Logistics Network Research Articles

Merchant Fleet Performance of Türkiye: A CRITIC-based TOPSIS Approach

The study focuses on assessing the merchant fleet performance of Türkiye, a country surrounded by seas on three sides and holding a pivotal position in global logistics networks. It considers mean age of vessels, total carrying capacity in dead weight tons, and the number of ships as indicators influencing Türkiye’s merchant fleet performance, with the importance weights of these indicators determined using the CRITIC method. Based on the determined weights, Türkiye’s merchant fleet performance for the years 2011-2023 is evaluated using the TOPSIS method. The findings reveal that mean age of vessels emerges as the indicator with the highest importance weight. The analysis indicates an improvement in Türkiye’s fleet performance between 2011 and 2013, followed by a subsequent decline from 2013 onwards. Notably, there is a significant shift observed in 2016 across all three indicators. Given Türkiye’s aging fleet, the study suggests a focus on acquiring young and high-capacity vessels to enhance fleet performance. This strategic approach can help Türkiye optimize its maritime trade capability and maintain competitiveness in the global shipping industry.

Low Carbon Planning of Multiple Integrated Energy Systems Considering Trans-Regional Battery Logistics Network

Low Carbon Planning of Multiple Integrated Energy Systems Considering Trans-Regional Battery Logistics Network

Aerial Mapping of the Peruvian Chira Valley Banana Production System to Monitor the Expansion of Fusarium Wilt Caused by Tropical Race 4

The recent incursion of Fusarium wilt of banana (FWB) caused by TR4 threatens banana production in the Chira Valley of Peru. To develop a management strategy, we mapped the entire production area from the air. During 12 flights at an altitude of 610 m (2,000 ft), we gathered 133,700 images in a timeframe of 2 weeks and constructed an orthomosaic map of 73,000 ha. This unveiled the complex logistic network across the banana-producing region, comprising 150 km of primary roads, as well as numerous secondary and tertiary unpaved paths through and bordering banana plantations. Moreover, the Poechos reservoir—with a total length of 450 km—feeds the entire Chira Valley irrigation system, which could further exacerbate TR4 expansion. Using georeferenced landscape details (digital terrain model), we determined areas prone to flooding, which is also relevant for disseminating TR4. Analyses of the maps resulted in the identification of many suspect areas for direct sampling and subsequent analyses to study the expansion of TR4. At four locations, we confirmed two cases, and by June 8, 2023, a total of 207 cases were reported. We conclude that TR4 is a serious threat to the entire region, which exports approximately 25% of the global organic bananas. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Increase inflation – major economic problem of the Republic of Moldova

The article describes the current economic and social situation, recorded in the Republic of Moldova, characterized by the accelerated evolution of the inflation rate. In order to maintain the macroeconomic balance at the level of the national economy, it is important to know the specifics of the challenges faced by the governing act. The development of financial policies capable of annihilating the action of the factors that triggered these inflationary trends is a pressing necessity for decision-making factors at the macroeconomic level. The situation is aggravated by the fact that the nature of the main causes, which influenced this socio-economic crisis, is of external origin, being the conjuncture created within the international resource markets, especially the energy ones. In the same way, the international political situation should not be neglected, destabilized by the armed conflict in the immediate vicinity of the national borders of the Republic of Moldova, a fact that created significant barriers for the country's foreign trade, regional communication logistics networks, etc. Thus, the author proposed to carry out an analysis of the inflationary processes registered in the Republic of Moldova, of the government's reaction to these challenges, with the elaboration of some conclusions and proposals on this subject.

Cyber-Physical Internet (CPI)-enabled logistics infrastructure integration framework in the greater bay area

The emerging digital economy has facilitated the digital transformation of the logistics industry toward a standard and collaborative state. The logistics infrastructure has accordingly attracted much attention as the basis for supporting digital economy implementation. Moreover, the initiatives of regional collaborative development, such as in the Greater Bay Area (GBA) of China, have accelerated the integration of logistics infrastructure and operation-level collaboration. Fortunately, the Cyber-Physical Internet (CPI) has created new opportunities for promoting infrastructure integration through the cyber, physical and internet dimensions. However, regional infrastructure integration in the GBA faces various challenges associated with CPI network operations. Thus, a bottom-up approach is proposed to facilitate logistics infrastructure integration in the CPI environment based on the conceptual fusion of computer networks. Considering the fundamental difference in network fusion and operation mechanisms between CPI and computer networks, a CPI-LI2 (logistics infrastructure integration) framework is designed as an integrated solution. Finally, a simulation study is conducted using real-life medical logistics data from the GBA to verify the effectiveness of the CPI-LI2 framework. Experiments show that the framework is well adapted to large-scale logistics networks with dynamically changing links.

