Problem Of Distribution Research Articles

Designing a new sustainable healthcare network considering the COVID-19 pandemic: Artificial intelligence-based solutions

The COVID-19 pandemic, which is still spreading its new mutations all around the world, is considered a healthcare challenge around the world. The best approach to forestall this pandemic is to avoid exposure to the virus. Therefore, medical protective equipment is essential for fighting this pandemic. This underlines the chief role of having a sustainable supply chain network (SCN) for producing and distributing personal protective equipment to avoid shortage and augmenting costs. The reality, however, is that the COVID-19 pandemic leads to many developments in countries and this study is another step for these purposes. This research developsa multi-period, multi-objective, multi-echelon, and multi-product medical protective equipment sustainable SCN considering the production, distribution, allocation, and inventory with “risk pooling” strategy effect with the aim of filling the existing gaps in health SCN research during the COVID-19 pandemic. By applying the risk pooling strategy, lower inventory levels or higher service levels can be achieved without increasing inventory costs. This model explores the possibility of lateral transshipments between distribution centers, as a way to increase the reliability of SCN performance. We model a new production, inventory, distribution, location, and allocation problem and consider four objectives for our suggested model (i) minimizing total SCN costs, (ii) minimizing environmental effects, (iii) minimizing social impacts, and (iv) maximizing the reliability of demand delivery. The proposedmodel simultaneously examines all three dimensions of sustainability (economic, environmental, and social) as well as the reliability of demand delivery. Considering all of these decisions and assumptions brings the studied problem closer to reality. We employ various algorithms for solving our developed model with different sizes: the improved version of the augmented ε-constraint (AUGMECON2) algorithm for small and medium-sized problems and two meta-heuristic algorithms, i.e., Multi-Objective Whale Optimization Algorithm (MOWOA) and Multi-Objective Variable Neighborhood Search (MOVNS) algorithm, for large-sized ones. The Taguchi approach is used to tune the parameters of meta-heuristic algorithms, and a comparison is performed using four evaluation metrics: Mean Ideal Distance (MID), Number of Pareto Solutions (NPS), Maximum Spread (MS), and Spread of Non-Dominance Solution (SNS). Proposed solving methods for the studied problem and making comparisons between them are another innovation of this study. A couple of numerical examples are provided to illustrate the applicability of the presented solution methods. Finally, sensitivity analysis for problem parameters is performed to validate our suggested model. Our study reveals the superiority of the MOWOA over the other algorithms.

ACHIEVED RESULTS AND PERSPECTIVES FOR FURTHER DEVELOPMENT OF SMALL AND MEDIUM-SIZED ENTERPRISES: STATISTICAL FINDINGS AND ANALYSIS

Small and medium-sized enterprises are a key part of every national economy and represent a factor of economic and social stability. In particular, entrepreneurial organizations are recognized as drivers of economic development, because they encourage the introduction of new technologies and have a direct impact on the level of investments and aggregate demand in terms of macroeconomic indicators. Also, this type of organizations contributes to solving the problem of unemployment and unequal distribution of wages. The need for strategic support and development of small and medium enterprises at the state level is permanent. In the past, these entities have been hit by the pandemic caused by the Covid-19 virus, inflation and digitization. The subject of the paper is the results review of the small and medium-sized enterprises development in Serbia, as well as the perspective of their further growth. Taking into account the effect of quality standards on business results, the connection of small and medium-sized enterprises with scientific and research institutions, but also the connection between the same organizations on the market, the authors propose certain improvements for their development. The key elements are based on the adequate and correct diagnosis of the mentioned connections. The research sample was purposive, which included companies in each of the 5 regions of the Republic of Serbia (n=112). The importance of quality standards and cooperation with faculties and scientific research institutions was established. It has been proven that the biggest obstacles in the development of the business of small and medium-sized enterprises are administrative procedures, as well as fiscal appropriation, and that the biggest impact on future business prospects is the adopted quality standard, investment in modern equipment, innovation, new knowledge and the introduction of new production lines.

