Problem Of Distribution Research Articles

APPLIED MODIFIED EXPONENTIAL APPROACH METHOD TO DETERMINE THE OPTIMAL SOLUTION

PT. IGM distributes vaccines to several cities within and outside West Kalimantan. Distribution can be carried out directly or through CV. XYZ. To maintain vaccine quality, an effective and efficient vaccine management plan is required, especially for storage and distribution, to prevent any deviations in these processes This is done to ensure the vaccine’s potency remains intact until it is ready for use. Distribution routes are chosen to be as efficient as possible. Therefore, this article discusses the application of the transportation method to manage vaccine distribution and minimize distribution costs. The distribution problem is formulated into a mathematical model and solved using the modified exponential approach method. This method is improvement on the improved Exponential Approach, focusing on the determination of initial solution and table revisions. Allocation is based on selecting cells with the smallest reduced cost entries. Based on research findings, PT IGM distributes vaccines to CV. XYZ, Pontianak and Kuburaya in amounts of 209.000 units, 151.000 units and 310.000 units, respectively. CV. XYZ distributes vaccines to Ketapang, Singkawang, Sintang and Bengkayang in amount of 40.000 units, 55.000 units, 45.000 units, and 9.000 units, respectively.

Distributed observers for linear systems with communication noises over Markovian switching topologies

This paper studies the distributed state estimation problem for a linear system that is jointly observable with the consideration of communication noises and Markovian switching topologies. To address this issue, a network of observers is proposed, where each observer only measures partial system output, which may not be sufficient to observe the complete state of the system. Subsequently, all observers within the network collaborate by sharing their estimated states with their neighbours through the network, facilitating a cooperative reconstruction of the entire state at each local observer. To better reflect the communication environment, we consider a situation in which communication links may fail and rebuild over time, and communication noises may occur when observers exchange information through Markovian switching topologies. Parameters of observers are designed properly, under which each observer fulfills the estimation task in the mean square sense. Illustrative examples to demonstrate the effectiveness of the distributed observers are also provided.

A reinforcement learning based memetic algorithm for energy-efficient distributed two-stage flexible job shop scheduling problem

Given the increasingly severe environmental challenges, distributed green manufacturing has garnered significant academic and industrial interest. This paper addresses the distributed two-stage flexible job shop scheduling problem (DTFJSP) under time-of-use (TOU) electricity pricing, with the objective of minimizing both makespan and total energy consumption costs (TEC). To tackle the problem, a hybrid memetic algorithm (HMA) is proposed. Initially, a three-tier vector encoding scheme and three population initialization strategies are devised. Subsequently, global search operators are tailored to the problem’s characteristics, and seven local search algorithms based on Q-learning are introduced. Additionally, an energy-saving operator is incorporated. Finally, orthogonal experimental design is employed to set algorithm parameters and validate the efficacy of some components. The numerical experimental results demonstrate that the proposed local search operator and energy-saving strategy are effective. Furthermore, the HMA exhibits superior diversity, breadth, and distribution compared to VNS, CMA, and NSGA-II, thereby validating the efficacy of the specialized designs of the HMA in addressing the DTFJSP.

Multi-Armed Bandit Approaches for Location Planning with Dynamic Relief Supplies Allocation Under Disaster Uncertainty

Natural disasters (e.g., floods, earthquakes) significantly impact citizens, economies, and the environment worldwide. Due to their sudden onset, devastating effects, and high uncertainty, it is crucial for emergency departments to take swift action to minimize losses. Among these actions, planning the locations of relief supply distribution centers and dynamically allocating supplies is paramount, as governments must prioritize citizens’ safety and basic living needs following disasters. To address this challenge, this paper develops a three-layer emergency logistics network to manage the flow of emergency materials, from warehouses to transfer stations to disaster sites. A bi-objective, multi-period stochastic integer programming model is proposed to solve the emergency location, distribution, and allocation problem under uncertainty, focusing on three key decisions: transfer station selection, upstream emergency material distribution, and downstream emergency material allocation. We introduce a multi-armed bandit algorithm, named the Geometric Greedy algorithm, to optimize transfer station planning while accounting for subsequent dynamic relief supply distribution and allocation in a stochastic environment. The new algorithm is compared with two widely used multi-armed bandit algorithms: the ϵ-Greedy algorithm and the Upper Confidence Bound (UCB) algorithm. A case study in the Futian District of Shenzhen, China, demonstrates the practicality of our model and algorithms. The results show that the Geometric Greedy algorithm excels in both computational efficiency and convergence stability. This research offers valuable guidelines for emergency departments in optimizing the layout and flow of emergency logistics networks.

