Reduce Logistics Costs Research Articles

The Logistics Cost of Cikarang Dry Port: A General Review

High logistics costs in Indonesia are thus one of the principal economic inefficiencies that restrain the country's growth and competitiveness in the global marketplace. In this context, the paper focuses on the contribution of the Cikarang Dry Port (CDP) to the national economy. This qualitative research draws on an extended narrative review of the Cikarang Dry Port. These results reflect how the CDP is pivotal in reducing logistics costs, benefiting clients and manufacturing firms around the dry port area through bettered transportation and logistics service provision. The study also puts much emphasis on the fact that logistics efficiency is not only crucial at the level of individual enterprise performance but also, more broadly, for Indonesia's economic landscape. The CDP consolidates supply chains and reduces transportation costs, ultimately building overall cost competitiveness for Indonesian enterprises in more state and international markets. The study calls for the Indonesian government to invest in developing and upgrading the infrastructure of the Cikarang and Bekasi areas. These strategic investments would also help reduce the overall cost of logistics, thereby creating an enabling environment for business operations and playing a role in the economic development and growth of the nation.

ДОСЛІДЖЕННЯ ЕВОЛЮЦІЇ МОДЕЛІ ЛІДЕРСТВА У СУЧАСНІЙ СИСТЕМІ ДИСТРИБУЦІЇ

The article examines the evolution of leadership models in a VUCA environment characterized by instability, uncertainty, complexity, and ambiguity. The impact of these factors on distribution companies is analyzed, including changes in consumer behavior, personnel, and business processes. The results of the study show that the shortening of product life cycles and the growth of customer expectations have significantly complicated management processes. For example, technological innovations have reduced the product update cycle to 6 months. We analyze examples of companies such as Amazon and Walmart, which use demand forecasting systems to reduce logistics costs by 15–20%. The article develops practical recommendations for improving management approaches and proposes a leadership model adapted to modern challenges, in particular through the introduction of personalization and innovation. This study provides relevant information for improving the efficiency of distribution management in the face of global changes.

The impact of industry structure on the value of national logistics costs/gross domestic product: Evidence from China

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.

Facilitating Circular Transition in the Construction Industry: Optimizing a Prefabricated Construction Site Layout Using a Novel BIM-Integrated SLP-GA Model

Prefabricated construction is being developed as one of the pathways toward circularity in the construction industry. However, compared to traditional cast-in-place construction methods, the design of prefabricated construction site layouts presents unique challenges, such as managing the space for prefabricated components and lifting equipment, and coordinating the precise timing between off-site fabrication and on-site assembly. Existing research has primarily focused on traditional cast-in-place construction, leaving room for improvement in optimisation models for prefabricated site layouts. This study develops a BIM-based System Layout Planning-Genetic Algorithm (SLP-GA) model specifically tailored for prefabricated construction site optimisation. The proposed model improves the accuracy and visualisation of layout planning through BIM technology, enabling dynamic adjustments and real-time data integration. It also incorporates genetic algorithms to address complex multi-objective optimisation problems, avoiding local optima and overcoming the limitations of traditional SLP methods that rely on subjective judgements. Unlike previous studies that do not consider secondary handling, the optimisation objectives of this study focus on minimising material handling costs associated with secondary handling and maximising comprehensive relationships, including efficiency, safety and space utilisation. The application of this model in a case study shows a reduction in logistics costs of 8.58% and an improvement in comprehensive relationships of 11.61%, indicating significant improvements. This research advances optimisation methods for prefabricated construction site layouts, enriches optimisation objectives by considering secondary handling, and provides practical guidance for improving the efficiency and effectiveness of prefabricated construction projects.

