Path Distribution Research Articles

Study on Multi-Objective Optimization of Logistics Distribution Paths in Smart Manufacturing Workshops Based on Time Tolerance and Low Carbon Emissions

In the Industry 4.0 environment, an ideal smart factory should be intelligent, green, and humanized, and the logistics transportation from raw materials to final products in the factory should be completed by smart logistics. In order to address the problems of low efficiency, poor workstation service satisfaction, high distribution costs, and non-greening during the logistics distribution processes in discrete smart manufacturing workshops are required. A mathematical model of optimized multi-objective green logistics distribution paths in a smart manufacturing workshop has been constructed in this study, with low costs, high efficiency, and workstation service satisfaction taken into consideration. Then, this mathematical model was solved with an improved ant colony optimization algorithm. A “time window span” was introduced in the basic ant colony optimization algorithm to prioritize the services to workstations with a relatively high urgency in material demand, with the aim of improving workstation service satisfaction. Lastly, in order to verify the effectiveness of the model and algorithm proposed in this study, a simulation experiment has been conducted on the workstation logistics distribution system in a smart manufacturing workshop to provide convincing evidence for optimizing workstation logistics distribution paths in workshops of discrete manufacturing enterprises.

Optimal ecological transition path of a credit portfolio distribution, based on multidate Monge–Kantorovich formulation

Optimal ecological transition path of a credit portfolio distribution, based on multidate Monge–Kantorovich formulation

Research on Cold Chain Logistics Distribution Path Optimization under the Application of Big Data

The perishable of fresh agricultural products and the improvement of people's requirements for food quality have put forward higher requirements for the distribution efficiency. This article for the cold chain of fresh agricultural products logistics distribution path optimization problem, in Chengdu shuangliu district A company agricultural products co., LTD., as A research object, using the particle swarm algorithm of the original raw agricultural products distribution path optimization design, calculate more reasonable distribution scheme, effectively improve the distribution efficiency, vehicle rate, and reduce the use of the vehicle, reasonable to reduce the logistics costs, ultimately improve the economic benefit of the enterprise and customer satisfaction, realize enterprise scientific logistics management.

Study on the Optimization of the Material Distribution Path in an Electronic Assembly Manufacturing Company Workshop Based on a Genetic Algorithm Considering Carbon Emissions

In order to solve the problems of high carbon emissions, low distribution efficiency and high costs related to the process of material distribution in manufacturing workshops, a multi-objective workshop material distribution path optimization problem model is established, and the model is solved using an improved genetic algorithm. The problem is processed using Gray code and crossover and variation operations with a genetic algorithm. To improve the search accuracy and convergence speed of the algorithm, an adaptive mutation method is proposed to enhance the diversity of the population and to achieve global optimal path objective finding. The improved algorithm is applied to workshop path multi-station logistics path planning, which effectively solves the transport path optimization and station solving problems in workshop logistics distribution, and the convergence speed and convergence accuracy of the algorithm are significantly improved. Finally, a simulation analysis is carried out on the optimization of the production material distribution of a smart gas meter workshop owned by K Company, which is an electronic assembly manufacturing company. We used MATLAB software for the case company logistics distribution route model for data analysis and solving. Due to the consideration of carbon emissions, we did not consider two kinds of experiments, which were two different cases of the optimal path. The experimental results verify that the distribution optimization scheduling model can meet the demands for immediate material distribution in the production workshop, which is conducive to improving material distribution efficiency, reducing logistics costs and achieving the goal of lowering carbon emissions. This optimization model has a certain utility in that in the current context of aiming for carbon neutral and carbon peaking, early low carbon distribution layout can reduce the environmental cost of the enterprise, making material distribution a more environmental economic path.

Особливості використання гуми як конструкційного матеріалу під час створення вузлів стикування систем термостатування

