Logistic Distribution Research Articles

Recent progress, challenges and future propects of applied deep reinforcement learning : A practicial perspecive in path planning

Path planning is one of the most crucial elements in the field of robotics, such as autonomous driving, minimally invasive surgery and logistics distribution. This review begins by summarizing the limitations of conventional path planning methods and recent work on DRL-based path planning methods. Subsequently, the paper systematically reviews the construction of key elements of DRL methods in recent work, with the aim of assisting readers in comprehending the foundation of DRL research, along with the underlying logic and considerations from a practical perspective. Facing issues of sparse rewards and the exploration-exploitation balance during the practical training process, the paper reviews enhancement methods for training efficiency and optimization results in DRL path planning. In the end, the paper summarizes the current research limitations and challenges in practical path planning applications, followed by future research directions.

An integrated optimization strategy for mission planning and control resource allocation for long-endurance unmanned aerial vehicle

PurposeWith burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.Design/methodology/approachWe consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.FindingsNumerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.Originality/valueDrawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

Excelling Green Supply Chain Management Practices Through Maturity Model: A Case Study of Fish 96 Company

This research aims to improve GSCM practices at Fish 96, an SME in tilapia aquaculture located in Bandung, West Java, Indonesia. It identifies and addresses some critical business issues regarding reducing the DOA rate, improving operational efficiency, and reducing environmental impacts. The literature review forms the theoretical basis of GSCM practices such as green procurement, manufacturing, distribution, and reverse logistics. A new GSCM maturity model is developed and validated with the case study of Fish 96, which eval_uates the company's current maturity levels and structured improvements. It assesses that Fish 96 stands at the initial stage in the journey of GSCM. Most practices are either nonexistent or at an initial point of implementation. The suggested maturity model includes five levels: Nonexistent, Aware, Intermediate, Advanced, and Sustainable, which represent a clear roadmap for Fish 96 to improve its GSCM performance. The research recommends adopting broad green practices across all supply chain dimensions, emphasising systematic strategies and enhanced supplier engagement by focusing on efficiency measures. With these recommendations, Fish 96 will contribute to reducing operational costs while reducing environmental impacts and improving social outcomes, thus improving the company's long-term sustainability and competitiveness.

Optimization of Storage Paths for Finished Cigarette Logistics Distribution Based on Improved GA-A*

Optimization of Storage Paths for Finished Cigarette Logistics Distribution Based on Improved GA-A*

Research on time-varying path optimization for multi-vehicle type fresh food logistics distribution considering energy consumption

With the increasing demand for fresh food markets, refrigerated transportation has become an essential component of logistics operations. Currently, fresh food transportation frequently faces issues of high energy consumption and high costs, which are inconsistent with the development needs of the modern logistics industry. This paper addresses the optimization problem of multi-vehicle type fresh food distribution under time-varying conditions. It comprehensively considers the changes in road congestion at different times and the quality degradation characteristics of fresh goods during distribution. The objectives include transportation cost, dual carbon cost, and damage cost, subject to constraints such as delivery time windows and vehicle capacity. A piecewise function is used to depict vehicle speeds, proposing a dynamic urban fresh food logistics vehicle routing optimization method. Given the NP-hard nature of the problem, a hybrid Tabu Search (TS) and Genetic Algorithm (GA) approach is designed to compute an optimal solution. Comparison with TS and GA algorithm results shows that the TS-GA algorithm provides the best optimization efficiency and effectiveness for solving large-scale distribution problems. The results indicate that using the TS-GA algorithm to optimize a distribution network with one distribution center and 30 delivery points resulted in a total cost of CNY 12,934.02 and a convergence time of 16.3 s. For problems involving multiple vehicle types and multiple delivery points, the TS-GA algorithm reduces the overall cost by 2.94–7.68% compared to traditional genetic algorithms, demonstrating superior performance in addressing multi-vehicle, multi-point delivery challenges.

Rural Logistics Distribution Countermeasures

Rural Logistics Distribution Countermeasures

Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions

Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global “double carbon” objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China.

The performance model of logistic distribution centers: Quality function deployment based on the Best-Worst Method.

