Logistics Network Research Articles

Efficient Expansion Algorithm of Urban Logistics Network for Medical Products Considering Environmental Impact

As society continues to age, people are becoming more concerned about their health care. This has led to an increase in the demand for medical products in urban areas, emphasizing the need for regular and prompt deliveries. However, the existing logistics centers are located in the suburbs of Seoul, a metropolitan city, which makes it challenging to ensure timely delivery. To address this issue, this study aims to establish new logistics centers in urban areas, particularly in Seoul, while minimizing CO2 emissions from delivery vehicles in alignment with sustainability efforts. The scientific gap addressed in and the novelty of this paper is that the input parameters are prepared based on actual data from a medical company in Korea to reflect reality, and the mathematical model-based optimization technique is applied to determine the optimal location of a new logistics center. The genetic algorithm is developed to solve the proposed mathematical model by deriving optimal or near-optimal solutions. Furthermore, the numerical experiment examined the impact of establishing a new logistics center in one of the candidate areas of local governments in Seoul by considering environmental impact. As a result, the new logistics network can reduce CO2 emissions by approximately 66.74% compared to the existing logistics network.

Optimal carrier Selection Based on Reliability Under Delivery Spoilage and Cost Constraint

In a highly competitive market environment, outsourcing of logistics service is one of the most significant strategies for enterprises to enhance competitiveness. However, how to construct an efficient and reliable logistics network by selecting suitable logistics carriers is a complex but important practical issue. In general, there are multiple carriers to provide freight service along each route of a logistics network. Moreover, the transportation capacity of each carrier has multiple states due to various factors, which can be portrayed by a capacity probability distribution. In addition, delivery spoilage and cost constraint are the key indicators affecting carrier selection. Therefore, this paper studies the logistics carrier selection problem from the perspective of multi-state network reliability under delivery spoilage and cost constraint, and apply the minimal path (MP) method to solve the problem. The practicality of the algorithm is verified by example analysis.

Integrative Dry Port Concept: A Chance for the Balkans

Dry ports can be in the hinterland of sea ports as well as river ports. In addition, they can provide the services of classic logistics centers. Combining these three roles leads to a kind of integration not only of functions, but also of transport modes, parts and participants of the intermodal network, also to synergistic effects of the joint action of the parties involved. Therefore, this extension of the dry port concept can be called an integrative dry port. This very concept can be one of the chances for overcoming some of the environmental, economic, and social challenges of the logistics and economy of the Balkan Peninsula, bearing in mind its specific position in the context of geography and the global logistics network. The main contribution of this paper is the description of the new concept of the integrative dry port and the consideration of its applicability in the Balkans.

Hub-and-Spoke Network Optimization with Flow Delay Cost: The Case of Goods Delivery on Urban Logistics Networks in Eastern China

With respect to a traditional point-to-point (P-P) network, a hub-and-spoke (H-S) network not only uses a smaller number of links/paths but also utilizes the scale economy advantage on consolidated flows on hub–hub links and at hubs. However, the inevitable delays through hubs have always been a critical concern. Therefore, this paper develops an H-S model considering flow delay costs and applies the model to a logistics case in Eastern China. The integer quadratic term in the model’s objective function is linearized using the algebraic method. Our model is applied to develop an H-S network for its 13-node express package delivery operation, using the particle swarm optimization (PSO) algorithm. The results show using the H-S can save more than 14.1% of the total cost annually. The model also provides an applied case to the H-S configuration, especially for urban express delivery logistics in China.

