Distribution Route Research Articles

Design of Fast Green Distribution Route Based on Greedy Algorithm

This study focused on the fast vehicle route optimization issue with carbon emission and time window constraints for on-time consumer demand based on the greedy approach. A greedy algorithm was established to rapidly plan the distribution route to obtain the lowest distribution cost and shortest distribution time to achieve a green distribution. The distribution cost model covering the costs of vehicle transportation time, time window deviation, fossil consumption, and PM2.5 emission was established based on on-time demand and green distribution characteristics. This study analyzed the milk distribution route for Guangxi A Diary Co., Ltd. based on the greedy algorithm. The results show that compared with the genetic algorithm, the algorithm running time is reduced by 2 s, although the greedy algorithm requires an additional cost of 21.73 CNY.

How to achieve sustainable distribution in the fast fashion industry? An electric vehicle solution under the "vehicle-battery separation" mode.

Exploring sustainable urban distribution based on electric vehicles is crucial given the rise in global greenhouse gas emissions, especially for fast fashion industries with extremely high distribution frequencies. However, most studies have overlooked the impact of deep discharge on the distribution route scheme, and few studies fit the characteristics of the fast fashion industry. As a result, this study presents a novel electric vehicle routing problem considering deep discharge model (EVRP-DD) for distribution route optimization, which fully considers deep discharge under the emerging mode of vehicle-battery separation. The characteristics of fashion consumers and products were also integrated into the model, such as consumer satisfaction and 3D loading constraints. To solve this complex programming problem, a sophisticated hybrid ant colony optimization (HACO) algorithm was designed by combining the advantages of ACO and A-star algorithms. Using real-life data, the experimental results verify the effectiveness and superiority of the proposed solution. EVRP-DD achieved reduced driving distance, total distribution cost, and deep discharge distance. HACO can enhance the computation speed and reduce the total distribution cost compared with the two conventional algorithms. The proposed solution showed excellent flexibility and could effectively adjust the optimal route scheme according to the ever-changing external environment. Thus, it can be concluded that this solution is a powerful tool for enterprises to achieve sustainable distribution. This study has realized theoretical innovation in sustainable distribution under the new mode of vehicle-battery separation, and its successful application in the fast fashion industry reflects its valuable application value.

Optimization path of agricultural products marketing channel based on innovative industrial chain

Optimization path is a complicated issue to manage the manufacture and distribution of fresh food, influenced by several parameters, such as its perishability. Producers cannot ensure that manufacturing and distribution decisions will be efficient and accurate. Using 15 prefecture-level cities in China as a case study, this paper gathers and organizes relevant data indicators of rural economy and agricultural ecological environment economic recovery for the period between 2008 and 2020, builds the composite evaluation index, determines the development index using the reporting system function, and finally employs a novel digital evaluation scheme to assess the coupling and coordination between the cities over time. The following inferences are made. To begin, linear growth with uneven regional development may be seen in the rural economy’s complete level index (values from 0.422 to 0.622). The objective of the study is to address the two-stage collaborative planning challenge to maximize farmer income by promoting the economic recovery. This article offers a mixed-integer processing paradigm to account for these restrictions. Calculations show that the suggested collaborative planning model may increase farmers’ distributing revenue by 7.98% compared to the existing independent decision-making distribution channel. Management thoughts have been developed based on various decision scenarios. Agricultural producers must sort and bundle their products with care and work with a fast and reliable delivery business and on time to deliver their items. Furthermore, the approach can assess the influence of distribution routes on producers’ revenues.

Biomass supply chain resilience: integrating demand and availability predictions into routing decisions using machine learning

ABSTRACT Biomass sources have the potential to mitigate carbon emissions as a renewable source while reducing waste and residues. Seasonality and disruption risks are some of the disadvantages of biomass resources requiring that biomass supply chains be managed such that to withstand disruptions. There has been very limited research on integrating predictions for smart management on supply or demand sides of biomass supply chains. In this study, a number of predictive models are investigated for building energy demand and biomass stock availability subject to forecasts of weather conditions. On that basis, an allocation algorithm is proposed for optimal collection and logistics of biomass from land to depots. Accordingly, Google Maps API will be used to identify the best distribution routes for delivering biomass from depots to end-users. A case study with real (supply and demand) data is considered. The proposed integrated data-driven approach aims at improving the accuracy of biomass supply and demand predictions and coordinating these predictions to enhance the resiliency of bioenergy supply chain routing decisions.