This paper aims to evaluate the performance of logistic distribution centers (LDCs) by Quality Function Deployment (QFD) based on the Best-Worst Method (BWM). The originalities of this paper include: (1) exploring five constructs with 20 customer requirement attributes (CRAs) for LDCs' operations based on the SERQUAL model, (2) using the QFD model to identify five primary divisions and 18 corresponding service technical requirements (STRs) for LDCs' operations, (3) recognizing the top five STRs should be prioritized for the allocation of limited resources from House of Quality (HoQ), including cargo order (7.15%), value-added activities (6.68%), document preparation (6.31%), consolidating and assembling (6.10%), and document management (6.00%), (4) applying the Best-Worst Method (BWM) to estimate CRAs' relative weight. The proposed research model can provide a methodological reference to the relevant literature in association with logistics operations and multiple-criteria decision analysis (MCDA).

Biochemical variables as predictors of in-hospital mortality in patients with acute coronary syndrome

Abstract Background There is a constant search of easily obtainable variables that can be put in relation with in-hospital mortality, enabling creation of prediction risk models in patients treated for acute coronary syndrome. Purpose The main goal of this study was to unravel what is the profile of the patients with acute coronary syndrome in risk of in-hospital mortality, based on simple biochemical variables acquired at the time of hospital admission. Methods This was a single-center cross-sectional cohort study on 3007 pts. treated for ACS (STEMI and NSTEMI). Venous blood sample at hospital admission was acquired for every patient. Patients were comparatively analyzed for their biochemical variables derived from venous blood sample, based on occurrence of in-hospital mortality. Results Out of 3007 pts, 988 (28.2%) were females, and 2519 (71.8%) males, at mean age 62.6 +/- 11.2 y. In-hospital mortality rate was 4.8% (145 out of 3007). We subjected to comparative analyzed hemogram, markers of renal function including sodium and potassium, glycemic profile, complete lipid panel (including ApoA1 and ApoB), and highly sensitive troponin. Significant association with in-hospital mortality was found for: hsTn, stress glycemia and glicated hemoglobin, BUN, creatinite, eGFR, sodium, potasium, full lipid panel [except for HDL-C and Lp(a)]. In the second step, markers proven to be significantly different were subjected to multivariate binary logistic regression (backward conditional). We designed two models, one that included ApoA1 and ApoB (including 1816 pts. for whom we had data), and the second without these two parameters (including 3007 pts.). With the first model we identified: Advanced age, stress glycemia, WBC, eGFR, HDL-C and ApoA1 as independent predictors. Surprisingly, HDL-C was found to be strong independent predictor in multivariate analyzis. While with second modeling we identified advanced age, hsTn, stress glycemia, RBC, WBC, BUN and eGFR as independent predictors. Conclusion Advanced age is a strong independent predictor in any modelling. When we have a possibility to obtain a complete lipid panel, beside stress glycemia and WBC as markers of the degree of neuro-hormonal activation and inflammation, and renal function, HDL-C and ApoA1 seems to be strong negative independent predictors. In the absence of this data beside aforementioned markers of neurohormonal activation and inflammation, and renal function, the degree of myocardial injury plays a significant role. According to our data it seems reasonable to obtain complete lipid panel at hospital admission.Multivariate logistic regression modelsAge distribution across genders

Analysis of Spatial Agglomeration Characteristics of Logistics Enterprises in Henan Province based on Poi

As an important industry connecting urban and rural areas and serving production and life, logistics enterprises, as the micro-foundation of the development of logistics industry, have a profound impact on the spatial reconstruction of regional economic and social development. National Development and Reform Commission issued the "Fourteenth Five-Year Plan" modern logistics development and "Fourteenth Five-Year Plan" modern circulation system construction planning "put forward new requirements for the development of logistics industry, emphasizing the optimization of logistics spatial layout and speed up the construction of national logistics hub, especially pointed out that to make up for the shortcomings of the central and western regions. As an important base for the development of China's logistics industry, the spatial layout of logistics enterprises in Henan province not only affects the high-quality development of the logistics industry in the province, but also plays an important role in the improvement of the national logistics network. In this paper, based on POI data, the mean nearest neighbor method, local Moran scatter plot analysis and kernel density visualization technology are used to analyze the spatial agglomeration characteristics of logistics enterprises in 157 counties in Henan Province. It is found that the spatial distribution of logistics enterprises in Henan Province has gradually evolved from multi-point dispersion to a single-core and multi-core collaborative model with Zhengzhou as the core, and presents a significant spatial positive correlation and agglomeration trend. This change has improved the logistics efficiency and promoted the strengthening of regional economic ties. At the same time, the agglomeration trend of logistics enterprises in Henan Province continues to strengthen, from the initial agglomeration in the core area to the diversified diffusion pattern, showing the continuous optimization and reconstruction of logistics network in the province.

