Logistics Operations Research Articles

A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations

AbstractProviding first aid and other supplies (e.g., epi‐pens, medical supplies, dry food, water) during and after a disaster is always challenging. The complexity of these operations increases when the transportation, power, and communications networks fail, leaving people stranded and unable to communicate their locations and needs. The advent of emerging technologies like uncrewed autonomous vehicles can help humanitarian logistics providers reach otherwise stranded populations after transportation network failures. However, due to the failures in telecommunication infrastructure, demand for emergency aid can become uncertain. To address the challenges of delivering emergency aid to trapped populations with failing infrastructure networks, we propose a novel robust computational framework for a two‐echelon vehicle routing problem that uses uncrewed autonomous vehicles (UAVs), or drones, for the deliveries. We formulate the problem as a two‐stage robust optimization model to handle demand uncertainty. Then, we propose a column‐and‐constraint generation approach for worst‐case demand scenario generation for a given set of truck and UAV routes. Moreover, we develop a decomposition scheme inspired by the column generation approach to generate UAV routes for a set of demand scenarios heuristically. Finally, we combine the decomposition scheme within the column‐and‐constraint generation approach to determine robust routes for both trucks (first echelon vehicles) and UAVs (second echelon vehicles), the time that affected communities are served, and the quantities of aid materials delivered. To validate our proposed algorithms, we use a simulated dataset that aims to recreate emergency aid requests in different areas of Puerto Rico after Hurricane Maria in 2017.

Logistics Performance Index of Africa: An Indicator for Türkiye and Africa Trade Relations?

The logistics performance of countries is important in trade flows. As part of the supply chain process, logistics is the backbone of trade processes. In this direction, the Logistics Performance Index (LPI) put forward by the World Bank can be considered as an indicator. With the LPI, countries have the opportunity to evaluate the effectiveness of their activities in trade and logistics. The aim of the study is to rank and analyze African countries with the integrated Entropy-MOORA Reference Approach using the LPI values for 2023. In order to increase the depth of the study, an evaluation is made based on the trade data between Türkiye and African countries for 2022. The study aims to rank African countries on the basis of six criteria defined by the MOORA Reference Approach and to provide a perspective on the development of Türkiye’s trade relations with African countries in line with trade figures. According to the results of the study, South Africa ranks first, Botswana second, and Libya last in the ranking of LPI countries. Türkiye’s trade volume with the continent, South Africa ranks fifth, Botswana fifty-third, and Libya third. Therefore, there is no correlation between LPI and trade data in current situation.

Design of an Arduino-based intelligent logistics cart

With the achievement of the first centenary goal, we will strive for another 15 years on this basis, aiming to achieve socialist modernization by 2035. The concept of intelligent production is receiving increasing attention, with the logistics industry being a representative sector. Currently, traditional logistics operations are unable to meet the high requirements of modern society due to problems such as high labor costs, poor safety, and low efficiency. In order to change the current situation of the traditional logistics industry, more intelligent equipment is needed to replace labor-intensive manual handling work. In this era, an Arduino-controlled intelligent logistics cart emerges as a development trend. This paper designs and develops an Arduino-controlled intelligent logistics cart, taking the Yunnan Province University Student Engineering Training Comprehensive Ability Competition as an application scenario. It achieves functions such as automatic tracking, intelligent recognition of block colors, automatic scanning of QR codes to obtain tasks, and all-round grasping of objects. The design of this intelligent logistics cart involves hardware selection, system design, and structural design.

Comparative Analysis of GDP Per Capita, Logistic Performance, and Carbon Dioxide Emissions: A Case of SAARC Countries

This study investigated the association between economic growth, environmental sustainability, and social development in SAARC countries. The study uses robust and balanced panel data from 2008 to 2021. The advanced econometric techniques used for empirical analyses, specifically Dynamic Ordinary Least Squares (DOLS). It provided valuable observations on the relationships between economic and environmental factors. The independent variable logistic infrastructure, gross capital formation, labour force participation rate, and carbon dioxide emission significantly affect our dependent variable growth rate The study results showed that logistic infrastructure positively affected our GDP per capita. Similarly, LAB and GCF also directly impacted your GDP per capita while CO2 negatively impacted the GDP per capita of the projected economies. These findings suggest that policymakers must adopt holistic approaches that prioritize sustainable development goals, mitigate environmental degradation, and promote inclusive economic growth to ensure a prosperous and resilient future for all.

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.

Neonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-term effects on both parents and infants. Timely screening and treatment can reduce these negative consequences. Our objective is to compare the performance of the traditional logistic regression with other machine learning (ML) models in identifying parents who are more likely to have depression symptoms to prioritize screening of at-risk parents. We used data obtained from parents of infants discharged from the NICU at Children's National Hospital (n = 300) from 2016 to 2017. This dataset includes a comprehensive list of demographic characteristics, depression and stress symptoms, social support, and parent/infant factors. Our study design optimized eight ML algorithms - Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network - to identify the main risk factors associated with parental depression. We compared models based on the area under the receiver operating characteristic curve (AUC), positive predicted value (PPV), sensitivity, and F-score. The results showed that all eight models achieved an AUC above 0.8, suggesting that the logistic regression-based model's performance is comparable to other common ML models. Logistic regression is effective in identifying parents at risk of depression for targeted screening with a performance comparable to common ML-based models.

Research on the Optimization Strategy of Last-Mile Distribution under the E-Commerce Logistics Model

With the rapid development of e-commerce, last-mile delivery, as a key link in e-commerce logistics, is essential to provide efficient and punctual delivery services. This study aims to explore the optimization strategy of last-mile delivery under the e-commerce logistics model to improve delivery efficiency, reduce costs, and provide a better user experience. This study adopts the method of literature review and case analysis, combined with the actual e-commerce logistics operation, to conduct an in-depth study on the problem of last-mile delivery.

Supply Chain Optimization in the Package Industry through Machine Learning Analysis

The package industry relies heavily on efficient supply chain management to meet customer demands and maintain profitability. However, managing complex supply chains involving multiple suppliers, transportation networks, and distribution channels poses significant challenges. This research proposes a machine learning-based approach to optimize supply chain operations in the package industry. By analysing historical data on supply chain activities, including procurement, inventory management, and distribution, our system aims to identify patterns and trends to improve decision-making processes. Machine learning algorithms such as support vector machine, naive Bayes, and logistic regression are utilized to forecast demand, optimize inventory levels, and streamline logistics operations. Experimental results demonstrate the effectiveness of the proposed approach in enhancing supply chain efficiency and reducing operational costs in the package industry.

Towards a cleaner and sustainable Europe and Central Asia: An investigation of the logistics‐environment nexus

AbstractThe logistics industry is the backbone of global trade and is crucial for the attainment of economic progress across the world. However, the industry involves activities that increase the consumption of energy resources including fossil fuels, which harms the environment. Thus, with a special focus on Europe and Central Asia, this study explores the logistics‐environment by evaluating the impact of logistics performance (LP) on environmental performance (EP) while controlling for key macroeconomic indicators such as income, trade openness, foreign direct investment, and industrialization. The motivation for this study is derived from the United Nations Sustainable Development Goals (SDGs‐11 and 13) which highlight sustainability and climate change mitigation. Using Poisson pseudo maximum likelihood with high‐dimensional fixed effects (PPMLHDFE) for 47 European and Central Asian countries over the period 2007–2018, the findings revealed that the overall logistic performance, and some of its components—customs, trade, and transport infrastructure, and tracing and tracking have a significant positive impact on the overall EP, environmental health, and ecosystem vitality. Additionally, the study confirms the detrimental effects of economic growth, trade, and industrialization on environmental quality and health from logistics and supply chain management (SCM) perspectives. Further policy caveats and suggestions are elucidated in the concluding section.

Development of metaheuristic algorithms for efficient path planning of autonomous mobile robots in indoor environments

The application of efficient path planning algorithms for two-wheeled Autonomous Mobile Robots (AMRs) in static environments with obstacles is a significant challenge in robotics research. Existing methods, such as the A star (A*) algorithm utilized in Robot Operating System 2 (ROS2), can provide optimal paths but may have high computational complexity in intricate environments. This study explores the potential of three metaheuristic algorithms - Improved Particle Swarm Optimization (IPSO), Improved Grey Wolf Optimizer (IGWO), and Artificial Bee Colony (ABC) - for planning efficient and smooth paths in static environments. These algorithms are selected due to their ability to efficiently find near-optimal solutions and avoid local minima. In this study, the researchers designed and built a two-wheeled AMR using a Raspberry Pi 4 microcontroller as the main processing unit, working in conjunction with an Arduino Mega for controlling the DC motor drive through an MDD10A motor driver circuit. The robot is equipped with an RPLiDAR A1 sensor to read 360-degree distance values for mapping and obstacle avoidance. The experimental results clearly indicate that the metaheuristic algorithms, especially ABC, can calculate paths up to 7% shorter than A* while requiring only one-tenth of the time. Moreover, ABC demonstrates superior motion smoothness when applied to the actual two-wheeled robot in static environments. This work represents a significant step in developing algorithms for two-wheeled robots that are ready to support real-world operations in industries, logistics, healthcare, or various service sectors, which can help increase efficiency and reduce operating costs in the future.

