Constraint Method Research Articles

Strategical and tactical supply chain optimisation for smart production planning and control 4.0

Industry 4.0 (I4.0) technologies generate new opportunities for developing smart models, algorithms and tools to support supply chain (SC) production planning and control (PPC), or smart PPC (SPPC 4.0). Paired with opportunities, challenges arise in integrating sustainability and resilience in SC network design (SCND) according under I4.0. The main novelty of this paper is to propose two multi-objective models that integrate strategical and tactical PPC decisions into a sustainable-resilient SC under I4.0 towards SPPC 4.0. Sustainability is incorporated in terms of reducing costs and CO2 emissions, and a job creation factor. Resilience is incorporated in terms of contracting support suppliers. To solve the multi-objective models, the augmented epsilon constraint method (AUGMECON) was used. This algorithm minimises a target objective function while it treats the others as constraints. Our experiments indicate that AUGMECON is sensitive to the choice of epsilon values. Furthermore, a synthetic data generation tool called SR1-SR2_SynthDataGenerator was developed. This tool generates input datasets to validate and evaluate our models against benchmarks. A stochastic model (SR2) is also proposed that, with small datasets, takes 8.39 seconds to solve. The proposed models can be useful for industries that seek sustainable and resilient SCs to adapt to changing environments.

Research on elastic parameter inversion method based on seismic facies-controlled deep learning network

Deep learning has been widely applied in the field of geophysics, and existing research literature has demonstrated that intelligent geophysical inversion methods have high vertical resolution but low horizontal resolution. The reason lies in the fact that existing horizontal constraint methods mainly adopt convolutional models, without fully considering other prior information of seismic data. Within the same sedimentary unit, seismic response characteristics vary gradually due to similar lithology and geological characteristics. Therefore, the seismic facies information extracted from seismic data is integrated into deep learning network to enhance the horizontal prediction stability of the network. Firstly, according to the spatial and temporal characteristics of seismic data, a fusion network of three-dimensional convolutional neural network (3D-CNN), gated recurrent unit (GRU) and attention mechanism is established to improve the vertical resolution of inversion results. Then, seismic facies classification of the target layer is achieved by applying the K-means clustering method. Subsequently, to improve the horizontal resolution of the inversion results, seismic facies classification is transformed into temporal encoding data using the position coding theory in natural language processing, to form a seismic facies-controlled deep learning network. Finally, the deep learning network is trained and tested in the thin interlayer model and practical application adopting a semi-supervised learning method. The results indicate that incorporating seismic facies-controlled technology in the deep learning network can improve the horizontal resolution of the inversion results.

Multi‐objective and multi‐stage low‐carbon planning of park integrated energy system considering random outages from superior power grid

AbstractThis article proposes a multi‐objective and multi‐stage low‐carbon planning approach for park integrated energy systems (PIES) considering the impacts of random outages from the superior electrical grid. This approach incorporates optimal multi‐stage construction sequencing and stepped carbon emission trading to leverage the economic and low‐carbon benefits of long‐term planning. First, the islanding modes of PIES are described using four random variables: island type, duration, start time, and typical day of occurrence, from which islanding scenarios are generated based on scenario tree. Next, a multi‐objective planning model that considers both economics and reliability is constructed, with the objectives of minimizing the total lifecycle planning cost and the expected economic loss during islanding. The improved Normalized Normal Constraint (NNC) method is proposed to solve the multi‐objective planning problem. Then, the fuzzy membership function is used to determine the optimal compromise solution, resulting in a planning scheme that balances economic efficiency and supply reliability. Finally, simulations indicate that, at the cost of a slight increase in planning expenses, the proposed model significantly reduces the loss costs under islanding modes compared with single‐objective economic‐focused planning. Additionally, the improved NNC method can achieve a more uniform Pareto frontier compared with the conventional NNC method.