Optimization of reverse logistics network for end-of-life vehicles: A Shanghai case study

With the surge in car ownership, the end-of-life vehicles recycling market has shown enormous development potential. As the reverse logistics network for recycling end-of-life vehicles suffers from high operating costs and low recycling rates, there is an urgent need to upgrade the actual recycling measures for end-of-life vehicles to an operable level. This article first uses the OGM (1, N) model to predict the number of end-of-life vehicles in the coming years. At the same time, a reverse logistics network model with the second-hand car market as the recycling centre was constructed with the goal of minimizing the total cost of the end-of-life vehicles reverse logistics network. The network model was simulated using mixed integer programming (MILP), and the optimal solution was solved through LINGO 12 programming. Through an example analysis of Shanghai, it is found that the market of end-of-life vehicles will embrace growth, and it is verified that the optimized reverse logistics network can effectively reduce the operation cost and logistics cost of recycling centres, and can effectively improve the actual recycling rate of end-of-life vehicles. Finally, the optimized site selection results are obtained, and a specific traffic distribution scheme is proposed, which is crucial for promoting cars that meet scrap standards to be recycled through formal channels and reducing logistics costs for recycling enterprises.

The Application of the Internet of Things in Intelligent Logistics Management

Starting from the application of the Internet of Things in intelligent logistics management, this paper first analyzes the main characteristics of intelligent logistics, including information, networking and intelligence, and then expounds the important value of the Internet of Things in intelligent logistics management, such as improving logistics efficiency, reducing logistics cost, optimizing resource allocation and so on. On this basis, the paper puts forward the key points of the application of the Internet of Things in intelligent logistics management, including the construction of the Internet of Things perception layer, building the Internet of Things network layer, improving the application layer of the Internet of Things, strengthening data analysis and mining, and paying attention to the security protection of the Internet of Things for reference.

Optimizing the organization of the first mile in agri-food supply chains with a heterogeneous fleet using a mixed-integer linear model

Consumers are increasingly demanding high-quality food, which presents significant challenges for agricultural supply chains. While the majority of research in the agri-food sector has concentrated on optimizing logistics costs and meeting demand by focusing on minimizing the last mile, the complexity of the first mile in the agricultural supply chain has been less explored. Farmers must efficiently manage the harvesting process and the transportation of harvested produce to consolidation centers to ensure the delivery of high-quality products. This paper addresses this research gap by introducing a mixed-integer programming model that leverages vehicle routing problem concepts to optimize the logistics processes involved in transporting harvested products from various fields to a central depot. The primary objective is to minimize total logistics costs associated with visiting different fields during a pick-up round using multiple vehicles. The model has been applied to a case study involving an agricultural cooperative in Greece as part of the European BBTWINS project, which aims to enhance agri-food value chain digitalization for improved resource efficiency. The results demonstrate that organizing the first mile of the agri-food supply chain with a cooled vehicle for pick-up rounds can reduce logistics costs by up to 40% compared to the current practices.

Research on optimization of multimodal hub-and-spoke transport network under uncertain demand

In the cargo transportation system, the hub-and-spoke transport network can make full use of the scale effect between logistics hubs and reduce logistics costs. Joint transportation of multiple modes of transportation can give full play to the advantages of different modes of transportation, which not only reduces logistics costs but also improves transportation efficiency. Therefore, this paper combines the advantages of multimodal transportation and hub-and-spoke network, and establishes an optimization model of multimodal hub-and-spoke transport network under demand uncertainty. The model takes into account hub capacity constraints and customer satisfaction with respect to transportation time, and to facilitate the model solution, we utilize the fuzzy expected value method and the fuzzy chance constraints based on credibility to clarify the uncertain variables in the model. We use mixed coding to describe the selection of hubs, assignment of nodes, and choice of transportation modes in this study and use the NSGA-II algorithm with local reinforcement to solve the problem. Finally, numerical experiments are designed to verify the validity of the model and algorithm through sensitivity analysis of relevant parameters, determine the reasonable number of hubs and confidence level, and obtain the influence of the change of hub capacity limit and the ratio of single and double hub transit on the research objectives. The results show that: the NSGA-II algorithm with local reinforcement can significantly improve the convergence speed of the algorithm; There is benefit inversion between economic cost and time cost, and the pursuit of economic cost minimization and time cost minimization, respectively, will lead to different choices of the number of hubs; Increasing the ratio of goods transfer between hubs is beneficial for fully utilizing the scale effect between hubs, achieving the goal of reducing economic costs, but at the same time, it will increase time costs.

