Different Types Of Constraints Research Articles

Multi-objective green vehicle scheduling problem considering time window and emission factors in ship block transportation

Logistics distribution is one of the main sources of carbon dioxide emissions at present, and there are also such distribution problems in the shipbuilding process. With the increasing attention paid to environmental problems, how to effectively reduce the energy consumption of block transportation and improve the utilization rate of resources in the factory is the key problem that China’s shipbuilding industry needs to solve at present. This article considers the time windows for block transportation tasks, as well as the self-loading constraints of different types of flat cars, and establishes an optimization model that minimizes the empty transport time and energy consumption of the flat cars as the optimization objective. Then, an Improved Genetic Whale Optimization Algorithm is designed, which combines the cross and mutation ideas of genetic algorithms and proposes a whale individual position updating mechanism under a mixed strategy. Furthermore, the performance and computational efficiency of the algorithm are verified through comparative analysis with other classical optimization algorithms on standard test examples. Finally, the shipyard’s block transportation example proves that the energy-saving ship block transportation scheduling method can effectively improve the efficiency of shipbuilding enterprise’s block transportation and reduce the energy consumption in the block transportation process. It proves the engineering practicality of the green dispatching method proposed in this paper, which can further provide a decision-making method for shipyard managers.

Many-objective joint optimization of computation offloading and service caching in mobile edge computing

The computation offloading problem in mobile edge computing (MEC) has received a lot of attention, but service caching is also a research topic that cannot be ignored in MEC. Due to the limited resources available on the Edge Server (ES), a wise computation offloading and service caching policy must be formulated in order to maximize system offload efficiency. In this paper, a many-objective joint optimization computation offloading and service caching model (MaJOCOSC) is designed. The model takes into account the limited computing and storage resources of ES, the delay and energy consumption constraints of different types of tasks, and multiple processing modes of user tasks, and sets delay, energy consumption, task hit service rate, service cache balancing, and load balancing as the five optimization objectives of MaJOCOSC. Meanwhile, a non-dominated sorting genetic algorithm (NSGAIII-ASF&WD) based on achievement scalar function (ASF) and the k-nearest neighbor weighted distance mating selection strategy is proposed for better solving the model. The ASF ensures that the given strategy performs well for each objective value, and the k-nearest neighbor weighted distance provides the user with a diversity of strategies. Simulation results show that NSGAIII-ASF&WD can obtain better objective values when solving the model compared with other many-objective evolutionary algorithms, and a suitable computation offloading and service caching strategy is obtained.

On-line posture measurement for automatic positioning of foldable-wing with priority-based multi-constraints

The digital assembly system is widely used in the automatic positioning of large aircraft components. Accurate posture measurement of the components remains challenging for high-precision assembly of the components with various engineering constraints. Existing methods often require repeated measurements of key features and are susceptible to the pronounced coupled effect, where various constraints mutually influence the solution of pose parameters. To enhance the efficiency and precision of posture measurement, this study proposes an on-line measurement method for the Foldable-wing Digital Assembly System. The key features and several reference points are measured once by the laser tracker. According to the assembly task sequence, a novel posture evaluation model with priority-based multi-constraints is established to calibrate these reference points which will be utilized as the new datum of posture measurement instead of the traditional key features. This model separately satisfies the various engineering constraints, such as coaxiality, gap, and basic positioning accuracy, in order of priority to avoid the coupling effect between different types of constraints. Consequently, features with higher priority can achieve higher accuracy without weight setting. Next, the reference points are automatically measured to calculate the posture parameters. Considering the uncertainty characteristics of the measuring instrument, the registration algorithm based on weighted total least squares adjustment is applied to reduce measurement uncertainty. Monte Carlo simulation results demonstrate the proposed method's ability to enhance positioning accuracy and reduce posture uncertainty, outperforming conventional methods. Finally, the practical application was successfully implemented, and all key features remained within tolerance.

Cognitive Inertia: Cyclical Interactions Between Attention and Memory Shape Learning

In explaining how humans selectively attend, common frameworks often focus on how attention is allocated relative to an idealized allocation based on properties of the task. However, these perspectives often ignore different types of constraints that could help explain why attention was allocated in a particular way. For example, many computational models of learning are well equipped to explain how attention should ideally be allocated to minimize errors within the task, but these models often assume all features are perfectly encoded or that the only learning goal is to maximize accuracy. In this article, we argue for a more comprehensive view by using computational modeling to understand the complex interactions that occur between selective attention and memory. Our central thesis is that although selective attention directs attention to relevant dimensions, relevance can be established only through memories of previous experiences. Hence, attention is initially used to encode features and create memories, but thereafter, attention operates selectively on the basis of what is kept in memory. Through this lens, deviations from ideal performance can still be viewed as goal-directed selective attention, but the orientation of attention is subject to the constraints of the individual learner.