Uncertain remanufacturing reverse logistics network design in industry 5.0: Opportunities and challenges of digitalization

Remanufacturing, a crucial step of reverse logistics, focuses on restoring or enhancing the functionality of waste products. The challenge in planning an effective remanufacturing reverse logistics system lies in the uncertainties from various sources. In addition, the evolving industrial landscape in Industry 5.0 necessitates adaptability to technological advancements. This paper proposes an integrated and digitalized architecture for uncertain reverse logistics network design. A fuzzy optimization model is first formulated to identify potential network configurations under varying demand-satisfying and capacity constraints. These solutions are automatically converted and assessed in a dynamic simulation environment with practical operational logic under a set of real-world scenarios. Numerical experiments are performed to validate the method and show the advantages of integrating optimization with dynamic simulation on a digital platform for strategic network planning. The results, built upon previous research, indicate that while initial investments in technology might be substantial, they may lead to long-term reductions in both costs and emissions. Moreover, collaborative decision-making is essential to mitigate potential disruptions and cascading effects. Our research contributes to the development of a novel integrated decision-support architecture and underscores the role of digitalization and Industry 5.0 in future smart and sustainable reverse logistics planning.

ARIMA-based Freight Forecasting and Network Optimization in E-commerce Logistics

Considering the proliferation of e-commerce platforms exemplified by Taobao and JD, the paradigm of online shopping has evolved into an integral facet of contemporary societal existence. To enhance network transport performance, it is essential to predict freight volume, assess existing capacity, and establish new sites to alleviate network pressure. This study focuses on a logistics transportation network, utilizing daily freight turnover data from January 1, 2021, to December 31, 2022. The ARIMA time series method is employed to forecast freight volumes at different sites within the e-commerce logistics network. Taking three logistics site route combinations, namely DC14→DC10, DC20→DC35, and DC25→DC62, as examples, two differencing operations are applied to meet the stationarity requirement of the ARIMA model. Autocorrelation and partial autocorrelation coefficients are used for preliminary model order determination. By minimizing the Akaike Information Criterion (AIC) value, an ARIMA (5) model is established. Single and multiple-step predictions are conducted, resulting in forecast curves for future freight volumes. Subsequently, an evaluation of the transportation network is performed, considering the importance of nodes and routes. The entropy weight method is applied to determine the weights of evaluation indicators. Importance indices for nodes and routes are calculated, leading to a ranking. For the assessment and selection of new site capabilities, Fisher's discriminant function is employed to classify different sites. The valuation level for site selection is determined, providing a scientific basis for the systematic choice of new sites.

A Study of Tennis Score Evaluation Based on Logistic Regression and LSTM Neural Networks

In order to study the performance of athletes in the competition, this paper establishes a comprehensive evaluation mechanism and prediction model through mathematical modeling. Firstly, this paper uses the clustering algorithm to categorize the data and establishes a score evaluation model according to the weights. Then, a momentum evaluation score calculation function was constructed and compared with the results of logistic regression algorithm. Finally, a multi-indicator data system was established to predict the results of the game using the LSTM neural network model. The experimental results show that the model proposed in this paper has a good prediction effect and provides suggestions to the participants.

Integrated relief pre-positioning and procurement planning considering non-governmental organizations support and perishable relief items in a humanitarian supply chain network

The escalating frequency and severity of disasters on a global scale have sparked inquiries into the efficacy of current disaster planning strategies in various scenarios. Despite the pivotal role of humanitarian supply chain planning in aiding impacted populations, much of the existing research is grounded in simplistic assumptions that limit their practicality. Addressing this gap, our proposed bi-objective model aligns response time and total cost, while also accommodating the collaboration between non-governmental organizations and governmental organizations to mirror real-world intricacies. This study comprehensively delves into various logistics aspects, encompassing pre- and post-disaster phases, including location, allocation, supplier selection, fleet size, supply contract, inventory, distribution, and transportation. This multifaceted approach enhances the model's suitability for managing genuine real-world emergencies. To mitigate disruption risks and unforeseen events, the model introduces pre-positioning, quantity flexibility contract, backup suppliers, and a multi-sourcing policy, thus enhancing the resilience and reliability of the logistics network. We present solutions for diverse scenarios through a scaled weighted sum method, while tackling uncertainty via a heuristic approach known as the backward scenario reduction method. Furthermore, to manage large-scale problems within an acceptable time frame, we propose an advanced hybrid algorithm. This algorithm synergizes a parallel differential evolution framework with reinforcement learning-enhanced local search mechanisms, aiming to improve both computational efficiency and solution accuracy. Finally, we validate the model's applicability through a real case study focusing on a flood scenario in Iran.