Mendorong Pertumbuhan Pangsa Pasar B2B untuk Sektor Ride-Hailing Menggunakan Segmentasi Pasar Strategis

The expansion of Indonesia's ride-hailing sector has been considerable, fueled by technological advancements and the widespread embrace of smartphones. Despite its swift growth, the industry faces difficulties concerning long-term viability, safety issues, and compliance with governmental regulations. Nevertheless, the integration of advanced technologies and strategic plans for service expansion into new regions presents significant opportunities. The competitive environment in Indonesia's ride-hailing market not only stimulates innovation but also shapes a lively and evolving market atmosphere. Originally designed for consumers, the ride-hailing sector has evolved into a versatile transportation solution for various business needs, including employee transportation and goods delivery. These services offer advantages for companies, such as enhanced operational efficiency and reduced logistics costs. Recognizing the diversity of the business market, segmentation becomes vital in comprehending customer needs. Through tailored marketing approaches, companies can deliver more pertinent solutions, boosting competitiveness and enlarging B2B market share. By using K-means clustering, it yields 5 clusters, namely cluster 1: Tech Innovators and Financial Players, cluster 2: Logistics Singular Focus, cluster 3: Logistics, Retail, and Automotive Synergy, cluster 4: Culinary, Logistics, and Travel Dynamics, cluster 5: Tech Titans, Healthcare Giants, and Financial Leaders. The analysis of user clusters on the B2B Ride Hailing Indonesia platform provides useful insights that guide strategic recommendations for improving service offerings, refining marketing strategies, and optimizing business operations. Targeting the millennial demographic through digital channels and influencers, examining marginal costs for high-traffic clusters to identify optimization opportunities, exploring expansion possibilities in clusters with growth potential, and tailoring business solutions for clusters with unique needs are among the recommendations. Keywords: K-means Clustering, Market Innovation, Market Share Expansion, Ride-Hailing Sector, Segmentation, Strategic Decisions.

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.

Logistics distribution route optimization of electric vehicles based on distributed intelligent system

Abstract The data management system of health cloud authentication plays an important role in the optimization of logistics vehicle routing. It can not only help logistics vehicles choose the best distribution path, but also save time and cost and improve economic efficiency. At present, logistics has not yet formed a complete service system. High distribution costs and low distribution efficiency limit the development of the entire logistics. The reduction of logistics costs and the improvement of distribution efficiency have become the top priorities of the society. The optimization of the distribution route is the key to cost saving and distribution logistics. It is particularly important to study and optimize the distribution route, because the distribution route affects the logistics transportation efficiency and the loss cost during transportation. Therefore, this paper adjusted the scheduling system of logistics vehicles through a distributed intelligent system, and optimized the path of logistics vehicles according to the improved genetic algorithm, thereby reducing the transportation cost of logistics and improving the efficiency of logistics distribution. This article first explains the definition, classification, and main components of the delivery vehicle routing problem. Then, using cloud authentication path optimization, a distributed intelligent system is constructed. Finally, an improved ant colony algorithm is used to analyze and study the distance constraints of vehicles. By improving the ant colony algorithm, it can be seen that the optimized path pheromone concentration and the optimized sub-function have gradually increased with time. The mean pheromone concentration was 40 %, and the seventh day was 15 % higher than the first. The mean value of the optimized subfunction was 0.34 %, and the seventh day was 20 % higher than the first. The distribution cost and distribution efficiency of the optimized logistics vehicle distribution path were much higher than those of the traditional logistics distribution path. Moreover, the distribution cost of the logistics distribution path was 9 % lower than the traditional one, and the distribution efficiency was 13 % higher. The average smoothness of the optimized logistics path is about 90 %, and the seventh day is 11 % higher than the first day. The average fitness of the optimized logistics path is 88 %, and the seventh day is 14 % higher than the first day. In a word, the data management system can uniformly schedule logistics vehicles and improve the efficiency of distribution.