Phenomenal consciousness and moral status: taking the moral option

ABSTRACT Intuitively, there is a close link between moral status and phenomenal consciousness. Taking the link seriously can serve as the basis of a proposal that appears to have a surprising number of theoretical benefits. This proposal is the moral option, according to which moral status is partly determinative of phenomenal consciousness, and phenomenal consciousness is sufficient for possession of a moral property I refer to as “moral status.” I argue for this view on the basis of its ability to shed light on the distribution problem of determining which systems are phenomenally conscious. I explicate it by drawing on a theory of attributions of phenomenal consciousness, and considering its relationship to positions on the metaphysics of consciousness. I defend it against a series of objections, including three based on previous theories of the link between phenomenal consciousness and moral status. The moral option should, in my view, be a serious contender among theories of phenomenal consciousness.

Topological Design of a Nanosatellite Structure with Optimal Frequency Responses Filled by Non-Uniform Lattices

Traditional structural forms are difficult to meet the lightweight requirements of subsequent spacecraft for load-bearing structures. In the aerospace industry, filling structure with lattices is a popular approach to reduce the weight of a spacecraft. However, this design strategy has deficiencies in the spatial distribution of lattice cells as well as its affection on the mechanical properties. In this study, a two-step topology optimization technique is proposed to solve the spatial distribution problem of nanosatellite. Firstly, an entire nanosatellite box composed of panels which filled with uniform lattices is sent to the vibration test to obtain the frequency data. Then, a finite element (FE) model of the nanosatellite structure which contains the same uniform lattices is built and validated with the obtained frequency data above. For the subsequent calculation of topology optimization. An equivalent model of the verified FE model is established by replacing the lattice cells in the sandwich layer with equivalent fictional elements. Subsequently, a topology optimization problem under the mass constraints is formulated for maximize the nature frequency, and a new light weighted nanosatellite which filled with non-uniform lattices is established by applying the density mapping method and the previous topology configuration result. By separating the design problem of nanosatellite into two steps, the proposed optimization design method achieves the maximum frequency design under the weight constraint. Furthermore, the frequency is also guaranteed to be around the nature frequency. The results reveal that the mass of the new structure with non-uniform lattices is reduced by 50.32% and the frequency is increased by 1.19%. An important technical importance and application value of this proposed technique is that it improves the performance and design efficiency of the load-bearing structures of a nanosatellite, and this method has significant technical significance and application value.

Adaptive Centroid-Connected Structure Matching Network Based on Semi-Supervised Heterogeneous Domain

Heterogeneous domain adaptation (HDA) utilizes the knowledge of the source domain to model the target domain. Although the two domains are semantically related, the problem of feature and distribution differences in heterogeneous data still needs to be solved. Most of the existing HDA methods only consider the feature or distribution problem but do not consider the geometric semantic information similarity between the domain structures, which leads to a weakened adaptive performance. In order to solve the problem, a centroid connected structure matching network (CCSMN) approach is proposed, which firstly maps the heterogeneous data into a shared public feature subspace to solve the problem of feature differences. Secondly, it promotes the overlap of domain centers and nodes of the same category between domains to reduce the positional distribution differences in the internal structure of data. Then, the supervised information is utilized to generate target domain nodes, and the geometric structural and semantic information are utilized to construct a centroid-connected structure with a reasonable inter-class distance. During the training process, a progressive and integrated pseudo-labeling is utilized to select samples with high-confidence labels and improve the classification accuracy for the target domain. Extensive experiments are conducted in text-to-image and image-to-image HDA tasks, and the results show that the CCSMN outperforms several state-of-the-art baseline methods. Compared with state-of-the-art HDA methods, in the text-to-image transfer task, the efficiency has increased by 8.05%; and in the image-to-image transfer task, the efficiency has increased by about 2%, which suggests that the CCSMN benefits more from domain geometric semantic information similarity.