Resampling Strategy in Sequential Monte Carlo for Constrained Sampling Problems

Sequential Monte Carlo (SMC) methods are a class of Monte Carlo methods that are used to obtain random samples of a high dimensional random variable in a sequential fashion. Many problems encountered in applications often involve different types of constraints. These constraints can make the problem much more challenging. In this paper, we formulate a general framework of using SMC for constrained sampling problems based on forward and backward pilot resampling strategies. We review some existing methods under the framework and develop several new algorithms. It is noted that all information observed or imposed on the underlying system can be viewed as constraints. Hence the approach outlined in this paper can be useful in many applications.

A Framework for Prioritization and Selection of Strategic Projects

Strategic projects are critical for the sustenance of an organization; however, they are subject to different types of constraints in terms of risk, management capabilities, resources, and political and cultural factors. Therefore, prioritizing and selecting these projects becomes a challenging task in each organization that focuses on multiple strategic projects spanning different sectors or industries. This article proposes a framework for analyzing strategic projects to rank heterogeneous projects across industries. The framework uses a multiple-criteria decision analysis model to prioritize strategic projects based on a set of criteria. The criteria were developed through clustering the relevant factors. Projects are first weighted with the criteria importance through intercriteria correlation method and then ranked using the technique for order of preference by similarity to ideal solution method. Vector weighting is then applied to rank the projects. The framework is demonstrated by its application to Qatar's real estate and transportation projects. This article contributes by providing a holistic framework and analytical methodologies to support decision-making for selecting one or more types of cross-industry strategic projects. Results show that the cross-industry project ranking obtained from the model is highly correlated with the ranks assigned by experts. The applicability of the model and the future research direction are also discussed.

Numerical and experimental investigations on shape optimization of submerged floating tunnels with a discrete adjoint method

The shape of the tube in submerged floating tunnels (SFTs) plays a critical role in determining their performance and safety in marine environments. In this study, a gradient optimization procedure based on the discrete adjoint method is performed to minimize the drag force under uniform currents action. The free form deformation approach is employed to parameterize the design variables. The physical mechanisms of the optimization process are revealed via unsteady numerical simulations and experimental tests. Moreover, the hydrodynamic performance of the tube with the optimal shapes subjected to a wave–current combination is also evaluated. The results show that the drag coefficient is significantly reduced through optimization by reducing the pressure amplitude in the negative-pressure area. Additionally, the lift oscillation is also suppressed to delay structural fatigue, owing to the strength reduction and increased distance of the wake vortex. The experimental analysis indicates the advantage of the optimal shape in reducing the loads under wave–current actions, accompanied by changes in the frequency distribution of the force and vortex structure. The elliptical shape of the SFT's tube provides significant advantages in drag reduction at high Reynold number. Suggestions on the shape design of the section of SFT are given according to different types of constraints.

An Arabic Probabilistic Parser Based on a Property Grammar

The specificities of Arabic parsing, such as agglutination, vocalization, and the relatively order-free words in Arabic sentences, remain major issues to consider. To promote its robustness, such parseing should define different types of constraints. Property Grammar (PG) formalism verifies the satisfiability of the constraints directly on the units of the structure, thanks to its properties (or relations). In this context, we propose to build a probabilistic parser with syntactic properties, using a PG, and we measure the production rules in terms of different implicit information and in particular the syntactic properties. We experimented with our parser on the treebank ATB, using the parsing algorithm CYK, and we obtained encouraging results. Our method is also automatic for implementation of most property types. Its generalization for other languages or corpus domains (using treebanks) could be a good perspective. Its combination with pre-trained models of BERT may also make our parser faster.

Supervised Video Cloth Simulation: Exploring Softness and Stiffness Variations on Fabric Types Using Deep Learning

Physically based cloth simulation requires a model that represents cloth as a collection of nodes connected by different types of constraints. In this paper, we present a coefficient prediction framework using a Deep Learning (DL) technique to enhance video summarization for such simulations. Our proposed model represents virtual cloth as interconnected nodes that are subject to various constraints. To ensure temporal consistency, we train the video coefficient prediction using Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM), and Transformer models. Our lightweight video coefficient network combines Convolutional Neural Networks (CNN) and a Transformer to capture both local and global contexts, thus enabling highly efficient prediction of keyframe importance scores for short-length videos. We evaluated our proposed model and found that it achieved an average accuracy of 99.01%. Specifically, the accuracy for the coefficient prediction of GRU was 20%, while LSTM achieved an accuracy of 59%. Our methodology leverages various cloth simulations that utilize a mass-spring model to generate datasets representing cloth movement, thus allowing for the accurate prediction of the coefficients for virtual cloth within physically based simulations. By taking specific material parameters as input, our model successfully outputs a comprehensive set of geometric and physical properties for each cloth instance. This innovative approach seamlessly integrates DL techniques with physically based simulations, and it therefore has a high potential for use in modeling complex systems.