Explosive bolts are widely used as actuating devices in the spacecraft separation systems. Explosive bolt body is divided into parts as a result of engagement of the pyromixture placed inside. Activated explosive bolts have negative mechanical effect on the interface elements and sensitive electronic devices installed nearby owing to explosive behavior of the pyromixture combustion, generating shock front with high pressure and velocities, impacts and collisions of the structural units. Cumulative effect of the above factors on the separated objects is called pyroshock. For separation systems with increased requirements to external actions and cleanliness, authors developed a shear explosive bolt or pyrobolt, divided into parts, cutting the body walls in segments, which are set in motion by action of the pressure of gases, released as a result of pyrocartridge activation. The basic sources of pyroshock for these shear explosive bolts with segments are: combustion of pyromixture, internal impacts of structural units against the bolt body; cutting of body wall in segments, release of preliminary deformed interface after activation. Structural solutions are presented to reduce the pyroshock per each of the components. Vibration impulsive loading during pyromixture combustion is reduced by optimization of explosive quantity, finding its minimum to provide the reliable activation of the device. To reduce the impact on the explosive bolt elements and shock front interface the rubber gasket is installed in the path of shock wave distribution, partially disseminating and absorbing its kinetic energy. Damper, made of easily deformable aluminum alloy, is also installed to decrease the internal impact of the rod against the explosive bolt body. Functional testing of the device, using the pendulum suspension and measuring separation speed and vibration impulsive loading, showed that body parts of the shear explosive bolt with segments are separated without significant impact loads and discharge of high-temperature gases and debris, providing reliable separation of compartments and units without damaging the sensitive equipment. Obtained values of the mechanical momentum, I = 0,4÷0,7 N•s and shock load spectrum – g-load 1950 g at the frequency range up to 5000 Hz, meet the up-to-date requirements to pyrotechnical devices. Key words: explosive bolt, pyroshock, shock wave, pyrocartridge, high-temperature gases, damper.

Near-junction phonon thermal spreading in GaN HEMTs: A comparative study of simulation techniques by full-band phonon Monte Carlo method

Accurate thermal simulation is essential for the near-junction thermal management and electro-thermal co-design of GaN HEMTs. While various methods have been employed to simulate phonon thermal transport in GaN, a comprehensive evaluation of their performance and reliability has yet to be conducted. In this work, first-principle-based steady-state full-band phonon tracing Monte Carlo (MC) simulations are conducted to study the thermal spreading resistance in GaN HEMTs. The results of full-band MC serve as a standard against which the applicability, accuracy, and computational efficiency of three widely-used approaches to simulate the near-junction phonon transport in GaN are thoroughly examined. The simulation techniques compared in this study include MC simulations with empirical isotropic phonon dispersion (isotropic MC), MC simulations with gray-medium approximation (gray MC), and finite-element methods (FEM) with effective thermal conductivities (FEM with keff). It is found that isotropic MC largely overestimates the thermal resistance due to the empirical model’s overestimation of phonon mean free path (MFP) distributions. By selecting an appropriate average MFP, gray MC can approximate the full-band results well, but due to its inability to reflect the contributions of different phonon modes, discrepancies are inevitable for some geometric parameters. For FEM-based analysis, although the diffusive nature of Fourier’s law precludes the reproduction of channel temperature distributions, the influence of phonon ballistic effects on the junction temperature can be accurately reflected in the well-chosen effective thermal conductivities. The comparison highlights the importance of directly incorporating first-principles-calculated phonon properties into device thermal simulations, and the paper can provide a clearer understanding of near-junction thermal transport in GaN and can be useful for thermal simulations of GaN-based devices.

Distribution Path Segmentation Using Route Relocation and Savings Heuristics for Multi-Depot Vehicle Routing

This paper uses routing segmentation optimization for the planning of optimal distribution networks between urban depots and their respective customers. In this experiment, three steps are proposed in concession: search for the initial solution using local search properties, improve the solution using route relocation and perturb the solution using tabu search incorporating the savings heuristic. By applying multi-depot simultaneous deployment with ideal scheduling strategies and routing heuristics ensuring cost-optimal routing, the study presents an alternative to enhanced scheduling system optimization. Based on repopulation and sequential insertion algorithms, the initial solution is created, while route relocation and tabu swap mechanisms constitute the improvement strategy and perturbation. Test results comparing the proposed solution strategy to the previous genetic algorithm solution result in a better arrangement of route segregation aspects representing customer clusters. This strategy has proven to be more successful in optimizing route segregation than the original genetic algorithm solution. This demonstrates a significant improvement in route optimization.

Green city logistics path planning and design based on genetic algorithm.

