Search Results (128)

Pharmaceutical supply chains face high inventory and stockout risks because of short product shelf lives and volatile demand. To enhance coordination efficiency and reduce drug waste, this study examines a two-echelon supply chain comprising a manufacturer and multiple medical institutions. We built a joint ordering and transshipment optimization model that simultaneously incorporates shelf-life constraints, the first-in–first-out (FIFO) policy, inventory capacity limits, and peer-level transshipment. Under deterministic and stochastic demand, we solved the model using Bayesian optimization and Monte Carlo simulation. The results show that moderate inventory transshipment effectively mitigates risk from demand uncertainty and increases total supply-chain profit; under stochastic demand, the optimal strategy relies more heavily on coordinated transshipment to reduce excess inventory and near-expiry waste. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

The continued growth of international maritime trade has driven automated container terminals (ACTs) to pursue more efficient operational management strategies. In practice, the horizontal yard layout in ACTs significantly enhances transshipment efficiency. However, the more complex horizontal transporting system calls for an effective approach to enhance automated guided vehicle (AGV) scheduling. Considering AGV charging and path conflicts, this paper proposes a multi-agent reinforcement learning (MARL) approach to address the AGV dispatching and path planning (VD2P) problem under a horizontal layout. The VD2P problem is formulated as a Markov decision process model. To mitigate the challenges of high-dimensional state-action space, a multi-agent framework is developed to control the AGV dispatching and path planning separately. A mixed global–individual reward mechanism is tailored to enhance both exploration and corporation. A proximal policy optimization method is used to train the scheduling policies. Experiments indicate that the proposed MARL approach can provide high-quality solutions for a real-world-sized scenario within tens of seconds. Compared with benchmark methods, the proposed approach achieves an improvement of 8.4% to 53.8%. Moreover, sensitivity analyses are conducted to explore the impact of different AGV configurations and charging strategies on scheduling. Managerial insights are obtained to support more efficient terminal operations. Full article

This article introduces an innovative hybrid methodology that integrates deterministic Mixed-Integer Linear Programming optimization with stochastic Agent-Based Simulation to address the PDP-TW. The approach is applied to real-world operational data from a luggage-handling company in Lisbon, covering 158 service requests from January 2025. The MILP model generates optimal routing and task allocation plans, which are subsequently stress-tested under realistic uncertainties, such as variability in travel and service times, using ABS implemented in AnyLogic. The framework is iterative: violations of temporal or capacity constraints identified during the simulation are fed back into the optimization model, enabling successive adjustments until robust and feasible solutions are achieved for real-world scenarios. Additionally, the study incorporates transshipment scenarios, evaluating the impact of using warehouses as temporary hubs for order redistribution. Results include a comparative analysis between deterministic and stochastic models regarding operational efficiency, time window adherence, reduction in travel distances, and potential decreases in CO₂ emissions. This work provides a contribution to the literature by proposing a practical and robust decision-support framework aligned with contemporary demands for sustainability and efficiency in urban logistics, overcoming the limitations of purely deterministic approaches by explicitly reflecting real-world uncertainties. Full article

Maritime logistics and railway logistics are crucial in cross-border logistics, and their integration forms a sea-rail cross-border intermodal logistics network. Against the backdrop of frequent unexpected events in today’s world, the normal operation of the sea-rail cross-border intermodal logistics network is under considerable threat. Therefore, researching the vulnerability of the intermodal network is extremely urgent. To this end, this paper first constructs a topological model of the sea-rail cross-border intermodal logistics network, designed to reflect the crucial process of “inter-layer transshipment” via transshipment nodes. Subsequently, a cascading failure model is developed to evaluate network vulnerability, featuring a load redistribution process that distinguishes between transshipment and non-transshipment nodes. The paper yields three primary findings. First, it identifies the optimal values for the capacity factor, overload factor, and inter-layer load transfer rate that most effectively mitigate the network’s vulnerability. Second, compared to a single sub-network (such as a maritime logistics network or a railway logistics network), the sea-rail cross-border intermodal network exhibits lower vulnerability when facing attacks. Third, it highlights the critical role of transshipment nodes, confirming that their failure will make the entire sea-rail cross-border intermodal logistics network more vulnerable. Full article