Effective cost management strategies in global supply chains

This review paper explores effective cost management strategies within global supply chains, highlighting the necessity for businesses to adapt and innovate in response to complex logistical operations, economic fluctuations, and geopolitical risks. It delves into strategic sourcing and procurement, inventory management techniques such as Just-In-Time (JIT) systems, logistics and transportation optimization, and the pivotal role of technology and automation in enhancing efficiency and reducing costs. The paper identifies gaps in current literature, particularly the need for more research on sustainable practices and risk management strategies. Recommendations for practical implementation are provided, emphasizing flexibility, resilience, and the importance of leveraging technology. Future research directions are suggested to further understand the dynamics of global supply chain cost management and the integration of sustainability practices. Keywords: Global Supply Chain, Cost Management, Strategic Sourcing, Inventory Management, Supply Chain Technology, Risk Management.

Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV

In contemporary logistics, the deployment of fixed-wing unmanned aerial vehicles (UAVs) as a transportation platform is experiencing rapid advancements, garnering substantial application within numerous logistic operations with pronounced efficacies. There are notable impediments to the utilization of commercial logistic-oriented fixed-wing UAVs, including elevated procurement and maintenance costs, extensive maintenance intervals, and unsuitability for small-volume, low-altitude transport tasks. These factors collectively exacerbate the risk associated with enterprise procurement and elevate the cost–benefit ratio. This study introduces the design and fabrication of a cost-efficient UAV for logistic delivery purposes, constructed primarily from cost-effective wood materials. This UAV is engineered to ferry payloads of up to 1000 g across a predefined aerial route at an altitude of 40 m. Upon reaching the designated location, the UAV is programmed to initiate the identification of the drop zone, thereafter descending to facilitate the release of the cargo. To mitigate the impact force during the landing phase, the payload was encapsulated within a sponge-damping layer, thereby preserving the integrity of the transported items. The empirical findings from outdoor delivery trials underscore the UAV’s ability to precisely execute payload drops at the targeted locations, confirming its potential to fulfill the logistical requirements for the transportation and delivery of small-volume items in a cost-effective, low-altitude framework. This investigation contributes to the burgeoning discourse on leveraging ultra-low-cost UAVs in logistics, offering a feasible solution to the challenges of cost and efficiency in UAV-operated delivery systems.

Future Trends: Predicting the Evolution of Rail and Road Transportation in Logistics

The logistics industry represents the vital circulatory system of the global economy, responsible for the efficient movement of goods across sprawling supply chain networks. Historically, rail and road transportation modes have been the workhorses of logistics operations, facilitated by their extensive infrastructure and versatility in cargo handling. However, the convergence of rapid technological advancements, evolving consumer expectations, and mounting environmental pressures is catalyzing a transformative shift in the future evolution of these pivotal transportation sectors. This comprehensive study delves into the emerging trends that are poised to reshape the rail and road transportation landscape within the logistics realm. By synthesizing insights from industry leaders, academic research, and cutting-edge technological developments, the study aims to provide a holistic perspective on the forthcoming changes and their far-reaching implications. The study’s main objective is to integrate semi-autonomous and autonomous cars into fleets of transportation vehicles. The advent of self-driving trucks and trains promises to revolutionize logistics operations by enhancing efficiency, reducing human error, and optimizing route planning. Concurrently, this transition raises critical concerns related to workforce displacement, regulatory frameworks, and cybersecurity vulnerabilities that necessitate proactive mitigation strategies. Moreover, the logistics industry faces mounting pressure to embrace sustainable practices and curtail its environmental footprint. Consequently, the rail sector is anticipated to experience a renaissance fueled by its inherent energy efficiency and lower carbon emissions compared to road transportation. Innovations in electric and hydrogen-powered locomotives, coupled with advancements in rail infrastructure, could position rail as a greener and more cost-effective solution for long-haul freight movement. Concurrently, the road transportation sector is poised for a transformation driven by the proliferation of electric and alternative fuel vehicles. This adoption is projected to accelerate as battery technology improves, charging infrastructure expands, and governments implement stricter emissions regulations. Furthermore, the integration of advanced telematics systems and realtime data analytics could optimize route planning, load management, and vehicle maintenance, further enhancing the operational efficiency of road-based logistics.

