Logistics Network Research Articles

Non-linear programming model proposal for mutual support distance of air defence systems

With the advancements in technology, the deployment of air defence systems required for the defence of a country after procurement is considered as an important problem in terms of defence effectiveness. It is seen that criteria such as coverage, strike effectiveness and logistics network are taken into account while deploying. In addition, principles such as remote countermeasures, defence in depth and all-round defence are also taken into account. In this study, two different deployment models are proposed for point and area air defence. In this model, the optimum deployment distance at which other systems can support each other in addition to a deployed air defence system is determined. In the nonlinear target programming model proposed for point air defence, the number of missiles is considered to be reasonably balanced for perimeter defence, while for area defence, the nonlinear optimization problem is addressed by calculating the equally important coverage and strike effectiveness criteria values. In this study, it is observed that the approach based on mutual support distance has been used for the first time in the defence literature. According to the results obtained, it is observed that this proposed model for determining the mutual support distance solves the deployment problem optimally.

Drone Integration in Last-Mile Delivery Operations

Purpose: This research examines the integration of drone technology into last-mile delivery operations, focusing on its impact on supply chain efficiency, cost reduction, and sustainability. The study explores operational challenges, such as air traffic management, payload limitations, regulatory compliance, and public perception, providing a comprehensive understanding of how drones can reshape last-mile logistics. Methodology: The study employs a multi-faceted approach, combining historical analysis, in-depth case studies, and scenario planning to assess the impact of drone technology on last-mile delivery efficiency, cost optimization, and accessibility. Data is gathered from industry reports, interviews with logistics professionals, and regulatory documents. Thematic analysis is conducted to identify critical patterns, while scenario-based modeling offers insight into the potential outcomes of various integration strategies. A comparative evaluation of existing last-mile delivery methods against drone-based delivery models is also provided. Findings: The research reveals that drone technology offers significant potential for enhancing last-mile delivery efficiency, particularly in urban areas and remote locations, aligning with findings from studies like those by Murray & Chu (2015). However, the study identifies considerable obstacles, including regulatory challenges, limited payload capacities, and public acceptance issues. Solutions such as airspace regulation frameworks, collaborative logistics networks, and advanced battery technologies are highlighted as critical enablers for successful drone integration. Contribution to Theory, Policy and Practice: This study contributes to the growing body of knowledge on drone technology's role in logistics by providing a comprehensive framework for businesses and policymakers to navigate the complexities of drone integration. It recommends actionable steps for integrating drones into last-mile delivery, bridging the gap between theoretical innovation and practical application. It provides policy recommendations for regulatory bodies to develop airspace management systems that support drone operations. For practitioners, the research offers a strategic guide to overcoming operational challenges, facilitating the adoption of drones as a sustainable solution for last-mile delivery.

Socio-Economical Analysis of a Green Reverse Logistics Network under Uncertainty: A Case Study of Hospital Constructions

This study addresses the critical issue of managing construction and demolition waste in urban environments. Effective waste management is not only essential for minimizing costs but also for enhancing sustainability and reducing environmental impact. In this context, the research introduces a green reverse logistics model designed for C&D waste management, integrating both sustainability considerations and current regulatory frameworks, such as LEED. A key innovation of this model is the incorporation of electric vehicles for waste collection, compared to traditional diesel vehicles, as part of the logistical process, as carbon emission is a significant concern. By evaluating the limitations and opportunities associated with electric vehicles, alongside robust optimization to manage uncertainties in waste collection, the model seeks to balance environmental, social, and economic objectives. It further incorporates decision-making tools like fuzzy logic to optimize multi-objective outcomes across various waste facilities, including separation labs, incineration centers, recycling centers, and landfills. A case study conducted in Tehran validates the model, highlighting the socio-economic and environmental benefits of using electric vehicles in waste collection. Sensitivity analysis indicates that hybrid and socially focused policies perform best under high-impact scenarios, although results can differ with varying data sets. Despite the complexity of managing reverse logistics networks, this research provides valuable insights for supply chain planners. It suggests potential future directions, such as the application of metaheuristic algorithms and improved stochastic planning methods.