Optimal Vehicle Routing Problem (VRP) For The Distribution Of Medical Devices By Applying The Clarke-Wright Algorithm

In the current era of modernization, the development of the need for medical devices is increasing,g so that the companies need goods distribution system with criteria that have speed, safety, and convenience to distribute various kinds of products according to optimal distribution routes. The aim of this study is to overcome the problem of the distribution route for medical devices from PT Alkes Medan because the number of demand for goods varies for each consumers, vehicle capacites, delivery time limits, average speed that can be reached on certain routes and times and the existence of the different conditions of multiple trips at the customer's location. A heuristic algorithm named Clarke-Wright Algorithm is applied in this problem. This algorithm will be later provide an optimal solution to route problem in the distribution of medical devices and provide effectiveness in finding the shortest optimal route of medical device goods distributed to consumers. The optimal route will be described in the results section of this study

Trends in human leptospirosis in Denmark, 2012-2021.

Leptospirosis is a zoonotic bacterial infection that can cause influenza-like symptoms and severe disease. In Denmark, leptospirosis is rare, non-endemic, and most commonly transferred to humans from mice and rats. Cases of human leptospirosis in Denmark are by law notifiable to Statens Serum Institut. This study aimed to describe trends in incidence of leptospirosis in Denmark from 2012 to 2021. Descriptive analyses were used to calculate the incidence, geographical distribution and possible routes of infection, as well as testing capacity and serological trends. The overall incidence rate was 0.23 per 100,000 inhabitants, with the highest annual incidence of 24 cases in 2017. Men between 40-49 years old were the demographic group most commonly diagnosed with leptospirosis. August and September were the months with highest incidence over the entire study period. The most common serovar observed was Icterohaemorrhagiae, although over a third of cases were diagnosed via polymerase chain reaction alone. The most common sources of exposure reported were travel abroad, farming, and recreational contact with fresh water, the latter being a new exposure compared to previous studies. Overall, a One Health approach would ensure better detection of outbreaks and milder disease. Additionally, preventative measures should be expanded to include recreational water sports.

Optimal selection algorithm of logistics distribution route based on machine vision

In order to improve the centralized planning ability of logistics distribution path data, improve the efficiency of logistics distribution and reduce the cost of logistics distribution, this paper proposes an optimal path selection algorithm based on machine vision. Using machine vision technology to calibrate the coordinates of logistics distribution path, combined with EMD decomposition method and wavelet denoising method to remove redundant data in logistics distribution data, particle swarm optimization algorithm to complete logistics distribution path planning, and ant colony algorithm to realize the optimal path selection of logistics distribution. The experimental results show that the average distribution cost of this method is only 766.7 yuan, the distribution time is less than 0.3 h, and the customer satisfaction is as high as 98%, which shows that this method can effectively optimize the distribution path.

Optimization of the COVID-19 Vaccine Distribution Route Using the Vehicle Routing Problem with Time Windows Model and Capacity Constraint

At this time the effectiveness of the COVID-19 vaccines has been proven, and it is crucial to carry out the complete vaccination of the population. Therefore, it is imperative to optimize the vaccine distribution fleets. This paper discusses the optimization of distribution routes for the Pfizer vaccine in Portugal in terms of transportation time, total costs, and CO2 emissions. To this end, the Vehicle Routing Problem with Time Windows (VRPTW) model with a vehicle capacity restriction was used. The VRPTW model was tested for two scenarios. The first scenario allowed the driver to work overtime (585 min). The second scenario considered that the driver works 8 h (480 min). The results are presented to compare and justify the proposed method with large significance placed in terms of safety concerns, economic savings, environmental protection, and energy consumption. This paper aims to contribute to the healthcare system by optimizing the COVID-19 vaccine distribution routes and minimizing this process’s carbon footprint.

