Improve Distribution Efficiency Research Articles

Electrical Substation Design for Safety, Reliability and Sustainability

Abstract: Electrical substations’ design is critical for safe, efficient and reliable electricity distribution. Safety and operational flexibility demand for optimized layout design. The fundamental ideas and elements of creating an efficient electrical substation structure that guarantees reliability and maximum performance. Selecting the optimum substation site in industrial operations necessitates a delicate balance of safety, efficiency, and environmental sensitivity. The main ideas and considerations that support the optimization of electrical substation design have been painstakingly addressed in this paper. Adequate space and smooth grid connectivity improve distribution efficiency and allow for future growth. Regulation adherence and effective security measures ensure ethical and secure siting. Adequate assessment of electromagnetic and radio interference is essential. Substation placement optimization connects industrial competence with environmental responsibility in this sophisticated decision-making process, establishing a healthy balance for reliability and sustainability. Beginning with the careful selection of a site that strikes a delicate balance between closeness to power sources and environmental sensitivity, each decision has far-reaching consequences for the grid's integrity. The thorough layout of equipment provides not only performance but also ease of maintenance, thus increasing the operational flexibility and reliability of the substation.

Strategies for Improving Irrigation Systems Based on Lamasba Regulation: A 2024 Field Study in Hoba Farms, Southeastern Algeria

In the Algerian province of El Oued, south-eastern Algeria, the irrigation system plays a vital role in supporting agricultural production, particularly in oil palm plantations, which have experienced significant expansion in recent decades. The development of irrigation techniques in the region dates back to antiquity, with the Lamasba system, discovered by Émile Masqueray in 1877 in the Belzma plain, being one of the most prominent examples of efficient water resource management. This ancient Roman system, which used precise schedules to distribute water from permanent sources, meticulously determined the quantities and times of water flow, reflecting a high level of social and economic organization at that time. During the French colonial period between 1957 and 1959, the Hoba area in the Righiba commune of El Oued province experienced a remarkable growth in oil palm plantations, with the implementation of advanced irrigation techniques, inspired by the ancient Roman system but modified to suit local conditions. As the project expanded, new challenges arose related to water distribution, particularly in fields far from water sources, leading to the construction of small, taller water towers to improve distribution efficiency. This study analyzes the current irrigation system in the Hoba region, focusing on the technical challenges these systems face, such as obsolete valves and the presence of weeds in the irrigation channels, which slow down the water flow and prolong the irrigation time. The study also examines the use of a new 5-meter-high tower that partially solved the problem of low water flow, while providing additional improvement suggestions to ensure the sustainability of agricultural production in these desert areas. The objective of this study is to offer a comprehensive vision on how to improve irrigation systems to meet the needs of modern agriculture and increase productivity in arid environments.

A Two-Echelon Multi-Trip Capacitated Vehicle Routing Problem with Time Windows for Fresh E-Commerce Logistics under Front Warehouse Mode

Given the swift expansion of fresh e-commerce, the front warehouse mode can respond quickly and ensure the quality of fresh products. However, the complexity of the supply chain structure under front warehouse mode poses a challenge in reducing logistics costs and improving distribution efficiency while meeting consumers’ immediate delivery demands. Therefore, this paper studies the vehicle routing problem of two-echelon fresh e-commerce under front warehouse mode. Considering trans-shipment time constraints between the two echelons and the characteristics of terminal distribution, this paper initially models the vehicle routing problem for front warehouses as a two-echelon multi-trip capacitated vehicle routing problem with time windows. A mixed-integer linear programming model is subsequently established. To solve the model, a hybrid genetic algorithm integrated with neighborhood search is developed. Matrix coding is employed to merge vehicle selection and route assignment decisions. Simultaneously, neighborhood search is applied to enhance the search capability of algorithms, thereby improving the quality of solutions. Furthermore, the effectiveness and efficiency of the model and algorithm are verified through experiments of varying scales. Finally, comparative strategies and sensitivity analysis highlight the advantages of multi-trip strategies and provide insights into the optimal vehicle capacity limit.

