Distribution Route Research Articles

Carbon-Efficient Scheduling in Fresh Food Supply Chains with a Time-Window-Constrained Deep Reinforcement Learning Model

Intelligent Transportation Systems (ITSs) leverage Internet of Things (IoT) technology to facilitate smart interconnectivity among vehicles, infrastructure, and users, thereby optimizing traffic flow. This paper constructs an optimization model for the fresh food supply chain distribution route of fresh products, considering factors such as carbon emissions, time windows, and cooling costs. By calculating carbon emission costs through carbon taxes, the model aims to minimize distribution costs. With a graph attention network structure adopted to describe node locations, accessible paths, and data with collection windows for path planning, it integrates to solve for the optimal distribution routes, taking into account carbon emissions and cooling costs under varying temperatures. Extensive simulation experiments and comparative analyses demonstrate that the proposed time-window-constrained reinforcement learning model provides effective decision-making information for optimizing fresh product fresh food supply chain transportation and distribution, controlling logistics costs, and reducing carbon emissions.

Soft Actor-critic-based Distributed Routing Scheme for Edge Computing Integrated with Dynamic IoT Networks

The rapid expansion of the Internet of Things (IoT) necessitates advanced routing schemes capable of meeting the stringent demands for low latency and high accuracy, which are critical for applications such as autonomous vehicles and telemedicine. Traditional edge computing methods often struggle with elevated latency, rendering them unsuitable for time-sensitive applications. Additionally, many reinforcement learning (RL) algorithms require action space discretization, which can introduce biases and dimensionality challenges. This paper introduces a novel Soft Actor-Critic (SAC)-based distributed routing scheme for edge computing, specifically designed to address these limitations. By integrating RL with Maximum Entropy principles and employing a decentralized approach, the proposed model enhances network performance, reduces delays, and effectively manages multi-optimality criteria. The distributed routing scheme operates independently of a centralized controller, allowing routers to make autonomous decisions and adapt seamlessly to changes in the network. This is accomplished through a Markov Decision Process (MDP) that optimizes routing paths based on various factors, including node depth, energy consumption, and transmission probability. The methodology encompasses local training phases for individual nodes, followed by federated training to refine the model across the network. Experimental results conducted on topologies of varying scales demonstrate the model’s efficacy in achieving high accuracy and efficient convergence, particularly in dynamic IoT environments. These findings underscore the potential of the proposed SAC-based distributed routing scheme as a robust solution for enhancing routing efficiency and reliability in the evolving landscape of IoT applications. IMPACT STATEMENT The rapid expansion of Internet of Things (IoT) applications demands advanced routing solutions to ensure low latency and high accuracy, crucial for sectors like autonomous vehicles and telemedicine. Traditional edge computing methods struggle with elevated latency, while many reinforcement learning (RL) algorithms face challenges with action space discretization, leading to biases and dimensionality issues. This study introduces a novel Soft Actor-Critic (SAC)-based distributed routing scheme to address these limitations. Integrating Maximum Entropy principles with RL enhances exploration and decision-making stability. The decentralized approach allows routers to make autonomous, real-time decisions based on local network conditions, optimizing routing paths through a Markov Decision Process (MDP). Experimental results from various simulated IoT network topologies show the model’s superior performance in reducing delays and maintaining bandwidth. This research paves the way for more reliable, low-latency IoT applications, significantly enhancing routing efficiency and network adaptability in dynamic IoT environments.