A quantum key distribution routing scheme for hybrid-trusted QKD network system

A quantum key distribution routing scheme for hybrid-trusted QKD network system

Desain Rute dan Optimasi Biaya Transportasi Pengisian Tabung Oksigen menggunakan Metode Complete Enumeration pada CV. Tasman Gases

The increasing demand for filling oxygen cylinders makes CV. Tasman Gases has always been consistent in planning transportation cost optimization strategies. The filling of oxygen cylinders in one distribution can transport up to 45 tubes sent to the concerned partner. The purpose of this study is to determine the shortest route and optimal cost in distributing. The method used is Complete Enumeration. The samples used include distribution locations and mileage. The procedure used begins with determining the number of routes, estimated mileage and fuel costs. There is the most optimal route selected from the 12 modeled routes. The selected route has the least transportation costs. This research makes CV. Tasman Gases has the best route, A-D-C-E-F with a distance of 10.9 km worth Rp. 10,423.13. This value is more effective than the existing A-E-C-D-F condition with a distance of 12.8 km obtaining fuel costs of Rp. 12,240.00. Thus, the distribution route and costs that have been optimal make CV. Tasman Gases can implement and identify future transportation constraints.

Penyelesaian Vehicle Routing Problem Dengan Algoritma Clarke And Wright Savings Di Perumahan Umum Bulog Medan Amplas

Distribution routes are generally a problem for every company, including in the public company BULOG Medan Amplas. Distribution to the Medan Amplas BULOG public company, namely having to serve every stall that is far from the warehouse with scattered locations, and limited vehicle capacity. So far, driver considerations in distributing products have only been based on random intuitions of driver and does not consider the efficiency of the route taken. Therefore, this research uses Clarke and Wright Savings algorithm to obtain optimal mileage by taking into account every consumer demand and vehicle capacity. Calculation results using the Clarke and Wright Savings Algorithm obtained the vehicle mileage of 695.08 km with a savings of 11 km or 1.56%.

A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution Costs

Under the background of the normalization of COVID-19 prevention and control and the rapid development of e-commerce, community group buying has occupied the market by providing low-priced, fast, and green consumer goods, but with it, the logistics and distribution volume of goods has also increased sharply. In order to reduce environmental pollution and the carbon emissions caused by transportation in the community group buying logistics distribution, it is necessary to investigate a suitable method to optimize vehicle distribution routes and reduce carbon emissions. Taking the lowest total costs of logistics and distribution and the smallest carbon emissions, this article introduces soft time window function and carbon emissions parameters, takes the delivery of goods from the community group buying distribution center in Wu’an Town, Hebei Province to customer points in 14 townships as an example, an optimization model for the distribution route of low carbon vehicles for community group buying based on improved genetic algorithm was constructed, AHP-EW fusion technology was used to calculate carbon emissions and cost weights, and compared with the traditional genetic algorithm and ant colony algorithm two typical heuristic algorithms, the feasibility of the proposed model and the advantages of the improved algorithm are verified, and the research results showed that it can reduce the costs and carbon emissions of vehicle distribution, provide decision-making reference for community group buying logistics enterprise distribution, and promote energy conservation and environmental sustainable development.

Distribution Route Optimization of Zakat Al-Fitr Based on the Branch-and-Bound Algorithm

The short interval between the collecting and distribution of zakat al-fitr is a recurring issue. As a result, ‘amil does not always pay attention to the ideal route, leading in inefficient transportation expenditures. This study aims to minimize the amount of vehicle mileage that affects fuel consumption. The branch-and-bound algorithm was employed to overcome the distribution route optimization problem by proposing the shortest circuit that traverses each district exactly once and returns to its original district. The procedures involve data collecting, graph analysis, branch-and-bound analysis, MATLAB code development, and the recommendation of the best route. The results indicate that the branch-and-bound algorithm can numerically solve the distribution route optimization corresponding to traveling salesman problem. Furthermore, according to a case study of zakat al-fitr distribution conducted by Eradication of Illiteracy Al Quran (PBHA), the total optimal distance of the computational-based algorithm was 152.9 km, with inter-village routes starting from Sidorejo and then via Sumberarum, Pendoworejo, Gerbosari, Banjaroyo, Banjarasri, Sendangagung, Tuksono, Argodadi, Triwidadi, Jatimulyo, Giripurwo, and ends in Sidorejo.

