Logistics Research Articles

Unlocking the Potential of Robotic Process Automation for Digital Transformation in Logistics and Supply Chain Management

Digital transformation (DT) and automation technologies have significantly impacted logistics and supply chain management (LSCM), enhancing business performance. Efforts to automate processes alongside machine and robotic automation aim to improve reliability, consistency, efficiency, and cost-effectiveness. While existing research has explored specific aspects of DT and robotic process automation (RPA), the deployment and implementation of RPA as a service (RPAaaS) in LSCM are under-explored. This study focuses on RPA in LSCM and aims to improve organisational processes associated with DT. A systematic review identifies five knowledge clusters regarding RPA deployment for DT in LSCM. Real-life RPA implementation cases in a third-party logistics company highlight challenges and opportunities related to RPA deployment. Insights on the timing and choice of the RPAaaS platform to facilitate DT in enterprises are discussed. This paper provides extensive theoretical insights into RPA for DT in LSCM and offers practical guidance for streamlined implementation.

Logistics Industry Agglomeration Affects New-Type Urbanization—An Empirical Test Based on Spatial Panel Models

The rapid development of the logistics industry and its cooperation with other production factors have an impact on the promotion of new-type urbanization (NTU), a more sustainable and inclusive model of urban growth. This article analyzes in-depth the impact mechanism of logistics industry agglomeration, a new way to promote innovation and improve industrial efficiency, on NTU from various dimensions, including population, economy, society, space, ecology, and urban-rural coordination. Using panel data from 31 provinces in China between 2002 and 2021, the level of NTU was calculated using the entropy weight method, and a spatial econometric model was employed to examine the impact of logistics industry agglomeration on NTU and its spatial spillover effects. The research findings are as follows. The study reveals a spatial correlation between logistics industry agglomeration and NTU across Chinese regions. Logistics industry agglomeration significantly enhances NTU levels and generates spatial spillover effects, facilitating urban development in surrounding areas. The influence of regional logistics industry agglomeration on NTU demonstrates notable spatial heterogeneity, with the central region exerting a greater impact than the eastern and western regions. It can help in the understanding of regional differences and help policy makers optimize resource allocation. The impact of logistics industry agglomeration on NTU is nonlinear, primarily characterized by a substantial improvement in NTU at the medium and low levels due to logistics industry agglomeration. It helps identify regional differences and supports personalized decision making. The level of human capital, the degree of government intervention, the degree of marketization, the level of technological industrialization, the ratio of elderly dependency, and the level of innovation have direct effects and spillover effects on NTU. This paper elaborates on the mechanism of logistics industry agglomeration on NTU in China and proposes targeted proposals for promoting the development of NTU, which is highly important for promoting urbanization in other regions of the world from the perspective of industry.

LOGISTICS MANAGEMENT OF LAND PACKAGE COMPANIES IN CIUDAD DEL ESTE

The main purpose of this study was to analyze the current situation and logistics management of parcel distribution companies in Ciudad del Este during the 2022 school year. The methodology used qualitative descriptive research has been used, which has served to develop the most important aspects of this work, from the approach to the contracting of the results obtained in the interview conducted. The interview technique was applied in a population of 10 companies located in Ciudad del Este, of which 3 companies were taken for the application of the research, the surveys were directed to the managers and officials in charge of logistics management. The research work consists of four units. Unit 1: Logistics planning system. Unit 2: Distribution logistics. Unit 3: Distribution planning. Unit 4: Quality control. The findings indicated that the different units of the logistics process such as: inventory management, load distribution and transport managers, information flow and order processing, storage and distribution have a favorable influence on the logistics management of the companies studied. Based on the results in all companies, in order to achieve competitive advantages, a comprehensive logistics system is necessary that allows all processes to be carried out in an orderly and efficient manner, thus reducing costs, organizing processes, exercising control and improving each of the company's areas. With the constant improvement of transport, and the correct distribution of materials in the construction process, it will be more efficient and this will lead to a reduction in direct and indirect costs.