With the surge in urban logistics demand, traditional surface transportation faces challenges, such as traffic congestion and environmental pollution. Leveraging metro systems in metropolitan areas for both passenger commuting and underground logistics presents a promising solution. The metro-based underground logistics system (M-ULS), characterized by extensive coverage and independent right-of-way, has emerged as a potential approach for optimizing urban freight transport. However, existing studies primarily focus on single-line scenarios, lacking in-depth analyses of multi-tier network coordination and dynamic demand responsiveness. This study proposes an optimization framework based on mixed-integer programming and an improved ICSA to address three key challenges in metro freight network planning: balancing passenger and freight demand, optimizing multi-tier node layout, and enhancing computational efficiency for large-scale problem solving. By integrating E-TOPSIS for demand assessment and an adaptive mutation mechanism based on a normal distribution, the solution space is reduced from five to three dimensions, significantly improving algorithm convergence and global search capability. Using the Nanjing metro network as a case study, this research compares the optimization performance of independent line and transshipment-enabled network scenarios. The results indicate that the networked scenario (daily cost: CNY 1.743 million) outperforms the independent line scenario (daily cost: CNY 1.960 million) in terms of freight volume (3.214 million parcels/day) and road traffic alleviation rate (89.19%). However, it also requires a more complex node configuration. This study provides both theoretical and empirical support for planning high-density urban underground logistics systems, demonstrating the potential of multimodal transport networks and intelligent optimization algorithms. Full article

The topic of environmental pollution by petroleum products is highly relevant due to rapid urbanisation, including industrial development, road infrastructure and fuel distribution. Potential threat areas include refineries, fuel stations, pipelines, warehouses and transshipment bases, as well as sites affected by accidents or fuel spills. This study aimed to determine whether organic and mineral materials could mitigate the effects of diesel oil pollution on the soil’s trace element content. The used materials were compost, bentonite and calcium oxide. Diesel oil pollution had the most pronounced effect on the levels of Cd, Ni, Fe and Co. The levels of the first three elements increased, while the level of Co decreased by 53%. Lower doses of diesel oil (2.5 and 5 cm³ per kg of soil) induced an increase in the levels of the other trace elements, while higher doses caused a reduction, especially in Cr. All materials applied to the soil (compost, bentonite and calcium oxide) reduced the content of Ni, Cr and Fe. Compost and calcium oxide also increased Co accumulation in the soil. Bentonite had the strongest reducing effect on the Ni and Cr contents of the soil, reducing them by 42% and 53%, respectively. Meanwhile, calcium oxide had the strongest reducing effect on Fe and Co accumulation, reducing it by 12% and 31%, respectively. Inverse relationships were recorded for Cd (mainly bentonite), Pb (especially compost), Cu (mainly compost), Mn (mainly bentonite) and Zn (only compost) content in the soil. At the most contaminated site, the application of bentonite reduced the accumulation of Pb, Zn and Mn in the soil, while the application of compost reduced the accumulation of Cd. Applying various materials, particularly bentonite and compost, limits the content of certain trace elements in the soil. This has a positive impact on reducing the effect of minor diesel oil pollution on soil properties and can promote the proper growth of plant biomass. Full article

Massive relief supplies inter-regional lateral transshipment (MRSIRLT) can significantly enhance the efficiency of disaster response, meet the needs of affected areas (AAs), and reduce deprivation costs. This paper develops an integrated allocation and intermodality optimization model (AIOM) to address the MRSIRLT challenge. A phased interactive framework incorporating adaptive differential evolution (JADE) and improved adaptive large neighborhood search (IALNS) is designed. Specifically, JADE is employed in the first stage to allocate the volume of massive relief supplies, aiming to minimize deprivation costs, while IALNS optimizes intermodal routing in the second stage to minimize the weighted sum of transportation time and cost. A case study based on a typhoon disaster in the Chinese region of Bohai Rim demonstrates and verifies the effectiveness and applicability of the proposed model and algorithm. The results and sensitivity analysis indicate that reducing loading and unloading times and improving transshipment efficiency can effectively decrease transfer time. Additionally, the weights assigned to total transfer time and costs can be balanced depending on demand satisfaction levels. Full article