Analysis of Book Distribution Routes Using the Capacity Vehicle Routing Problem (CVRP) Method Using the Sweep Algorithm

Distribution is the process of distributing products from producers to consumers. The ease of consumers in obtaining the desired product is the top priority of every company. In the distribution system, the chosen route is the most important element in determining the distance to be traveled and the costs to be incurred. If the chosen route is optimal, then the distribution system becomes more effective and efficient. Basedon the results of the author's observations, the delivery of goods in the company is still manual and irregular, where the goods are sent from the depot to the consumer and back to the depot so that it takes a lot of time and costs. One solution in solving the distribution problem is the Capacity Vehicle Routing Problem (CVRP). The Sweep algorithm is the simplest clustering method to solve CVRP. In this study, the author used a sweep algorithm because this algorithm is one of the methods that can solve the problem of the distribution route of Quranic books. Based onthe results of the study, researchers compared the distribution route usingthe sweep algorithm and the distributionroute of the previous company, obtained a percentage of total mileage savings of 35.35%, namely from the previous total company mileage of 135.2 Km to 87.4 Km by using the Sweep algorithm. This shows that the CVRP method using the sweep algorithm can provide optimal distance and save company expenses.

The Effectiveness of the Clarke & Wright Savings Algorithm in Determining Logistics Distribution Routes (case study PT.XYZ)

A good logistics system is one factor that affects the smooth running of company activities. Currently, transportation costs account for about two-thirds of total logistics costs, so to increase efficiency, companies try to maximize the use of transportation equipment and personnel by determining the optimal distribution route. Route optimization can be done using the Vehicle Routing Problem and the Clarke- Wright Method. PT. XYZ is a company engaged in maintaining ATM machines (Automated Teller Machines). The form of this activity can be in the form of installing air conditioners, TVs and repairing ATM machines. This company faces problems in the logistics section, where the installation target is often not achieved from the specified target. This is because the distance from one point to another is coupled with the congestion level in big cities, so this causes the company to pay large operational costs. Therefore, a distribution route is needed to save time and distribution costs. This study uses the Clarke and Wright Savings Algorithm to solve the problems faced by companies. The results showed that after using the Clarke and Wright Savings Algorithm, the total distance and shipping costs were reduced by saving 97.9 Km of distance and saving costs.

Research on delivery problem based on two-stage multi-objective optimization for takeout riders

From the perspective of takeout riders, in order to ensure the safety of riders, reasonably arrange the distribution tasks of riders and improve the distribution efficiency of the platform, this paper proposes a new distribution mode. In the future, efficiency is the core competitiveness of the takeout platform, and humanization is an important condition for the long-term development of the takeout platform. Both are indispensable. This paper simplifies the traditional many to many distribution mode into one to many distribution mode, and realizes the new distribution mode through two-stage multi-objective optimization algorithm. In the first stage, genetic algorithm is applied to analyze and optimize the new distribution mode for the first time; In the second stage, the large-scale neighborhood search algorithm is applied to destroy and reorganize the distribution route obtained in the first stage according to the objective function to obtain the optimal distribution route. Aiming at the three scenarios of consumer group distribution generated by random simulation, high-density and low-density consumer group distribution in Shenyang, this paper uses MATLAB tools to simulate. Through the analysis of simulation results, the working hours and delivery distances of takeout riders are reduced, and the maximum delivery volume is limited. So the method proposed in this paper can not only improve the intelligent level of distribution, but also propose a more humanized distribution mode from the perspective of takeout riders, which greatly supports the sustainable development of the delivery system of network connected takeout platform.

Green room temperature synthesis of silver-gold alloy nanoparticles.