Bi-objective robust optimisation approach for the management of reliable emergency logistics with uncertain disruptions during a rainstorm

ABSTRACT A reliable emergency logistics system is crucial for the urban safety and public security during a rainstorm disaster. However, the impact of uncertain issues and the mitigation of psychological risks have not been well studied in the existing works. We develop a scenario-based robust optimisation approach for the emergency logistics management under uncertain disruptions. A bi-objective model, minimising total risk and cost, is formulated to decide facility locations and route plans for emergency rescue, transfer as well as materials’ transportation. The scenarios for random disruption with partial and full degrees in nodes and edges are addressed with probabilities. A psychological risk assessment model is developed to estimate the panic degree of trapped people as time varies. The complexity of the resulting mathematical model motivated the adaption and comparison of four multi-objective methods. A real-life problem in Jinzhong, China, and several tests are conducted to provide managerial insights.

A comprehensive survey of recent advances in facility location problems: Models, solution methods, and applications

Logistics management research has significantly benefited from the increased prominence of warehouse management challenges in recent years, which are now extensively utilized in analyzing logistics distribution and transportation networks. Concerning Facility Location Problems and their variations, this work attempts to perform an extensive literature assessment of current model advances and improvements. This study also aims to ascertain the influence and developments in Facility Location Problems, which are classic optimization problems with numerous applications in manufacturing, distribution, transportation, and supply chain management decisions. This study thoroughly analyzes current developments in facility location problems, a category of optimization issues frequently used in network architecture, logistics, and supply chain management. The review analyzes literature from the past decade, categorizing works based on problem variants, mathematical formulations, solution methods, and exact approaches. Key findings highlight significant progress in solving complex facility location problems through advances in mathematical programming, scalable exact methods, and hybrid metaheuristics. Emerging trends such as uncertainty modeling, multi-objective optimization, and sustainability are also discussed. Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action

Diagnostic value of tissue plasminogen activator-inhibitor complex in sepsis-induced liver injury: A single-center retrospective case-control study

BACKGROUND Sepsis often causes severe liver injury and leads to poor patient outcomes. Early detection of sepsis-induced liver injury (SILI) and early treatment are key to improving outcomes. AIM To investigate the clinical characteristics of SILI patients and analyze the associated risk factors, to identify potential sensitive biomarkers. METHODS Retrospective analysis of clinical data from 546 patients with sepsis treated in the intensive care unit of the 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force between May 2018 and December 2022. The patients were divided into the sepsis group (n = 373) and SILI group (n = 173) based on the presence of acute liver injury within 2 hours of admission. We used the random forest algorithm to analyze risk factors and assessed potential diagnostic markers of SILI using the area under the receiver operating characteristic curve, Kaplan-Meier survival curves, subgroup analysis and correlation analysis. RESULTS Compared with the sepsis group, tissue plasminogen activator-inhibitor complex (t-PAIC) levels in serum were significantly higher in the SILI group (P < 0.05). Random forest results showed that t-PAIC was an independent risk factor for SILI, with an area under the receiver operating characteristic curve of 0.862 (95% confidence interval: 0.832-0.892). Based on the optimal cut-off value of 11.9 ng/mL, patients at or above this threshold had significantly higher levels of lactate and Acute Physiology and Chronic Health Evaluation II score. The survival rate of these patients was also significantly worse (hazard ratio = 2.2, 95% confidence interval: 1.584-3.119, P < 0.001). Spearman’s correlation coefficients were 0.42 between t-PAIC and lactate, and 0.41 between t-PAIC and aspartate transaminase. Subgroup analysis showed significant differences in t-PAIC levels between patients with different severity of liver dysfunction. CONCLUSION T-PAIC can serve as a diagnostic indicator for SILI, with its elevation correlated with the severity of SILI.