Analysis and Research on Intelligent Logistics Data under Internet of Things and Blockchain

ABSTRACT To explore the security performance of logistics data transmission in an Intelligent Logistics System (ILS) under the Internet of Things (IoT) and Blockchain (BC), IoT is introduced to optimize the logistics transportation process. First, BC is adopted to improve the ILS under IoT, and ILS under IoT combined with BC is established. Next, an analysis of the relationship between the information service provider and the transportation provider in the logistics transaction process is conducted to perform data analysis and to establish an incentive mechanism for logistics transportation under BC. Finally, the simulation analysis of the constructed model is implemented. The results reveal that the constructed model is highly efficient. It can effectively solve the traceability problem of logistics transaction information, and the calculation cost is stable within 1,000 ms. In the comparative analysis of system transmission performance, the model exhibits outstanding results in transmission latency, consistently maintaining an average delay of approximately 350 ms. This stability is primarily attributed to the seamless integration of BC technology, which optimizes data packet transmission paths and mitigates waiting time. Furthermore, the system efficiently manages high data volumes while upholding robust data processing capabilities, owing to the optimization of smart contracts and decentralized storage mechanisms. As for energy consumption, the model notably reduces overall energy usage within the logistics network by curtailing unnecessary data transmission and computation, particularly in mobile nodes and wireless communication channels. The proposed algorithm excels across various transmission metrics, including transmission delay, data successful acceptance rate, network throughput, and energy consumption are all shown to be the best transmission performance of the proposed algorithm, in which the data successful acceptance rate is close to 1. As a result, the developed ILS model not only guarantees low latency performance but also demonstrates high data transmission security, facilitating more efficient and precise real-time information transmission. In this study, within the ILS framework, the relationship between information service providers and transportation service providers is established through BC, and an incentive mechanism is constructed. However, some shortcomings in this incentive mechanism are recognized. For instance, during the initial stages of BC integration, there are issues of information asymmetry concerning factors such as credit, transportation capacity, and effort level. Moreover, the open access policy of couriers in the model raises concerns, as the access policy itself constitutes sensitive information, which may result in privacy breaches. Hence, as part of future work, the introduction of attribute encryption schemes is planned to obfuscate access policies, thereby safeguarding the privacy of logistics personnel’s access policies.

Chaotic laser time series prediction based on an improved logistic mapping algorithm echo state network

Chaos synchronization plays vital functions in the fields of optical chaos secure communication. The synchronization performance can be significantly degraded by parameter mismatches between the chaotic transmitter and receiver. In this paper, the Deep-Logistical Mapping Echo State Network (D-LMESN) is proposed to enhance the performance of chaos synchronization. The network is upgraded by using an improved logical mapping algorithm and a deep reserve pool structure with phase space reconstruction. Results show that D-LMESN exhibits better performance in the prediction of chaotic time series, thanks to the adaptive parameter adjustment, which increases the ability to capture the dynamic characteristics of complex systems. Compared with ESN, the mean square error of this model is reduced by 55% and 72%, respectively, in chaotic laser simulation and actual data experiments. This provides a new possibility, to our knowledge, for the development of chaotic secure communication.

Optimization of household medical waste recycling logistics routes: Considering contamination risks.

The escalating generation of household medical waste, a byproduct of industrialization and global population growth, has rendered its transportation and logistics management a critical societal concern. This study delves into the optimization of routes for vehicles within the household medical waste logistics network, a response to the imperative of managing this waste effectively. The potential for environmental and public health hazards due to improper waste disposal is acknowledged, prompting the incorporation of contamination risk, influenced by transport duration, waste volume, and wind velocity, into the analysis. To enhance the realism of the simulation, traffic congestion is integrated into the vehicle speed function, reflecting the urban roads' variability. Subsequently, a Bi-objective mixed-integer programming model is formulated to concurrently minimize total operational costs and environmental pollution risks. The complexity inherent in the optimization problem has motivated the development of the Adaptive Hybrid Artificial Fish Swarming Algorithm with Non-Dominated Sorting (AH-NSAFSA). This algorithm employs a sophisticated approach, amalgamating congestion distance and individual ranking to discern optimal solutions from the population. It incorporates a decay function to facilitate an adaptive iterative process, enhancing the algorithm's convergence properties. Furthermore, it leverages the concept of crossover-induced elimination to preserve the genetic diversity and overall robustness of the solution set. The empirical evaluation of AH-NSAFSA is conducted using a test set derived from the Solomon dataset, demonstrating the algorithm's capability to generate feasible non-dominated solutions for household medical waste recycling path planning. Comparative analysis with the Non-dominated Sorted Artificial Fish Swarm Algorithm (NSAFSA) and Non-dominated Sorted Genetic Algorithm II (NSGA-II) across metrics such as MID, SM, NOS, and CT reveals that AH-NSAFSA excels in MID, SM, and NOS, and surpasses NSAFSA in CT, albeit slightly underperforming relative to NSGA-II. The study's holistic approach to waste recycling route planning, which integrates cost-effectiveness with pollution risk and traffic congestion considerations, offers substantial support for enterprises in formulating sustainable green development strategies. AH-NSAFSA offers an eco-efficient, holistic approach to medical waste recycling, advancing sustainable management practices.