Improved NSGA2 algorithm for recovery of interrupted departure flights

To solve the problem of airline flight disruption caused by emergencies, the disrupted departing flights are restored, a dual-objective optimization model is constructed to minimize the total delay cost of airlines and minimize the total delay time of passengers. An adaptive non-dominated sorting genetic algorithm based on dominance intensity is designed, and three improved operations are proposed: a fast dominance sorting method, a new congestion distance and an adaptive elite retention strategy. The proposed algorithm is verified by the operation data of an airline at Fuzhou Airport. The experimental results show that compared with the traditional plan-first-serve method, the solution obtained by the proposed algorithm is greatly optimized. Compared with the constraint method, the solution time is generally lower than the constraint method and the solution result is close to the optimal result obtained by the constraint method. Compared with multi-objective optimization algorithms such as NSGA2 and MOEAD, the proposed algorithm shows better performance and can solve the problem effectively and efficiently, providing a basis for airlines to achieve optimized solutions.

EPVC: A Novel Initialization Approach of Visual-Inertial Integrated Navigation

Abstract The fusion of visual and inertial measurements in robotics community is growing in popularity since both of them have complementary perceptual information. Pre-initializing gyroscope bias and accelerometer bias of the Inertial Measurement Unit (IMU) is a critical issue to achieve a better fusion performance, and the metric scale is another crucial element to be estimated. Current mainstream loosely-coupled initialization methods are unstable as they do not incorporate IMU information into the visual structure from motion(SfM). In addition, the accuracy of the tightly-coupled methods is limited since they do not use visual observations to remove gyroscope bias and usually ignore them in close-form solution. In this paper, a visual-inertial initialization method which we refer to as epipolar plane normal vectors coplanarity (EPVC) constraint method is proposed to solve gyroscope bias. A step further, a novel analytical solution is presented to optimize other parameters. Comparing our proposed method with VINS-Mono and inertial-only optimization through the publicly available EuRoC dataset, the results demonstrate that our method outperforms the existing methods in estimating the gyroscope bias and scale factor, and with the increase of initialization time, the accelerometer bias error and gravity direction error have a clear diminishing tendency.

YPR-SLAM: A SLAM System Combining Object Detection and Geometric Constraints for Dynamic Scenes

Traditional SLAM systems assume a static environment, but moving objects break this ideal assumption. In the real world, moving objects can greatly influence the precision of image matching and camera pose estimation. In order to solve these problems, the YPR-SLAM system is proposed. First of all, the system includes a lightweight YOLOv5 detection network for detecting both dynamic and static objects, which provides pre-dynamic object information to the SLAM system. Secondly, utilizing the prior information of dynamic targets and the depth image, a method of geometric constraint for removing motion feature points from the depth image is proposed. The Depth-PROSAC algorithm is used to differentiate the dynamic and static feature points so that dynamic feature points can be removed. At last, the dense cloud map is constructed by the static feature points. The YPR-SLAM system is an efficient combination of object detection and geometry constraint in a tightly coupled way, eliminating motion feature points and minimizing their adverse effects on SLAM systems. The performance of the YPR-SLAM was assessed on the public TUM RGB-D dataset, and it was found that YPR-SLAM was suitable for dynamic situations.

Simulating noise and vibrations on passenger ships using Statistical Energy Analysis

Various sources aboard a passenger ship contribute significantly to noise and vibration concerns. Notably, fluctuating pressure loads on the hull can result in elevated noise levels within rooms near the propulsion. Expensive solutions of sound insulation such as floating floors are employed to reduce these levels. Nowadays, designing these treatments is mainly based on empirical studies. However, the limited availability of the ship, the laborious nature of experiments, and the possibility of solutions being oversized have only increased the method's constraints. Utilizing calculation methods can save time, reduce additional weight on the ship, and provide more tailored design solutions. A Statistical Energy Analysis (SEA) has been used as deterministic approaches are often not suitable for the frequency range of interest for full-scale structures. The validation process for the wave-based SEA method involves several steps. Initially, simple models are constructed to establish correlations between local airborne and structure-borne energy transmission using simple acoustic loads, as well as to assess the method's scope of application. Subsequently, this approach is applied to larger models, such as the aft full part of the ship, enabling the prediction of noise levels and energy transmission paths under real loads.