Scenario analysis on CO2 emission reductions in hinterland transport of Japan through intermodal logistics network simulation

Because transport in the port hinterlands heavily depends on roads with negative externalities, much social interest has been in modal shifts (MSs) to rail and shipping with lower environmental impact. Currently, leading industry sectors to ambitiously embrace technological innovations to decarbonize—consequently, in line with these changes, the significance of MSs should change; however, no previous studies have comprehensively presented the overall CO2 emissions and transport mode shares for future regional cargo transport. This paper presents a scenario analysis of CO2 emission reductions in the Japanese domestic hinterland of international maritime container transport based on future scenarios of technological innovation and change in shipper behavior. The simulation results using the global logistics intermodal network simulation model showed that up to 87% CO2 reduction is possible, and the primary driver for this is technological innovation. Moreover, the significance of MSs fluctuates depending on the advancement of technological innovation, suggesting that MSs hold a degree of import as a decarbonization measure during the transitional phase toward carbon neutrality (CN) as technological innovation advances. The results of this study, which fluctuated considerably by scenario and timing, have policy implications with respect to the path to be followed toward CN, including whether to promote MSs.

A COMPARATIVE STUDY ON COMSUMER LOYALTY OF E-COMMERCE COMPANIES FLIPKART AND AMAZON

This study compares Flipkart and Amazon in particular to delve into the nuances of client loyalty in the Indian e-commerce business. It uses a mixed-method approach to find the key variables affecting customer loyalty by combining surveys with other methods. According to the study, developing a following of devoted clients requires a variety of factors. On the one hand, it is essential to have concrete features like a user-friendly platform, competitive pricing, variety, and high-quality products delivered quickly. The report does stress, though, that loyalty is more than simply business. Strong brand identity and ethical business practices are critical, as demonstrated by the value of brand reputation and trust. The study demonstrates how Flipkart and Amazon use various approaches to foster customer loyalty. Flipkart creates a feeling of community by customizing the shopping experience for its customers through engaging marketing campaigns, special deals, and tailored suggestions. On the other hand, in order to expedite the shopping experience and encourage recurring transactions, Amazon places a high priority on a wide range of products, a strong logistical network, and reward programs like Amazon Prime. Notably, both businesses use artificial intelligence and data analytics to improve customer satisfaction and personalize services.One of the main challenges facing the e-commerce industry is security issues. While consumers like the ease of online purchasing, the survey notes that distrust and loyalty can be hampered by worries about data privacy, cybersecurity, and fake goods. According to the study, Flipkart and Amazon have taken steps to allay these worries, including putting strong security measures in place, guaranteeing authenticity, and keeping open lines of communication with their patrons. KEY WORDS: INDIA, E-COMMERCE, LOYALTY, FLIPKART, AMAZON, STRATEGY, FACTORS, CUSTOMER.

The Redesign of Logistics Network of Fresh Fruit in Guangxi Province of China

This study aims to investigate the distribution characteristics of China's agricultural industry and its logistics network design. The study is based on the theory and method of hub-and-spoke logistics network design, takes the logistics of agricultural products in China as the research area, and focuses on the fresh fruit industry. The specific case study took the fruit industry in Guangxi Province as an object and analyzed the overall spatial distribution from 2014 to 2022. By considering the factors of economic development, logistics development and fruit industry development, 11 indicators were selected to construct a comprehensive evaluation index system of logistics-level node cities within the logistics network. This index system aims to provide a holistic assessment of the logistics capabilities of these cities. The hub cities and spoke cities of the fresh fruit industry logistics network were determined by cluster analysis based on factor analysis. The cities are identified using the gravitational model and the logistics affiliation degree. As a result, a hub-and-spoke logistics network for the fresh fruit industry is centered on Nanning, Liuzhou and Guilin, which are designated as the key hub cities. Yulin, Qinzhou, Guigang and Baise are the secondary spoke nodes, while Beihai, Wuzhou, Chongzuo, Fangchenggang, Hezhou, Laibin and Hechi are the tertiary and quaternary spoke nodes. In order to better develop the fresh fruit industry in Guangxi, proposals are made to guide the layout of the fruit industry, improve the logistics infrastructure in the node cities, and ensure the quality of fruit transportation.