Strategic Response Practices and Performance of Maersk Logistic Company in Mombasa County, Kenya

Organizations need to embrace agile and precise strategies to swiftly respond to dynamic market conditions, referred to as strategic response practices. Organizations must be able to change and thrive in a constantly evolving marketplace while maintaining competitiveness and sustainable growth through these practices. The aim of this research was to figure out how the strategic response practices in Maersk Kenya, a logistics firm, impact its performance. The research methodology was based on theories such as Stakeholder Management Theory, Schumpeter's Theory of Innovation, Competitive Advantage Theory of Michael Porter, Strategic Choice Theory, and Balance Scorecard Theory. To meet the study goals, the study made use of a descriptive research design and then set a specific target. The sample population of 112 respondents comprising of senior managers, middle managers, customer experience agents, sales agents, customs officers as well as port authority officers. A total of 54 individuals was selected as the sample size, which accounts for 48 % of the entire population. The research tool was tested in a pilot study to ensure its efficacy and reliability. The sample was one-tenth the size of the original. This research study applied a method of sampling known as stratified sampling and made use of both secondary and primary sources of data. We gathered primary data by administering a structured questionnaire that included a 5-point Likert scale. The survey was designed with limited options, requiring the participant to choose one out of five available choices. Both qualitative and quantitative methods was utilized to analyse the data. The quantitative approach involved calculating percentages, frequencies, means, and standard deviations. On the other hand, the qualitative approach involved conducting content analysis. Correlation analysis and regression analysis was to figure out the existing correlations between strategic response methods and performance. Correlation analysis found a statistically significant association between performance and stakeholder involvement, innovation, and differentiation strategies, as well as logistics considerations. The study recommended that there was need for organization to develop methods or ways to consistently evaluates and improves its logistics network to ensure optimal performance and customer satisfaction.

Momentum Study Based on Logistic Regression Models and BP Neural Networks

In the context of a game, "momentum" usually refers to a set of events (e.g., consecutive points scored) that create momentum or a trend in a game, which may have a significant impact on the outcome of the game. The purpose of this paper is to investigate how momentum affects the outcome of a game through mathematical modeling. First, an AHP-TOPSIS model is built to calculate the score of each athlete at each moment to quantify and visualize momentum. Second, based on real game data, a Kruskal-Wallis H model test is established to assess the correlation between momentum and score. To predict the fluctuation of players' momentum, logistic regression model was used to predict the momentum fluctuation. Finally, GABP neural network and genetic algorithm are established to visualize the performance data of the players and predict the inflection points during the game. The model proposed in this paper effectively solves the problem of momentum during the game and improves the ability to predict and analyze the game results.

The multidepot vehicle routing problem with intelligent recycling prices and transportation resource sharing

The increasing focus on environmental regulations and the economic advantages of recycling has spurred interest in the design of multidepot reverse logistics networks (MDRLNs). In these networks, the growing use of intelligent recycling bins (IRBs) has been beneficial for both product recycling and standardizing recycling product pricing. Furthermore, collaboration and resource sharing enhance the efficiency of resource utilization and recycling. This study proposes a multidepot vehicle routing problem with time windows that incorporates intelligent recycling prices (IRPs) and transportation resource sharing (MDVRPTW-IRPTRS). Initially, a linear function is developed to define the relationship between the volume of returned products and IRPs. Subsequently, the problem is expressed as a mathematical model aiming to minimize total operating costs and maximize total recycling profits. Additionally, a hybrid algorithm that combines a three-dimensional (3D) k-means clustering algorithm with a self-adapting genetic algorithm-particle swarm optimization (SGA-PSO) is devised to determine the optimal solution for MDVRPTW-IRPTRS. The 3D k-means clustering algorithm is utilized to categorize IRBs within an MDRLN. The SGA-PSO algorithm incorporates elite preservation and self-adaptive update mechanisms to enhance the solution quality and algorithm convergence. A transportation resource sharing (TRS) strategy is integrated into the SGA-PSO, facilitating the allocation of shared vehicles to alternative recycling routes. A comparative analysis of SGA-PSO against other algorithms, including a hybrid genetic algorithm, an improved particle swarm optimization algorithm, and a hybrid genetic algorithm with variable neighborhood search, demonstrates its superiority in solving the MDVRPTW-IRPTRS. The model and algorithm are applied in a real-world case study in Chongqing, China, and the study discusses the optimized results under varying TRS strategies and IRP schemes, contributing to the development of an efficient and synergistic urban reverse logistics network. Moreover, the superior performance of the proposed approach is validated through the ablation experiments. This study offers valuable decision-making support for fostering an environmentally sustainable and resource-efficient city.