Development of Rice Distribution Model to Support Food Security in East Java Province

This study aims to analyze problems in food distribution from upstream to downstream and the efficiency of the distribution values of the food chain, and formulate an efficient food distribution system model in Pacitan Regency. A mixed method, which combines quantitative and qualitative analyses, was used. The first objective was analyzed with the fishbone diagram, the second objective with the value chain model, and the third objective with the Matrix of Alliances and Conflicts: Tactics, Objectives, and Recommendations (MACTOR). This study used primary and secondary data obtained from observations, surveys, in-depth interviews, and focus group discussions. Respondents consisted of stakeholders in agriculture and distribution channels, namely farm laborers, landowners, wholesalers, warehousing, brokers, agents, retailers, bankers / financial institutions, and information providers. The objectives associated with product flow included transportation, risk, price, distribution, profit. The results show that there are problems in the four areas of production, post-harvest, trading system, and institution. There are three food distribution systems in Pacitan East Java but the most efficient model is farmers-millers-retailers-consumers. Although farmers must allow more time to reimbursement, prices are more affordable for consumers. The results of stakeholder analysis found that the stakeholders with the highest competitiveness are retailers, agents, information and warehousing, while those with the lowest are farm laborers and landowners. The results of the convergence show that close relationship is observed between farm laborers, collectors, warehousing and landowners in Order One 1; between warehouses, landowners, and collectors in Order Two 2; and between agents and collectors in Order Three 3. This shows that collectors have the strongest potential to play the central role in the agricultural chain.

Dual-Path Neural Network based on Deep Features and Transfer Learning for Bearing Fault Diagnosis

The safe operation of mechanical equipment is a basic requirement in the industrial manufacturing process. As an important component of mechanical equipment, rolling bearings are prone to unforeseen failures due to long-term operation under complex conditions. The occurrence of failures, no matter how big or small, will cause economic losses. Therefore, reliable diagnosis of rolling bearings is crucial. Aiming at the problem of insufficient feature recognition of convolutional neural networks under strong noise background, a deep feature extraction network integrating multiscale convolutional neural network (MSCNN) and bidirectional gated recurrent unit (BiGRU) is proposed. MSCNN and BiGRU are used to extract multiscale features and temporal features from noisy vibration signals respectively, and different weights are assigned to the fused features through the attention mechanism module to achieve important feature selection. Furthermore, in order to solve the problem of different feature distributions between the source domain and the target domain under variable working conditions, transfer learning is introduced in the proposed deep feature extraction network. The difference in feature distribution between the source domain and the target domain is measured by a multi-level distance formula, and the measurement result is added to the loss function. The back propagation of the loss is used to achieve the alignment of feature distribution between the source domain and the target domain. Finally, the model uses the SoftMax function as a classifier for rolling bearing fault diagnosis. Experimental comparison and analysis show that the proposed model has good migration ability and achieves a higher fault identification accuracy.

Synthesis and characterization of Fe3O4@SiO2 core-shell nanoparticles for mirror and efficient magnetic compound fluid polishing of TC4 titanium alloy

To fully investigate the polishing performance of the Fe3O4@SiO2 core–shell abrasive for TC4 titanium alloy plate and solve the problem of uneven distribution of abrasive particles in magnetic compound fluid polishing (MCFP) and obtain ultra-smooth titanium alloy surface. In this paper, the functional Fe3O4@SiO2 core–shell abrasives were prepared and characterized by XRD, FTIR, VSM and TEM to demonstrate the successful encapsulation of SiO2 in Fe3O4 first. Second, a new polishing device was built to perform polishing experiments. Four different fractions of Magnetic compound fluid (MCF) were configured and utilized in polishing experiments for MCFP of TC4 titanium alloy to verify the polishing properties. Finally, the contact model between abrasive particles and workpiece is established and the material removal mechanism is discussed. The results show that SiO2 was successfully coated on Fe3O4. The polishing results show that the MCF containing the core–shell structure had the best polishing effect. The surface roughness Ra decreased from 0.201 μm to 0.025 μm after 20 min of polishing. The surface roughness of TC4 titanium alloy with an initial roughness Sa of 0.248 μm is reduced to 0.023 μm after polishing by Fe3O4@SiO2 core–shell abrasive. It is shown that the application of core–shell abrasive to process titanium alloys can obtain smooth and defect-free surfaces with good polishing performance. The surface quality of titanium alloy can be improved effectively by using core–shell abrasive, and a new idea for polishing titanium alloy is proposed. This study plays an important role in solving the problem of uneven particle distribution in MCFP. These results pave the way for further research into the application of core–shell abrasives in precision machining and expand the application scope of particles with core–shell structure.