Effective logistics distribution paths are crucial in enhancing the fundamental competitiveness of an enterprise. This research introduces the genetic algorithm for logistics routing to address pertinent research issues, such as suboptimal scheduling of time-sensitive orders and reverse distribution of goods. It proposes an enhanced scheme integrating the Metropolis criterion. To address the limited local search ability of the genetic algorithm, this study combines the simulated annealing algorithm's powerful local optimization capability with the genetic algorithm, thereby developing a genetic algorithm with the Metropolis criterion. The proposed method preserves the optimal chromosome in each generation population and accepts inferior chromosomes with a certain probability, thereby enhancing the likelihood of finding an optimal local solution and achieving global optimization. A comparative study is conducted with the Ant Colony Optimization, Artificial Bee Colony, and Particle Swarm Optimization algorithms, and empirical findings demonstrate that the proposed genetic algorithm effectively achieves excellent results over these algorithms.

A voltage waveform design method for shortening current response time of a coil

SummaryCoils are widely used in magnetorheological dampers, solenoid valves, and electron guns, where a quick response of the magnetic induction to the excitation voltage is needed. To achieve a quick response, the response time of the magnetic induction relative to the current and that of the current relative to the voltage should be shortened as much as possible. The latter is dominant in the total response time and can be shortened by using voltage control with an increased resistance or current control. The current time responses of the Helmholtz coils based on voltage control with an increased resistance and current control are measured. The results indicate that the current response times of the coils based on voltage control with resistances of 23.7 and 162 Ω and current control with a resistance of 23.7 Ω are 235.3, 42.3, and 62.1 μs, respectively. The current time responses of the voltage control are smooth, while those of the current control have large oscillations. Based on Kirchhoff's equilibrium equation, an overshoot voltage waveform design method is proposed to shorten the current response time and eliminate the current oscillations. The experimental results show that the current time response is smooth, and the current response time can be shortened by 91.8%, 54.4%, and 68.9% in comparison with those of the former three control cases. The demonstration experiments of the effect of the different control methods on the electron‐beam scanning path and hot spot distribution show that the scanning path and dwell duration are closer to the expected ones with the proposed method. The proposed method is of great value for improving the damping performance of a magnetorheological damper, the accuracy of the fluid injection of a solenoid valve, and the accuracy of the electron‐beam thermal assessment.

Improving Efficacy of Tsunami Warnings Along the West Coast of the United States

Tsunamis cause significant damage and loss of life, particularly for the nearest communities, where the tsunami may arrive in minutes. These local communities often do not receive an informed or timely alert under traditional warning pathways. In response, numerous tsunami early warning (TEW) algorithms have been developed with the goal of providing informed tsunami source characterization for use in rapid, localized warning. An overlooked aspect of TEW is the means that this crucial information is disseminated. Current operations focus heavily on the time an alert is issued from a warning center, however, that alert passes through multiple groups and agencies before it is conveyed to affected communities. This distribution path can create further delays and contributes to inconsistencies in the message timeliness and content. In this study, we provide the framework and advocate for the use of a rapid dissemination tool, that we call WaveAlert, that would leverage preexisting advances in earthquake early warning systems to provide timely, clear, and consistent alerts to the public by use of the MyShake EEW phone app. This proposed tsunami dissemination tool would be able to provide consistent, public facing tsunami alerts over the duration of the hazard with the added benefit of low message latencies and high spatial resolution in who can be targeted for messages. We illustrate the need for rapid alerting strategies through a retrospective look at the alerting process during the 2022 Tonga tsunami and through a modeled potential near-field Cascadia timeline example affecting the west coast of the US.

Multi-objective route optimization of urban cold chain distribution using electric and diesel powered vehicles

Increasingly serious urban road congestion and environmental pollution have led some cities to impose restrictions such as limiting certain types of vehicles and the areas in which they can operate and introducing subsidies and other incentives for electric refrigerated vehicles. As a result, both internal combustion engine (ICE) refrigerated vehicles and electric refrigerated vehicles will be used together in urban cold chain distribution for a long time into the future. Most current studies are concerned with mixed vehicle type allocation or mixed vehicle routing problems. Although there are some studies that combine the two, there has been little research into the effects of low carbon emission and multiple objective optimization on distribution and routing for a mixture of vehicle types. This paper describes the creation of an optimized decision-making cold chain distribution model that includes different types of distribution vehicles and different route optimization criteria that represent the three enterprise objectives of minimal cost, minimum carbon emissions and minimum distance. The decision criteria are influenced by conflicting benefits offered by ICE and electric refrigerated vehicles as well as government subsidies for electric refrigerated vehicles and carbon emission trading by logistics enterprises. A chaotic particle swarm optimization algorithm was used to analyze a cold chain distribution system in Wuhan, Hubei Province, China. The results show that: (1) The choice of enterprise distribution models and the vehicles and routes determined are closely related to enterprise goals. To minimize total cost, ICE refrigerated trucks with less load capacity and electric refrigerated trucks with greater load capacity are preferred. To minimize carbon emissions, ICE refrigerated trucks and electric refrigerated trucks with less load capacity are preferred. To minimize total distance, ICE refrigerated trucks and electric refrigerated trucks with greater load capacity are preferred. (2) As the value of government subsidies increases, the total load capacity of electric refrigerated vehicles also increases, the distribution routes change, and total carbon emissions and total distribution costs decrease. (3) When carbon emission trading prices increase, the number of electric refrigerated vehicles gradually increases, the distribution path changes, the total distribution costs increase, and total carbon emissions decrease. Some suggestions are made for government to determine subsidies for electric refrigerated vehicles and carbon emission trading policies that will guide the choice of distribution vehicles and affect the criteria used for route optimization by cold chain logistics enterprises.