Designing a Reverse Logistics Network for End-of-Life Vehicles in an Uncertain Environment

The strategic development of reverse logistics networks is crucial for addressing the common challenge of low recovery rates for end-of-life vehicles (ELVs) in China. To minimize the total cost of the reverse logistics network for ELVs, this paper proposes a mixed-integer linear programming (MILP) model. The model considers the recycling volume of different vehicle types, facility processing capacity, and the proportions of parts and materials. Building on this foundation, a fuzzy mixed-integer nonlinear programming (FMINLP) model is developed to account for the inherent uncertainty associated with recycling volumes and facility processing capacities. The model was solved using Lingo, and its effectiveness was validated using Jiangsu Province of China as a case study, followed by a sensitivity analysis. The results indicate that dismantling and machining centers incur the highest processing costs. Variations in recycling volume and facility handling capacity significantly impact total costs and site selection, with the former having a more pronounced effect. Increasing facility processing capacity effectively increases the recovery rate. Moreover, a higher confidence level corresponds to higher total costs and a greater demand for facilities.

Emotion dysregulation links pathological eating styles and psychopathological traits in bariatric surgery candidates.

Approximately one-third of bariatric surgery patients experience weight regain or suboptimal weight loss within five years post-surgery. Pathological eating styles and psychopathological traits (e.g., emotion dysregulation) are recognized as potential hindrances to sustain weight loss efforts and are implicated in obesity development. A comprehensive understanding of these variables and their interplays is still lacking, despite their potential significance in developing more effective clinical interventions for bariatric patients. We investigate the prevalence of and interactions between pathological eating styles and psychopathological traits in this population. 110 bariatric surgery candidates were characterized using the Binge Eating Scale (BES), Hamilton Depression/Anxiety Scales (HAM-D/A), Barratt Impulsiveness Scale (BIS-11), Experiences in Close Relationships (ECR), Difficulties in Emotion Regulation Scale (DERS). We analyzed these variables with multiple logistic regression analyses and network analysis. Patients with pathological eating styles showed more pronounced anxiety/depressive symptoms and emotion dysregulation. Network analysis revealed strong connections between BES and DERS, with DERS also displaying robust connections with HAM-A/D and ECR scales. DERS and attention impulsivity (BIS-11-A) emerged as the strongest nodes in the network. Our findings demonstrate the mediating role of emotion dysregulation between pathological eating styles and psychopathological traits, supporting existing literature on the association between psychopathological traits, insecure attachment styles, and pathological eating behaviors. This research emphasizes the significance of emotion regulation in the complex network of variables contributing to obesity, and its potential impact on bariatric surgery outcomes. Interventions focusing on emotion regulation may thus lead to improved clinical outcomes for bariatric patients.

Taxonomy of key practices for a sustainable Last-Mile logistics network in E-Retail: A comprehensive literature review

ObjectiveThe growth of e-commerce has transformed last-mile logistics, with direct-to-consumer deliveries and frequent delivery failures. This has exacerbated pollution and congestion issues, negatively impacting urban life. Given this scenario, the present study aimed to develop a taxonomy that enables the design of a sustainable last-mile logistics network specifically tailored to the context of e-retail. MethodA systematic literature review was employed for collecting robust scientific research, with the PRISMA diagram used to represent the steps followed in this process. Content analysis was applied to categorize the sustainability practices identified in the literature, grouping them according to the last-mile logistics process. RelevanceThis research contributes to understanding the last-mile logistics field in the e-retail context and provides valuable insights for formulating sustainable strategies and practices to mitigate the negative impacts of e-retail growth on the urban environment and society’s quality of life. ResultsThe research presents a structured taxonomy with 31 categorized practices across last-mile logistics processes: fulfillment, transportation, and delivery modalities. The results indicate that a sustainable last-mile network includes consolidation centers, shared micro-depots, shared freight transportation, electric vehicles such as bicycles and tricycles, and the implementation of collection and delivery points (CDPs). Social/Management ContributionsThe taxonomy offers elements that may contribute to operational efficiency, environmental impact reduction, and improving society’s quality of life by providing practical guidelines for the sustainable management of last-mile logistics in the e-retail context.