DEEP LEARNING FOR AUTOMATIC CLASSIFICATION OF AVOCADO FRUIT MATURITY

Avocado (Persea Americana), a fleshy fruit with a single seed, has increased in popularity globally, especially in tropical and Mediterranean climates, thanks to its commercial and nutritional value. Rich in bioactive compounds, avocados contribute to the prevention and treatment of various diseases, including cardiovascular problems and cancer. Avocado production in Indonesia, for example, is showing a significant increase, reflecting the growing demand. Avocado ripeness affects shelf life and quality, making the determination of ripeness level a critical aspect of postharvest management. Skin color and pulp firmness change during storage, affecting quality and nutritional value. Proper classification of ripeness is important to reduce post-harvest losses, improve quality and optimize export costs. Recent research shows the use of technologies such as machine learning and YOLO (You Only Look Once) version 9 in real-time detection of avocado ripeness, offering innovative solutions to reduce post-harvest losses and improve distribution efficiency. This approach not only benefits farmers and consumers but also ensures consumer satisfaction and reduces economic losses. This study highlights the importance of real-time detection in monitoring avocado ripeness, where the training process was conducted for 89,280 iterations resulting in a new model for avocado ripeness detection. The final model has a mean Average Precision (mAP) validation value of 84.3%, mAP 84.3% signifies the optimal level of accuracy in object recognition in avocado fruit maturity images using the YOLO model that has undergone an intensive training process.

ACSICS: Joint Distribution Mode Integrating Agricultural Industry Chain Logistics Under the Background of Artificial Intelligence

The purpose is to study the new application of artificial intelligence (AI) technology in the Agro logistics industry distribution modes. This work summarizes the problems in agricultural logistics by investigating the current situation of the logistics distribution modes (LDMs) of Agro products and establishes a joint LDM integrating logistics and agricultural industry chain (AIC). An intelligent virtual center is established for the joint LDM according to the specific situation of Xi’an. Experts are invited to evaluate the proposed LDM. The results show that the existing third-party logistics (3PL), agricultural supermarket docking LDM, and company+farmer shared LDM have respective advantages and disadvantages. The virtual center for the proposed joint LDM multiplies the weight matrix [Formula: see text] and fuzzy evaluation matrix A to obtain a comprehensive fuzzy evaluation result. It evaluates the result according to the maximum membership criterion. Most experts have a relatively good evaluation of the joint LDM. In the comprehensive fuzzy evaluation results, the comprehensive “excellent” score is 0.3755, and the comprehensive “good” score is 0.2678. According to the principle of maximum subordination, the AIC logistics integrating Agro products logistics has an excellent performance, and the overall satisfaction level of performance has exceeded 85%. In addition, Yonghui supermarket adopts the joint LDM, and the price of fruits and vegetables is lower than that of other LDMs. Therefore, the proposed AI-based joint LDM can optimize the distribution route, improve distribution efficiency, and save logistics costs to a great extent.

Heterogeneous Fleets for Green Vehicle Routing Problem With Traffic Restrictions

Suffering from environmental distress like carbon emissions, traffic restrictions have been enforced extensively in distribution logistics. Reasonable arrangement of urban freight transportation can effectively improve distribution efficiency, reduce distribution costs, and alleviate the impact of traffic restrictions in distribution logistics. In response to increasingly stringent traffic restrictions, we establish a multi-objective optimization model, including the minimum distributions and the minimum carbon emissions. Given that the limits of battery capacity and cargo capacity, we build a green vehicle routing problem with soft time windows (GVRPTW) model with heterogenous fleets. In this study, three different factors, that is restricted area, travel time of vehicles, and carbon tax prices, are discussed in details. In order to solve the NP-hard model, we propose an improved ant colony optimization algorithm (IACO) by optimizing the state transition probability, and verifies the worth of the algorithm. The experimental results can explore the impacts of traffic restriction policies on the formulation of distribution scheme and offer reference opinions for the government to formulate reasonable restriction policies and better guide logistics enterprises to reduce carbon emissions.

