Search Results (23)

This study investigated the impact of a Yard Management System (YMS) implemented at a third-party logistics distribution center in the United States. Five years of operational data (2018–2022), including 72 monthly observations of inbound and outbound freight performance (measured in pounds) and detention occurrences (measured in US dollars), were analyzed using one-way ANOVA to assess pre- and post-implementation performance. The results indicated that the YMS significantly improved inbound and outbound freight volume, reduced detention occurrences, and enhanced operational efficiency within the third-party logistics distribution center. These findings suggest that YMS can be an effective tool for enhancing yard-level operational efficiency, reducing delays, and supporting broader supply chain optimization strategies in third-party logistics environments. Full article

In the context of rapidly growing logistics demand, traditional warehouse management methods are inadequate in meeting contemporary efficiency and accuracy requirements. The present study proposes the development of an intelligent warehouse visualization platform, the objective of which is to address issues such as high labor dependency, opaque inventory, and operational inefficiencies. The construction of a virtual warehouse environment was undertaken using Unity3D, with the aim of simulating real-world zones. These comprised storage areas, automatic guided vehicle (AGV) pathways, and operational spaces. The platform incorporates radio frequency identification devices (RFID) for item tracking and a role-based access system, enabling real-time monitoring and management of inbound, inventory, and outbound processes. In order to optimize AGV path planning, the proposed algorithm incorporates a dynamic weighted heuristic, a five-neighborhood search, a bidirectional search, and Bézier curve-based smoothing. The efficacy of these enhancements has been demonstrated through a reduction in searched nodes, computation time, and path length, while simultaneously enhancing smoothness. As demonstrated by simulations conducted in Unity3D, the optimized algorithm exhibits a reduction in search nodes of 59.19%, in time of 45.41%, and in path length of 18%, in comparison with the conventional A-star algorithm. The platform offers a safe, efficient, and scalable solution for enterprise training and operational simulation, contributing valuable insights for intelligent warehouse upgrading. Full article

Cross-docking has emerged as a critical logistics strategy to reduce lead times, lower inventory levels, and enhance supply chain responsiveness. However, in high-throughput terminals, efficient coordination of inbound and outbound trailers remains a complex task, especially under uncertain and dynamically changing conditions. We propose a practical framework that helps logistics terminals assign trailers to docks in real time. It links live sensor data with a mathematical optimization model, so that the system can quickly adjust trailer plans when traffic or workload changes. Real-time data from IoT sensors, GPS, and operational records are preprocessed, enriched with predictive analytics, and used as input for a Mixed-Integer Linear Programming (MILP) model solved in rolling horizons. This enables the continuous reallocation of inbound and outbound trailers, ensuring synchronized flows and balanced dock utilization. Numerical experiments compare the adaptive approach with conventional first-come-first-served scheduling. Results show that average inbound dock utilization improves from 68% to 71%, while the share of periods with full utilization increases from 33.3% to 41.4%. Outbound utilization also rises from 57% to 62%. Moreover, trailer delays are significantly reduced, and the overall makespan shortens from 45 to 40 time slots. These findings confirm that adaptive, real-time trailer assignment can enhance efficiency, reliability, and resilience in cross-docking operations. The proposed framework thus bridges the gap between static optimization models and the operational requirements of modern, high-throughput logistics hubs. Full article

The rapid growth of e-commerce and advances in information and communication technologies have placed increasing pressure on last-mile delivery companies to enhance operational productivity. As investments in logistics infrastructure require long-term planning, maximizing the efficiency of existing terminal operations has become a critical priority. This study proposes a mathematical model for inbound truck scheduling that simultaneously minimizes truck waiting times and balances workload across temporary inventory storage located at outbound chutes in cross-docking terminals. The model incorporates a dynamic rescheduling strategy that updates the assignment of inbound trucks in real time, based on the latest terminal conditions. Numerical experiments, based on real operational data, demonstrate that the proposed approach significantly outperforms conventional strategies such as First-In First-Out (FIFO) and Random assignment in terms of both load balancing and truck turnaround efficiency. In particular, the proposed model improves workload balance by approximately 10% and 12% compared to the FIFO and Random strategies, respectively, and it reduces average truck waiting time by 17% and 18%, thereby contributing to more efficient workflow and alleviating bottlenecks. The findings highlight the practical potential of the proposed strategy for improving the responsiveness and efficiency of parcel distribution centers operating under fixed infrastructure constraints. Future research may extend the proposed approach by incorporating realistic operational factors, such as cargo heterogeneity, uncertain arrivals, and terminal shutdowns due to limited chute storage. Full article