Chance-constrained optimization for active distribution networks with virtual power lines

Active distribution networks (ADNs) experience uncertainties because of the high integration of distributed renewable energy resources (RESs). Stochastic analysis approaches are attracting increasing attention owing to the uncertain nature of RESs and load demands. This study proposes a chance-constrained optimal operation framework for ADNs considering virtual power lines (VPLs) solved via the second- order cone programming (SOCP) optimal power flow (OPF) problem. VPLs comprise virtual lines connected to each network’s energy storage systems (ESSs). If one ESS is charged, another ESS is discharged by the same amount. We reformulate the constraints into tractable forms using a simplified Z-bus sensitivity matrix and first-order Taylor expansion to solve via SOCP OPF because the chance constraints on voltage magnitudes are generally non-convex and intractable. The efficacy of the proposed approach was verified through its application to two modified IEEE 33-bus systems, each featuring different consumer units, including commercial, residential, and industrial units. The effectiveness of the proposed methods was demonstrated by comparing three different types of constraints: conventional hard constraints, soft constraints with penalties, and chance constraints. The results showed that although the chance constraints case had a slightly higher voltage violation rate, the network power loss was reduced by 3.4% compared to the conventional hard constraints case. Furthermore, modified 85-bus and 65-bus systems are carried out to verify the algorithm’s scalability. The proposed approach achieves a trade-off between voltage regulation and power loss minimization, with a voltage violation rate of 0.88% and a power loss reduction to 1.02%.

A Stochastic Control Approach for Constrained Stochastic Differential Games with Jumps and Regimes

We develop an approach for two-player constraint zero-sum and nonzero-sum stochastic differential games, which are modeled by Markov regime-switching jump-diffusion processes. We provide the relations between a usual stochastic optimal control setting and a Lagrangian method. In this context, we prove corresponding theorems for two different types of constraints, which lead us to find real-valued and stochastic Lagrange multipliers, respectively. Then, we illustrate our results for a nonzero-sum game problem with the stochastic maximum principle technique. Our application is an example of cooperation between a bank and an insurance company, which is a popular, well-known business agreement type called Bancassurance.

Adaptive formation control architectures for a team of quadrotors with multiple performance and safety constraints

AbstractIn this work, we propose a novel adaptive formation control architecture for a group of quadrotor systems, under line‐of‐sight (LOS) distance and relative distance constraints as well as attitude constraints, where the constraint requirements can be both asymmetric and time‐varying in nature. The LOS distance constraint consideration ensures that each quadrotor is not deviating too far away from its desired flight trajectory. The LOS relative inter‐quadrotor distance constraint is to guarantee that the LOS distance between any two quadrotors in the formation is neither too large (which may result in the loss of communication between quadrotors, for example) nor too small (which may result in collision between quadrotors, for example). The attitude constraints make sure that the roll, pitch, and yaw angles of each quadrotor do not deviate too much from the desired profile. Universal barrier functions are adopted in the controller design and analysis, which is a generic framework that can address system with different types of constraints in a unified controller architecture. Furthermore, each quadrotor's mass and inertia are unknown, and the system dynamics are subjected to time‐varying external disturbances. Through rigorous analysis, an exponential convergence rate can be guaranteed on the distance and attitude tracking errors, while all constraints are satisfied during the operation. A simulation example further demonstrates the efficacy of the proposed control framework.

$\Pi$-strategy for a differential game of pursuit with integral constraints of a generalized type

The paper investigates a differential game of simple pursuit, when the controls of two opposing players are subject to integral constraints of a generalized type. The generalization of the proposed restriction lies in the fact that it includes previously known restrictions such as integral, geometric, linear, exponential and their mixtures. In general, it includes 25 types of pursuit problems with such different types of constraints. To solve the pursuit problem under such generalized constraints, we propose a parallel pursuit strategy ($\Pi$-strategy for short) and find sufficient conditions for the solvability of this problem. At the end of the article, tables are provided that list each particular type of game, the conditions for its solvability, the resolving function (which determines the corresponding $\Pi$-strategy), and the time of capture.