Multi-objective Logistics Distribution Path Optimization Based on Annealing Evolution Algorithm

Logistics distribution is a collection of interrelated organizations and facilities. There is a waste of cost and time in many links. Therefore, it is particularly important to use information technology to improve distribution efficiency. Under the constraints of delivery vehicle cost and time, this paper proposes an improved genetic simulated annealing algorithm (SAGA), which combines the global search ability of the genetic algorithm (GA) and the simulated annealing algorithm (SA) with strong local search ability to solve the vehicle routing problem with time windows (VRPTW). The perturbation factor is introduced to improve the local search, and the crossover method is optimized to obtain more efficient genetic operators by using population information. In this paper, combined with the actual application case, the simulation experiment is carried out in MATLAB. The experimental results show that, compared with the traditional genetic algorithm and simulated annealing algorithm, the total cost of the improved genetic simulated annealing algorithm is reduced by about 15%, which provides a more suitable vehicle route planning scheme.

Research on the Optimization of Fresh Agricultural Products Trade Distribution Path Based on Genetic Algorithm

This study, taking the R fresh agricultural products distribution center (R-FAPDC) as an example, constructs a multi-objective optimization model of a logistics distribution path with time window constraints, and uses a genetic algorithm to optimize the optimal trade distribution path of fresh agricultural products. By combining the genetic algorithm with the actual case to explore, this study aims to solve enterprises’ narrow distribution paths and promote the model’s application in similar enterprises with similar characteristics. The results reveal that: (1) The trade distribution path scheme optimized by the genetic algorithm can reduce the distribution cost of distribution centers and improve customer satisfaction. (2) The genetic algorithm can bring economic benefits and reduce transportation losses in trade for trade distribution centers with the same spatial and quality characteristics as R fresh agricultural products distribution centers. According to our study, fresh agricultural products distribution enterprises should emphasize the use of genetic algorithms in planning distribution paths, develop a highly adaptable planning system of trade distribution routes, strengthen organizational and operational management, and establish a standard system for high-quality logistics services to improve distribution efficiency and customer satisfaction.

Application and Communication Optimization Technology of Unmanned Distribution Car under Deep Learning in Logistics Express of COVID-19.

This work aims to solve the problem that the daily necessities of urban residents cannot be delivered during coronavirus disease 2019 (COVID-19), thereby reducing the possibility of the delivery personnel contracting COVID-19 due to the need to transport medicines to the hospital during the epidemic. Firstly, this work studies the application and communication optimization technology of unmanned delivery cars based on deep learning (DL) under COVID-19. Secondly, a route planning method for unmanned delivery cars based on the DL method is proposed under the influence of factors such as maximum flight time, load, and road conditions. This work analyzes and introduces unmanned delivery cars from four aspects combined with the actual operation of unmanned delivery cars and related literature: the characteristics, delivery mode, economy, and limitations of unmanned delivery cars. The unmanned delivery car is in the promotion stage. A basic AVRPTW model is established that minimizes the total delivery cost without considering the charging behavior under the restriction of some routes, delivery time, load, and other factors. The path optimization problem of unmanned delivery cars in various situations is considered. A multiobjective optimization model of the unmanned delivery car in the charging/swap mode is established with the goal of minimizing the total delivery cost and maximizing customer satisfaction under the premise of meeting the car driving requirements. An improved genetic algorithm is designed to solve the established model. Finally, the model is tested, and its results are analyzed. The effectiveness of this route planning method is proved through case analysis. Customer satisfaction, delivery time, cost input, and other aspects have been greatly improved through the improvement and optimization of the unmanned delivery car line, which has been well applied in practice. In addition, unmanned delivery cars are affected by many factors such as load, and the service time required for delivery is longer. Therefore, this work chooses an unmanned distribution car with strong endurance to improve distribution efficiency. The new hospital contactless distribution mode discussed here will play an important role in promoting future development.