The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors. Full article

Cross-docking operations in Eastern and Central European markets face increasing complexity amid persistent uncertainty and inflationary pressures. This study provides the first comprehensive comparative analysis integrating economic efficiency with sustainability indicators across strategic locations. Using mixed-methods analysis of 40 bibliographical sources and quantitative modeling of cross-docking scenarios in Bratislava, Prague, and Budapest, we integrate environmental, social, and governance frameworks with activity-based costing and artificial intelligence analysis. Optimized cross-docking achieves statistically significant cost reductions of 10.61% for Eastern and Central European inbound logistics and 3.84% for Western European outbound logistics when utilizing Budapest location (p < 0.01). Activity-based costing reveals labor (35–40%), equipment utilization (25–30%), and facility operations (20–25%) as primary cost drivers. Budapest demonstrates superior integrated performance index incorporating operational efficiency (94.2% loading efficiency), economic impact (EUR 925,000 annual savings), and environmental performance (486 tons CO₂ reduction annually). This is the first empirically validated framework integrating activity-based costing–corporate social responsibility methodologies for an emerging market cross-docking, multi-dimensional performance assessment model transcending operational-sustainability dichotomy and location-specific contingency identification for emerging market implementation. Findings support targeted infrastructure investments, harmonized regulatory frameworks, and public–private partnerships for sustainable logistics development in emerging European markets, providing actionable roadmap for EUR 142,000–EUR 187,000 artificial intelligence implementation investments achieving a 14.6-month return on investment. Full article

Efficient material handling is crucial in the logistics operations of modern salt warehouses, where Rail Guided Vehicles (RGVs) and Air Sorting Robots (ASRs) are often deployed to manage inbound and outbound tasks. However, as the number of tasks increases within a given period, conflicts and deadlocks between simultaneously operating RGVs and ASRs become more frequent, reducing efficiency and increasing energy consumption during transportation. To address this, the research frames the inbound and outbound problem as a task allocation issue for the RGV/ASR system with a finite buffer, and proposes a collision avoidance strategy and a zero-wait strategy for loaded machines to reallocate tasks. To improve computational efficiency, we introduce an adaptive multi-neighborhood hybrid search (AMHS) algorithm, which integrates a dual-sequence coding scheme and an elite solution initialization strategy. A dedicated global search operator is designed to expand the search landscape, while an adaptive local search operator, inspired by biological hormone regulation mechanisms, along with a perturbation strategy, is used to refine the local search. In a case study on packaged salt storage, the proposed AMHS algorithm reduced the total makespan by 30.1% compared to the original task queue. Additionally, in 15 randomized test scenarios, AMHS demonstrated superior performance over three benchmark algorithms—Genetic Algorithm (GA), Discrete Imperialist Competitive Algorithm (DICA), and Improved Whale Optimization Algorithm (IWOA)—achieving an average makespan reduction of 12.6% relative to GA. Full article

In the four-way shuttle system, the efficiency of path planning directly affects the overall effectiveness of logistics and warehousing operations. Traditional path planning methods for multiple four-way shuttles do not take into account the fact that the map status will change as the inbound and outbound tasks are completed. To address this issue, a path planning algorithm for dynamic environments based on an improved Conflict-Based Search (CBS) mechanism is proposed. Firstly, by introducing turning constraints and a node expansion strategy, the A* algorithm is improved, reducing the number of turns and optimizing the node expansion process. Secondly, based on the improved A* algorithm, a path planning algorithm for dynamic environments based on CBS is designed. This algorithm adopts the inbound/outbound task priority strategy and the nearby-task priority strategy to resolve conflicts. It effectively manages the changes in the map status by establishing and maintaining a “ChangeList” and revises the path set of the four-way shuttles. Based on the layout of the intelligent vertical warehouse with four-way shuttles of a certain enterprise, simulation experiments were carried out using a rasterized map. The algorithm was compared with the DCBS-PFM and RRT-A algorithms, verifying the effectiveness and superiority of the algorithm. Full article

Digital Twins (DT) are an emerging trend in diversified industrial sectors and their value chains. This study aims to explore current DT applications within SCs, focusing on sustainable inbound and outbound logistics and identifying key enablers that will facilitate the successful adoption of DTs in SCs. Using the PEO model and employing the PRISMA framework, this study screened articles from the Scopus database to explore the existing knowledge related to this topic. A steep increase in articles related to DTs over the past 10 years indicates that there is growing attention in exploring and leveraging this technology for various applications. The successful adoption of DTs is driven by several key factors, including advanced technological infrastructure; standardized processes; continuous improvement; knowledge workers; technologies like IoT, IIoT, AR/VR; and managerial support. Full article

Rising carbon emissions are worsening global climatic conditions, posing a grave threat to the environment. Analysing and reducing carbon footprints are vital for combating climate change. A carbon footprint analysis categorises emissions into three scopes, aiming to identify re- duction areas and promote sustainable practices, such as energy efficiency, renewable energy use, and eco-friendly choices in consumption. The current study’s purpose is to track and analyse the carbon emissions in the inbound and outbound logistics of a process industry belonging to Micro Small and Medium Enterprises using life cycle analysis. Ultimately, this study recommends mitigation strategies to bring down the carbon footprint. Full article