The Effect of Entrepreneurship and Digital Technology on Customer Relationship Management: Dependability Study Among Entrepreneur Logistic Operators

This study aims to determine the effect of marketing and entrepreneurship on customers relationship management. The study factors include functional value, time compression, reliability, flexibility, and capacity. This study was led at Pulau Pinang, Selangor, and Johor Bharu. Furthermore, individual customers who use logistic services were sampled. In addition, the total respondents to the questionnaires were 391. The method used in the study is applied research. A questionnaire on the Likert scale was applied for the survey. Results showed that the customer intention effect is significant to the study in entrepreneurship. The study was also conducted using SEM analysis. Further study is required on the subject. Keywords: planning, technology, virtual leadership

Poetic Baggage: Representations of Camp Followers in the Latin Verse Inscriptions

Each unit of the Roman army constituted a major logistic operation that cannot be reduced to the fighting men who formed their core and purpose. While camp followers are depicted in literary sources especially as an hindrance to war operations, inscriptions provide a different picture of the world that gravitated around the military forts and fortresses. In the military settlements that guarded the limes, the presence of paramilitary and non-military personnel did not represent a burden, but an important part of the military economy, which in some cases even led to the emergence of a new business class. In this paper, we provide an anthology of verse inscriptions dedicated by, or to, camp followers, with a view to showcase how their everyday interactions with the army were conceptualised and verbalised, ultimately contributing to a richer picture of the limes social ecosystem.

Chartering policies and operational efficiency of shipping lines: exploring strategic changes in response to the COVID-19 pandemic

Shipping is a highly cyclical, volatile, and unpredictable industry. In recent history, economic shocks like the COVID-19 pandemic have heavily disrupted supply chains and tested the resilience and adaptability of shipping lines from both operational and financial perspectives. Therefore, the commercial success of shipping lines is increasingly determined by an optimal balance between reliable logistics operations and financial performance. Given this, chartering strategies of shipping lines are pivotal in achieving fleet optimisation and financial sustainability. Using a Mann–Whitney U test and a data envelopment analysis (DEA), this study provides a view on chartering strategies and efficiency levels of shipping lines in response to the COVID-19 pandemic. The findings reveal that chartering strategies have changed considerably since 2020 and that most shipping lines have pursued a peculiar strategy with a different mix in terms of chartered vessels’ size, age, and period of charter. Shipping lines which showed the most marked differences in chartering strategies also yielded higher efficiency scores under DEA, whereas those that did not sustain large changes scored lower in terms of efficiency. This study provides significant insights on and managerial guidance for understanding and benchmarking chartering strategies and their impact on efficiency gains.

КРИТЕРІАЛЬНИЙ ВПЛИВ НА ВИБІР ВИДУ ТРАНСПОРТНОГО СПОЛУЧЕННЯ ПРИ ОРГАНІЗАЦІЇ ДОСТАВКИ ВАНТАЖІВ В УМОВАХ МІЖНАРОДНОЇ ІНТЕГРАЦІЇ

Introduction. The organisation of cargo delivery in various transport connection options requires the identification of the most optimal parameters of transportation by certain transport modes and the development of better transport connections. The systematisation of transport and logistics processes requires a comprehensive solution to the problems of organising the delivery of goods in international multimodal connections. The growing demands of the freight clientele are becoming a key factor in the competition between both carriers and logistics operators to serve more profitable cargo flows. Problem Statement. International integration processes involve interaction between all participants in international cargo delivery, and its reliability depends on the effectiveness of its organisation. Ensuring high-quality transport services for a particular cargo delivery is only possible if the criteria are correctly selected and adequately assessed. Purpose. To determine the criteria of influence on the choice of transport mode in the organisation of cargo delivery, taking into account the factor influence characterising the carrying capacity of each transport mode serving a particular order, as well as the synergy of their interaction.

A probabilistic bi‐objective model for a humanitarian location‐routing problem under uncertain demand and road closure

AbstractEffective planning and execution of humanitarian aid logistics activities ensure that disaster‐related losses are minimized. This study addresses a tactical‐level pre‐disaster humanitarian logistics problem where a decision‐maker decides on cross‐dock locations by taking potential vehicle routes into account. A decision support model is proposed for the location selection and distribution operations in humanitarian logistics with explicit fuel consumption estimation. In the addressed problem, the demand amount of each node depends on probabilistic disaster scenarios. Probabilities of whether each arc/road is open or closed and heterogeneous vehicle fleet in terms of vehicle sizes are also respected. The model is formulated as probabilistic bi‐objective mixed integer linear programming, whose objectives are minimization of the total cost (i.e., fuel cost, vehicle fixed cost, and fixed opening cost) and total travel time. To the best of our knowledge, the proposed decision support model is unique in terms of the features considered simultaneously. The applicability of the model is demonstrated by the case study and subsequent numerical analyses of a possible earthquake in the Kartal district of Istanbul. The proposed model is shown to have the potential to support decision‐makers in preparation for a disaster. A solution approach based on a clustering algorithm has been also proposed to solve larger instances of the problem. The effectiveness of this heuristic has been demonstrated through its application to larger‐scale problems.