The Saving Matrix Method for Improving Distribution Efficiency

The delivery route for the Bandung 40400 Mail Processing Center is determined based on the estimation using the zoning system, and this may trigger delays. This happens because of each vehicle can only deliver to one distribution center at a time. This makes the load factor level of vehicle utilization below 20%. This study aims to obtain the optimal route and delivery schedule to improve distribution efficiency and increase the load factor of vehicle capacity utilization. This quantitative research method uses the Saving Matrix algorithm on all distribution channels. The results obtained indicate that the optimal delivery routes are 5 to 6 cluster. This saves mileage of up to 144 km per route per day and saves distribution costs of up to Rp. 39,146.50 for one delivery. In addition, there was an increase in the load factor of 18.37% for dropping 1 and 16.49% for dropping 2. By using the proposed route, a resume of the delivery schedule is obtained with clearer departure and arrival times and saving times up to 275 minutes. Furthermore, the expansion of the scope of research and comparing the level of effectiveness between distribution centers in various regions can be carried out as a follow-up study.
 Keywords—Saving matrix algorithm; Delivery routes; Load factor; Scheduling

Intelligent Distribution of Fresh Agricultural Products in Smart City

With the construction of smart cities and the continuous improvement of people's living standards, residents’ demand for fresh agricultural products (FAPs) has increased dramatically. Therefore, reasonable arrangement for intelligent distribution of FAP in smart cities can effectively guarantee product quality, improve distribution efficiency, reduce distribution cost, and increase customer satisfaction. In actual distribution in smart city, road conditions are one of the important factors that affect the distribution. Therefore, according to the influence of road conditions on refrigerated vehicle's (RV's) speed, the RV's speed characteristic models are established. Meanwhile, according to the characteristics of FAP, the penalty cost function based on the time window is constructed. According to the idea of fuzzy logic, the customer satisfaction evaluation model is established. Then, in order to minimize the distribution costs and maximize customer satisfaction as the optimization goal of intelligent distribution in smart city, the mathematical model is built. For solving this model, an improved quantum-behaved particle swarm optimization algorithm (IQPSO) is proposed. Finally, the effectiveness of IQPSO is verified by simulation. The results show that IQPSO also achieves good results, and the model constructed can effectively balance the relationship between the distribution costs and customer satisfaction when distributing FAP in smart city.

Research on E-commerce Logistics Distribution Path Planning Based on Improved Genetic Algorithm

With the rapid development of the Internet, e-commerce business has gradually emerged. However, its logistics distribution route planning method has problems such as redundancy of logistics data, which cannot achieve centralized planning of distribution paths, resulting in low e-commerce logistics distribution efficiency and long distribution distances, higher cost. Therefore, in order to improve the ability of logistics distribution path planning, this paper designs an e-commerce logistics distribution path planning method based on improved genetic algorithm. Optimize the analysis of e-commerce logistics distribution nodes, establish a modern logistics distribution system, and optimize the total transportation time and transportation cost under the location model of the logistics distribution center. Using hybrid search algorithm and improved genetic algorithm parameters, an improved genetic algorithm distribution path planning model is established to select the optimal path of logistics distribution, and realize e-commerce logistics distribution path with high accuracy, low error and good convergence. planning. According to the experimental results, the method in this paper can effectively shorten the distance of e-commerce logistics distribution path, reduce the number of distribution vehicles, reduce distribution costs, improve distribution efficiency, and effectively achieve centralized planning of logistics distribution. Therefore, the e-commerce logistics distribution route planning method based on improved genetic algorithm has high practical application value.

A Study on the Analysis of the Economic Effects of Livestock Products Grading on Eggs

This study is conducted to find ways to improve distribution efficiency through the expansion of livestock products’ grading on eggs. The economic effects of livestock products’ grading on eggs is analyzed. The benefits of the operator following the implementation of the self-quality evaluation system are estimated. The data of the Korea Institute for Animal Products Quality Evaluation (KAPE) are analyzed using the Equilibrium Displacement Model (EDM). The economic effect of the operation of livestock products’ grading on eggs is found to be 11.04 billion won in 2019. If the ratio of livestock products’ grading is expanded to 10~50% the economic effects are estimated to be 14.15 billion to 70.76 billion won. In addition, the cost-saving effect of the self-quality evaluation system is calculated to be about 46.8%.