Predictive Modeling in IoT-Driven Agriculture: A Comparative Study of Regression and Classification with LIME Interpretability

Abstract. This study aims to compare the performance of regression and classification machine learning models in crop prediction by analyzing data from IoT-driven agriculture. Linear regression and random forest regression models were used to predict the percentage of root growth dry matter, while logistic regression and support vector machines were employed to classify crop production regions. To enhance model interpretability, the LIME tool was applied to analyze feature importance. The experimental results demonstrate that the models perform well in terms of prediction accuracy, and LIME provided clear feature explanations, helping identify the variables with the greatest impact on prediction outcomes. This research offers data-driven insights for optimizing resource management in smart agricultural systems.

Environmental Impact Minimization Model for Storage Yard of In-Situ Produced PC Components: Comparison of Dung Beetle Algorithm and Improved Dung Beetle Algorithm

If PC components are produced on site under the same conditions, the quality can be secured at least equal to that of factory production. In-situ production can reduce environmental loads by 14.58% or more than factory production, and if the number of PC components produced in-situ is increased, the cost can be reduced by up to 39.4% compared to factory production. Most of the existing studies focus on optimizing the layout of logistics centers, and relatively little attention is paid to the layout of PC parts for in-situ production. PC component yard layout planning for in-situ production can effectively reduce carbon dioxide emissions and improve construction efficiency. Therefore, the purpose of this study is to develop an environmental impact minimization model for in-situ production of PC components. As a result of applying the developed model, the optimization of the improved dung beetle optimization algorithm was verified to be efficient by improving the neighboring correlation by 22.79% and reducing carbon dioxide emissions by 18.33% compared to the dung beetle optimization algorithm. The proposed environmental impact minimization model can support the construction, reconstruction, and functional upgrade of logistics centers, contributing to low carbon dioxide in the logistics industry.

Решение задачи оптимизации системы транспортного обслуживания района промысла

The conditions of possible ways to improve the transport and logistics support of fishing activities are considered. The organization of the work of the fishing fleet in a fishing expedition is described. The general scheme of the functioning of the system for the delivery of fish products from the fishing area to the final consumer is formulated, taking into account its structure and the requirements of the divisions of this system for each other. Based on a detailed analysis, taking into account the development strategy of the fishing industry and the prospects for the delivery of fish products to consumers along the Northern Sea Route, the specified requirements for the projected organization of the fleet's work in a traditional and alternative logistical manner are formulated and described. In search of possible ways to optimize the system of transport and logistics support for fishing, examples of the operation of receiving and transport vessels in the organization of fishing services under the scheme “fishing area – fishing flotilla – floating base – transport fleet – port – hub – transport fleet – port” were considered. A systematic approach using the linear programming method was used to solve the problem. The mathematical model was developed based on the distributive linear programming problem of placing shipping vessels on the “shoulders” of delivering fish products to transshipment ports. The mathematical model of the problem uses two types of variables, and the total cost of transporting fish products is considered as an objective function, which should be minimized taking into account a number of restrictions. Based on the results of preliminary calculations, the “Solution Search” add-in of the MS EXCEL program was used to solve the problem in accordance with the mathematical model. The simulation results made it possible to formulate the logistical component of servicing fishing areas and justify the advantages of this approach for optimizing the choice of cluster ports, as well as the choice of transport vessels and their placement along the routes of transportation of fish products to the consumer at the lowest cost.

Evaluating the Impact of Logistics on Customer Satisfaction in the Nigerian Retail Industry

The retail industry in Nigeria has seen significant growth in recent years, with an increasing number of players entering the market and an increasing demand from consumers for convenience and efficient service. However, the industry faces significant challenges in terms of logistics, including poor infrastructure, inadequate transport networks, and inefficient delivery systems. These challenges have a direct impact on customer satisfaction, with consumers reporting delays in delivery, damaged goods, and missing items. The objectives of this research are as follows: To identify the key challenges facing logistics in the Nigerian retail industry.; To assess the impact of these challenges on customer satisfaction; To propose solutions to address these challenges and improve customer satisfaction. The methodology for this research will include a literature review to explore the existing research on logistics, retail, and customer satisfaction. Data collection was done through surveys and interviews, using both quantitative and qualitative methods. This study is significant in addressing environmental problems in the Nigerian retail industry because logistics and transportation are major sources of greenhouse gas emissions and other pollutants. Inefficient logistics and transportation systems can result in increased vehicle miles traveled, more fuel consumption, and higher emissions. By identifying the challenges facing logistics in the retail industry and proposing solutions to address them. The findings provided recommendations for the retail industry and policy makers to improve the sustainability and efficiency of logistics in the country.