Comparison of Rigid Pavement Planning Using PD T-14-2003 and NAASRA 1987 Methods in Industrial Areas

As vital infrastructure, roads are a means of transportation access and function as a distribution route for goods and services. Road pavement plays a crucial role in highway construction, necessitating proper planning according to Indonesian standards and planning criteria to ensure smooth land transportation and provide comfort and safety for its users. This study aims to provide information on road conditions, drainage dimensions, and construction costs. Planning is crucial in every road construction design, particularly for rigid pavement, based on survey data collected from the road section. Numerous methods exist for calculating the design value of rigid pavement. We use the Pd T-14-2003 method and the NAASRA 1987 method (National Association of Australian State Road Authorities) to plan rigid pavement thickness in the industrial area of PT. Bukit Muria Jaya Kudus. The Pd T-14-2003 method yielded a JSKN of 4x107 and a CBR of 35% for the subgrade, resulting in a plate thickness of 150 mm. While the Naasra 1987 planning method achieved a JSKN of 6x107 and a CBR of 35% for the base soil, a plate thickness of 160 mm was obtained with a value of K = 80 kPa/mm. The reinforcement planning using the Pd T-14-2003 and Naasra 1987 methods involves longitudinal reinforcement D10 mm at a distance of 150 mm and transverse reinforcement D10 mm at 250 mm. The drainage dimensions at the cross-section location are width x height (1 m x 1 m), and the guard's height is 0.2 m.

Optimization of emergency material distribution routes in flood disaster with truck‐speedboat‐drone coordination

AbstractTo improve the effectiveness of flood disaster relief operations, by ensuring timely and accurate delivery of urgently needed supplies to affected areas, this study focuses on the problem of emergency material distribution during floods. With the objective of minimizing the overall delivery time of emergency materials, we propose a coordinated optimization model that integrates trucks, speedboats, and drones for effective distribution of emergency supplies in flood‐affected areas. To solve this optimization problem, we introduce an improved adaptive large neighborhood search (IALNS) algorithm, which builds on the traditional ALNS framework through refined tuning of deletion and insertion operators. Comparative analyses are conducted with a genetic algorithm, simulated annealing algorithm, and tabu search algorithm. The results reveal that the average performance gap of IALNS compared to these methods is 91.13%, 152.72%, and 16.92%, respectively. The experimental results demonstrate that the efficiency of the proposed model and algorithm in addressing the emergency supply distribution problem during flood disasters, highlighting the superior performance of IALNS. This research contributes to enhancing disaster response strategies, ultimately leading to improved outcomes for flood‐affected communities.

Electric Vehicle Distribution Route Optimisation and Charging Strategy Considering Dynamic Loads

Electric Vehicle Distribution Route Optimisation and Charging Strategy Considering Dynamic Loads

Phloem sap from melon plants contains extracellular vesicles that carry active proteasomes which increase in response to aphid infestation.

The morphogenesis of higher plants requires communication among distant organs throughout vascular tissues (xylem and phloem). Numerous investigations have demonstrated that phloem also act as a distribution route for signalling molecules being observed that different macromolecules translocated by the sap, including nucleic acids and proteins, change under stress situations. The participation of extracellular vesicles (EVs) in this communication has been suggested, although little is known about their role. In fact, in the last decade, the presence of EVs in plants has originated a great controversy, where major concerns arose from their origin, isolation methods, and even the appropriate nomenclature for plant nanovesicles. Phloem sap exudates from melon plants, either aphid-free or infested with Aphis gossypii, were collected by stem incision. After sap concentration (Amicon), phloem EVs (PhlEVs) were isolated by size exclusion chromatography. PhlEVs were characterised using Nanoparticle Tracking Analysis, Transmission electron microscopy and proteomic analysis. Here we confirm the presence of EVs in phloem sap in vivo and the detection of changes in the particles/protein ratio and composition of PhlEVs in response to insect feeding, revealing the presence of typical defence proteins in their cargo as well as components of the proteasome complex. PhlEVs from infested plants showed lower particles/protein ratio and almost two times more proteolytic activity than PhlEVs from aphid-free plants. In both cases, such activity was inhibited in a dose-dependent manner by the proteasome inhibitor MG132. Our results suggest that plants may use this mechanism to prepare themselves to receive infectious agents and open up the possibility of an evolutionary conserved mechanism of defence against pathogens/stresses in eukaryotic organisms.