Research on compressor cascade flow field modeling method based on finite volume flux-informed neural network

For compressor cascade flow field modeling, there exists strong velocity shear in the leading edge separation flow, boundary layer, and wake, which leads to increased modeling errors. To improve the accuracy of the flow field modeling method, this paper introduces the concept of numerical flux from the finite volume method into the loss function to implement Euler equation physics-informed learning, and a finite volume flux-informed neural network (FVFI-net) is constructed. Selecting a high-load, large-turning-angle compressor cascade as the study object, a comparative analysis is conducted on the advantages and disadvantages of purely data-driven, weak physical constraint, and finite volume flux-informed methods in compressor cascade flow field modeling. The study found that compared to purely data-driven and weak physical constraint methods, FVFI-net can reduce the average error of aerodynamic parameters in the flow field by approximately 45.6% and 29.5%, respectively, at a 0° angle of attack. For the flow separation problem occurring at the suction side leading edge and the blade wake area caused by a 5° angle of attack, FVFI-net can effectively reduce modeling errors near the leading edge, in the wake region, and near the periodic boundaries, thus reducing the average error of the aerodynamic parameters of the flow field by about 49.2%and 31.3%, respectively, compared to pure data-driven and weak physical constraint methods.

Norm constraints pyramid for image dehazing

Dark channel prior-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on the accuracy of the assumptions used in the target scenes, which incurs color distortion and brightness reduction when the models are used for real-world hazy images.We propose a norm constraints pyramid framework to improve the generalization performance of dehazing. First, a local color adaptive correction approach is devised to ascertain whether there is any color bias and thereafter rectify it automatically. Furthermore, multiple norm constraint methods are developed to improve the transmission and accomplish the first image removal. Finally, a non-linear enhancement method is created via this restriction that precisely modifies the brightness of an image.Through extensive experiments, we demonstrate that our framework establishes the new state-of- the-art performance for real-world dehazing, in terms of visual quality assessed by no-reference quality metrics as well as subjective evaluation and downstream task performance indicator.

Emergency constraint-based CMIP6 predictions for future droughts on the Mongolian Plateau

Accurate predictions of future drought changes in the context of warming play an important role for both human social and ecological environments. This study aims to predict future droughts on the Mongolian Plateau using the latest Coupled Model Comparison Project dataset. First, the historical and future relationships of 17 Global Climate Models (GCMs) were constrained using the emergent constraints method to establish new predictions for future droughts under SSP2-4.5 and SSP5-8.5 scenarios. Based on two scenarios, spatial and temporal variations of future droughts on the Mongolian Plateau were analyzed. Future droughts will be worse compared to historical periods. In the SSP2-4.5 scenario, the future drought intensity shows an increasing trend, the frequency of drought gradually increases, and the spatial variation of both decreases gradually from the southwest to the northeast. Under the SSP5-8.5 scenario, the intensity and frequency of droughts increases from 2020 to 2060; from 2061 to 2100, the intensity and frequency of droughts show a decreasing trend. The spatial variation is uniform, with high values in the southwest and low values in the northeast.

Providing a Framework to Optimize the Sustainable Construction Material Supply Chain with the Possibility of Horizontal Transfers