Development of Gresik – Mojokerto Railway Infrastructure to Accelerate Economic Growth in Gerbangkertosusila

The most important economic area in East Java is the Gerbangkertosusila (GKS) area. The GKS area between Gresik – Mojokerto is not yet connected directly, almost all goods have to go through Surabaya and Sidoarjo first. This results in high logistics costs and long travel times. The research method used in this research is a qualitative research method on economic growth factors in the GKS area. The factors considered in economic growth are population, number of people of productive age, number of people who are not yet working, minimum income/month, number of poorer people, percentage of poorer people, length of roads, number of people accessing the internet, number of telecommunication towers, length of roads damaged, investment value, export value, import value, and gross regional domestic product (GRDP). The analytical tool used in the research is a descriptive method to measure and assess the quality of economic growth and changes in regional economic growth factors, assisting strategic planning for the construction of new railway lines as the right solution for developing regional economic growth. Therefore, to accelerate economic growth and reduce logistics costs in the GKS area, a new railway line between Gresik - Mojokerto should be built. If logistics costs are low, it will reduce the price of goods and increase the opportunity to order goods and open up new jobs. If new job opportunities are opened, it will be possible to reduce the number of unemployed people by 375,392 people and the poorer population by 719,944 people. So it is hoped that it will increase economic growth to reach 7%. Only the Gresik and Sidoarjo regions have met this economic growth. Mojokerto’s economic growth is still 5.56% and lags behind other regions. If the construction of the new Gresik – Mojokerto train line is realized, then economic growth in the GKS region of at least 7% will not be difficult to achieve.

Design of Rural E-commerce Logistics Information Management System Based on Spring Boot

With the improvement of science and technology, the Internet has more and more affected people's daily life. People can stay at home and learn the latest information, movies, animations, music, etc. through the Internet. They can also buy goods, learn knowledge, and even find jobs through the Internet. It can be seen that the development of the Internet has brought great convenience to people's lives, and many industries have been born. As an emerging Internet industry, e-commerce has provided a large number of job opportunities for the market. Merchants can promote through e-commerce platforms, and customers can purchase goods through the platform. To ensure the normal and orderly flow of this business activity, efficient logistics systems are indispensable. With the rise of the concept of smart logistics, it not only requires employees to have professional theoretical knowledge and management service knowledge, but also professional technical knowledge such as electronic information technology. Designing an efficient logistics information management system requires cultivating smart logistics talents. E-commerce logistics, through information management systems, can not only achieve intelligent management and control of the logistics process, improve logistics efficiency and reduce logistics costs, but also enhance the automation and intelligence level of logistics, promoting the development of the logistics industry towards a more intelligent, efficient, and sustainable direction.

Edge intelligence empowered delivery route planning for handling changes in uncertain supply chain environment

Traditional delivery route planning faces challenges in reducing logistics costs and improving customer satisfaction with growing customer demand and complex road traffic, especially in uncertain supply chain environment. To address these challenges, we introduce an innovative two-phase delivery route planning method integrating edge intelligence technology. The novelty of our approach lies in utilizing edge computing devices to monitor real-time changes in road conditions and dynamically adjust delivery routes, thereby providing an effective solution for efficient and flexible logistics. Initially, we construct a mixed-integer programming model that minimizes the total cost under constraints such as customer destinations and time windows. Subsequently, in the cloud-edge collaborative mode, edge computing devices are utilized to collect real-time road conditions and transmit it to the cloud server. The cloud server comprehensively considers customer demand and road condition changes and employs adaptive genetic algorithms and A-star algorithms to adjust the delivery routes dynamically. Finally, comprehensive experiments are conducted to validate the effectiveness of our method. The results demonstrate that our approach can promptly respond to changes in customer demands and road conditions and flexibly plan the optimal delivery routes, thereby significantly reducing overall costs and enhancing customer satisfaction.