Harnessing the Power of Optimization: Vehicle Routing Problems in Logistics and Distribution

Harnessing the Power of Optimization: Vehicle Routing Problems in Logistics and Distribution

Solving distributed assembly blocking flowshop with order acceptance by knowledge-driven multiobjective algorithm

In the era of Industry 4.0, industrial artificial intelligence technologies make production planning and scheduling systems more flexible. A new distributed assembly blocking flowshop problem with order acceptance and scheduling decisions (DABFSP_OAS) was investigated in this paper. Specifically, three objectives—the makespan, total energy consumption (TEC), and total profit (TP)—were addressed simultaneously. To address this problem, we established a knowledge-driven non-dominated sorting genetic algorithm-II (KDNSGAII). First, three initialization schemes based on the problem-specific property were introduced to generate diverse initial population. Then, to accelerate the convergence process, we developed multiple Pareto-based crossover and mutation operators. In addition, two novel destructive reinsertion strategies based on product and job sequence length were implemented to enhance the development ability of the algorithm. Finally, the designed strategies were evaluated. Comparisons and discussions showed that the KDNSGAII outperformed the other state-of-art multi-objective algorithms in solving DABFSP_OAS.

AGN-201K educational and research reactor benchmark analyses by McCARD Monte Carlo calculations

In this study, a benchmark model for the AGN-201K educational and research reactor was newly developed based on its Final Safety Analysis Report data, with any unclear or missing specifications obtained from different AGN-201 reactors. The AGN-201K benchmark model was verified for criticality problems at various critical rod positions, the approach-to-criticality experiment problem, the fine rod worth measurement problem, and the flux distribution problem. The model was evaluated by comparing experimental values with calculated results by the McCARD Monte Carlo neutronic transport code, with the McCARD results of the AGN-201K benchmark showing good agreement with the experimental results. The most noteworthy point is that the recently developed McCARD TDMC (Time-Dependent Monte Carlo) transient module was successfully demonstrated through AGN-201K transient benchmark analyses. It is also noted that the reactor periods from McCARD results were very similar to the experimental results, confirming the suitability of TDMC analysis for realistic applications. Furthermore, the new AGN-201K benchmark model is expected to serve as a practical and useful tool for verifying and validating newly developed neutronic codes through various steady-state and transient analyses.

A topological network connectivity design problem based on spectral analysis

How to improve network connectivity and which parts of the network are vulnerable are critical issues. We begin by defining an equal distribution problem, in which supplies are distributed equally to all nodes in the network. We then derive a topological network connectivity measure from the convergence speed, which is the second minimum eigenvalue of a Laplacian network matrix. Based on the equal distribution problem, we propose a method for identifying critical links for network connectivity using the derivative of the second minimum eigenvalue. Furthermore, we develop a network design problem that maximizes topological connectivity within a budget creating strengthening network links. The problem is convex programming, and the solution is global. Furthermore, it can be converted into an identical semidefinite programming problem, which requires less computational effort. Finally, we test the developed problems on road networks in the Japanese prefectures of Ishikawa and Toyama to determine their applicability and validity.