Cold Chain Distribution Route Optimization for Mixed Vehicle Types of Fresh Agricultural Products Considering Carbon Emissions: A Study Based on a Survey in China

With the improvement of people’s living standards and the widening of circulation channels, the demand for fresh agricultural products continues to increase. The increase in demand will lead to an increase in delivery vehicles, costs, and carbon emissions, among which the increase in carbon emissions will aggravate pollution and is not conducive to sustainable development. Therefore, it is very important to balance economic and environmental benefits in the distribution of fresh agricultural products. Based on the analysis of the distribution characteristics of fresh agricultural products, this paper studies the optimization of the cold chain distribution route of fresh agricultural products considering carbon emission. Firstly, the cold chain distribution route planning of fresh agricultural products was investigated and analyzed by the interview method, and the basis for establishing the model objective and constraint conditions was obtained. Then, taking the minimum total cost including carbon emission cost as the optimization goal, the cold chain distribution route optimization model for mixed vehicle types is established considering electric refrigerated vehicles, gasoline refrigerated vehicles, and so on. Genetic algorithm was used to solve the model, and MATLAB2018b was used to substitute specific case data for simulation analysis. The analysis results show that increasing the consideration of carbon emission and mixed vehicle types in the distribution route of fresh agricultural products can not only reduce the distribution cost but also reduce the carbon emission. To some extent, the research content of this paper can provide a reference for enterprises in planning cold chain distribution routes of fresh agricultural products.

Comparison of Djikstra, Hybrid-PSO algorithms for optimizing the distribution route of papaya seeds and honey products (Case Study: PT. Agro Apiari Mandiri)

Dynamic global competencies in the industrial sector drive fierce competition in capturing markets and increasing customer satisfaction, which requires efficiency in various aspects of business including distribution. PT. Agro Apiari Mandiri faces challenges in optimizing delivery routes to avoid delays. This study aims to compare the Dijkstra and Hybrid-PSO algorithms to determine the optimal distribution route in the Bogor, West Java, and Lebak, Banten regions, in order to reduce the distance and delivery time. The research methods include literature study, data collection, and route optimization model creation. The results show that PSO is more efficient in optimizing delivery routes than other methods, with variations in PSO parameters affecting total travel time, number of vehicles, and computing time. Implementation uses hardware and software such as MSI Laptops and Matlab. In conclusion, the use of PSO in distribution route optimization makes a significant contribution to the company's cost and distribution efficiency and can be a reference for further research in distribution route optimization.

Optimization of distribution paths in cold chain logistics management based on IFW-ABC algorithm

The current cold chain logistics management and distribution routes generally face problems such as environmental pollution and high costs. Therefore, the study proposed an improved fireworks-artificial bee colony for optimizing delivery paths. Firstly, the artificial bee colony was improved by using neighborhood search. Secondly, for the optimal solution, the fireworks explosion operator was used for the final result search. The benchmark function validation showed that the proposed algorithm performed better than other algorithms in solving optimal value, average value, and standard deviation. In the simulation analysis of a fresh enterprise in China, the total delivery cost was reduced by an average of 3.30% and 4.93% compared to other algorithms, considering carbon emission and not considering it, respectively. This proposed algorithm had more advantages in reducing quality loss, pollutant emissions, and CE. Therefore, this improved fireworks-artificial bee colony can effectively reduce the economic costs and environmental pollution of cold chain logistics distribution routes. This ensures economic benefits while considering social benefits, achieving the optimal path planning for logistics distribution.

Vehicle-UAV Integrated Routing Optimization Problem for Emergency Delivery of Medical Supplies

In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm’s tendency toward early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach which successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system’s logistics during critical situations.