Addressing the challenges of using autonomous robots for last-mile delivery

The importance of last-mile delivery (LMD) in today's logistics networks has increased recently due to the rising popularity of e-commerce and home delivery. Various LMD modes, such as drones and autonomous vehicles, have emerged to solve the problems encountered during LMD activities and to reduce human intervention in the process. The purpose of this paper is to determine the challenges that arise when using autonomous delivery robots (ADRs) in LMD. Throughout the research, the challenges have been revealed, categorized into six titles and their significance has been calculated via the intuitionistic fuzzy (IF) pivot pairwise relative criteria importance assessment (PIPRECIA) method in conjunction with PESTEL Analysis. IF-PIPRECIA is employed for the first time in the literature with PESTEL Analysis to evaluate the challenges for the implementation of ADRs in LMD. According to the proposed methodology results, challenges related to “Energy consumption,” “Battery life and energy management,” “Cybersecurity,” “Air pollution,” and “Uncertain return on investment” are the most critical factors to consider when implementing ADRs in LMD.

The Poultry Red Mite, Dermanyssus gallinae, travels far but not frequently, and takes up permanent residence on farms

Management of Dermanyssus gallinae, a cosmopolitan hematophagous mite responsible for damage in layer poultry farming, is hampered by a lack of knowledge of its spatio-temporal population dynamics. Previous studies have shown that the circulation of this pest between farms is of strictly anthropogenic origin, that a mitochondrial haplogroup has been expanding on European farms since the beginning of the 21st century and that its local population growth may be particularly rapid. To refine our understanding of how D. gallinae spreads within and among farms, we characterized the genetic structure of mite populations at different spatial scales and sought to identify the main factors interrupting gene flow between poultry houses and between mitochondrial haplogroups. To this end, we selected and validated the first set of nuclear microsatellite markers for D. gallinae and sequenced a region of the CO1-encoding mitochondrial gene in a subsample of microsatellite-genotyped mites. We also tested certain conditions required for effective contamination of a poultry house through field experimentation, and conducted a survey of practices during poultry transfers. Our results confirm the role of poultry transport in the dissemination of mite populations, but the frequency of effective contamination after the introduction of contaminated material into poultry houses seems lower than expected. The high persistence of mites on farms, even during periods when poultry houses are empty and cleaned, and the very large number of nodes in the logistic network (large number of companies supplying pullets or transporting animals) undoubtedly explain the very high prevalence on farms. Substantial genetic diversity was measured in farm populations, probably as a result of the mite's known haplodiploid mode of sexual reproduction, coupled with the dense logistic network. The possibility of the occasional occurrence of asexual reproduction in this sexually reproducing mite was also revealed in our analyses, which could explain the extreme aggressiveness of its demographic dynamics under certain conditions.

Modelling a Logistics and Financial Supply Chain Network during the COVID-19 Era

Background: Supply chain networks (SCNs) have been interrupted by the COVID-19 pandemic, leaving them open to financial losses. SCs have been impacted by the pandemic, necessitating the adoption of sustainable practices and dynamic capacities to ensure resilience and performance. Several studies have focused on this subject, offering insights into the importance of sustainable supply-chain management, corporate governance, big data management activities, and digital technology in minimising the consequences of the pandemic and fostering sustainability. Methods: This study suggests an analytical framework for assessing environmentally friendly procedures and dynamic capacities to assure performance in a disruptive environment. Results: The following are some of the important details and contributions in this article: (1) developed a conceptual framework for assessing dynamic capacities and sustainable behaviours considering COVID-19, (2) concentrates on financial ratios during COVID-19, and (3) established drivers for sustainable practices and competencies during disruption and unpredictable business settings. Conclusions: The suggested model can assist practitioners in creating and implementing sustainable supply chain (SC) activities and tracking and assessing their effects on the sustainability of businesses. So, the proposed model can assist managers in creating and implementing sustainable supply-chain activities and tracking and analysing their effects on the sustainability of businesses.