The application of Supply Chain Management (SCM) principles has gained considerable attention across various industries to enhance operational efficiency and business performance. In the construction sector, SCM can guide managers in strategic planning and foster collaboration with suppliers, leading to more effective project execution. Despite its importance, limited research has focused on designing sustainable supply chains for construction materials under uncertain conditions. This study aims to bridge this gap by introducing a robust optimization framework that incorporates sustainable development criteria, specifically the minimization of harmful environmental effects and the reduction of worker unemployment. Additionally, the research introduces an innovative concept of horizontal transfers between downstream members, which not only reduces supply deficiencies but also indirectly increases profitability for these members. This article is a part of a Doctoral dissertation. It follows a two-step approach. First, suppliers of construction materials are ranked based on sustainable development criteria using a multi-criteria decision-making method. The best-ranked supplier is then integrated into a mathematical model for optimizing the supply chain design. Given the NP-hard nature of the problem, the CPLEX 12.1 solver with the epsilon constraint method is used to solve small-scale models. For larger-scale problems, three meta-heuristic algorithms are developed: multi-objective ant-lion, multi-objective gray wolf, and multi-objective dragonfly. The results show that while both the ant-lion and gray wolf algorithms deliver comparable average performance, the gray wolf algorithm excels in terms of solution time, making it more effective for real-time problem-solving. This makes the gray wolf algorithm the most efficient choice for addressing large-scale, multi-objective supply chain challenges. This research introduces a novel framework for designing sustainable construction supply chains under uncertain conditions. By focusing on criteria such as minimizing harmful environmental effects and reducing worker unemployment, this study addresses key aspects of sustainable development. Additionally, the innovative horizontal transfer mechanism and the insights into algorithmic performance provide valuable contributions to the field, especially for real-world applications in the construction industry.

Role of decoupling process on the configurations of compact stars induced by Thomas-Fermi dark matter with null complexity in f(T) gravity

Our goal in this work is to find an anisotropic solution for a self-bound compact object composed of dark matter with a null complexity factor in f(T)-gravity theory. We use a well-known gravitational decoupling via complete geometric deformation (CGD) technique to examine the role of decoupling parameters on the configuration of compact objects. Initially, we derive the null complexity condition for f(T)-gravity decoupled system which leads to a relation between gravitational potentials. Next, we apply the CGD approach to split the decoupled system into two subsystems. The initial system refers to a pure f(T) gravity system consisting of an isotropic fluid distribution, where the isotropy criterion is equivalent to the condition in Einstein's gravity. The solution of the first system is solved through the Vlasenk-Pronin space-time metrics while the second system associated with the deformation function is solved by the density constraints method by mimicking a new source with Thomas-Fermi dark matter density profile that generates the anisotropy in the decoupled system. The physical validity of the anisotropic solution is checked by the graphical analysis of the pressure, density, energy, and stability conditions. We have also shown the effect of torsion and decoupling parameters on the configuration of anisotropic compact objects. The energy exchange (ΔE) of fluid distribution is also discussed. We found that ΔE is positive throughout the stellar configuration, which implies that energy is effectively transmitted to the surrounding environment.

Stochastic optimal scheduling of a combined wind-photovoltaic-CSP-fire system accounting for electrical heat conversion

Aiming at the consumption problem caused by the increasing scale of wind power and photovoltaic (PV) grid-connected, a multi-energy co-generation system is constructed with wind power, PV, concentrated solar power (CSP), and thermal power; in addition, in order to reduce the impact of the prediction errors of wind power, PV, and loads on the system’s economic operation, the photovoltaic and thermal power plants are used to provide the system's backup capacity together, and the opportunity constraint model of the reliability of the backup capacity is established, so as to satisfy the system reliability constraints at a certain higher confidence level; finally, a sampling-based deterministic transformation method is introduced to simplify the model. The model is simplified by introducing a sampling-based deterministic transformation method of opportunity constraint; finally, a stochastic optimal dispatch model of the combined wind-photovoltaic-CSP-fire system, which accounts for the conversion of electricity and heat, is constructed with the objective of minimising the integrated operating cost of the combined system, and the effectiveness of the proposed model is analysed by simulation verification.

Effect of hydrogen constraints on predicting thermal conductivity of hydrocarbons in molecular dynamics simulation

Large overestimation has been reported while predicting the thermal conductivity of hydrocarbons using an all-atom force field model in molecular dynamics (MD) simulations. Although it has been guessed as resulting from the high-frequency vibration of the hydrogen atoms and hydrogen constraints method is suggested to be employed to reduce the deviation, how hydrogen constraints affect the heat conduction and local structure of hydrocarbons in MD simulation is still not fully understood. In this work, the effect of hydrogen constraints on the prediction of thermal conductivity of n-decane in MD simulations with the all-atom force field is studied. The results show that the deviation of the thermal conductivity of n-decane can be narrowed down by 72.48 % in simulations if the hydrogen constraints with a SHAKE algorithm is employed. The analysis of heat flux decomposition indicates that employing hydrogen constraints can reduce the contribution of transport term and non-bonded interactions to the heat flux, which in turn can help improve the accuracy while predicting the thermal conductivity in MD simulations. Furthermore, results of the vibrational density of states show that hydrogen constraints can dismiss the high-frequency vibration mode of molecules, thus effectively reducing the overestimation of thermal conductivity of hydrocarbon systems in MD simulations. The findings of this work sheds light on the molecular mechanism of heat transfer in hydrocarbon systems.