영남지방 초기 국의 등장 배경과 사로국 구조

Through the long-distance trade network and ritual structures of base settlements that were intensively developed in the Yeongnam region in the 2nd century BC, the background of the establishment of the early politics and the method of solidarity were examined. Long-distance trade beyond the Yeongnam region is concentrated in the 2nd century BC, and the bases where foreign trade is active overlap with regions(mainly Daegu, Gyeongsan, and Gyeongju) found in the early days of the clustering of wooden tombs. In some regions, politics that internally united regional units appeared in order to form a socio- economic coalition suitable for logistics distribution, redistribution, cost reduction, and information solidarity amid intensively developed foreign trade in the 2nd century BC. Thus, it is inferred that the clustering of wooden tombs appeared from the time when these early politics reached villages and townships as local political bodies to maintain active foreign trade, iron production, and distribution. In this situation, by linking topographic conditions, traffic routes and literature records, and archaeological data, focusing on Saro-guk, the composition of the euprak and the structure of the country were explained. Around the middle of the 2nd century BC, it is expected that the early Saroguk was established, and it is interpreted that the 6 villages, symbolized as major towns and villages, were united through the conference. Early Saroguk is a device that binds and maintains local communities while showing various forms for each town and village, and the phenomenon of establishing altar-style stone tombs, high- altitude enclosure, standing-ritual structures, and early wooden tombs by base is similar. In addition, the title of ChaChawoong, which was mentioned in early literature records, also weighed on the ideological and religious power base. Given the ritual structure of the settlement base and the power base of the early ruler, it was difficult to sustain the national structure only by pursuing the interests of foreign trade, and it is assumed that even by transforming the traditionally important megalithic monuments and high-altitude enclosure, it led to community cohesion.

Research on a Joint Distribution Vehicle Routing Problem Considering Simultaneous Pick-Up and Delivery under the Background of Carbon Trading

In order to explore the positive impact of the joint distribution model on the reduction in logistics costs in small-scale logistics enterprises, considering the demand on enterprises for simultaneous pick-up and delivery, as well as the cost of carbon emissions, this study considers the vehicle routing problem of simultaneous pick-up and delivery under a joint distribution model. First of all, an independent distribution model and a joint distribution model including fixed transportation, variable transportation, time penalty, and carbon emissions costs are established; second, by adding the self-adaption cross-mutation probability and the destruction and repair mechanism in the large-scale neighborhood search algorithm, the genetic algorithm is improved to adapt to the solution of the model in this paper, and the effectiveness of the improved algorithm is verified and analyzed. It is found that the improved genetic algorithm is more advantageous than the original algorithm for solving the problems of both models designed in this paper. Finally, the improved genetic algorithm is used to solve the two models, and the results are compared and analyzed. It is found that the joint distribution model can reduce the total cost by 6.61% and the carbon emissions cost by 5.73%. Additionally, the impact of the carbon trading mechanism on the simultaneous pick-up and delivery vehicle routing problem under the joint distribution model is further explored. The results of this study prove that enterprises can effectively reduce costs, improve profits, reduce carbon emissions, and promote the sustainable development of logistics enterprises under the condition of joint distribution.