Bitcoin price movement prediction by news sentiment using machine learning approach

This study investigates the potential of news sentiment, derived from Google News, in predicting Bitcoin price movements. It explores the correlation between sentiment in news headlines and Bitcoin's market behavior. Employing a data set of news headlines related to Bitcoin from Google News, this research applies sentiment analysis and various machine learning algorithms, including Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Naïve Bayes, and KNN. The approach involves extracting sentiment scores and correlating these with historical Bitcoin price data. The analysis revealed that Decision Tree and Random Forest algorithms provide a balanced prediction for Bitcoin price movements. Logistic Regression and Support Vector Machine showed high AUC scores but with unbalanced class predictions. Naïve Bayes and KNN were less effective. Overall, sentiment analysis of news headlines can predict short-term Bitcoin price movements to a reasonable degree. This research offers a novel tool for investors and market analysts, enhancing understanding of the impact of news sentiment on cryptocurrency prices. It provides a predictive model to assist in investment decision-making and market analysis. This study contributes to the literature on financial forecasting by integrating sentiment analysis with machine learning for cryptocurrency price prediction. It's one of the few studies to analyze the impact of news sentiment on Bitcoin price, offering a new perspective in the realm of financial technology and digital currencies.

DEVELOPMENT OF A CREDIT SCORING MODEL USING MACHINE LEARNING FOR COMMERCIAL BANKS IN VIETNAM

In an increasingly competitive financial market, developing an accurate and efficient credit scoring model is crucial for banks to make informed credit decisions, and to help customers obtain credit products that match their financial capabilities. This study aims to develop a credit scoring model for individual customers of Vietnamese banks using machine learning techniques. This article will use a set of financial and non-financial indicators as inputs for various machine learning models such as Logistic Regression, K-nearest Neighbor, Decision Tree, Random Forest, LightGBM, and Support Vector Method. The obtained results from these models will be carefully compared and evaluated to ultimately select the best credit scoring model for the bank. This study leverages Google Colab, a cloud-based platform, for comprehensive data analysis and model development, ensuring a robust and data-driven approach. This study hopes to provide a useful solution for Vietnamese banks in managing credit risk and improving their business performance.

Factors Influencing Officer Compliance in the Implementation of Integrated Management of Childhood Illness (IMCI) at Community Health Center in Banda Aceh City

Integrated Management of Sick Toddlers (IMCI) is an integrated approach to improving child health and well-being, which aims to reduce morbidity and mortality and promote growth and development of children under five. Despite its importance, challenges in the quality of IMCI implementation in PHC centers still exist. This research aimed to identify factors that influence staff compliance in implementing IMCI at health centers in Banda Aceh city. This research method used an analytic observational research with a cross-sectional design, involving 64 IMCI service providers from November 2023 to June 2024. Data were collected using questionnaires and analyzed using the chi-square test and multivariate logistic regression. The results showed that staff compliance in implementing IMCI was 39%. Internal factors that significantly influenced compliance were motivation (OR=14.3, 95% CI: 3.039-67.505) and knowledge (OR=8.63, 95% CI: 1.694-43.978). External factors included the availability of complete logistics facilities (OR=8.05, 95% CI: 1.706-38.04). This research identified Ulee Kareng Health Center as having the highest number of PIA service providers (17%), while Banda Raya Health Center had the lowest number (7%). Most of the PIA service providers were aged 19-44 years (68.8%), mostly female (96.9%), and had high educational qualifications (93.7%). The implications of this research underscore the important role of motivation, knowledge, and logistical support in improving staff adherence to IOP protocols. The results of this research have implications for strengthening health care systems and improving the quality of IYCF implementation to meet the health needs of children under five in Banda Aceh city.