Optimization Algorithms for Sustainable Operation of Multi-Unit Hydropower Plants

The work presented in this article concerns numerical studies on optimization methods used for the sustainable utilization of the energy potential of water, converting it into electricity in a hydropower plant equipped with more than one unit. These methods allow for maximization of production in given hydrological conditions, leading to the balanced, lossless, and environmentally friendly use of the renewable energy source that is water. Methods are selected from three groups, i.e., analytical, enumeration, and randomized. The results of calculations of optimal points of selected test functions carried out using the Broyden–Fletcher–Goldfarb–Shanno Limited-Memory Version (L-BFGS-B), Explicit Complete Enumeration (ECE), and Genetic Algorithm (GA) methods provided basic information on the features of these methods. Based on these tests, the GA method was selected to solve the problem of the optimal load distribution in a hydropower plant equipped with three identical hydro units. The defined optimization problem consisted of finding a configuration of hydro units in operation that would guarantee the maximum efficiency of the power plant under the imposed hydrological conditions. During the numerical studies, a number of calculations were performed to identify the impact of procedures and parameters characteristic of the optimization methods on the obtained results. Particular attention was paid to the GA method and penalty functions, enabling the elimination of results from the area of prohibited solutions.

IMPROVING THE EFFICIENCY OF ENERGY COMPANIES BY OPTIMISING THE DISTRIBUTION OF COSTS FOR MAINTENANCE AND REPAIR OF ELECTRICAL EQUIPMENT

At present, there is a high emergencies probability in the electric power system as an electrical equipment failures result caused primarily by the objectively existing ageing and its service life significant exhaustion. Emergencies arising from electrical equipment failures lead to the dynamic stability violation of power systems and power system load nodes by voltage, cascading accidents and, as a result, technological processes disruption of consumer enterprises, which is accompanied by significant losses. One of the ways to prevent sudden failures or minimise the risk of their occurrence is to ensure rational and efficient operation management and reduce the operating equipment cost without risking a decrease in the power system reliability. To solve the optimal distribution problems of power company operating costs, taking into account the objectively existing limiting factors, a generalised balance sheet model of cost allocation by electrical equipment groups and units is proposed, which is a total costs function of an operating factors number. Mathematical models for calculating the electrical equipment share participation coefficients in the costs formation for its repair and maintenance for each of the identified factors have been obtained. A linguistic mathematical model for determining generalised estimates of the electrical equipment units and groups significance degree in the repair and maintenance costs distribution formation, taking into account the main operational factors, has been proposed. An algorithm and software for modelling the modes of the electric power system with an electrical connections scheme of electric networks with a 35-330 kV voltage have been developed to assess the operation risk index and power company repair and maintenance costs distribution. The estimated power company repair and maintenance costs distribution according to the current technical equipment characteristics and power system subsystem modes was performed.

Challenging (mis)understandings of labour productivity for levelling-up: a broader research agenda for regional development

ABSTRACT Policy work typically conflates measures of real and nominal labour productivity, using them interchangeably, often leading to misdiagnoses of the causes of regional disparities, with potentially flawed policy as a response. The Levelling Up White Paper is no exception, reflecting a fundamental misunderstanding of what is being measured and why it matters. This article makes two contributions to the literature on this point. First, it shows theoretically and empirically that there is no necessary correlation between real and nominal labour productivity for either sectors or regions. Secondly, it builds and extends an industry-sector typology to argue that interregional nominal labour productivity differences are best understood within an augmented post-classical power-over-rents framework, where the ability to capture value is critical. We trace the consequences of this for understanding the industrial mix in high nominal productivity regions and the effect on local prices and real wages. While we suggest some potential measures for rebalancing economic activities across regions, we argue that levelling up of living standards is a problem of distribution as much as efficiency. It requires moving beyond the obsession with attempting to directly target misdiagnosed differences in regional labour productivity, and rather engaging with regional development holistically and sustainably.