Error and attack vulnerability of Apollonian networks

Abstract This article examines the resilience of different Apollonian network (AN) types—deterministic, random, and evolutionary—to systematic attacks. ANs, members of the family of maximal planar graphs, possess unique properties such as high clustering coefficients, small-world properties, scale-free behavior, Euclidean and space-filling properties, and modularity. These peculiarities require a thorough investigation of their robustness. This work presents a novel approach to studying ANs by implementing evolutionary Apollonian networks (EANs). These EANs include various probabilities distribution functions, including exponential, degenerate, logistic, Pareto, and stable (Cauchy, Lévy, Normal) distributions. To improve the robustness of these networks, we propose a novel edge rewiring mechanism using a genetic algorithm (GA). The GA aims to optimize a combined metric that includes the Flow Robustness of Degree (SFRD), Betweenness (SFRB), and Dangalchev's closeness (SFRC) centralities while preserving the original degree distribution and structural properties of the network. To evaluate the effectiveness of this approach, we use various robustness measures to assess the resilience of different AN types. The results show that SFRB, SFRD, and SFRC effectively rank ANs based on their robustness.

Effects of nano-MoO3 on growth, quality and toxicity of soybean.

Metal nanoparticles are widely used in agricultural production. As a new type of molybdenum fertilizer, MoO3NPs have the properties of nanomaterials and the characteristics of molybdenum nutrition. Previous studies have focused on their role in promoting crop growth. However, it is unknown whether excessive MoO3NPs will affect crop quality and nutritional value. In this study, the effects of different concentrations of MoO3NPs (0, 0.15, 0.5, 1.0, 5.0, 10, 50, 100 mg kg-1) on the growth and quality of soybean were investigated by pot experiments to analyze the plant effects caused by MoO3NPs. The results showed that the effects of MoO3NPs treatment on plant biomass and nodule number were promoted at low concentrations (0.15-5 mg kg-1) and inhibited at high concentrations (10-100 mg kg-1). According to the logistic distribution model, it was predicted that MoO3NPs would have the strongest toxic effect on soybean flowering stage. The contents of MoO3NPs which reduced the yield of soybean by 10% and 20% were 12.38 and 30.81 mg kg-1. NP0.15 could significantly improve the total amount of amino acids in grains, while NP100 reduced the total amount of amino acids in grains, both of them significantly increasing the contents of linolenic acid and linoleic acid in soybean seeds. A change of MoO3NPs concentration had no negative effect on the nutritional value of soybean grains. The research could lead to a better understanding of the potential impact of nutritional changes caused by MoO3NPs on human health. © 2024 Society of Chemical Industry.

Analysis of Behaviour Pattern of Bicycle-sharing Users Based on Complex Network: a Case Study of Beijing Downtown Area

Abstract. The introduction of the Bicycle-sharing System (BSS) has provided great convenience for residents for short travel in city. However, the parking lot planning and bicycle logistics distribution remain suboptimal, adversely affecting traffic order. Studying the spatial and temporal usage patterns can be helpful for better management of BSS. In this paper, we propose an analytical framework for detecting the bicycle-sharing pattern based on the community detection algorithms from complex networks to study the spatial-temporal patterns of usage.Firstly we identify the potential demand for bicycle among urban residents with a dataset from the BSS in Beijing downtown area. Then we construct a linkage network for modelling the BSS by applying community detection algorithms to identify regions of both high and low connectivity. Following this, based on the division of sub-areas, three indicators (graph density, cross-area travel demand, and confidence ellipses) are established for characterizing these sub-areas. Our findings include: (1) The usage pattern of the BSS within the downtown area consistently exhibits relatively stable clustering phenomena over time. (2) The usage pattern is related to the urban spatial structure, with significant differences between weekdays and weekends. (3) Residents tend to complete their cycling within the current sub-areas. (4) Generally, smaller sub-areas tend to have denser bicycle travel behaviour. These insights are vital for improving BSS parking lot planning and logistics distribution.