Preliminary model development for archipelago country logistic system

Indonesia, the largest island country in the world, requires a reliable logistics system to meet its people's needs. The National Logistics System Development Blueprint (SISLOGNAS) supports this process, increasing trade, economic productivity, infrastructure investment, and employment opportunities. However, Indonesia's logistics performance index is currently ranked 61st out of 139 countries surveyed. To address these issues, an initial model was developed for national logistics system variables for archipelagic countries. This research uses an exploratory factor analysis approach with Jamovi software. The study found that 50 indicators in the logistics system of an archipelagic country like Indonesia are classified into six categories: services, commodities, logistics infrastructure, modes of transportation, timeliness, and technology. These indicators interconnect to develop an effective and efficient national logistics network

Research on emergency transport planning for E-commerce logistics network based on simulated annealing algorithm

In response to the issue of insufficient demand for logistics facilities during holidays or the temporary or permanent closure of logistics facilities due to sudden events such as epidemics, natural disasters, etc., this paper proposes a planning model and solution algorithm based on multi-objective dynamic decision-making to optimize the emergency transport plan and structure of the e-commerce logistics network. Firstly, an LSTM model is utilized to predict the transportation demand for a certain period in the future. Decision variables are introduced to indicate whether to open or close new routes after closing a logistics facility. The objective functions are set to minimize the number of routes with changes in cargo volume, minimize the load imbalance of each route, and minimize the cumulative total of parcels that cannot circulate normally. Considering factors such as normal circulation of parcels, circulation capacity, transportation capacity after opening or closing facilities, and time constraints, a multi-objective planning model is constructed. Finally, the simulated annealing algorithm is employed for solution, obtaining a circulation plan that meets the requirements. Simulation experiment results demonstrate that the model can rapidly and accurately respond to the paralysis of the e-commerce logistics network caused by sudden events, showcasing high efficiency and practical application value.

The effects of costs on drone uptake in multi-modal logistics systems within a healthcare setting

Uncrewed Aerial Vehicles (UAVs; commonly known as drones) have been gaining interest as a potential transport mode for logistics (i.e., payload delivery), bringing suggested benefits such as reduced transit times and improved access in hard-to-reach locations. However, drones have yet to become widely established in routine logistics systems, with a postulated reason being that the higher costs associated with operating drones relative to traditional surface transport modes (e.g., vans, bicycle couriers) present a barrier to uptake. Based on case studies of two real-world logistics networks transporting patient pathology samples in a healthcare setting in the United Kingdom, this study investigated the effects of the relative costs of drones on mode choice in integrated, multi-modal logistics systems. Results suggested that drones could be a financially viable option if their costs reduced below ∼19% of current values, although such a reduction may not be feasible, even in a future involving increased drone automation. Drones reduced sample transit times by up to ∼70% compared to vans but benefits to the wider healthcare system were negligible because level of service requirements for transit times could be achieved by all modes.

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

Integrated decisions for global supply chain network configuration: a dynamic multi-modal perspective

ABSTRACT This paper proposes a mathematical model to optimally design a multi-modal global production and logistics network consisting of multiple plants, warehouses, and marketplaces. The proposed model is a mixed integer linear program (MILP), which optimally selects different facilities and warehouses required to be active in each time period to fulfil the demand of various marketplaces. The objective function is cost minimization pertaining to setup, production, inventory holding, and transportation. Along with this, the model also decides the optimal quantities to be transported among the plants, warehouses, and marketplaces. The model also optimally selects the transportation mode to be deployed. The paper then expands the proposed model to include a time-cost trade off by imposing a penalty cost for delays and compares and critiques the results of the two scenarios. The proposed MILP has been validated on a randomly generated data set consisting of 4 plants, 6 warehouses, 4 marketplaces, and 3 time periods (4P-6W-4 M-3T) using LINGO 10 optimization package. The illustration considers three modes of transportation namely, road, rail, and air. Last, the paper concludes with discussing managerial implications of the work along with suggesting some research directions for future.

FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records

With the rapid growth of Internet of Vehicles (IoV) technology, the performance and privacy of IoV terminals (IoVT) have become increasingly important. This paper proposes a federated learning model for IoVT classification using connection records (FLM-ICR) to address privacy concerns and poor computational performance in analyzing users' private data in IoV. FLM-ICR, in the horizontally federated learning client-server architecture, utilizes an improved multi-layer perceptron and logistic regression network as the model backbone, employs the federated momentum gradient algorithm as the local model training optimizer, and uses the federated Gaussian differential privacy algorithm to protect the security of the computation process. The experiment evaluates the model's classification performance using the confusion matrix, explores the impact of client collaboration on model performance, demonstrates the model's suitability for imbalanced data distribution, and confirms the effectiveness of federated learning for model training. FLM-ICR achieves the accuracy, precision, recall, specificity, and F1 score of 0.795, 0.735, 0.835, 0.75, and 0.782, respectively, outperforming existing research methods and balancing classification performance and privacy security, making it suitable for IoV computation and analysis of private data.