ECONOMIC AND FUNCTIONAL DIMENSIONS OF THE DEVELOPMENT OF INTERNATIONAL TRANSPORT CORRIDORS IN THE CONTEXT OF GLOBALIZATION

Under current conditions of growing interdependence of national economies, global supply chains are being transformed and trade flows are being reoriented. The dynamics and scale of these processes are largely determined by the efficiency of international transport corridors. Investments in transport corridors have a significant multiplier effect on the entire economy by stimulating the development of transport and logistics and related sectors of the economy, creating a growing demand in labour markets, and developing global value chains, international production, trade and logistics networks. The purpose of the article is to study the impact of international transport corridors on the development of territories at the national and regional levels. The use of descriptive statistics allowed to assess the impact of international transport corridors on the level of economic development, trade openness and foreign trade volumes based on the analysis of data from 121 countries. The links between the zones of influence of international transport corridors and other functional types of territories in Ukraine were investigated. The territorial coefficient of environmental load in the context of Ukrainian regions was calculated. The results of the study will be useful for both researchers in the field of logistics and transport infrastructure development and policymakers in the field of transport and economic policy.

Optimizing the location choice of reverse logistics recycling network for end-of-life photovoltaic modules based on the EPR principle

With the rapid development of the photovoltaic (PV) industry, the collection and recycling of end-of-life PV modules is facing a new challenge. Based on the EPR principle (Extended Producer Responsibility principle), this paper investigates the location choice of reverse logistics recycling network for end-of-life PV modules from the perspective of different stakeholders. In this study, a mixed integer programming (MILP) model is established to optimize the reverse logistics network of end-of-life PV modules with the objective of minimizing the total cost of the reverse logistics network, taking into account a four-tier recycling network system with customers going to the recycling centre, dismantling and processing centre, remanufacturing centre and waste disposal agency, and three recycling solutions are designed according to the EPR principle, and the optimal solution is found with the help of CPLEX software. Optimal solution, so as to determine the siting of facilities in the network and the distribution among each node. We take Zhejiang Province, one of the leading regions in China for promoting distributed PV applications, as the research object, and plan and design the recycling network of end-of-life PV modules in Zhejiang Province, determine the siting of the network as well as the distribution of the traffic to validate the validity of the MILP model, and provide opinions and references for the recycling problem of end-of-life modules in the future.