Configuration and optimisation of a green closed-loop supply chain with delivery time and green investment considering government subsidy under meta-heuristics algorithms

Consumers' environment consciousness, together with the fierce rivalries among companies, has attracted increasing attention from scholars and industries to the configuration and optimisation of green closed-loop supply chain. However, current studies of closed-loop supply chains rarely consider both the uncertainty from the market and the quality from the recycled used products. Therefore, this article constructs a green closed-loop supply chain total profit model that considers government subsidies, delivery times, green investment and the above uncertainties. Fuzzy chance constraint method is used to solve the uncertain demand; genetic and particle swarm optimisation algorithms are adopted to calculate the model. This study finds (1) enterprises can formulate flexible and adaptable strategies based on different demands; (2) their delivery time decisions rely on both consumer demand and their logistics development levels; (3) enterprises should adjust their delivery times and green investment decisions according to consumers' different sensitivities; (4) enterprises can increase profits by recycling products within a certain range of greenness; (5) for improving recycling and remanufacturing rates, the government can provide secondary subsidies to enterprises for product recycling within the green range of 0.35 ∼ 0.40. This study contributes to the optimisation of strategies developed by enterprises under conditions of subsidies and multiple uncertainties.

PathLAD+: Towards effective exact methods for subgraph isomorphism problem

The subgraph isomorphism problem (SIP) is a challenging problem with wide practical applications. In the last decade, despite being a theoretical hard problem, researchers designed various algorithms for solving SIP. In this work, we propose five main strategies and develop an improved exact algorithm for SIP. First, we design a probing search procedure to try whether the search procedure can successfully obtain a solution at first sight. Second, we design a novel matching ordering strategy as a value-ordering heuristic, which uses some useful information obtained from the probing search procedure to preferentially select some promising target vertices. Third, we discuss the characteristics of different propagation methods in the context of SIP and present an adaptive propagation method to make a good balance between these methods. Moreover, to further improve the performance of solving large graphs, we propose an enhanced implementation of the edge constraint method and a domain limitation strategy, which aims to accelerate the search process. Experimental results on a broad range of classic and graph-database benchmarks show that our proposed algorithm performs better than several state-of-the-art algorithms for the SIP.

Surface complexation of rare earth elements on goethite in sea-floor hydrothermal environment: Insight from first principles simulations

Deep-sea mud shows tremendous resource potential for rare earth elements (REEs) and its formation is closely associated with sea-floor hydrothermal activities. Iron (oxyhydr)oxides link the sources and sinks of REEs in sea-floor hydrothermal systems. However, the complexation mechanisms of REEs on iron (oxyhydr)oxides have not been well understood yet. In this study, by using the first principles molecular dynamics technique, we first calculated the pKa’s of surface groups on goethite (110) surface at elevated temperatures relevant to sea-floor hydrothermal systems and then evaluated the complexation structures and free energies of REEs on goethite (110) and (010) surfaces using the method of constraint by taking Sc3+, Y3+, and La3+ as model REE cations. The results show that REE complexation occurs in mildly acidic to neutral conditions. The most thermodynamically stable complexes of REEs are bidentate complexes on two neighboring FeOH sites on goethite (110) surface and tridentate complexes on two neighboring FeOH sites plus one Fe2OH site on goethite (010) surface. Sc3+ complexes match the goethite lattice and can be incorporated into the lattice. The stabilities of REE complexes increase with the distance from hydrothermal vents. Complexation of Y3+ is less favored on goethite compared to other REEs whereas Sc3+ prefers complexation on goethite (010) surface and La3+ exhibits similar stabilities on both (010) and (110) surfaces. The derived atomic level complexation mechanisms would be helpful for the interpretation of experimental data and the prediction of REEs’ behavior in the sea-floor. The findings presented here provide valuable insights into REEs fractionation and enrichment in deep-sea muds.