Thickness-Dependent Thermal Conductivity and Phonon Mean Free Path Distribution in Single-Crystalline Barium Titanate.

Nanosized perovskite ferroelectrics are widely employed in several electromechanical, photonics, and thermoelectric applications. Scaling of ferroelectric materials entails a severe reduction in the lattice (phonon) thermal conductivity, particularly at sub-100nm length scales. Such thermal conductivity reduction can be accurately predicted using the information of phonon mean free path (MFP) distribution. The current understanding of phonon MFP distribution in perovskite ferroelectrics is still inconclusive despite the critical thermal management implications. Here, high-quality single-crystalline barium titanate (BTO) thin films, a representative perovskite ferroelectric material, are grown at several thicknesses. Using experimental thermal conductivity measurements and first-principles based modeling (including four-phonon scattering), the phonon MFP distribution is determined in BTO. The simulation results agree with the measured thickness-dependent thermal conductivity. The results show that the phonons with sub-100nm MFP dominate the thermal transport in BTO, and phonons with MFP exceeding 10nm contribute ≈35% to the total thermal conductivity, in significant contrast to previously published experimental results. The experimentally validated phonon MFP distribution is consistent with the theoretical predictions of other complex crystals with strong anharmonicity. This work paves the way for thermal management in nanostructured and ferroelectric-domain-engineered systems for oxide perovskite-based functional materials.

A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method

Network layer multicast is a powerful method for transmitting data from sources to multiple group members. When joining a multicast group, a group member first sends a request to a designated router (DR). Then, the DR selects a node in the existing multicast tree (known as a multicast joining node, or MJN) to establish a multicast distribution path from the MJN to itself. The MJN selection method runs on the DR and has a significant impact on the distribution of the multicast tree, that directly affects the load distribution in the network. However, the current MJN selection method cannot effectively detect the load status of the downlink multicast path in the case of asymmetric routing, leading to network congestion and limiting the number of multicast groups that the network can accommodate (multicast capacity). To solve this problem, we propose an MJN selection method based on the reverse shortest path tree (RSPT). RSPT can effectively detect the load status of downlink multicast paths in case of routing asymmetry. Based on the detection results of RSPT, DR can select the MJN with the lowest path load to join the multicast tree. Our experimental results indicate that compared to existing multicast methods, our method has a lower cost and delay, and can effectively balance the network load in the case of asymmetric routing, increasing multicast capacity by more than two times.

Pseudo almost periodicity for stochastic differential equations in infinite dimensions

In this article, we introduce the concept of p-mean θ-pseudo almost periodic stochastic processes, which is slightly weaker than p-mean pseudo almost periodic stochastic processes. Using the operator semigroup theory and stochastic analysis theory, we obtain the existence and uniqueness of square-mean θ-pseudo almost periodic mild solutions for a semilinear stochastic differential equation in infinite dimensions. Moreover, we prove that the obtained solution is also pseudo almost periodic in path distribution. It is noteworthy that the ergodic part of the obtained solution is not only ergodic in square-mean but also ergodic in path distribution. Our main results are even new for the corresponding stochastic differential equations (SDEs) in finite dimensions.

Riparian Invasive Alien Plant Species in Southern Transylvania (Romania): History of Introduction, Chorology and Ecology

Abstract The establishment and spread of invasive alien plant species (IAPS) outside their natural habitats have irreversibly affected native biodiversity in almost every habitat type. For Southern Transylvania, we recognise that there are about 200 adventive plants. From those, we selected 18 IAPS that occupy riparian habitats, the most common, abundant, frequent or with higher impact on habitats and human health. We studied the history of their introduction in the study area, their distribution paths and means of spreading in the wild, their chorology and ecology.