How Important Are Functional and Developmental Constraints on Phenotypic Evolution? An Empirical Test with the Stomatal Anatomy of Flowering Plants.

AbstractQuantifying the relative contribution of functional and developmental constraints on phenotypic variation is a long-standing goal of macroevolution, but it is often difficult to distinguish different types of constraints. Alternatively, selection can limit phenotypic (co)variation if some trait combinations are generally maladaptive. The anatomy of leaves with stomata on both surfaces (amphistomatous) present a unique opportunity to test the importance of functional and developmental constraints on phenotypic evolution. The key insight is that stomata on each leaf surface encounter the same functional and developmental constraints but potentially different selective pressures because of leaf asymmetry in light capture, gas exchange, and other features. Independent evolution of stomatal traits on each surface imply that functional and developmental constraints alone likely do not explain trait covariance. Packing limits on how many stomata can fit into a finite epidermis and cell size-mediated developmental integration are hypothesized to constrain variation in stomatal anatomy. The simple geometry of the planar leaf surface and knowledge of stomatal development make it possible to derive equations for phenotypic (co)variance caused by these constraints and compare them with data. We analyzed evolutionary covariance between stomatal density and length in amphistomatous leaves from 236 phylogenetically independent contrasts using a robust Bayesian model. Stomatal anatomy on each surface diverges partially independently, meaning that packing limits and developmental integration are not sufficient to explain phenotypic (co)variation. Hence, (co)variation in ecologically important traits like stomata arises in part because there is a limited range of evolutionary optima. We show how it is possible to evaluate the contribution of different constraints by deriving expected patterns of (co)variance and testing them using similar but separate tissues, organs, or sexes.

Physics informed neural networks for electric field distribution characteristics analysis

Electric field calculations based on the Poisson equation have been widely used in high voltage and plasma technology. However, in practical applications, the electric field distribution in space is relatively complex, and the simulation technology based on the traditional method is often a simplification of reality, which leads to a large error between the simulation and the actual measured value. In the actual application process, due to the limitation of measurement methods, it is necessary to infer the electric field data at other locations in space according to the measurement results. Physics informed neural networks (PINNs) are introduced into the electric field calculation. PINNs are considered partial differential equation solvers based on deep neural networks. In this paper, 2D and 3D electric field distributions are discussed and compared with the finite element method. A method of dividing the dielectric distribution based on the sigmoid function is proposed, which can be effectively used to construct the spatial electric field model of the homogeneous dielectric. The combination of the data and physical model based on PINNs establishes a method to solve the inverse problem of the relative permittivity in the electric field. The results show that PINNs can calculate the distribution of the electric field according to the physical equations and different types of constraints and parameters.

An Energy Trading Framework for Interconnected AC–DC Hybrid Smart Microgrids

This paper proposes a three-stage energy trading framework (ETF) for interconnected AC-DC hybrid microgrids (IHMGs) in the presence of renewable distributed generators (DGs) and battery energy storage systems (BESSs). The proposed ETF aims at minimizing the operating cost of each microgrid (MG), and it includes three stages: a pre-trading stage, a trading stage, and a post-trading stage. The ETF determines the optimal hourly trading prices and powers while preserving the privacy of information for each MG. The proposed framework allows each MG to schedule its operation economically and reliably through optimizing its local resources (i.e., DGs and BESSs) as well as exchanging power with neighboring MGs and the main grid. The proposed ETF takes into account the internal topology and power flow constraints for different types of MGs (i.e., AC, DC, and AC-DC hybrid MGs). The proposed three-stage ETF is tested using a case study that includes different types of MGs with different types of loads and DGs. The effectiveness of the proposed ETF is verified through a comparison with the base-case system for both grid-connected and isolated modes of operation.

General Trends of the Camelidae Antibody VHHs Domain Dynamics

Conformational flexibility plays an essential role in antibodies' functional and structural stability. They facilitate and determine the strength of antigen-antibody interactions. Camelidae express an interesting subtype of single-chain antibody, named Heavy Chain only Antibody. They have only one N-terminal Variable domain (VHH) per chain, composed of Frameworks (FRs) and Complementarity Determining regions (CDRs) like their VH and VL counterparts in IgG. Even when expressed independently, VHH domains display excellent solubility and (thermo)stability, which helps them to retain their impressive interaction capabilities. Sequence and structural features of VHH domains contributing to these abilities have already been studied compared to classical antibodies. To have the broadest view and understand the changes in dynamics of these macromolecules, large-scale molecular dynamics simulations for a large number of non-redundant VHH structures have been performed for the first time. This analysis reveals the most prevalent movements in these domains. It reveals the four main classes of VHHs dynamics. Diverse local changes were observed in CDRs with various intensities. Similarly, different types of constraints were observed in CDRs, while FRs close to CDRs were sometimes primarily impacted. This study sheds light on the changes in flexibility in different regions of VHH that may impact their in silico design.