Formation of New Technology in Automated Highway System in Peripheral Highway

There will be a dramatic shift in the transportation sector with the advent of autonomous highway systems, which might boost safety, efficiency, and sustainability. Modernizing India's transportation system may be as simple as installing state-of-the-art equipment along the Western Peripheral Highway. This abstract explores the potential impacts of autonomous highway systems on the Western Peripheral Highway, a vital piece of infrastructure. The development of autonomous highway systems is facilitated by a number of critical technologies, including artificial intelligence, sensor fusion, communication networks, and autonomous vehicles. These innovations enable cars to navigate themselves, exchange data with each other and roadside infrastructure, and make split-second decisions, all with the goal of enhancing traffic flow and roadway safety. By using high-definition mapping and precise localization systems, vehicle positioning and navigation are assured to be accurate, even in complex urban environments. The installation of Formation of new technology in Automated highway system in peripheral highway along the Western Peripheral Highway in India is widely anticipated as a potential solution to the country's dire transportation issues. Formation of new technology in Automated highway system in peripheral highway offer a realistic solution to increase mobility, decrease emissions, and alleviate congestion, all of which are contributing factors to the growing demand for efficient logistics networks brought about by rapid urbanization. Smart grid technology and renewable energy sources, when integrated into highway infrastructure, can make the transportation sector more resilient and eco-friendly. To make Formation of new technology in Automated highway system in peripheral highway a reality, many things and challenges must be taken into account. Make sure the communication protocols and car manufacturers are compatible for a smooth integration and operation. Urgent action is needed to address privacy and cybersecurity concerns in order to ensure the security of data and autonomous driving systems. The development of autonomous highway systems along India's Western Peripheral Highway finally presents a chance to drastically change transportation and urban mobility. It is possible for India to use cutting-edge technology and promote cooperation between the government, businesses, and universities to build highways that are safer, more efficient, and less harmful to the environment in the future. With sufficient investment, innovative thinking, and meticulous planning, Formation of new technology in Automated highway system in peripheral highway have the potential to transform urban landscapes and improve the quality of life for countless Americans.

Development and validation of a machine-learning model for predicting the risk of death in sepsis patients with acute kidney injury

The mortality rate of patients with sepsis-induced acute kidney injury (S-AKI) is notably elevated. The initial categorization of prognostic indicators has a beneficial impact on elucidating and enhancing disease outcomes. This study aimed to predict the mortality risk of S-AKI patients by employing machine learning techniques. The sample size determined by a four-step procedure yielded 1508 samples. The research design necessitated the inclusion of individuals with S-AKI from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The patients were initially admitted to the Intensive Care Unit (ICU) for their hospital stay. Additionally, these patients (aged from 18 to 89 years old) had encountered S-AKI on the day of their admittance. Forty-two predictive factors were analyzed, with hospitalization death as the outcome variable. The training set (4001 cases) consisted of 70 % of the participants, and the remaining (1714 cases) participants were allocated to the validation set. Furthermore, an additional validation set (MIMIC-III) consisted of 1757 patients from the MIMIC-III database. Moreover, an external validation set from the Intensive Care Department of Beijing Friendship Hospital (BFH) comprised 72 patients. Six machine learning models were employed in the prediction, namely the logistic, lasso, rpart, random forest, xgboost, and artificial neural network models. The comparative efficacy of the newly developed model in relation to the APACHE II model for predicting mortality risk was also assessed. The XGBoost model exhibited a superior performance with the training set. With the internal validation set and the two external validation sets (MIMIC-III and BFH), the xgboost algorithm demonstrated the highest performance. Meanwhile, APACHE II performed poorly at predicting the mortality risk with the BFH validation set. The mortality risk was influenced by three primary clinical parameters: urine volume, lactate, and Glasgow Coma Scale (GCS) score. Thus, we developed a prediction model for the risk of death among S-AKI patients that has an improved performance compared to previous models and is a potentially valuable tool for S-AKI prediction and treatment in the clinic.

Designing a sustainable plastic bottle reverse logistics network: A data-driven optimization approach

Management of recovery options for plastic beverage containers involves some challenges. Most materials of used containers are recycled and are used to produce new products. The locations of collection facilities are the strategic decisions, affecting the amount collected and the total cost of a Reverse Logistics Network (RLN). In this study, a multi-objective (MO) optimization model is introduced to configure a plastic beverage container RLN, considering economic, environmental, and social objectives. This study also implements a scenario-based possibilistic approach to handle the uncertainty of the parameters. Furthermore, a data-driven fuzzy optimization framework is developed to consider the overlapping and multi-clustered characteristics of historical data samples. The application of the proposed method is demonstrated by considering a network in Vancouver, Canada. The numerical results reveal that the optimal configuration of the RLN resulting from the proposed MO model exhibits significant sensitivity to fluctuations in costs, demands, and the prioritization of the objective functions. Additionally, the proposed data-driven framework can incorporate decision makers' preferences when tuning the conservatism degree of uncertain parameters and the preference level of different objectives of the MO model. Moreover, the developed data-driven algorithm can reduce over-conservatism by 14% and guarantee the feasibility of optimal solutions compared to other data-driven strategies.