Research on logistics distribution route optimization based on deep learning model and block chain technology

The growing data age is reflected in all aspects of today's society. In the field of logistics, especially when the road conditions in urban areas are complex, how to select the optimal distribution path and reduce the distribution time is a problem worthy of attention. Aiming at the problems faced by traditional algorithms in solving the distribution of logistics vehicles in urban areas, however, the method based on regional chain technology can better solve the path optimization problem. A deep reinforcement learning algorithm based on attention mechanism and LSTM model is designed and applied to the distribution path planning of logistics vehicles. The distribution optimization path of logistics vehicles is obtained through sample training experiments, Thus, it provides a new idea for the optimization of logistics distribution path.

Optimization of Sustainable Bi-Objective Cold-Chain Logistics Route Considering Carbon Emissions and Customers’ Immediate Demands in China

To meet the national green development trend and realize the sustainable development of human society, the carbon emission in cold-chain distribution is costed. We plan the vehicle distribution path reasonably and optimize the distribution path locally for immediate demand to balance the economic benefits of enterprises and customer satisfaction while reducing the environmental pollution. To minimize distribution cost and maximize customer satisfaction, we design an improved ant colony algorithm to solve the initial distribution path and use the insertion method to solve the immediate customer demand. Taking the actual data of enterprise M as an example, we obtain the optimal distribution path using MATLAB software and optimize the distribution path locally according to the immediate customer demand. The results show that the proposed model and the designed algorithm are practical in satisfying the sustainable development of cold-chain logistics in China.

Sub-optimal Deep Pipelined Implementation of MIMO Sphere Detector on FPGA

Sphere detector (SD) is an effective signal detection approach for the wireless multiple-input multiple-output (MIMO) system since it can achieve near-optimal performance while reducing significant computational complexity. In this work, we proposed a novel SD architecture that is suitable for implementation on the hardware accelerator. We first perform a statistical analysis to examine the distribution of valid paths in the SD search tree. Using the analysis result, we then proposed an enhanced hybrid SD (EHSD) architecture that achieves quasi-ML performance and high throughput with a reasonable cost in hardware. The fine-grained pipeline designs of 4 × 4 and 8 × 8 MIMO system with 16-QAM modulation delivers throughput of 7.04 Gbps and 14.08 Gbps on the Xilinx Virtex Ultrascale+ FPGA, respectively.

Pharmaceutical distribution path optimization based on K-nearest neighbor algorithm

With the changing situation of the prevention and control of the new crown epidemic, the demand for various types of pharmaceutical supplies is rising, which puts forward very high requirements on the timeliness of distribution. In view of this, this paper establishes a pharmaceutical logistics distribution path optimization model by comprehensively considering constraints such as vehicle loading and latest delivery time in pharmaceutical distribution. In order to solve this type of problem, a nearest neighbor algorithm is proposed, which introduces correlation coefficients to realize the improvement of the performance of the original algorithm. At the end of the paper, the effectiveness of the model and the algorithm is verified by applying a comparative analysis with the existing correlation algorithm with real data from a pharmaceutical company. The experimental results show that the method effectively reduces the delivery mileage and improves the pharmaceutical delivery timeliness.

An artificial-intelligence-enabled sustainable supply chain model for B2C E-commerce business in the international trade

This work aims to improve the efficiency of logistics distribution in the international trade environment by optimizing the distribution path of the e-commerce supply chain model (SCM). First, this work analyzes the international trade and e-commerce backgrounds and unveils the existing problems in the sustainable supply chain model (SCM). Then, an experiment is designed to study sustainable SCM under the Business to Consumer (B2C) E-commerce business model and compare it to the Consumer to Consumer (C2C) business model. This research has important reference value for improving resource efficiency in the logistics field. The optimal experimental results of the model show that given an order quantity of 2000, the vehicle competition ratio (VCR) is only 1.1 when the vehicle capacity (VC) is 12. In contrast, when the VC is 72, the VCR is 1.45. Therefore, the greater that the load of a single vehicle in the logistics management system is, the higher the logistics distribution efficiency of the supply chain. The research content has practical application value for improving the efficiency of logistics distribution under the current e-commerce environment and promoting the digital and electronic development of international trade logistics.