The sustainability of large economies is one of the most important challenges in today’s world. As the world strives to create a greener and more efficient future, it becomes necessary to accurately analyze and forecast freight volumes. By developing a reliable freight transportation forecasting model, the authors will be able to gain valuable insights into the trends and patterns that determine the development of economic systems. This will enable informed decisions on resource allocation, infrastructure development, and environmental impact mitigation. Such a model takes into account various factors such as market demand, logistical capabilities, fuel consumption, and emissions. Understanding these dynamics allows us to optimize supply chains, reduce waste, minimize our carbon footprint, and, ultimately, create more sustainable economic systems. The ability to accurately forecast freight volumes not only benefits businesses by enabling better planning and cost optimization but also contributes to the overall sustainable development goals of society. It can identify opportunities to shift to more sustainable modes of transportation, such as rail or water, and reduce dependence on carbon-intensive modes, such as road or air. In conclusion, the development and implementation of a robust freight forecasting model is critical to the sustainability of large-scale economic systems. Thus, by utilizing data and making informed decisions based on these forecasts, it is possible to work toward a more sustainable future for future generations. Full article

In recent years, sustainable development has become an emerging trend in the logistics industry. Smart manufacturing factories pursue green logistics processes with lower energy consumption and reduced carbon emission. The environmental sustainability of the logistics process is widely acknowledged as an important issue. However, a standardized methodology for assessing the environmental cost of logistics-process-aided smart manufacturing is lacking. This paper presents a concept for determining the inbound logistics environmental cost (ILEC) of a gigafactory. Additionally, a novel structured methodology for ILEC assessment is proposed to uniformly describe the gigafactory’s logistics environmental cost, regarding the “double carbon” goal (peak carbon dioxide emissions and carbon neutrality). First, eight types of basic logistics activities and six logistics phases associated with the gigafactory’s inbound logistics are defined. The mapping relationships between the logistics phases and the basic logistics activities are constructed. Then, the novel concepts of environmental price cost (EPC) and environmental impact cost (EIC) are defined and presented. Finally, the ILEC of the gigafactory, including EPC and EIC, is assessed based on mapping relationships and an environmental cost model. We validate this model using the advanced Geely Automobile factory in China in order to analyze the actual inbound logistics environmental costs and how to assess its environmental price and environmental impact. Results from the data model show the environmental costs throughout the whole process and the detailed composition ratio of EPC and EIC in the inbound logistics. Based on the implementation of the ILEC model, our study helps gigafactories to identify critical logistics nodes through energy consumption and to measure the environmental performance of the inbound logistics process. Furthermore, our study helps gigafactories to develop practical environmental strategies. Full article

This paper analyzes how a robust and dynamic forecasting system was designed and implemented to predict material volumes for the inbound logistics network of an international automotive company. The system aims to reduce transportation logistics costs and improve demand capacity planning for freight forwarders. The forecasting horizon is set for 4 months and 12 months ahead in the future. To solve this problem, a time series modeling approach was carried out by using different time series forecasting methods like ARIMA, Neural Networks, Exponential Smoothing, Prophet, Automated Simple Moving Average, Multivariate Time Series, and Ensemble Forecast. Additionally, important data preprocessing methods and a robust model selection framework were used to train the models and select the best-performing one. This is known as Forward Chaining Nested Cross Validation with origin recalibration. The system performance was assessed using the Symmetric Mean Absolute Error (SMAPE). The final version of the forecasting system can deliver 4-month-ahead forecasts with a SMAPE lower than 10% for 86% of all material flow connections. The system’s forecast output is updated on a monthly basis and was integrated into the inbound logistics network system of the company. Full article

The 6G Internet of Things (IoT) is of utmost importance when it comes to running and controlling contemporary supply chains. Blockchain and machine learning (ML) are two upper-layer technologies that can assist with securing and automating the IoT. First, we propose integrating blockchain technology into modern supply chains to facilitate effective communication among all partners. Second, for inbound logistics task prediction, we develop Multi-Head Attention (MHA)-Based Gated Recurrent Unit (GRU). Finally, numerical findings demonstrate that the MHA-Based GRU model has satisfying fitting efficiency and prediction precision compared to its competitors. Full article

Using shared resources has created better opportunities in the field of sustainable logistics and procurement. The Multi-Depot Traveling Purchaser Problem under Shared Resources (MDTPPSR) is a new variant of the Traveling Purchaser Problem (TPP) in sustainable inbound logistics. In this problem, each depot can purchase its products using the shared resources of other depots, and vehicles do not have to return to their starting depots. The routing of this problem is a Multi-Trip, Open Vehicle Routing Problem. A tailored integer programming model is formulated to minimize the total purchasers’ costs. Considering the complexity of the model, we have presented a decomposition-based algorithm that breaks down the problem into two phases. In the first phase, tactical decisions regarding supplier selection and the type of collaboration are made. In the second phase, the sequence of visiting is determined. To amend the decisions made in these phases, two heuristic algorithms based on the removing and insertion of operators are also proposed. The experimental results show that not only can purchasing under shared resources reduce the total cost by up to 29.11%, but it also decreases the number of dispatched vehicles in most instances. Full article