A scenario-based game theory integrating with a location-allocation-routing problem in a pre- and post-disaster humanitarian logistics network under uncertainty

PurposePre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.Design/methodology/approachIn this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.FindingsManagers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.Originality/valueThe model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Research on Optimization of Power Battery Recycling Logistics Network

With the popularity and development of electric vehicles, the demand for power batteries has increased significantly. Power battery recycling requires a complex and efficient logistics network to ensure that used batteries can be safely and cost-effectively transported to recycling centers and properly processed. This paper constructs a dual-objective mathematical model that minimizes the number of recycling centers and minimizes the logistics cost from the service center to the recycling center, and designs the power battery disassembly and recycling process and the recycling logistics network, and finally uses a genetic algorithm to solve it. Finally, this article takes STZF Company as an example to verify the effectiveness of this method. The verification results show that the logistics intensity of the optimized power battery recycling logistics network has been reduced by 36.2%. The method proposed in this article can provide certain reference for power battery recycling logistics network planning.

Multi-period fourth-party logistics network design from the viability perspective: a collaborative hyper-heuristic embedded with double-layer Q-learning algorithm

In the ‘new normal’ setting of a mega-crisis, the viability becomes the driving force for the fourth party logistics (4PL) network design. In this paper, the viability is characterised in terms of agility, resilience and survival sustainability as the response to changes in demand, disruption and survivability. The fortification and recovery strategies are considered in possible disruptions at transfer centres and third-party logistics providers. A novel mixed integer non-linear programming model is proposed to obtain the multi-period 4PL network solution with minimum total cost under viability constraints. Considering the NP-hard characteristic of problem and the non-convex of proposed model, the hyper-heuristic algorithm is designed. To take advantage of both global optimality seeking and local search ability, a collaborative hyper-heuristic embedded with double-layer Q-learning (CHHDLQL) algorithm is proposed. The effectiveness and efficiency of the proposed algorithm is demonstrated by the promising numerical results. By stress-testing the existing network, appropriate adjustments to fortification and recovery strategies can effectively cope with changes in demand and disruption. Furthermore, the impact of 4PL strategy, fortification and recovery strategies, and viability constraints are investigated. The demand satisfaction, network resilience and capacity can be improved by adjusting agility, resilience and survival sustainability to influence different component network costs. Highlights A novel multi-period 4PL network model under viability constraints is presented. The CHHDLQL algorithm for construction and perturbation coevolution is proposed. The stress tests apply to the 4PL network, including demand, disruption and 3PL. 4PL improves demand satisfaction, resilience and capacity by adjusting viability.

Remote Intelligent Medical Monitoring Data Transmission Network Optimization based on Deep Learning

A hospital operating status evaluation data analysis system was established based on the autoencoder's network. The Gibbs sampling method is used to obtain the approximate distribution of RBM. In addition, the Autoencoder neural network can also select feature dimensions that can better characterize the characteristics of financial operation data from a large amount of financial operation data. Deep learning methods are used to study the redundant information elimination method and the generation mechanism of multi-source heterogeneity in multi-source heterogeneous networks. The principle of intrinsic compression is used to reduce the dimensionality of the redundancy in the network and obtain the compression redundancy objective function. This article sets thresholds for information classification on the Internet. The approach was tested using financial data from a medical institution. Use smart encoders to extract 17 financial indicators from financial data that can be used for modeling. The evaluation results are used as the output vector of the model. Comparative experiments show that the AUC value and accuracy of the method proposed in this article can be improved by 0.84 and 83.33% compared with the AUC value of shallow logistic regression and BP neural network. This algorithm has apparent improvements.