Understanding Viral Haemorrhagic Fevers: Virus Diversity, Vector Ecology, and Public Health Strategies.

Viral haemorrhagic fevers encompass a diverse group of severe, often life-threatening illnesses caused by viruses from multiple families, including Arenaviridae, Filoviridae, Flaviviridae, Hantaviridae, Nairoviridae, Peribunyaviridae, and Phenuiviridae. Characterised by fever and haemorrhagic symptoms, these diseases challenge public health systems by overwhelming healthcare facilities, complicating diagnostic processes, and requiring extensive resources for containment and treatment, especially in resource-limited settings. This discussion explores the intricate relationships between VHFs and their transmission vectors-both animal and arthropod-and examines the impact of ecological and geographic factors on disease spread. The primary transmission of VHFs typically occurs through direct contact with infected animals or via bites from haematophagous arthropods, facilitating zoonotic and, at times, human-to-human transmission. With an emphasis on the role of diverse wildlife, domesticated animals, and vectors such as mosquitoes and ticks in the epidemiology of VHFs, there is a recognised need for robust surveillance and strategic public health responses to manage outbreaks. This review discusses the necessity of interdisciplinary approaches that integrate virology, ecology, and public health to enhance diagnostic capabilities, develop vaccines and antivirals, and improve outbreak interventions. Exploring the ecological and biological dynamics of VHFs will help bolster a deeper understanding of these emerging viruses and underpin preparation for future outbreaks. The importance of enhanced global cooperation, continuous research, and collaboration to mitigate the public health threats posed by these complex infections is a central theme, serving as a foundational strategy to reinforce worldwide preparedness and response efforts. Future directions include addressing gaps in vaccine development and tailoring public health strategies to the unique challenges of managing VHFs, such as the rapid mutation rates of viruses, the need for cold chain logistics for vaccine distribution, and socio-economic barriers to healthcare access, in order to ensure readiness for and effective response to emerging threats worldwide.

Research on Underwriting Decision-Making of Insurance Companies in Areas with Frequent Extreme Weather Based on Comprehensive Evaluation Method

The purpose of this research is to develop an insurance company underwriting model by pre-processing and model construction of collected data in order to assess whether to underwrite insurance in extreme weather-prone areas and to analyse the factors that influence underwriting decisions. In this paper, data such as insurance company payout rate, number of insured, homeowners' income in selected areas, health insurance coverage, year of construction of historical landmarks, GDP per capita, and tourism income were processed, and minimum-maximum normalisation and zero-mean normalisation were used to eliminate differences in feature magnitude. In the model construction, the logistic regression model and BP neural network were used to derive the underwriting probability, and the three main factors of natural disaster risk assessment, payout rate, and homeowner influence were introduced, and the relationship between these factors and the underwriting probability was described by the logistic distribution function, which determined that the underwriting probability should be underwritten when the underwriting probability Pi≥0.5. The natural disaster risk assessment model selects disaster factors, disaster environment and disaster receptors as assessment indicators, constructs the CEDP model, and calculates the weights using the AHP model, the Topsis model and the CPITIC weighting method, and carries out fuzzy clustering analysis on the CEDP index of 25 regions to classify the risk into five levels. For insurer profits, solvency, premium income and claims ratios were analysed, ratios based on annual claims to total premiums were calculated, and the impact of homeowners' incomes, property and historic buildings on underwriting decisions was assessed. Insurance company underwriting decision factors in areas prone to extreme weather are ultimately determined.

Data-driven Approximation of Cumulative Distribution Function Using Particle Swarm Optimization based Finite Mixtures of Logistic Distribution

This paper proposes a data-driven approximation of the Cumulative Distribution Function using the Finite Mixtures of the Cumulative Distribution Function of Logistic distribution. Since it is not possible to solve the logistic mixture model using the Maximum likelihood method, the mixture model is modeled to approximate the empirical cumulative distribution function using the computational intelligence algorithms. The Probability Density Function is obtained by differentiating the estimate of the Cumulative Distribution Function. The proposed technique estimates the Cumulative Distribution Function of different benchmark distributions. Also, the performance of the proposed technique is compared with the state-of-the-art kernel density estimator and the Gaussian Mixture Model. Experimental results on κ−μ distribution show that the proposed technique performs equally well in estimating the probability density function. In contrast, the proposed technique outperforms in estimating the cumulative distribution function. Also, it is evident from the experimental results that the proposed technique outperforms the state-of-the-art Gaussian Mixture model and kernel density estimation techniques with less training data.