Low-carbon scheduling strategy for electric vehicles considering carbon emission flow and dynamic electricity prices

As the global environmental pollution problem intensifies, the carbon reduction transformation of the power system is urgent. In order to solve the problem of unclear carbon flow and distribution in the operation of the power grid, as well as the mismatch between static time-of-use electricity prices and peak and valley periods in the scheduling of electric vehicle charging loads, a multiperiod dynamic electricity price guidance strategy based on carbon emission flow theory is proposed. First, based on the accurate power flow results of the power system, a complex power distribution matrix of the power system is constructed to obtain the distribution of the power generated by the generator units in each node of the network. Then, the Monte Carlo random sampling method is used to simulate the load situation of electric vehicles in a disordered charging state. A mathematical model based on the carbon trading model is established to minimize the load difference at the grid end and maximize the cost of charging on the user side. Finally, the proposed ordered charging method with a multiperiod dynamic electricity pricing strategy is compared with unordered charging, and considering the participation of electric vehicles in carbon trading, this strategy effectively reduces the peak valley difference between the power grid and user charging costs.

Gradient adaptive data augmentation and locally compensated feature fusion network for strip steel surface defect classification based on long tailed distribution

Abstract Defect classification is a crucial aspect of strip production. The existing methods for strip steel surface defect classification still require improvement due to the multi-category nature and complexity of defects, as well as the long-tailed distribution characteristics of the defect dataset. In this paper, a gradient-adaptive data augmentation and locally compensated feature fusion network for strip steel surface defect classification is proposed. Firstly, we put forward a gradient-adaptive data augmentation method based on the number of category samples to solve the long-tail distribution problem, which uses gradient-adaptive sample expansion to avoid increasing inter-class variation and preserves the probabilistic information contained in the category distribution through an average-based undersampling operation of the category samples. Secondly, a locally compensated feature fusion network is proposed, which is the first method to increase the multi-scale perception capability of the network using local compensation, mimicking the human behaviour of observing complex image patterns, which is compensating the feature information of the global image with the help of the feature information from the local image. The method not only improves the model’s ability to perceive defects, but also mitigates the negative effects of image compression. The experimental results on one public dataset and three private datasets demonstrate that the proposed method achieves significant classification performance improvement. Thus, it is founded that our method provides a promising solution for the strip surface defect classification problem.