Exploring supply chain and regional resilience through the analysis of the transport dimension

Despite the key role that global supply chains (GSC), and more specifically, the transport and logistics function, might play in the performance of regions, there is a lack of analytical tools that turn the data generated by supply chain operations into quantitative metrics allowing policymakers to identify early signals of potential vulnerabilities and assess how resilient the GSC are at regional level. In an effort to fill this gap, we present a Transportation Resilience Index (TRI) that permits evaluating how transport chains affect supply chain resilience and, consequently, regional resilience. Our index, based on publicly available data –statistics on foreign trade flows provided by the Spanish Customs, the World Bank’ Logistics Performance Index and the UNCTAD’ Bilateral Liner Shipping Connectivity Index − differentiates between the determinants over which the company has direct decision-making control, and those linked to the selection of the supply market (market and macro dimensions). The TRI is applied to the Spanish regions (NUTS2) by using the Benefit-of-the-Doubt (BoD) approach to Data Envelopment Analysis (DEA) and applying a Common Set of Weights (CSW) for the selection of the weights to the index components. In our specific analysis, the market and macroeconomic components account for 84% of the total weight of the TRI. Despite the lower relative weight of the company dimension, the high degree of homogeneity in the market and macroeconomic scores means the company dimension plays a key role in the relative resilience of the Spanish regions.

Study on the Impact of RCEP National Logistics Performance on Vietnam's Cross-Border E-Commerce Export Trade

In the era of global economic integration, the Regional Comprehensive Economic Partnership (RCEP) plays a pivotal role in shaping trade dynamics, particularly by enhancing Vietnam's cross-border e-commerce exports through improved logistics. This study meticulously reviews a vast array of both domestic and international literature and employs Stata 17.0 software to analyze panel data spanning from 2014 to 2022 across RCEP member countries. It focuses on six core logistics performance indicators: customs and logistics management, logistics infrastructure, timeliness of shipments, quality of logistics services, tracking and tracing capabilities, and ease of international shipments.The findings from this analysis reveal that these logistics performance indicators significantly influence Vietnam's e-commerce exports. Infrastructure quality and the ease of shipments have the most substantial impact, suggesting that robust logistics infrastructure and efficient shipment processes are crucial for enhancing trade efficiency. Despite the overall positive trends, the study notes significant variations in logistics development within the RCEP region. While countries like Singapore exhibit advanced logistics capabilities, ASEAN countries generally lag behind, indicating a disparate level of logistics progress across the region.The research highlights the potential for targeted logistics improvements to directly boost Vietnam's cross-border e-commerce exports. By advancing logistics performance, especially in areas lagging behind, there is an opportunity to not only enhance trade but also to level the playing field within the RCEP. This study underscores the critical importance of developing cohesive and efficient logistics systems to support the burgeoning e-commerce sector, thereby fostering economic growth and deeper regional integration within the RCEP framework.

A Genetic Algorithm with Lower Neighborhood Search for the Three-Dimensional Multiorder Open-Size Rectangular Packing Problem

This paper addresses the multiorder open-dimension three-dimensional rectangular packing problem (3D-MOSB-ODRPP), which involves packing rectangular items from multiple orders into a single, size-adjustable container. We propose a novel metaheuristic approach combining a genetic algorithm with the Gurobi solver. The algorithm incorporates a lower neighborhood search strategy and is underpinned by a mathematical model representing the multiorder open-dimension packing scenario. Extensive experiments validate the effectiveness of the proposed approach. The LNSGA algorithm outperforms Gurobi and the traditional genetic algorithm in solution quality and computational efficiency. For small-scale instances, LNSGA achieves optimal values in most cases. LNSGA demonstrates significant optimization improvements over Gurobi and the genetic algorithm for large-scale instances. The superior performance is attributed to the effective integration of the lower neighborhood search mechanism and the Gurobi solver. This study offers valuable insights for optimizing the packing process in e-commerce warehousing and logistics operations.