Reliability Estimation of Scale Parameter in Type II Generalized Half-Logistic Distribution from Doubly Censored Samples Based on Monte Carlo Simulation

Objectives: This article deals with estimation of the reliability function for one-parameter Type II Generalized Half- Logistic distribution (Type II GHLD). Methods: The Maximum likelihood (ML) and Approximate Maximum likelihood (AML) methods provided by Jilani et.al. 1 . Findings: Comparisons between the estimators were made using Monte Carlo Simulation based on statistical indicter bias and mean squared error (MSE). We have generated 3000 random samples of size n=10(5)20 from a standard one-parameter Type II GHLD with = 0.5, 1.5 and 2.0. Each sample is then ordered and from this sample a doubly (including left and right) censored sample is considered, by censoring r smallest observations and s largest observations with all possible choices of r and s corresponding to this resultant sample we compute MLEs (using Newton-Raphson method), the LAMLE and unbiased LAMLE . Finally, the results are explained with tables. Novelty: This study has empirically proves that all the three reliability estimators have the same asymptotic variance. Hence, in this case, any one of the three estimators can be used but preferable estimator based unbiased estimator. Keywords: Maximum likelihood estimation, Approximate Maximum likelihood estimation, Bias, Mean squared error, Variances

Economic Design of Generalized Multiple Dependent State Sampling Plan Under Exponentiated Half Logistic Distribution

The main objective of this study is to propose an economic design of a generalized multiple dependent state sampling plan based on time truncated life test under exponentiated half logistic distribution for assuring median life. In this study, the optimal plan parameters are determined for various median ratios using the approach of two points on the operating characteristic curve. The performance of the proposed plan is compared with other sampling plans designed under exponentiated half logistic distribution. An investigation on the effect of misspecification of shape parameters on probabilities of lot acceptance is done and the industrial application of the proposed plan is explained with real time data. The results obtained from both conventional as well as economic designing show that the proposed plan outperforms the other existing sampling plans, such as multiple dependent state sampling plan, multiple dependent state repetitive group sampling plan, and single sampling plan.

Assessing the Bridge Structure’s System Reliability Utilizing the Generalized Unit Half Logistic Geometric Distribution

Reliability is now widely recognized across various industries, including manufacturing. This study investigates a system composed of five components, one of which is a bridge network. The components are assumed to follow the generalized unit half logistic geometric distribution (GUHLGD) with equal failure rates over time. The following three improvement methods are considered: reduction, cold duplication, and hot duplication. The reliability function and mean time to failure (MTTF) are employers liability equivalence factors (REFs). Additionally, the λ fractiles of both the original and enhanced systems are obtained. Numerical results illustrate the effectiveness of these techniques, with cold duplication shown to be the most effective, offering higher reliability and MTTF compared to hot duplication. The enhanced system outperforms the original system overall.

Optimization of frozen goods distribution logistics network based on k-means algorithm and priority classification.

Maintaining the quality and integrity of frozen goods throughout the supply chain necessitates a robust and efficient cold chain logistics network. This research proposes a machine learning-based method for optimizing such networks, resulting in significant cost reduction and resource utilization improvement. The method employs a three-phase approach. First, K-means clustering groups sellers based on their geographical proximity, simplifying the problem and enabling more accurate demand prediction. During the second phase of the proposed method, Gaussian Process Regression models predict future sales volume for each seller cluster, leveraging historical sales data. Finally, the Capuchin Search Algorithm simultaneously optimizes distributor location and resource allocation for each cluster, minimizing both transportation and holding costs. This multi-objective approach achieved a 34.76% reduction in costs and a 15.6% reduction in resource wastage compared to the existing system. This novel method offers a valuable tool for frozen goods distribution networks, with advantages such as considering multiple goals for optimization, focusing on demand prediction, potential for reduced complexity, and focusing on managerial insights over compared methods.