OPTIMIZATION OF LOGISTICS MANAGEMENT TO INCREASE EFFICIENCY AT CONSTRUCTION SITES: RESEARCH OF BEST PRACTICES AND STRATEGIES

Problem statement. Problem Statement. This article provides a detailed examination of the importance of optimizing logistics management on construction sites, which serves as a key element for enhancing overall efficiency and productivity in the modern construction industry. In the context of increasing competition and complexity of construction projects, effective logistics management becomes a critical success factor. Significant budget overruns and delays in the execution of construction projects are often associated with inefficient logistics management, especially due to rework, errors in material supply, and deficiencies in coordination among different project participants. This leads to unnecessary expenses, loss of time, and a decrease in the quality of work performed. The purpose of this study. The article focuses on the development and implementation of new strategies and methods for optimizing logistics on construction sites. This includes effective management of material resources, improvement of storage and delivery processes of construction materials, as well as the integration of various aspects of the logistics chain using modern information technologies. The research involves an in-depth analysis of existing literature and current practices on construction sites, which allows identifying the main problems and determining key challenges and opportunities for improvement. Methodology. To achieve the set goal, methods of system analysis, comparative studies, and modeling of logistics processes are used. Real cases from construction practice are analyzed, and surveys of experts and specialists in the fields of logistics and construction are conducted. Conclusions. The research results can be useful for improving logistics management processes on construction sites, which will contribute to reducing resource losses and increasing efficiency in the construction industry. The article also makes an important contribution to understanding the critical importance of logistics management in modern construction and offers specific recommendations and practical tools for its improvement. This can promote the competitiveness of construction companies and the development of the industry as a whole.

Challenges and Implementation of Drug Inventory Control in Hospitals: Literature Review

One of the most important hospital management functions is the management of pharmaceutical drug logistics. Drug planning is the process of selecting the type and quantity of drugs required for procurement. Various managerial theories can be used to analyze drug control in pharmaceutical installations, and three research journals on pharmaceutical drug logistics control in hospitals are used for this literature review. The results of the analysis show that there are four methods in drug control: EOQ (economic purchase amount), ABC (better control at all times), ROP (point of repurchase), and VEN (vital, essential, and non-essential). One way to help pharmaceutical logistics stock inventory be more efficient, efficient, and responsive to patient needs and service dynamics is to apply one or a combination of these four methods. Good drug stock management can ensure optimal inventory control, improve service quality, reduce costs, and better manage risks.

Research on Innovative Management of Smart Cold Chain Logistics: Taking Cobia Shanwei of Guangdong as an Example

Research on Innovative Management of Smart Cold Chain Logistics: Taking Cobia Shanwei of Guangdong as an Example

Research on Evolutionary Game of Low-Carbon Logistics in Two-level Supply Chain under Carbon Tax Policy

Aiming at the problem of low carbon emission reduction in the sustainable development of the logistics industry, this study adopts the research method combining game theory and dynamic system analysis. A tripartite evolutionary game model including the logistics company, the manufacturer, and the government is constructed, and the strategy choice and mutual influence of each participant in the process of low-carbon emission reduction are discussed. At last, MATLAB software is used to simulate the model. By adjusting the values of different parameters, the process and results of the dynamic evolution of the system are displayed intuitively. It was found that the government's sound penalty and subsidy policy plays a key role in promoting the evolution of the manufacturer and the logistics company to adopt low-carbon strategies. Specifically, higher subsidies and moderate penalties can encourage enterprises to take low-carbon measures and promote the system's evolution to a low-carbon and stable equilibrium point. These conclusions help the logistics company and the manufacturer to identify the key factors and paths of low-carbon development, promote growth in the direction of low-carbon, green, and sustainable development, and provide theoretical support and empirical basis for the government to formulate and improve relevant policies on carbon emission reduction, and improve the effectiveness and pertinency of policies.