Logistics distribution model and storage planning design based on multi-source information positioning in smart city development

The research mainly focuses on the inefficient planning of autonomous distribution and storage modes of distribution vehicles in smart city logistics and distribution, which in turn leads to poor customer experience, rising distribution costs, and wasted distribution resources. From the perspective of information processing in the logistics distribution process, the study takes multi-angle and multi-source information collection and fusion processing strategy as the main basis to help smart delivery robots realize map construction and autonomous positioning in the distribution process, and then facilitate real-time logistics distribution and storage mode planning by robots in combination with their own states. The research results show that the algorithm accuracy, standard error, and average running time of the extended Kalman filter localization model designed in the study are 0.96, 1.52, and 105s, respectively, with the algorithm accuracy being the highest and the other two values being the lowest in its class. Meanwhile, in the simulation logistics planning, the research-designed logistics planning model has the strongest information capturing ability, and the planning of distribution routes and storage modes is more reasonable, which can provide more efficient autonomous planning solutions.

Peculiarities of Arctic Ichthyofauna

An audit of the marine and freshwater ichthyofauna of the Arctic has been carried out. According to new data, the ichthyofauna of the Arctic is represented by 4 classes, 33 orders, 103 families, 251 genera and 479 species of cyclostomes and fish (or about 1% of the world fauna), of which 214 species or 45% of the number of species in the Arctic are true Arctic species. Arctic fish are characterized by polymorphism and euryphagy, which are associated with the variable and low food supply of northern water bodies. The ichthyofauna of the Arctic is well represented by taxa with a wide adaptive radiation (genera Coregonus, Salvelinus, Lycodes, etc.) and species with a complex intraspecific structure such as superspecies or species-complex. Another characteristic feature of the Arctic is low endemism of ichthyofauna at the level of families (5%), genera (2%) and species (10%). Possible routes of distribution of whitefish and pike in the water bodies of the North have been discussed.

Sedimentary provenance and U-PB detrital zircon in the mesozoic record of the Parecis Basin, Rôndonia and Juruena sub-basin

Sediment provenance analysis is a tool increasingly used in the study of basins. This technique allows identifying the mineralogical and lithological composition of source areas, inferring paleogeography, climate and relief, determining the main sand distribution routes, and estimating the distance and transport time. The Mesozoic volcanic-sedimentary packages of the Parecis Basin mark a significant part of the South American Platform evolution, regarding tectonism, subsidence, sedimentation, basic and alkaline magmatism, uplift and erosion. Also, these packages registers the initial stages of Andean Orogeny subduction. Different authors have established opposite stratigraphic proposals for Parecis Basin regional correlations, and also for its tectonics evolution. Given this context, there are controversies regarding the chronostratigraphic positioning and relationships between the intervals, especially the units called Salto das Nuvens and Utiariti formations. This article presents the faciological framework and facies association, heavy mineral assemblage, ZTR index and sediment sources, and U-Pb detrital zircon ages to both sub-basins. Detrital Zircon data reveal minimum ages of 124 Ma for the Rondônia sub-basin and 245 Ma for the Juruena sub-basin for sedimentary deposition attributed to the base of the Cretaceous units. The detrital age of zircons points to a varied contribution, between elements from different provinces and ages of the Amazon Craton, the Paraguay Fold Belt and records of alkaline magmatism of ages Permo-Triassic and Cretaceous in the different kimberlithic fields of Rondônia and Mato Grosso.

IMPROVEMENT OF THE TECHNOLOGICAL PROCESS OF PERISHABLE GOODS DELIVERY IN THE CITY OF KHARKIV

The article analyses the existing methods for building perishable goods delivery technologies and identifies strategies to study the prospects for developing perishable goods delivery systems, which allowed the authors to formulate the research objective in this development. The chosen topic is quite relevant since the formation of rational transport and technological schemes for the delivery of perishable goods allows for increasing the efficiency of resource use, decreasing the cost of their delivery, and reducing the delivery time of perishable goods. One of the ways to improve the efficiency of perishable goods delivery and reduce delivery time is to determine rational distribution routes and rational brands of rolling stock. The study established that foreign scientists have proposed solutions related to the construction of transport and technological schemes for the delivery of perishable goods, taking into account the choice of vehicles and routes for the delivery of goods. Most of these solutions focused on determining the appropriate loading capacity of rolling stock and the optimal delivery routes for perishable goods. A result of the conducted research was a proposed methodology for a comprehensive improvement of the perishable goods delivery process, consisting of determining rational distribution routes for a selected set of vehicles with different loadings. The authors implemented the software for the algorithm for choosing routes for vehicles of various capacities with the definition of the core technical and operational and technical and economic indicators of efficiency of the delivery process of perishable goods. The software implementation of the algorithm for selecting routes for vehicles of different capacities with the determination of the core technical and operational and technical and economic indicators of the efficiency of the delivery process of perishable goods allows one to quickly, in real-time, perform calculations and determine the rational parameters of the transport process and choose the appropriate routes and rolling stock. Keywords: perishable goods, cargo delivery, distribution routes, truck capacity, costs.