Background: Sustainability is a critical concern in transportation, notably in light of governmental initiatives such as cap-and-trade systems and eco-label regulations aimed at reducing emissions. In this context, collaborative approaches among carriers, which involve the exchange of shipment requests, are increasingly recognized as effective strategies to enhance efficiency and reduce environmental impact. Methods: This research proposes a novel collaborative planning model for multimodal transport designed to minimize the total costs associated with freight movements, including both transportation and $C{O}_2$ emissions costs. Transshipments of freight between vehicles are modeled in the proposed formulation, promoting carrier coalitions. This study incorporated eco-labels, representing different emission ranges, to capture shipper sustainability preferences and integrated authority-imposed low-emission zones as constraints. A bi-objective approach was adopted, combining transportation and emission costs through a weighted sum method. Results: A case study on the Naples Bypass network (Italy) is presented, highlighting the model’s applicability in a real-world setting and demonstrating the effectiveness of collaborative transport planning. In addition, the model quantified the benefits of collaboration under low-emission zone (LEZ) constraints, showing notable reductions in both total costs and emissions. Conclusions: Overall, the proposed approach offers a valuable decision support tool for both carriers and policymakers, enabling sustainable freight transportation planning. Full article

This paper presents an enhanced hub location model tailored to port–hinterland logistics planning, grounded in the Capacitated Multiple-Allocation Hub Location Problem (CMAHLP). The formulation incorporates nonlinear cost structures, hub-specific operating costs, adaptive capacity constraints, and a feasibility condition based on the Social Net Present Value (NPVsocial) to support the design of intermodal freight networks under asymmetric spatial and socio-environmental conditions. The empirical case focuses on Spain, leveraging its strategic position between Asia, North Africa, and Europe. The model includes four major ports—Barcelona, Valencia, Málaga, and Algeciras—as intermodal gateways connected to the 47 provinces of peninsular Spain through calibrated cost matrices based on real distances and mode-specific road and rail costs. A Genetic Algorithm is applied to evaluate 120 scenarios, varying the number of active hubs (4, 6, 8, 10, 12), transshipment discounts (α = 0.2 and 1.0), and internal parameters. The most efficient configuration involved 300 generations, 150 individuals, a crossover rate of 0.85, and a mutation rate of 0.40. The algorithm integrates guided mutation, elitist reinsertion, and local search on the top 15% of individuals. Results confirm the central role of Madrid, Valencia, and Barcelona, frequently accompanied by high-performance inland hubs such as Málaga, Córdoba, Jaén, Palencia, León, and Zaragoza. Cities with active ports such as Cartagena, Seville, and Alicante appear in several of the most efficient network configurations. Their recurring presence underscores the strategic role of inland hubs located near seaports in supporting logistical cohesion and operational resilience across the system. The COVID-19 crisis, the Suez Canal incident, and the persistent tensions in the Red Sea have made clear the fragility of traditional freight corridors linking Asia and Europe. These shocks have brought renewed strategic attention to southern Spain—particularly the Mediterranean and Andalusian axes—as viable alternatives that offer both geographic and intermodal advantages. In this evolving context, the contribution of southern hubs gains further support through strong system-wide performance indicators such as entropy, cluster diversity, and Pareto efficiency, which allow for the assessment of spatial balance, structural robustness, and optimal trade-offs in intermodal freight planning. Southern hubs, particularly in coordination with North African partners, are poised to gain prominence in an emerging Euro–Maghreb logistics interface that demands a territorial balance and resilient port–hinterland integration. Full article

The spatial separation of port yards and railway hubs, which relies on external truck drayage as a necessary link, hampers the seamless transshipment of sea–rail intermodal containers between ports and railway hubs. This creates challenges in synchronizing yard cranes (YCs) at the port terminal, external trucks (ETs) on the road, and rail-mounted gantry cranes (RMGs) at the railway hub. However, most existing studies focus on equipment scheduling or container transshipment organization under the port–railway integration mode, often overlooking critical time window constraints, such as train schedules and export container delivery deadlines. Therefore, this study investigates the collaborative scheduling of YCs, ETs, and RMGs for synchronized loading and unloading under the port–railway separation mode. A mixed-integer programming (MIP) model is developed to minimize the maximum makespan of all tasks and the empty-load time of ETs, considering practical time window constraints. Given the NP-hard complexity of this problem, an improved genetic algorithm (GA) integrated with a “First Accessible Machinery” rule is designed. Extensive numerical experiments are conducted to validate the correctness of the proposed model and the performance of the solution algorithm. The improved GA demonstrates a 6.08% better solution quality and a 97.94% reduction in computation time compared to Gurobi for small-scale instances. For medium to large-scale instances, it outperforms the adaptive large neighborhood search (ALNS) algorithm by 1.51% in solution quality and reduces computation time by 45.71%. Furthermore, the impacts of objective weights, equipment configuration schemes, port–railway distance, and time window width are analyzed to provide valuable managerial insights for decision-making to improve the overall efficiency of sea–rail intermodal systems. Full article