Metallic alloy nanoparticles (NPs) exhibit interesting optical, electrical and catalytic properties, dependent on their size, shape and composition. In particular, silver-gold alloy NPs are widely applied as model systems to better understand the syntheses and formation (kinetics) of alloy NPs, as the two elements are fully miscible. Our study targets product design via environmentally friendly synthesis conditions. We use dextran as the reducing and stabilizing agent for the synthesis of homogeneous silver-gold alloy NPs at room temperature. Our approach is a one-pot, low temperature, reaction-controlled, green and scalable synthesis route of well-controlled composition and narrow particle size distribution. The composition over a broad range of molar gold contents is confirmed by scanning transmission electron microscopy-energy-dispersive X-ray spectroscopy (STEM-EDX) measurements and auxiliary inductively coupled plasma-optical emission spectroscopy measurements (ICP-OES). The distributions of the resulting particles in size and composition are obtained from multi-wavelength analytical ultracentrifugation using the optical back coupling method and further confirmed by high-pressure liquid chromatography. Finally, we provide insight into the reaction kinetics during the synthesis, discuss the reaction mechanism and demonstrate possibilities for scale-up by a factor of more than 250 by increasing the reactor volume and NP concentration.

Shifts in breeding distribution, migration timing, and migration routes of two North American swift species

Shifts in breeding distribution, migration timing, and migration routes of two North American swift species

Digital Transformation in Pharmaceutical Supply Chain: An African Case

Abstract Background: The pharmaceutical supply chain, pivotal in ensuring timely and efficient delivery of medical goods, has experienced significant challenges in the African context. Infrastructure deficiencies, fragmented distribution networks, and limited transparency have historically been obstacles in ensuring the consistent flow of essential drugs and treatments. Materials and Methods: The transformative power of digital technologies in reconfiguring the pharmaceutical supply chain across Africa. Drawing on multiple case studies, this paper presents evidence of how technologies such as blockchain, artificial intelligence, and the Internet of Things (IoT) are being employed to enhance traceability, improve inventory management, and optimize distribution routes. These digital innovations are not only ensuring the integrity of medicines by reducing counterfeit products but are also enhancing the agility of the supply chain to respond to sudden market and health demands. Results: Executed in a thorough data analysis strategy to interpret the results and extract significant insights by investigating the challenges faced in this digital transformation, which include the need for skills development, infrastructure investment, and the creation of supportive regulatory environments. The study underscores the role of partnerships between governments, international organizations, and private enterprises in driving these innovations. Conclusion: While the journey of digital transformation in Africa’s pharmaceutical supply chain is ongoing, there are promising strides being made. By embracing digital solutions, Africa is positioning itself to better tackle health challenges and ensuring that its populace has access to genuine and essential medicines. This research contributes to a deeper understanding of digital transition strategies and offers actionable insights for stakeholders involved in global health and supply chain management.

Intelligent Vehicular Communication Using Vulnerability Scoring Based Routing Protocol

Internet of Vehicles (IoV) is an intelligent vehicular technology that allows vehicles to communicate with each other via internet. Communications and the Internet of Things (IoT) enable cutting-edge technologies including such self-driving cars. In the existing systems, there is a maximum communication delay while transmitting the messages. The proposed system uses hybrid Co-operative, Vehicular Communication Management Framework called CAMINO (CA). Further it uses, energy efficient fast message routing protocol with Common Vulnerability Scoring System (CVSS) methodology for improving the communication delay, throughput. It improves security while transmitting the messages through networks. In this research, we present a unique intelligent vehicular infrastructure communication management framework. This framework includes additional stability for both short and long-range mobile communications. It also includes built-in cooperative intelligent transport system (C-ITS) capabilities for experimental verification in real-world contexts. In addition, an energy efficient-fast message distribution routing protocol (EE-FMDRP) has been presented. This combines the benefits between both temporal and direction oriented routing methods. This has been suggested for distributing information from the origin ends to the predetermined objective in a quick, accurate, and effective manner in the event of an emergency. The critical value scale score(CVSS) employ ratings to measure the assault probability in Markov chains. Probabilities of chained transitions allow us to statistically evaluate the integrity of a group of IoV assets. Thus the proposed method helps to enhance the vehicular systems. The CAMINO with energy efficient fast protocol using CVSS (CA- EEFP-CVSS) method outperforms in terms of shortest transmission latency achieves 2.6 sec, highest throughput 11.6%, and lowest energy usage 17% and PDR 95.78%.