Changing Epidemiology of Chronic Hepatitis C: A University Hospital Experience

Background: Hepatitis C virus (HCV) infection remains a significant public health problem worldwide, with varying epidemiological trends over time necessitating continual surveillance and intervention strategies. Objectives: This retrospective study aimed to examine trends in demographics, transmission routes, and disease characteristics among 635 patients diagnosed with HCV infection between 2003 and 2023. Methods: Data on demographics, HCV RNA levels, liver enzymes, genotype distribution, and transmission routes were retrospectively analyzed. The study period was divided into four segments for detailed analysis. Results: There was a gradual increase in the diagnosed cases over the study period, particularly noticeable after 2012. The mean age at diagnosis remained stable from 2003 - 2007 to 2008 - 2012 but decreased significantly in the following years (P = 0.001). Male predominance was observed throughout the study, with a notable increase in the male/female ratio over time (P < 0.001). Intravenous drug use (IVDU) emerged as the predominant transmission route, reaching 68.3% in 2018 - 2022 (P < 0.001). The prevalence of incarceration history among patients increased significantly, reaching 24.6% in 2018 - 2022 (P < 0.001). Disease characteristics varied between periods; median HCV RNA levels were highest in 2018 - 2022, and median alanine aminotransferase (ALT) levels differed significantly between periods (P = 0.001). The genotype distribution showed a shift over time, with genotype 1 predominating initially and genotypes 3, 4, and 5 becoming more prevalent in later years (P < 0.001). Conclusions: This study highlights the dynamic changes in hepatitis C epidemiology over two decades in southern Turkey, highlighting the increasing burden in younger populations, the increasing prevalence of IVDU as a transmission route, and the changing genotype distribution.

Pengoptimalan Rute untuk Minimasi Biaya Distribusi pada CV.DNY

The distribution route problem at CV.DNY is related to the lack of optimal route determination because delivery routes are determined only based on the order of goods received first. Determining the route in the distribution process is an important factor for the company because it affects the company's distribution costs. CV. DNY is a logistics company that operates in the sector of delivering goods to consumers. Based on field studies conducted, the company has not been able to determine the optimal route. The aim of this study is to increase the efficiency of the routes used so that shipping costs can be saved. The method used in this research is a minimum spanning tree. The results of the research show that the minimum spanning tree method obtains a shorter route with a distance of 4557 km compared to the company's initial route of 5318 km, experiencing distance savings of 761 km and minimizing distribution costs of IDR 5,174,800. So it can be concluded that the minimum spanning tree method uses POM-software The company's QM (Program Operations Management – ​​Quantitive Method) achieves delivery route savings and cost minimization.

PENYELESAIAN CAPACITATED VEHICLE ROUTING PROBLEM PADA PENDISTRIBUSIAN AIR GALLON MENGGUNAKAN ALGORITMA KOLONI LEBAH

Distribution is one of the problems in the business and industrial world, for example the problem of delivering supplies of raw materials and other goods to several customers, where the delivery must be carried out exactly once by one or more vehicles to each customer with a certain capacity. In its development, this problem was formulated as the Capacitated Vehicle Routing Problem (CVRP). Based on the definition, CVRP is one of the most common variations of the Vehicle Routing Problem (VRP) problem to determine the optimal route with the addition of constraints in the form of homogeneous vehicle capacity. Here, we discussed the CVRP problem of the distribution route for Pak Nurrofiq's gallons of water from the depot to its customers in the Taman Sei Raya Complex, Serdam Asri Complex and Kapuas Mas Complex. The aim of this research is to create a mathematical model for the CVRP problem in the distribution of Pak Nurrofiq's drinking water and solve it using the bee colony algorithm. The bee colony algorithm is a metaheuristic algorithm based on the behavior of honey bees in searching for food sources. The research results show that the case of gallon water distribution was successfully implemented into CVRP and completed using the bee colony algorithm, 4 routes were obtained with a total distance of all routes, namely 9,068 km.