Global supply chain flow planning for Chinese manufacturing under the BRI: An SFG-DRO method

The Belt and Road Initiative (BRI) has fostered the free flow of commodities and optimal distribution of resources for the global manufacturing supply chain (GMSC), meanwhile confronted with risks and uncertainties. This paper proposes the SFG-DRO method, which can well solve the flow distribution problem of GMSC under the BRI with uncertainties. The proposed method combines trend prediction from the stochastic frontier gravity (SFG) model with 1-Wasserstein fuzzy set-based distributionally robust optimization (DRO). Additionally, this paper integrates the principles of the column and constraint generation (C&CG) algorithm with the particle swarm optimization (PSO) algorithm to handle the above two-stage nonlinear problems efficiently. The results show that the SFG-DRO distribution method significantly reduces transportation costs by 25% and enhances security. By examining flow distribution across three key BRI channels, this paper identifies underexploited markets along the southward channel and substantial market potential with security risks along the northeast sea-land channel, proposing relevant improvement suggestions.

Method for solving distributional problems of mathematical programming under conditions of fuzzy initial data

The object of this study is a large class of mathematical programming problems under conditions of uncertainty of initial data. The formulated object generates a subclass of problems of rational distribution of a limited resource under conditions of initial data described in terms of fuzzy mathematics. The conventional, standardly used method for solving such problems is based on optimization on average. To obtain such a solution, it is sufficient to replace all fuzzy initial data with their modal values in the analytical description of the mathematical model of the corresponding problem. To solve the resulting deterministic problem, one can use known methods of mathematical programming. However, the results of such a solution can be used in practice if the carriers of fuzzy parameters are specified compactly, that is, the intervals of possible values of fuzzy parameters of the problem are small. Otherwise, the implementation of this solution may lead to unpredictably large losses. Other alternative approaches are based on the use of insufficiently informative estimates of the best or worst possible values of fuzzy parameters of the problem. These circumstances make the statement of the problem and the objective of the study relevant: devising a method for solving the problem of rational distribution of a limited resource under conditions of fuzzy initial data. To solve the stated problem of rational distribution of a limited resource, a productive idea of constructing the proposed general optimization method under conditions of uncertainty of the initial data has been constructively implemented. In this case, the initial problem is reduced to a clear problem of optimizing a complex criterion constructed on the basis of the objective function of the initial problem and a set of membership functions of its fuzzy parameters. An example of solving the problem has been considered, leading to a solution that is better than that obtained on the basis of the modal values of the fuzzy parameters of the problem

The equitable non-split domination number of graphs

Finding a group of dominant servers is a necessary step towards optimising service provisioning to all clients in the complex world of server-client networks. This crucial decision rests on the non-split dominance number in the graph architecture. We present the idea of an equitable non-split domination number in graphs to address the possible problem of unequal task distribution among servers. When the vertices of a dominating set, Dens, have the same colour in an equitable colouring scheme, the set is said to be equitable. The induced subgraph ⟨V −D⟩ also maintains its connectivity. In particular, the application of equitable non-split domination numbers to graphs like the Triangular Grid, Antiprism, Wheel, and Complete Graphs is explored in this study.

Inverse problem for thermal radiation distribution on turbine blade under quartz lamp irradiation

Due to the strong time-varying characteristics and complex geometry of aerospace components, rapid changes in the distribution of radiative heat flux on the test surface are often required during non-uniform aerodynamic heating tests. However, it takes time to adjust the geometric parameters of the lamp array, which cannot meet the requirement for rapidly changing radiative heat flux distribution. To address this issue, a new method for calculating radiant heat flux and a fast linear analysis method of quartz lamp power are proposed which can calculate radiant heat flux distribution of complex surface and meet the need of timeliness and rapidity in radiant heat flux distribution, making it more suitable for engineering applications. Through numerical verification under single lamp and quartz lamp array, the maximum difference between the theoretical analysis method and the Monte Carlo method is less than 2.25% under single lamp, and less than 5% under quartz lamp array. Finally, turbine blade model and plane model are taken as research objects to verify the feasibility and reliability of the fast linear analysis method of quartz lamp power. The results show that the relative average error of the calculated quartz lamp power is 5.86% and 14.83%, respectively, compared with the actual power. This provides a reference and basis for the rapid simulation design of thermal radiation environment during the experiment.