Fertilizer Logistics in Brazil: Application of a Mixed-Integer Programming Mathematical Model for Optimal Mixer Locations

Background: Brazil is one of the largest consumers of fertilizers and is highly dependent on the international market to meet its demand for agricultural production inputs. The complexity of the fertilizer supply chain motivated us to carry out this study on redesigning the fertilizer logistics chain and evaluate strategies for reducing logistics costs by redesigning the fertilizer mixing network in Brazil, a country that is heavily dependent on imported fertilizers for agriculture. Methods: We introduce a multi-product mixed-integer linear programming optimization model encompassing the logistics network, from import ports to mixing factories and agricultural fertilizer supply centers. This model includes logistics infrastructure and taxes, accounting for greenhouse gas emissions (specifically carbon dioxide) in fertilizer logistics. Results: The results indicate that expanding the port capacity for fertilizer importation can significantly reduce logistics costs and greenhouse gas emissions by up to 22.5%, decreasing by 23.9% compared to the baseline. We also observed that removing taxes on fertilizer importation can reduce logistics costs by approximately 11%, but it increases greenhouse gas emissions by 2.25% due to increased reliance on road transport. We identified 15 highly resilient regions for establishing mixing factories, evaluated various scenarios and determined the importance of these locations in optimizing the fertilizer supply network in the country. Moreover, the results suggest a significant potential to enhance the role of Brazil’s Northern Arc region in fertilizer import flows. Conclusions: Public policies and private initiatives could be directed toward encouraging the establishment of mixing factories in the identified regions and increasing transport capacity in the Northern Arc region. Improving the logistical conditions of the fertilizer network would contribute to food security by reducing the costs of essential inputs in food production and promoting sustainability by reducing greenhouse gas emissions.

Intensification of oil production through the use of hydraulic fracturing fluid based on polyacrylamide

The article considers the technology of Hydraulic Fracturing (HF) based on polyacrylamide (Co-PAM), identifies parameters for evaluating the quality of the fluid both on a guar and polyacrylamide base. Fluid viscosity is a main criteria of success of HF operations. Thus, fluid thermal stability, determine the pumping time. The composition of the Co-PAM-based HF fluid differs from the guar-based one, consisting of two main chemical reagents - a gelling agent and a crosslinker(x-linker), in that only a synthetic gelling agent is used. Recommended concentration starts from 4 l/m3. At this concentration the synthetic gel starts to exhibit properties of a sand-carrying fluid, suitable for standard pump rate (4.0-5.0 m3/min) HF. A significant advantage is compliance with the requirements for HF fluids which not only improves the filtration properties of the formation but also considers the economic feasibility of using such a chemical reagent. Co-PAM gel using provides reduction of logistics costs, water heating. Also it allows to exclude hydration unit, reduced crew, decreased wear on pumping units, including reduced consumption of lubricants during the HF operation.

Metaverse Algorithm in Automated Management of Intelligent E-Commerce People and Goods Yards

The article mainly studies the Metaverse algorithm in the automated management of intelligent e-commerce people and goods yards, aiming to optimize the logistics operation and inventory management in the e-commerce people and goods yards through the Metaverse algorithm. The Metaverse algorithm based on the automated management of intelligent e-commerce people and goods yards refers to a method based on Metaverse scenarios to achieve intelligent logistics operations and inventory management with minimal investment in manpower, material resources, and financial resources. Finally, the article verifies the feasibility and superiority of the Metaverse algorithm through a practical application case. The research results show that the Metaverse algorithm can increase the efficiency of automated management of intelligent e-commerce people and goods to a maximum of 93%, which is 4.9% higher than the general management method. Therefore, it can be concluded that after optimizing the logistics operation and inventory management of the people and goods yard, the Yuanverse algorithm has improved the efficiency of logistics operation and inventory management level, and reduced logistics costs.

The Collaborative Application of Internet of Things and Artificial Intelligence in Smart Logistics

This article introduces the current application status and development trends of the Internet of Things and artificial intelligence technology in smart logistics, analyzes how the Internet of Things and artificial intelligence technology work together to achieve efficient operation of smart logistics systems, as well as the challenges and opportunities they face. This article believes that the Internet of Things and artificial intelligence technology are the core driving forces of smart logistics. They can achieve informatization, automation, and intelligent processing in various aspects of logistics, improve logistics efficiency, reduce logistics costs, and promote green and sustainable development of logistics.