Integration of a parameter combination discriminator improves the accuracy of chlorophyll inversion from spectral imaging of rice

The PROSPECT model, widely employed for leaf radiation transfer analysis, relies heavily on input biochemical parameters to calculate spectral reflectance. This dependence often results in similar simulated spectra for different parameter combinations, which complicates the inversion of leaf chlorophyll content (Cab). To address this ill-posed problem, we enhanced the model's application by integrating a support vector machine (SVM)-based parameter combination discriminator with the Look-Up Table (LUT) constructed from the PROSPECT model. We marked samples in the LUT to reflect their closeness to measured parameters, facilitating the identification of reasonable versus unreasonable parameter combinations. The discriminator could effectively discriminate between reasonable and unreasonable parameter combinations, achieving accuracies of 0.894 and 0.888 in the training and test sets, respectively. The discriminator was then employed to refine the LUT, and an improved third-generation non-dominated ranking genetic algorithm (NSGA-III) was used to optimize the extreme learning machine. The inversion of rice Cab using the refined LUT and the NSGA-III demonstrated substantial improvements. The LUT was significantly improved after integration with the discriminator, yielding R2 and RMSE of 0.665 and 7.220 ​μg ​cm−2, respectively. The NSGA-III inversion, which utilized the “constraint method” with discriminator results as optimization objectives, achieved the best inversion accuracy, with R2 and RMSE values of 0.809 and 4.788, respectively. This study demonstrates that the effective use of a parameter discriminator can significantly enhance the accuracy of Cab inversion based on the PROSPECT model, offering a substantial advancement in addressing its inherent ill-posed challenges.

Adaptive Surface Normal Constraint for Geometric Estimation From Monocular Images.

We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric context in existing methods impedes their ability to accurately enforce the consistency between the different geometric properties, thereby leading to a bottleneck of geometric estimation quality. We therefore propose the Adaptive Surface Normal (ASN) constraint, a simple yet efficient method. Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints. By dynamically determining reliable local geometry from randomly sampled candidates, we establish a surface normal constraint, where the validity of these candidates is evaluated using the geometric context. Furthermore, our normal estimation leverages the geometric context to prioritize regions that exhibit significant geometric variations, which makes the predicted normals accurately capture intricate and detailed geometric information. Through the integration of geometric context, our method unifies depth and surface normal estimations within a cohesive framework, which enables the generation of high-quality 3D geometry from images. We validate the superiority of our approach over state-of-the-art methods through extensive evaluations and comparisons on diverse indoor and outdoor datasets, showcasing its efficiency and robustness.

A train trajectory optimization method based on the safety reinforcement learning with a relaxed dynamic reward

Train trajectory optimization (TTO) is an effective way to address energy consumption in rail transit. Reinforcement learning (RL), an excellent optimization method, has been used to solve TTO problems. Although traditional RL algorithms use penalty functions to restrict the random exploration behavior of agents, they cannot fully guarantee the safety of the process and results. This paper proposes a proximal policy optimization based safety reinforcement learning framework (S-PPO) for the train trajectory optimization, including a safe action rechoosing mechanism (SARM) and a relaxed dynamic reward mechanism (RDRM) combining a relaxed sparse reward and a dynamic dense reward. SARM guarantees that the new states generated by the agent consistently adhere to the environmental security constraints, thereby enhancing sampling efficiency and facilitating algorithm convergence. RDRM makes it easier for agents to obtain successful samples by relaxing time constraints, which also offers a better balance between exploration and exploitation. The experimental results show that S-PPO can significantly improve performance and obtain better train operation trajectories than soft constraint methods, and the convergence process is smoother. Finally, it was demonstrated that S-PPO exhibits good adaptability across various speed limit tracks.