The sea–rail intermodal container terminal serves as a key transportation hub for green logistics, where efficient resource coordination directly enhances multimodal connectivity and operational synergy. To address limited storage capacity and trans-shipment inefficiencies, this study innovatively proposes a resource-sharing strategy between the seaport and the railway container terminal, focusing on the joint allocation of yard space and internal trucks. For indirect trans-shipment operations between ships, the port, the railway container terminal, and trains, a mixed-integer programming model is formulated with the objective of minimizing the container trans-shipment cost and the weighted turnaround time of ships and trains. This model simultaneously determines yard allocation, container transfers, and truck allocation. A two-layer hybrid heuristic algorithm incorporating adaptive Particle Swarm Optimization and Greedy Rules is designed. Numerical experiments verify the model and algorithm performance, revealing that the proposed method achieves an optimality gap of only 1.82% compared to CPLEX in small-scale instances while outperforming benchmark algorithms in solution quality. And the shared yard strategy enhances ship and train turnaround efficiency by an average of 33.45% over traditional storage form. Sensitivity analysis considering multiple realistic factors further confirms the robustness and generalizability. This study provides a theoretical foundation for sustainable port–railway collaboration development. Full article

Accurately predicting the storage area of prefabricated components facilitates transshipment scheduling and prevents the waste of storage space. Due to the influence of numerous factors, precise prediction remains challenging. Currently, limited research has addressed the prediction of storage areas for prefabricated components, and effective solutions are lacking. To address this issue, a GRU model with an attention mechanism based on ensemble learning was proposed. The model employed the Bo-Bi-ATT-GRU approach to address the time series prediction of storage areas. A Bayesian optimization algorithm was utilized to enhance parameter tuning and training efficiency, while an ensemble learning framework improved model stability. In this study, a port container dataset was used for experimentation, with root mean square error (RMSE) and mean absolute percentage error (MAPE) as evaluation metrics. Compared with the GM model, the R² of the proposed model improved by 3.38%. Experimental results demonstrated that the ensemble learning-based prediction model offered superior performance in forecasting the storage area of prefabricated components. Full article

Optimizing last-mile delivery operations is an essential component in making a modern city livable, particularly in the face of rapid urbanization, increasing e-commerce activity, and the growing demand for fast deliveries. These factors contribute significantly to traffic congestion and pollution, especially in densely populated urban centers like Casablanca. This paper presents an innovative approach to optimizing last-mile delivery by integrating public transportation into the logistics network to address these challenges. A custom-built environment is developed, utilizing public transportation nodes as transshipment nodes for standardized packets of goods, combined with a realistic simulation of traffic conditions through the integration of the travel time index (TTI) for Casablanca. The pickup and delivery operations are optimized with the proximal policy optimization algorithm within this environment, and experiments are conducted to assess the effectiveness of public transportation integration and three different exploration strategies. The experiments show that scenarios integrating public transportation yield significantly higher mean rewards—up to 1.49 million—and more stable policy convergence, compared to negative outcomes when public transportation is absent. The highest-performing configuration, combining PPO with segmented training and public transport integration, achieves the best value loss (0.0129) and learning stability, albeit with a trade-off in task completion. This research introduces a novel, scalable reinforcement learning framework to optimize pickup and delivery with time windows by exploiting both public transportation and traditional delivery vehicles. Full article

The Vehicle-Routing Problem (VRP) represents a critical challenge in logistics, encompassing numerous variations, such as time window considerations, multi-depot systems, two-echelon routing aspects, and Satellite Locations (SL). SLs are intermediate facilities that support cross-docking, storage, and transshipment operations. However, inconsistencies in defining “satellite” have hindered precise research and implementation. This study presents a systematic review of the use of satellites for VRP, employing the PRISMA methodology to ensure a comprehensive and reproducible analysis. The findings indicate that about 50% of the reviewed papers include a path-splitting variant. At the same time, there is a notable gap in addressing random demands and pickup and delivery within cross-docking environments. A major limitation is the lack of a well-known public dataset, as about 50% of the datasets are created or adapted for specific studies. Additionally, the analysis reveals significant gaps in dataset standardization and the integration of dynamic routing under uncertainty. These findings underscore the potential of satellite-based systems to optimize urban logistics and supply chains while pointing to critical avenues for future research. Full article