An adversarial-based domain generalization method for the health evaluation of axial piston pumps

Abstract The axial piston pump is the power component in hydraulic systems and evaluating its health status is of great importance to the safe operation of hydraulic systems. Discharge pressure signals are common monitoring signals for axial piston pumps, but it is difficult to obtain satisfactory health evaluation results by directly using raw discharge pressure signals since the degradation information lies in some specific frequency bands. Furthermore, the axial piston pump often operates under different operating conditions and most existing deep learning models have low diagnostic accuracies due to the problems of insufficient degradation data and different data distribution. This paper proposes an adversarial-based domain generalization (DG) method by integrating time-frequency analysis and data augmentation, to accurately predict the health status of axial piston pumps under unknown working conditions. First, discharge pressure signals under various operating conditions are decomposed by using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and effective intrinsic mode functions (IMFs) are selected to train multiple convolutional neural network (CNN) models. Second, a novel data augmentation method based on the modified discrete cosine transform-composite spectrum (DCS) algorithm is introduced to fuse the IMFs from different domains and generate pseudo data sets. Finally, the adversarial training is adopted between the real data and the pseudo data to capture domain-generalized features. The discharge pressure signal of an actual axial piston pump at different health levels were collected on a test bench to verify the effectiveness of the proposed method. Results indicate that the proposed method has a higher prediction accuracy than the comparative methods.

An improved prairie dog optimization algorithm integrating multiple strategies and its application

Aiming at the problems in prairie dog optimization (PDO), such as uneven population distribution at initialization, slow convergence, imbalance between global exploration and local exploitation, and the tendency to get trapped in the local optimum, this study proposes an Improved prairie dog optimisation algorithm integrating multiple strategies (IMSPDO). Firstly, the population is initialized using spatial pyramid matching (SPM) chaotic mapping combined with improved random opposition-based learning (IROL) to solve the problems of uneven distribution and poor diversity of the population. Secondly, the prey escapes energy formula mentioned in the harris hawks optimization (HHO) is introduced to achieve the smooth transition between the exploration phase and the exploitation phase, balancing the algorithm’s global exploration capability and local exploitation capability. Additionally, the idea of the particle swarm optimization (PSO) is applied to enhance the global optimization capability of the algorithm. Finally, the ideas of simulated annealing (SA), polynomial mutation and Cauchy mutation are also introduced to improve the ability that individuals to jump out of the local optimum. The performance of the improved algorithm is verified on a set of 21 classical benchmark functions and 8 CEC2020 test functions. The proposed IMSPDO is also evaluated against original PDO, and six other commonly used algorithms. The result of the Wilcoxon rank-sum test shows that there is a significant difference between the selected algorithms and IMSPDO. Furthermore, 3 engineering examples are used to further test the superiority of IMSPDO in dealing with real-world problems.

Feasibility analysis of the Sanchez-Pomraning method to treat the particle size distribution in particle-dispersed fuel

The non-uniform size of particles appears in the particle-dispersed fuel generally, but this phenomenon is usually neglected. This study aims to evaluate the feasibility of the Sanchez-Pomraning method to solve the particle size distribution problem in particle-dispersed fuel. The Pu-rich agglomerate phenomenon in the MOX fuel and manufacturing tolerance in the Fully Ceramic Microencapsulated (FCM) fuel are calculated by ALPHA-DH code, which implements the Sanchez-Pomraning method based on the MOC transport calculation and improved subgroup resonance calculation. Numerical results show that the Sanchez-Pomraning method has the capability to treat the particle size distribution effect accurately, where the differences between the Sanchez-Pomraning method and the Monte Carlo method are generally lower than 160 pcm. For the Pu-rich agglomerate phenomenon, the results show that ignoring the Pu-rich agglomerate effect introduces the reactivity difference of around 600 pcm, and ignoring the size distribution of the Pu agglomerate will cause the reactivity of up to 200 pcm in the extra-high or extra-low Pu enrichment. The more discrete size distribution and the smaller Pu-spots, the more significant influence will occur in the MOX fuel. For the FCM fuel, the influence brought by the TRISO particle size distribution can be ignored when only TRISO particles are filled. However, ignoring the size distribution when TRISO particles and poison particles are both added into the FCM fuel introduces the differences reaching 600–1000 pcm. These results demonstrate the importance of considering the particle size distribution for analyzing the MOX fuel and FCM fuel in practice. And the Sanchez-Pomraning method is reliable to analyze the effect of particle size distribution.