Factors inducing cutaneous adverse reactions in cancer patients treated with PD-1 and PD-L1 inhibitors: a machine-learning algorithm approach

Background Immune checkpoint inhibitors (ICIs) show promise in cancer treatment but can lead to immune-related adverse events (irAEs), notably affecting the skin. Understanding the factors behind these skin reactions is crucial for effective management during treatment. Hence, the aim of this study was to uncover associations between patient characteristics and cutaneous adverse reactions among cancer patients undergoing ICI treatment. Methods The study involved 209 cancer patients receiving ICIs. Statistical methods, including the chi-square test, Fisher’s exact test, and multivariable logistic regression, were employed to analyze variables such as hypertension, antihistamine use, cancer metastasis, diabetes, and opioid usage. Additionally, machine learning techniques, including logistic regression, elastic net, random forest, and support vector machines (SVM), were utilized to develop predictive models anticipating skin-related adverse events. Results Results highlighted significant associations between specific patient attributes and the incidence of skin reactions post-ICI treatment. Notably, patients using antihistamines or with cancer metastasis exhibited higher rates of skin adverse reactions, while those with diabetes or using opioids displayed lower incidence rates. Robust performance in forecasting these adverse events was observed, particularly in the predictive models employing logistic regression and elastic net. Conclusions This pioneering study contributes crucial insights into predictive modeling for ICI-induced skin reactions, emphasizing the importance of personalized treatment strategies. By identifying risk factors and utilizing tailored predictive models, healthcare providers can proactively manage adverse events, optimizing the benefits of ICIs while mitigating potential side effects.

Detection of hate: speech tweets based convolutional neural network and machine learning algorithms

There is no doubt that social media sites have provided many benefits to humanity, such as sharing information continuously and communicating with others easily. It also seems that social media sites have many advantages, but in addition to these advantages, there are disadvantages that we always strive to find a solution. One of these disadvantages is sharing hate speech. In our study, we’re discussing a way to solve this phenomenon by using Term Frequency-Inverse Document Frequency (TF-IDF) based approach to feature engineering on eleven classifiers for machine and deep learning that can automatically identify hate speech. Three different databases were used, the first of which “Hate speech offensive tweets by Davidson et al.”, the second called "Twitter hate speech" and finally we merged the second data with (Cyberbullying dataset (toxicity_parsed_dataset)". The classifiers involved are Logistic Regression (LR), Naive Bayes (NB), Multi-layer Perceptron (MLP), and Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), K-Means, Decision Tree (DT), Gradient Boosting classifier (GBC), and the Extra Trees (ET) in addition to the convolutional neural network (CNN). Maximum accuracy was attained, which exceeded 99%.

Nomogram models for predicting myocardial ischemia under high altitude exposure: a cohort study

Background: Exposure to high altitude increases the risk of myocardial ischemia (MI) and subsequent cardiovascular death. Nomogram is a graphical regression model, but there are no reports on using nomogram to predict myocardial ischemia under high altitude exposure. Our goal was to establish prediction models based on pre-high-altitude physical exposure examination data and identify key risk factors. Methods: We prospectively enrolled a total of 2,855 healthy individuals who underwent physical examination at the 920th Hospital of Joint Logistics Support Force and were scheduled to undergo high-altitude (3000–3500 m) training within six months. These participants were randomly divided into a training cohort (75%) and a validation cohort (25%). In the training set, single-factor analysis of variance and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were used to select variables, and two nomograms were established based on clinical features (CF) and clinical features + blood tests (CF + BT), respectively. The performance of the nomograms was evaluated using the area under the receiver operating characteristic curve (ROC), the concordance index (C-index), and calibration curves. Results: The C-index for the prediction models CF and CF + BT were 0.652 and 0.804, respectively. In the training cohort, the AUC for prediction models CF and CF + BT were 0.61 and 0.80, respectively. In the validation cohort, the AUC for prediction models CF and CF + BT were 0.61 and 0.81, respectively. Conclusion: We have successfully established two nomogram models to predict myocardial ischemia under high-altitude exposure and identified some risk factors.