Study and design of a retrofitted smart water meter solution with energy harvesting integration

The reduction of water resources due to climate change and the increasing demand associated with population growth is a renewed concern. Water distribution monitoring and smart metering are essential tools to improve distribution efficiency. This paper reports on the study, design, and implementation of a smart water meter (SWM) prototype, designed for mechanical water meters that need to undergo a retrofitting process to enable automatic metering readings. Metering data is transmitted through innovative narrowband internet of things (NB-IoT) technology with low power, long-range, and effective penetration. A flexible power management design allows the introduction of an energy harvester that recovers energy from the surrounding environment and charges the internal battery. The energy harvesting feasibility was demonstrated with two proof-of-concept configurations, light and water-turbine based. The details on the performance of the proposed solution are presented, including the output voltages and harvested power. Although the energy harvesting technologies have not been integrated yet in commercial SWM applications, the results show that the integration is feasible and, once employed in a controlled environment, it can create business advantages by reducing the size and capacity of the internal batteries, enabling one to reduce the operation cost and mitigate long-term ecological problems associated with the use and disposal of batteries.

Collaborative logistics pickup and delivery problem with eco-packages based on time–space network

Collaboration among logistics companies offers a simple and effective way of increasing logistics operation efficiency. This study designs an optimal collaboration strategy by solving the collaborative logistics pickup and delivery problem with eco-packages (CLPDPE). This problem seeks to minimize the total operational costs by forming collaborative alliances and allocating trucking resources based on time–space (TS) network properties. The synchronization of two-echelon logistics networks is improved by solving this problem. Moreover, this study considers the stability of collaboration (i.e., the willingness of logistics companies to join and remain in collaborative alliances) by comparing different profit allocation strategies in the CLPDPE solving process. A novel methodology that combines multi-objective mixed integer programming, multidimensional K-means clustering, reference point based non-dominated sorting genetic algorithm-II (RP-NSGA-II), forward dynamic programming and improved Shapley value method is developed to formulate and solve CLPDPE. Our results show that the proposed algorithm outperforms most other algorithms in minimizing the total cost, waiting time and number of vehicles. An empirical case study in Chongqing city, China suggests that the proposed collaborative mechanism and transportation resource sharing strategy based on TS network can reduce cost, improve distribution efficiency, and contribute to efficient, smart, intelligent and sustainable urban logistics and transportation systems.

Location Model of Fresh Agricultural Products Logistics Distribution Center Based on Network Nodes

The high logistics cost of fresh produce is the main problem that restricts the distribution of produce. How to reduce the logistics cost of fresh agricultural products, improve the utilization rate of logistics facilities, and improve distribution efficiency and service levels has become the key to research. As an important hub for fresh agricultural products, the fresh agricultural product distribution center can effectively integrate supply and demand information and is the key to ensuring the efficiency of fresh agricultural product distribution, especially to meet the urban fresh agricultural product distribution needs. The fresh agricultural product urban logistics distribution center has the characteristics of small service radius, small scale, many distribution batches and high distribution frequency. The service radius and the scale of the distribution center determine the function of the distribution service. In order to save costs while ensuring distribution efficiency and logistics service levels, it is necessary to scientifically and rationally plan the location of urban distribution centers for fresh agricultural products. Despite extensive research on the location of logistics distribution centers, there are still some shortcomings. First, by analyzing and summarizing the existing literature, the theory and research methods of urban logistics distribution centers for fresh agricultural products are summarized, the characteristics of urban logistics distribution centers for fresh agricultural products are analyzed, and the status of logistics centers for fresh agricultural products is summarized. Influencing factors and site selection process introduced the basic principles and steps of immune algorithm. Secondly, based on the location theory of distribution centers, a cost model for the location of urban logistics distribution centers for fresh agricultural products is established. The model takes into account economic and social factors, including distribution costs, fixed costs, operating costs, waste costs and carbon emission costs. In the case of traffic congestion, a carbon emission factor is introduced to calculate the environmental cost of the allocation process. Finally, combined with the calculation steps of the immune algorithm, the solution steps are designed for the location model of the urban fresh agricultural products logistics distribution center, and the actual calculation examples are studied using MATLAB software programming, and the distribution center is obtained based on the low model. Through the method of this article, the transportation speed of fresh agricultural products logistics distribution center location has been increased by 13%.