Literature Review on Fresh Products Cold Chain Logistics and Distribution Issues

In this paper, the literature on the concepts of fresh product cold chain logistics, the container operation problems related to cold chain transport, product order picking, distribution and the integration of the two problems are sorted out to provide theoretical references for the further study of the transport and distribution problems of fresh product cold chain logistics. Through combing the literature at home and abroad, the current fresh product cold chain logistics transport and distribution mainly exists in the container intermodal transport research object is single, mostly focusing on the railway container centre station; order distribution problems related research is less, vehicle path problem solving algorithms and related mathematical models are less; order picking and distribution of the joint research is less, and most of them stay in the theoretical stage, the method of landing is more difficult and other problems.

The method of distributing the costs of growing perennial non-bloodless grasses by types of products

The introduction of modern technologies for growing perennial grasses in feed production has caused the need to improve the methodology for allocating costs by product type. The article describes the current method of distributing the costs of cultivation. The problems of distribution of these costs are revealed. It is proposed to open two analytical accounts for the sowing of a grass mixture of perennial uncovered grasses for a more accurate distribution of costs in the future, in the year of sowing: «The cost of tillage and sowing of perennial grasses », «The cost of caring for crops and harvesting perennial grasses». A new method of cost allocation by types of perennial herb products has been developed.

Multi-scale large kernel convolution and hybrid attention network for remote sensing image dehazing

Remote sensing (RS) image dehazing holds significant importance in enhancing the quality and information extraction capability of RS imagery. The enhancement in image dehazing quality has progressively advanced alongside the evolution of convolutional neural network (CNN). Due to the fixed receptive field of CNN, there is insufficient utilization of contextual information on haze features in multi-scale RS images. Additionally, the network fails to adequately extract both local and global information of haze features. In addressing the above problems, in this paper, we propose an RS image dehazing network based on multi-scale large kernel convolution and hybrid attention (MKHANet). The network is mainly composed of multi-scale large kernel convolution (MSLKC) module, hybrid attention (HA) module and feature fusion attention (FFA) module. The MSLKC module fully fuses the multi-scale information of features while enhancing the effective receptive field of the network by parallel multiple large kernel convolutions. To alleviate the problem of uneven distribution of haze and effectively extract the global and local information of haze features, the HA module is introduced by focusing on the importance of haze pixels at the channel level. The FFA module aims to boost the interaction of feature information between the network's deep and shallow layers. The subjective and objective experimental results on on multiple RS hazy image datasets illustrates that MKHANet surpasses existing state-of-the-art (SOTA) approaches. The source code is available at https://github.com/tohang98/MKHA_Net.

Optimal distribution of limited resource in a context of uncertainty

Object of research: methods of rational distribution of limited resource in a context of uncertainty. It is assumed that the system consists of a set of independently functioning elements, and effectiveness of these elements depends on the level of investment. The complexity of solving the problem using traditional methods depends on and is determined by the type of analytical description of production functions of the system elements. Significant difficulties arise if parameters of production functions are uncertain quantities, specified, for example, in terms of fuzzy mathematics. This circumstance emphasizes the relevance of studying approaches to solving the set problem of resource distribution for the case when its parameters are not clearly defined, and this circumstance also determines the goal of the work. Problems arising from the goal consist in developing mathematical models and methods for rational resource distribution for the main types of production functions with their parameters being fuzzy numbers of (L –R) type. To solve these problems, a method is proposed that transforms the original fuzzy problems into clear ones that can be solved using standard constrained optimization technologies. An important result of the research consists in the fact that the proposed method is universal, that is, it is applied in the same way to solve problems of resource distribution in systems wherein production functions of the elements can be of arbitrary nature. Refs.: 11 titles.