SUPPLY CHAIN RISK MANAGEMENT: STRATEGIC SOLUTIONS FOR REDUCING TRANSPORTATION AND LOGISTICS RISKS

This study investigates strategic approaches to mitigating risks in transportation and logistics within global supply chains, focusing on the integration of advanced technologies, flexibility, collaboration, and sustainability. By employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the study systematically reviews 37 key articles to provide a comprehensive understanding of modern risk management practices. The findings reveal the increasing reliance on technologies such as predictive analytics, the Internet of Things (IoT), and blockchain for enhancing visibility, monitoring, and decision-making. Flexibility in logistics networks, including alternative sourcing and diversified transportation routes, emerged as crucial for mitigating disruptions, while collaboration among supply chain partners, particularly through real-time information sharing, significantly reduces risk exposure. Additionally, the study highlights the growing integration of sustainability into risk management, addressing climate change and environmental risks. This research underscores the need for proactive, adaptable, and sustainable risk management strategies to maintain supply chain resilience in the face of evolving global challenges.

Leveraging Blockchain for Transparent and Efficient Supply Chain Management: Business Implications and Case Studies

Blockchain technology has surfaced as a revolutionary response to important challenges in supply chain management, such as a scarcity of transparency, inefficiencies, and potential for fraud. This study looks into the implementation of blockchain in supply chain systems, focusing especially on improving transparency, operational efficiency, and the trust among stakeholders. The analysis of genuine case studies in retail, logistics, and manufacturing illustrates the ways blockchain facilitates better traceability, cuts transaction times, and increases security via immutable and valid records. This study uses a mixed-methodology by blending quantitative data analytics from secondary sources with qualitative observations from reports in the industry. Findings point to the fact that adopting blockchain technology has improved supply chain efficiency by 15-30% and reduced fraud by as much as 40%, together with a striking decrease in processing times throughout global logistics networks. The originality of this research is found in its complete examination of the economic and operational effects of blockchain, offering business leaders tactical insights on maximizing their supply chains through this technology. The paper culminates with suggestions for resolving implementation issues and tactics for extending blockchain solutions in complex international networks.

Multi-period emergency facility location-routing problems under uncertainty and risk aversion

This paper addresses a multi-period emergency facility location-routing problem, in which the uncertainties in material demands and transportation time, as well as dynamic inventory replenishment and carryover are incorporated in the design of multi-level emergency logistics networks. To measure the risks stemming from uncertain transportation time, mean-CVaR method is used. Then, a risk-averse stochastic programming model for the presented problem is formulated to minimize the total rescue time of the network. Moreover, a genetic algorithm is developed to solve the proposed model. Extensive numerical experiments including the randomly generated instances and a case study on the Wenchuan earthquake in China are conducted to verify the effectiveness of the presented model and algorithm. Experimental results show that the genetic algorithm significantly performs better than the Gurobi solver in terms of both solution quality and solution time.

Collaboration and resource sharing in the multidepot time-dependent vehicle routing problem with time windows

Concerns about energy conservation and emission reduction have highlighted the importance of environmentally sound logistics networks in urban areas. These networks are deeply intertwined with urban traffic systems, where variations in transit speeds can significantly increase the energy consumption and carbon emissions of delivery vehicles, compromising the environmental sustainability of urban deliveries. To address this, we propose a multidepot time-dependent vehicle routing problem with time windows, enhancing route planning flexibility and resource configuration. Our approach begins with a route spatiotemporal decomposition method to estimate vehicle travel times and emissions based on varying vehicle speeds. We then develop a multiobjective mixed integer linear programming model that aims to minimize total operating costs, the number of vehicles, and carbon dioxide emissions. A hybrid heuristic algorithm combining spectral clustering, multiobjective ant colony optimization, and variable neighborhood search is proposed to solve the model. This algorithm incorporates collaboration and resource sharing strategies, a pheromone initialization mechanism, a novel heuristic operator that accounts for time dependency, and a self-adaptive update mechanism, enhancing both solution quality and algorithm convergence. We compare the performance of our algorithm with that of the CPLEX solver, multiobjective ant colony optimization, non-dominated sorting genetic algorithm-Ⅲ, and multiobjective particle swarm optimization. The results demonstrate the superior convergence, uniformity, and spread of our proposed algorithm. Furthermore, we apply our model and algorithm to a real-world case in Chongqing, China, analyzing optimized results for different time intervals and vehicle speeds. This study offers robust methodologies for theoretically and practically addressing the multidepot time-dependent vehicle routing problem with time windows, contributing to the development of economical, efficient, collaborative, and sustainable urban logistics networks.