Study on Optimal Loading of Zero Load Cargo

In recent years, thanks to the transformation and upgrading of domestic consumption and the continuous improvement of logistics network, the total freight volume of China’s part-load logistics market is also increasing, among which urban logistics network plays an important role in part-load logistics enterprises. Reasonable and perfect urban network is helpful to reduce the total cost of market logistics and improve distribution efficiency. This paper introduces the problems existing in part-load logistics, studies the urban logistics model of part-load logistics enterprises, introduces three ways of part-load transportation, and analyzes the urban logistics model of part-load logistics enterprises. At the end of this paper, the above contents are briefly summarized.

Electric Vehicle Routing Problems with Stochastic Demands and Dynamic Remedial Measures

With the rapid development of e-commerce, logistic enterprises must better predict customer demand to improve distribution efficiency, so as to deliver goods in advance, which makes logistics stochastic and dynamic. In order to deal with this challenge and respond to the concept of “green logistics,” an electric vehicle routing problem with stochastic demands (EVRPSD) and proactive remedial measures is investigated, and an EVRPSD model with probability constraints is established. At the same time, a hybrid heuristic algorithm, combining a saving method and an improved Tabu search algorithm, is proposed to solve the model. Moreover, two insertion strategies with the greedy algorithm for charging stations and dynamic nodes are introduced. Finally, a large number of experimental data show that the heuristic algorithm proposed in this paper is feasible and effective.

Research on logistics distribution path optimization based on PSO and IoT

Logistics information tends to be large-scale and complicated, and must be handled by intelligent means to promote the sustainable and healthy development of the logistics industry. The key issue currently facing is the optimization of the logistics distribution path. First, analyze the current research status of Internet of Things (IoT) and the basic application of Particle Swarm Optimization (PSO); Then according to the development needs of logistics industry analyze the connection and restriction of optimal path planning. With this as a constraint, the optimization model is built with the goal of comprehensive cost; Then studied how to design and implement the logistics information system based on the IoT. The paper focuses on the design of information mechanism to ensure information security and the intelligent mechanism design to improve distribution efficiency. Finally, an optimization model of logistics distribution path based on IoT is established. The improved PSO algorithm is used to optimize and compare it with the traditional PSO algorithm. It is concluded that based on the logistics distribution model of the IoT, intelligent logistics is formed, so that goods and articles, goods and trucks, goods and people can all be contacted, and the distribution route can be rationally planned.

City logistics in an omnichannel environment. The case of Brussels

Consumers live in an omnichannel environment, in which they use both online and offline channels for their shopping activities. The result is an increase in parcels shipped to consumers’ homes, challenging urban freight systems in terms of efficiency and sustainability. Many measures exist, that aim to optimise and improve activities of logistics service providers in urban areas. However, research on the parcel market and its actors is limited, so it is unclear how parcel distribution is organised and which city logistics measures are (not) adopted. This information is needed to re-think parcel distribution and support policy-making that sustains and improves urban sustainability and quality of life. Therefore, we carried out case-study research with focus on Brussels, Belgium’s largest city and capital. Findings demonstrate that logistics service providers are focused on measures that improve distribution efficiency, which is determined by parcel volume, stop density, delivery failure and urban regulation. To this end, pick-up points and lockers and tools to affect receiver behaviour are implemented most. Generally, Brussels’ parcel distribution market is dominated by conventional vehicles and traditional modes of operation, but regulations and policies are advancing the use of micro-hubs in combination with cargo-bikes. Local authorities can facilitate adoption of city logistics measures for parcel distribution by cooperating and exchanging information with the private sector and by developing a harmonised, long-term vision on freight transport policy across cities and regions.