Kasus Pendayagunaan Sumber Daya Pendidikan

Education is an important sector in the development of a country, and effective utilization of educational resources plays a major role in achieving these goals. However, in Indonesia, the utilization of educational resources still faces various challenges, especially related to the unequal distribution of resources between urban and remote areas. This study aims to analyze the utilization of educational resources in Indonesia, both in terms of the quality of educators, educational facilities, and available technology. The research method used is a qualitative approach with descriptive analysis to describe the current conditions and existing challenges. The results of the study show that despite government efforts, there is still a large gap in access to education between developed and disadvantaged areas. Assistance programs such as the School Operational Assistance Fund (BOS) and the Smart Indonesia Program (PIP) have not fully reached areas in need. The quality of educators is also a major obstacle, especially in the 3T (frontier, outermost, and disadvantaged) areas, where many teachers have not received adequate training. In addition, the problem of uneven distribution of educational infrastructure is a major obstacle in creating a conducive learning environment. This study suggests the need for equal distribution of education budgets, improvement of teacher quality through continuous training, and more transparent and efficient management of existing education resources. The implementation of educational technology is also expected to improve the quality of learning in areas with physical limitations. In the long term, more inclusive and locally data-based policies will strengthen Indonesia's education system. This study is expected Keywords: Educational Resources, Utilization, Quality of Educators Education is an important sector in the development of a country, and effective utilization of educational resources plays a major role in achieving these goals. However, in Indonesia, the utilization of educational resources still faces various challenges, especially related to the unequal distribution of resources between urban and remote areas. This study aims to analyze the utilization of educational resources in Indonesia, both in terms of the quality of educators, educational facilities, and available technology. The research method used is a qualitative approach with descriptive analysis to describe the current conditions and existing challenges. The results of the study show that despite government efforts, there is still a large gap in access to education between developed and disadvantaged areas. Assistance programs such as the School Operational Assistance Fund (BOS) and the Smart Indonesia Program (PIP) have not fully reached areas in need. The quality of educators is also a major obstacle, especially in the 3T (frontier, outermost, and disadvantaged) areas, where many teachers have not received adequate training. In addition, the problem of uneven distribution of educational infrastructure is a major obstacle in creating a conducive learning environment. This study suggests the need for equal distribution of education budgets, improvement of teacher quality through continuous training, and more transparent and efficient management of existing education resources. The implementation of educational technology is also expected to improve the quality of learning in areas with physical limitations. In the long term, more inclusive and locally data-based policies will strengthen Indonesia's education system. This study is expected

Perturbation Theory Methods for the Semiconductor Plasma Diode Simulation

A technique for solving a nonlinear singularly perturbed boundary value problem of predicting the state of an electron-hole plasma in the active region of p-i-n (plasma) diodes is considered. The problem is formulated for system of equations of the electron and hole currents continuity and the Poisson's equation. The solution to the problem is sought by the asymptotic method of boundary corrections. It is shown that boundary corrections play a key role in forming the electric field in the active region of the diode. Under certain conditions, boundary corrections demonstrate non-monotonic behavior. The presence of boundary corrections makes it possible to solve problems of heat distribution in the semiconductor device under study, problems of the influence of external electromagnetic fields on electron-hole plasma, etc. A series of computer experiments were conducted.

Optimization of cargo distribution for high-speed freight trains to overcome strong wind conditions

The high-speed freight train (HSFT) is an emerging freight transportation mode that can play a significant role in the development of remote cities. In this study, we propose an optimization method for cargo distribution to prevent HSFT from overturning in strong winds. The multibody dynamics (MBD) HSFT model is built and imposed with aerodynamic loads derived through the computational fluid dynamics method. Based on the MBD simulation results, the prediction model of overturning safety for HSFT is established using gradient-boosting decision tree to make the value of cargo height continuous. Consequently, a nonlinear programming model for the cargo distribution problem is built and solved. The MBD simulation results reveal that the outer side crosswind is good for large vehicle velocity and the vehicle in the rear position of HSFT is safer. The case study demonstrates that the proposed optimization method can decrease the overturning risk for HSFT by more than 10% compared with the even distribution scheme. This research will contribute to making the loading plan for the rail company, ensuring the operational safety of HSFT in the windy area.

Variational graph neural network with diffusion prior for link prediction

Recently, Graph neural networks(GNNs) has achieved tremendous success in a variety of fields. Many approaches have been proposed to address data with graph structure. However, many of these are deterministic methods, therefore, they are unable to capture the uncertainty, which is inherent in the nature of graph data. Various VAE(Variational auto-encoder)-based approaches have been proposed to tackle such problems. Unfortunately, due to the simple a posterior and a prior assumption problems of such methods, they are not well suited to handle uncertainty in graph data. For example, VGAE(Variational graph auto-encoder) assumes that the posterior and prior distributions are simple Gaussian distributions, which can lead to overfitting problems when incompatible with the true distributions. Many methods propose to solve the posterior distribution problem, but most ignore the effect of the prior distribution. Therefore, in this paper, we proposed a novel method to solve the Gaussian prior problem. Specifically, in order to enhance the representation power of the prior distribution, we use the diffusion model to model the prior distribution. We incorporate the diffusion model into VGAE. In the forward diffusion process, noise is gradually added to the latent variables, and then the samples are recovered by the backward diffusion process. To realize the backward diffusion process, we propose a new denoising model which predicts noise by stacking GCN(Graph Convolution Network) and MLP(Multi-layers Perceptron). We perform experiments on different datasets and the experimental results demonstrate that our method obtains state-of-the-art results.