Alternative Fuels in Sustainable Logistics—Applications, Challenges, and Solutions

Logistics is becoming more cost competitive while customers and regulatory bodies pressure businesses to disclose their carbon footprints, creating interest in alternative fuels as a decarbonization strategy. This paper provides a thematic review of the role of alternative fuels in sustainable air, land, and sea logistics, their challenges, and potential mitigations. Through an extensive literature survey, we determined that biofuels, synthetic kerosene, natural gas, ammonia, alcohols, hydrogen, and electricity are the primary alternative fuels of interest in terms of environmental sustainability and techno-economic feasibility. In air logistics, synthetic kerosene from hydrogenated esters and fatty acids is the most promising route due to its high technical maturity, although it is limited by biomass sourcing. Electrical vehicles are favorable in road logistics due to cheaper green power and efficient vehicle designs, although they are constrained by recharging infrastructure deployment. In sea logistics, liquified natural gas is advantageous owing to its supply chain maturity, but it is limited by methane slip control and storage requirements. Overall, our examination indicates that alternative fuels will play a pivotal role in the logistics networks of the future.

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.

AGV Scheduling for Optimizing Irregular Air Cargo Containers Handling at Airport Transshipment Centers

Airport transshipment centers play a pivotal role in global logistics networks, enabling the swift and efficient transfer of cargo, which is essential for maintaining supply-chain continuity and reducing delivery times. The handling of irregularly shaped air cargo containers presents new constraints for automated guided vehicles (AGVs), as these shapes can complicate loading and unloading processes, directly impacting overall operational efficiency, turnaround times, and the reliability of cargo handling. This study focuses on optimizing the scheduling of AGVs to enhance cargo-handling efficiency at these hubs, particularly for managing irregular air cargo containers. A mixed-integer linear programming (MILP) model is developed, validated for feasibility with the Gurobi solver, and designed to handle large-scale operations. It incorporates a novel approach by integrating a simulated annealing optimized genetic algorithm (GA). The experimental results demonstrate that the designed algorithm can solve models of considerable size within 8 s, offering superior time efficiency compared to the solver, and an average solution quality improvement of 12.62% over the genetic algorithm, significantly enhancing both the model’s efficiency and scalability. The enhanced AGV scheduling not only boosts operational efficiency but also ensures better integration within the global logistics framework. This research provides a robust foundation for future advancements in logistics technology, offering both theoretical and practical insights into optimizing complex transportation networks.

Forecasting material flows of modern logistics centres of railway freight stations exemplifi ed by Xi’an (the People’s Republic of China)

The purpose of the study is to create a more accurate material fl ow forecasting model of Xi’an freight railway station in China. The combined forecasting model is more validated for forecasting freight flows of regional logistics compared to three methods: grey forecasting, Markov chains, entropy weighting. Through the creation of the combined model, the grey forecasting method is combined with Markov chain correction, and the projected data is compared with the actual data to obtain higher accuracy of the forecasting model. A combined model using the grey forecasting method combined with Markov chain correction is created, with the forecast data compared with the actual data to obtain high accuracy of the forecasting model. The practical significance is that in the context of the present post-pandemic economic development, the logistics enterprises that do not operate in accordance with the modern logistics methods may be displaced by competitors. If the railway does not improve its logistics infrastructure, logistics equipment, railway logistics network platform, etc., it will lose out to other modes of transport. In order to meet the needs of logistics and improve the market competitiveness, the main indicator of a freight station is loading and logistics flow. Therefore, exact prediction of future changes in the logistics flow of a freight station can help to determine whether the station needs to be upgraded as a railway station or transformed into a certain type of a logistics centre.

Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs.

Mapping the Social Relations of Labor in Contemporary Algorithmic Society

Abstract Moritz Altenried's The Digital Factory (2022) accomplishes in just under two hundred pages what many other books twice that length have struggled with: assembling a concise yet readable introductory map to the global, fragmented, and too-often hidden landscape of digitally-mediated capitalism. But the digital factory itself is an incomplete concept, almost always requiring us to look for the external and contingent labor support hidden just outside of its supposedly totalizing network of logistics, robotics and algorithms.