Determining Book Distribution Routes by Implementing Vehicle Routing Problems Using Excel Solver at Gunadarma University

Gunadarma University operates multiple campuses, each equipped with its own library. To ensure equitable access to newly acquired titles, it is imperative that these materials are disseminated across all campus libraries. So, when a new book title is added, this book must also be owned by the library at each campus location. In this research, book distribution routes with the shortest distance and time will be determined using the Vehicle Routing Problem (VRP) method with Excel Solver. Gunadarma University has a central library which is the starting point for distributing books to other campuses. The central university library serves as the distribution hub, from which books are dispatched to seven additional campus locations. Simulation results using Excel Solver showed that the shortest route for sending books to seven library locations on the Gunadarma University campus using one vehicle was a distance of 137.14 km in 3 hours 12 minutes. This optimized route yielded substantial cost savings, amounting to a 12.5% reduction compared to previous distribution practices.

Optimisation of Urban Logistics Distribution Routing with Time Window

Reasonable optimization of the path can not only reduce unnecessary logistics cost expenditure; efficient circulation speed can also enhance the economic development of the city. In the process of the transformation of the model, it will be directly affected by the change of the importance of logistics enterprises to logistics distribution work. Based on the background of urban logistics, the optimization process of multi-logistics distribution path with time window is comprehensively studied. The relevant theoretical knowledge involved in the distribution link is reviewed, and through the analysis of the existing problems of urban logistics and the vehicle path optimization problem, the heuristic algorithm is used to establish the path optimization model of urban logistics center with time window, which reduces the vehicle transportation cost, thus reducing the total cost of logistics, and at the same time, improves the efficiency of the internal circulation of urban logistics and accelerates the economic development.

A Study on Problems of the Street Vendors in Lalbandi Municipality

Due to their lack of education and skills, street vendors are among the most vulnerable and impoverished groups in the urban informal labor market and business sector. They are also unable to obtain permanent positions in the lucrative formal sector. They constantly attempt to use their limited financial means to address the issues affecting their way of life. They serve as the primary distribution routes for a wide range of goods used on a daily basis, including fruits, vegetables, shoes, ready-made clothes, toys, household appliances, stationery, newspapers, and periodicals. A serious dilemma would arise for small-scale businesses that cannot afford to retail their products through pricey formal sector distribution networks, as well as fruit and vegetable growers, if they were to be removed from urban markets. So is found in the case of street vendors of Lalbandi Municipality. Thus, main purpose of conducting this study is to find out the problems faced by street vendors in Lalbandi Municipality. For this, descriptive research design is adopted to explore street vendors’ problems. Out of two hundred seventy-three street vendors of Lalbandi Municipality, thirty street vendors are chosen randomly. The street vendors who sell fruits, vegetables, prepared foods, and household appliances via local vehicles and permanent shelter are the subjects of the current study. The study showed that there is a noteworthy correlation between the issues encountered by the street vendors. The respondents’ general opinion of street sellers and the issues they encountered do not significantly correlate. In nutshell, the street vendors of Lalbandi Municipality have been facing various sorts of problems such as lack of security, price fluctuation, lack of sufficient amount, bribes to the authorities etc. and for that a comprehensive strategy that includes social protection measures, infrastructure and service investments, policy reforms, and initiatives to organize and empower street vendors is needed. Furthermore, the present study is useful to the stakeholders, policy makers and to those who have just started up their small-scale businesses.