Problems of the Russian economy in 2023–2024: the catch–up development trap, parallel imports, distribution of national income and development of competition

The authors of the article focused on the current problems of the Russian economy in 2023–2024, such as: import substitution and the catch–up development trap, parallel import, insufficiently efficient distribution of national income and the need to develop competition in the domestic Russian market. The authors revealed the prerequisites, current state and impact on the general state of the country’s economy of all the above problems. It was shown that catch–up development, import substitution and parallel import are effective tools for solving a number of economic problems arising under the influence of both economic and political historical conditions. The catch–up development of the Russian economy does not allow us to fully effectively solve the problems of efficient distribution of national income, poverty, demography, as well as consistently develop domestic competition. The authors concluded that solving the entire complex of the identified problems of the Russian economy requires a scientifically based systems approach, the best theoretical basis for which, of course, is the work of domestic scientists in the field of systemic economic theory.

Linear quadratic control and estimation synthesis for multi‐agent systems with application to formation flight

AbstractThis paper concerns the optimality problem of distributed linear quadratic control in a linear stochastic multi‐agent system (MAS). The main challenge stems from MAS network topology that limits access to information from non‐neighbouring agents, imposing structural constraints on the control input space. A distributed control‐estimation synthesis is proposed which circumvents this issue by integrating distributed estimation for each agent into distributed control law. Based on the agents' state estimate information, the distributed control law allows each agent to interact with non‐neighbouring agents, thereby relaxing the structural constraint. Then, the primal optimal distributed control problem is recast to the joint distributed control‐estimation problem whose solution can be obtained through the iterative optimization procedure. The stability of the proposed method is verified and the practical effectiveness is supported by numerical simulations and real‐world experiments with multi‐quadrotor formation flight.

Optimization Strategies for Modern Transportation and Distribution Network

In the contemporary logistics landscape, it is a challenge to figure out the transportation and distribution problem, especially between the factory and the market. Given the increasing consumer expectation for fast delivery, the need for highly effective distribution strategies is more pressing than ever because the manufacturers and retailers have to minimize costs while maximizing efficiency. This paper aims to devise a method to optimize for costs and efficiency, and has certain reference value for how to reduce costs and improve efficiency in the freight industry. The current approach only considers the cost of products, but does not consider the cost occurring from the transportation distance. Therefore, this paper puts forward a model that considers both transportation distance and costs in optimization.

Kernel Stein Discrepancy on Lie Groups: Theory and Applications.

Distributional approximation is a fundamental problem in machine learning with numerous applications across all fields of science and engineering and beyond. The key challenge in most approximation methods is the need to tackle the intractable normalization constant present in the candidate distributions used to model the data. This intractability is especially common for distributions of manifold-valued random variables such as rotation matrices, orthogonal matrices etc. In this paper, we focus on the distributional approximation problem in Lie groups since they are frequently encountered in many applications including but not limited to, computer vision, robotics, medical imaging and many more. We present a novel Stein's operator on Lie groups leading to a kernel Stein discrepancy (KSD) which is a normalization-free loss function. We present several theoretical results characterizing the properties of this new KSD on Lie groups and its minimizer namely, the minimum KSD estimator (MKSDE). Properties of MKSDE are presented and proved, including strong consistency, CLT and a closed form of the MKSDE for the von Mises-Fisher, the exponential and the Riemannian normal distributions on . Finally, we present several experimental results depicting advantages of MKSDE over maximum likelihood estimation.