When increasing vegetable production may worsen food availability gaps: A simulation model in India

Translating agricultural productivity into food availability depends on food supply chains. Agricultural policy and research efforts promote increased horticultural crop production and yields, but the ability of low-resource food supply chains to handle increased volumes of perishable crops is not well understood. This study developed and used a discrete event simulation model to assess the impact of increased production of potato, onion, tomato, brinjal (eggplant), and cabbage on vegetable supply chains in Odisha, India. Odisha serves as an exemplar of vegetable supply chain challenges in many low-resource settings.Model results demonstrated that in response to increasing vegetable production 1.25-5x baseline amounts, demand fulfillment at the retail level fluctuated by + 3% to −4% from baseline; in other words, any improvements in vegetable availability for consumers were disproportionately low compared to the magnitude of increased production, and in some cases increased production worsened demand fulfillment. Increasing vegetable production led to disproportionately high rates of postharvest loss: for brinjal, for example, doubling agricultural production led to a 3% increase in demand fulfillment and a 19% increase in supply chain losses. The majority of postharvest losses occurred as vegetables accumulated and expired during wholesale-to-wholesale trade.In order to avoid inadvertently exacerbating postharvest losses, efforts to address food security through agriculture need to ensure that low-resource supply chains can handle increased productivity. Supply chain improvements should consider the constraints of different types of perishable vegetables, and they may need to go beyond structural improvements to include networks of communication and trade.

Sampling constrained continuous probability distributions: A review

AbstractThe problem of sampling constrained continuous distributions has frequently appeared in many machine/statistical learning models. Many Markov Chain Monte Carlo (MCMC) sampling methods have been adapted to handle different types of constraints on random variables. Among these methods, Hamilton Monte Carlo (HMC) and the related approaches have shown significant advantages in terms of computational efficiency compared with other counterparts. In this article, we first review HMC and some extended sampling methods, and then we concretely explain three constrained HMC‐based sampling methods, reflection, reformulation, and spherical HMC. For illustration, we apply these methods to solve three well‐known constrained sampling problems, truncated multivariate normal distributions, Bayesian regularized regression, and nonparametric density estimation. In this review, we also connect constrained sampling with another similar problem in the statistical design of experiments with constrained design space.This article is categorized under: Applications of Computational Statistics > Computational Mathematics Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Statistical and Graphical Methods of Data Analysis > Sampling

Why don’t we go outside? – Perceived constraints for users of urban greenspace in Sweden

Urban greenspace (UGS) is important for human wellbeing, particularly physical and mental health, and is claimed to support social cohesion. However, the expansion and densification of urban centres in recent decades has occurred largely at the expense of UGS. This risks its attractiveness for users. Although recent research has identified various factors that influence the use of UGS in different contexts, few studies have taken an explicit, bottom-up approach to understand which factors constrain willingness to use it. This study analyses responses from an online survey in Sweden (N = 2806) to identify the main constraints to UGS usage, and for whom these are constraints. Respondents could select from 22 different types of constraints – relating to incivilities, management, accessibility and availability, safety, and personal issues. Respondents could also provide comments to identify additional constraints. Incivilities were the most perceived problem. For example, litter was selected by 53% of respondents, while vandalism was selected by 24%. At the same time, many respondents perceived few or no constraints – 59% stated that nothing prevented them from using UGS, while 35% stated that they saw no problems. Safety-related and accessibility/availability constraints were not strongly identified although many respondents commented that UGS in Sweden is inadequate given the large number of users, and that the UGS that people want to use the most is often inaccessible without a car. Multiple binary logistic regression was used to investigate relationships between perceptions of constraints and fourteen predictor variables. Odds ratios were used to determine whether significant (p < 0.05) relationships were meaningful. Our findings show that different groups have starkly divergent perceptions of constraints relating to UGS. Several key factors – including age, self-reported nature-connectedness, distance to UGS, and frequency of use – were associated with a heightened likelihood of perceiving different groups of constraints. However, relationships between constraints and factors relating to environmental justice were not straightforward. These findings indicate the complexity of UGS planning challenges relating to densification, the New Urban Agenda and promotion of societal benefits, and a need to further integrate multiple user perspectives, especially of younger adults and infrequent users.