidelity sample and employs a low-fidelity infilling strategy that maximizes the minimum Euclidean distance for selecting new low-fidelity samples. Moreover, a standard synthetic benchmark is used to elaborate the procedure of optimization and show the capability and effectiveness of the framework. Finally, the framework is applied to two aerodynamic shape optimization problems: maximizing the lift-to-drag ratio for the Royal Aircraft Establishment 2822 (RAE2822) airfoils and minimizing the cruise drag coefficient for the three-dimensional (3D) drooped and scarfed non-axisymmetric nacelles. The framework increases the lift-to-drag ratio by 51.21% over the baseline and achieves an 18.79% reduction in the cruise drag coefficient for nacelle optimization, outperforming traditional multi-fidelity deep neural network optimization framework. Sufficiently utilizing the implicit relations between different fidelity levels of data through defined local perceptual fields and convolution, our MFCNN-based optimization framework signifies a step forward in the efficiency and accuracy of aerodynamic shape optimization.

Large-Scale Data-Driven Optimization in Deep Modeling With an Intelligent Decision-Making Mechanism.

This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies how specific design choices in the large-scale data-driven optimization and creation process affect the balance between the accuracy and effectiveness of the designed deep intelligent model. To this end, this study presents a novel architecture unit, which is termed as the "Activate-and-Freeze" block on popular and highly competitive datasets. In order to extract informative features by fusing spatial and channel-wise information together within local receptive fields and boost the representation power, this study constructs a Dense-attention module (pyramid spatial channel (PSC) attention) to perform feature recalibration, and through the PSC attention to model the interdependence among convolution feature channels. We join the PSC attention module in the activating and back-freezing strategy to search for one of the most important parts of the network for extraction and optimization. Experiments on various large-scale datasets demonstrate that the proposed method can achieve substantially better performance for improving the ConvNets representation power than the other state-of-the-art deep models.

Research on the Optimization of Multi-Class Land Cover Classification Using Deep Learning with Multispectral Images

With the advancement of artificial intelligence, deep learning has become instrumental in land cover classification. While there has been a notable emphasis on refining model structures to improve classification accuracy, it is imperative to also emphasize the pivotal role of data-driven optimization techniques. This paper presents an in-depth investigation into optimizing multi-class land cover classification using high-resolution multispectral images from Worldview3. We explore various optimization strategies, including refined sampling strategies, data band combinations, loss functions, and model enhancements. Our optimizations led to a substantial increase in the Mean Intersection over Union (mIoU) classification accuracy, improving from a baseline of 0.520 to a final accuracy of 0.709, which represents a 35.2% enhancement. Specifically, by optimizing the classic semantic segmentation network in four key aspects, we improved the mIoU by 15.5%. Further improvements through changes in data combinations, sampling methods, and loss functions led to an overall 17.2% increase in mIoU. The proposed model optimization methods enabled the OUNet to outperform the baseline model by providing more precise edge detection and feature representation, while reducing the model parameters scale. Experimental evidence shows that in the application of multi-class land surface classification, increasing the quantity and diversity of samples, avoiding data imbalance issues, is equally valuable for improving overall classification accuracy as it is for enhancing model performance.

Small-disturbance input-to-state stability of perturbed gradient flows: Applications to LQR problem

This paper studies the effect of perturbations on the gradient flow of a general nonlinear programming problem, where the perturbation may arise from inaccurate gradient estimation in the setting of data-driven optimization. Under suitable conditions on the objective function, the perturbed gradient flow is shown to be small-disturbance input-to-state stable (ISS), which implies that, in the presence of a small-enough perturbation, the trajectories of the perturbed gradient flow must eventually enter a small neighborhood of the optimum. This work was motivated by the question of robustness of direct methods for the linear quadratic regulator problem, and specifically the analysis of the effect of perturbations caused by gradient estimation or round-off errors in policy optimization. We show small-disturbance ISS for three of the most common optimization algorithms: standard gradient flow, natural gradient flow, and Newton gradient flow.

On regularization schemes for data-driven optimization based on Cressie–Read divergence and CVaR

On regularization schemes for data-driven optimization based on Cressie–Read divergence and CVaR

Dynamic Decoupling Method Based on Motor Dynamic Compensation with Application for Precision Mechatronic Systems

Motors are widely employed in mechatronic systems, especially in precision multiple degrees of freedom motion systems. In most applications, the dynamic equation between the motor instruction and the actual driving force is simplified as a constant. Subsequently, the static decoupling method can be utilized to design the feedback controller. However, in high-precision mechatronic systems, motor dynamics cannot be neglected, and the static decoupling performance is compromised due to discrepancies between motors. In this paper, a dynamic decoupling method is developed to improve the decoupling performance of the multiple-input multiple-output systems. The effects of transmission delays, motor dynamics, and discrepancies between different motors are taken into consideration in the dynamic decoupling method. Furthermore, a data-driven optimization method is developed to estimate the parameters of the dynamic decoupling controller. The effectiveness and superiority of the proposed method are demonstrated through numerical simulations. The experimental results show that the dynamic decoupling control method can achieve a 97.75% performance improvement at least compared to the static decoupling control method.

Designing a sustainable plastic bottle reverse logistics network: A data-driven optimization approach

Management of recovery options for plastic beverage containers involves some challenges. Most materials of used containers are recycled and are used to produce new products. The locations of collection facilities are the strategic decisions, affecting the amount collected and the total cost of a Reverse Logistics Network (RLN). In this study, a multi-objective (MO) optimization model is introduced to configure a plastic beverage container RLN, considering economic, environmental, and social objectives. This study also implements a scenario-based possibilistic approach to handle the uncertainty of the parameters. Furthermore, a data-driven fuzzy optimization framework is developed to consider the overlapping and multi-clustered characteristics of historical data samples. The application of the proposed method is demonstrated by considering a network in Vancouver, Canada. The numerical results reveal that the optimal configuration of the RLN resulting from the proposed MO model exhibits significant sensitivity to fluctuations in costs, demands, and the prioritization of the objective functions. Additionally, the proposed data-driven framework can incorporate decision makers' preferences when tuning the conservatism degree of uncertain parameters and the preference level of different objectives of the MO model. Moreover, the developed data-driven algorithm can reduce over-conservatism by 14% and guarantee the feasibility of optimal solutions compared to other data-driven strategies.

DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation

In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411–1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.