The Russian Arctic in the international economic and geopolitical agenda

The article considers the key economic and geopolitical aspects in the activities of states having strategic interests in the Arctic region. Attention is paid to potential challenges that Russia may face in terms of international cooperation, including militarization. The current development of the Northern Sea Route with the assessment of its integration prospects into the global logistics network is considered. The state of the infrastructure in the Arctic is evaluated, along with identification of possible development directions.

A Two-Stage Robust Optimization for Reliable Logistics Network Design via Evolutionary Computation

This paper presents a novel two-stage robust optimization model for designing a dependable logistics network that integrates evolutionary computation techniques. The proposed model considers both the normal and disrupted states of the logistics network and seeks to reduce the overall network cost and operating time in different disruption situations. The challenge is a multi-objective optimization problem addressed using a hybrid evolutionary method that combines the advantages of the non-dominated sorting genetic algorithm with the large neighborhood search heuristic. Numerical experiments are conducted on various test instances to demonstrate the effectiveness and efficiency of the proposed model and algorithm. The results show that the proposed algorithm can generate robust and reliable logistics network designs resilient to disruptions and uncertainties, leading to significant improvements in logistics performance and cost savings compared to traditional methods.

GIS-technologies and algorithm for developing regional systems for handling medical waste on the example of three regions

Introduction. GIS is a modern computer technology designed for collecting, storing, analyzing and visualizing spatial data – mapping. GIS is also actively used in healthcare to solve many regional problems, including optimization and management of healthcare resources. Over last decade, many researchers have used mathematical software models for healthcare waste management. A review of the literature shows facility location, risk to the public, and economic feasibility to be among the issues that have been extensively covered in the literature. Designing a reverse logistics network using mathematical software tools is an efficient and effective way to manage healthcare waste. The objective of the study. To determine the optimal approach to creating a functional model of a system for managing medical waste of class B and C using GIS-technologies for each of the three regions, based on the characteristics of each. Materials and methods. Medical institutions in three regions (St. Petersburg, Leningrad Region and Krasnoyarsk Territory) as sources of medical waste generation were considered taking into account their capacity and specialization. The composition and volumes of waste by class were determined using a calculation method based on indicative waste generation standards. The identification of local technologies for the neutralization of medical waste existing in medical institutions was carried out. The obtained data was processed statistically and displayed on electronic maps for subsequent use in GIS technologies, as well as for the development and justification of a waste management system for medical institutions, taking into account the prospects for the development of healthcare. Results. Based on the analysis of data on medical waste of classes B and C in individual medical institutions, as well as an analysis of the power of existing technologies for neutralizing this waste using GIS technologies, proposals were developed to improve the medical waste management system in three regions. The proposals are aimed at improving the sanitary and epidemiological situation in the regions over a five-year period and take into account the features of the existing transport infrastructure and the requirements of sanitary legislation. Limitations. The model of a medical waste management system using GIS technologies was used only for managing waste of classes B and C. Conclusion. To ensure the safe and environmentally sustainable functioning of the waste management system of classes B and C in the regions, it is necessary to apply an integrated approach based on GIS technologies. This approach should take into account the regional characteristics of the management of the medical waste disposal system and its economic feasibility and take into account the regional characteristics of transport logistics and the prospects for the development of healthcare.

Optimising Drone-Assisted Logistics for Urban Last-Mile Delivery: An Overview of Applications, Methodologies, and Emerging Trends

The rapid evolution of drone-assisted logistics for urban last-mile (ULM) delivery has garnered significant interest from both academia and industry. This article presents a comprehensive review of the state-of-the-art research and practical implementations of ULM systems, focusing on the use of unmanned aerial vehicles (UAVs) for the final stage of goods and parcel delivery in urban environments. The applicability of UAV-based logistics across various contexts, including urban and rural areas, is examined, with real-world case studies highlighted to demonstrate practical uses. Key methodologies and models employed in optimising UAV routing and operations are discussed, particularly those that enhance the efficiency and reliability of ULM. The critical advantages and limitations of drone-assisted last-mile logistics are analysed, providing insights into the operational, regulatory, and technological challenges. The discussion is further expanded by addressing emerging trends in UAV technology, as well as innovations in drone deployment strategies and the evolving regulatory landscape. In conclusion, potential theoretical advancements and future applications of ULM systems are outlined, with an emphasis on integrating drones into broader logistics networks and smart city frameworks. The insights offered aim to guide future research and practical developments in this rapidly advancing field.