Under the background of regular epidemic prevention and control, the frontiers and prospects of humanitarian logistics (HL) have substantial value in enhancing emergency capacity. Taking the relevant articles collected in Web of Science and Scopus databases as the research object, this study discusses the basic characteristics, hot topics, and the potential directions of HL, using the bibliometric analysis and statistical analysis methods. Results show that the number of articles presents an overall increase with fluctuation. The major driving force of HL research is from the USA and China. Although a certain cooperation size among authors has been formed, close contact among research teams from different institutions in different countries is lacking. In addition, the four main topics of HL are highlighted through the co-occurrence analysis of keywords by VOSviewer. Further research directions and suggestions can be identified under the analysis of each topic and the knowledge map of CiteSpace. It is expected that results will provide a theoretical reference to scholars when identifying innovative ideas in this field.

This study develops a multi-objective optimization framework for sustainable container truck dispatching in port logistics, addressing the limited joint consideration of environmental compliance, worker-sensitive assignment, and operational efficiency in traditional dispatching practice. The problem is formulated as a constrained assignment-and-scheduling model under time-window, compliance, capacity, and service requirements. To balance optimality and real-time responsiveness, a dual-path solution strategy is proposed, combining a mixed-integer linear programming (MILP) model for small-scale instances with a Priority-Based Constructive Heuristic with Conflict Resolution (PBCH-CR) for medium-to-large-scale scenarios. Computational experiments on scenario-based synthetic instances calibrated to empirical port-operation distributions show that PBCH-CR maintains 100% environmental compliance for assigned orders, improves familiarity-oriented matching relative to the FCFS baseline, and sustains strong emergency-response performance within sub-minute computation times. Sensitivity analysis further shows that improving urgency-oriented performance entails a reduction in freight-revenue-oriented performance. Overall, the framework provides a practical approach to balancing environmental compliance, operational efficiency, and worker-sensitive dispatching, with relevance to Sustainable Development Goals 11 and 13 and to SDG 8-related objectives.

Static pre-scheduling in inland container depot (ICD)-centered drayage must coordinate tractors, detachable load units, factory loading, and port deadlines before next-day execution. Conventional order-based routing is too rigid for mixed direct haulage, drop-and-pull, relay pickup, street-turn, and buffering operations. This study proposes a task-pooling framework that decomposes logistics orders into atomic tasks and recombines them across tractors in a unified static planning space. A compact route-based MILP is used for reduced-scale calibration, and an enhanced adaptive large neighborhood search (E-ALNS) is developed around ICD-oriented relay recombination and temporal-slack shifting. On a realistic synthetic benchmark with 100 generated order records (90 active executable orders), 60 available tractors, and 330 executable tasks, the proposed method reduces the internal search-ledger value from 42,213.29 to 34,421.22 and the compact ex post blueprint value from 53,802.28 to 47,717.99 relative to the greedy construction baseline. The resulting blueprint preserves an average inter-task slack of 89.86 min and a 5th-percentile slack of 61.73 min. A generic adaptive-neighborhood baseline reaches a slightly lower ex post value of 46,722.48 only with a longer runtime and much lower temporal reserve. The results support a cost–reserve–runtime tradeoff interpretation rather than unconditional cost dominance.

Handwritten mathematical expression recognition aims to recover symbol sequences and hierarchical relations from two-dimensional layouts. Existing methods often rely on linear sequence modeling or local structural composition, making it difficult to jointly capture the main chain and nested dependencies, leading to structural entanglement and unstable parsing. To address this issue, this paper proposes a structural re-indexing and nested decoding network. A structural re-indexing module separates the main chain and hierarchical relations on shared features and uses mutual constraints to model their dependency. An offset field rearranges features into a disentangled representation, reducing relational ambiguity. Based on this, a nested decoder progressively constructs expressions. The main chain provides the global framework, and local structures are expanded at key positions according to relation predictions. Experiments show that the structural re-indexing and nested decoding network achieves higher accuracy, robustness, and structural consistency, especially for complex expressions and long sequences.

Cross-border e-commerce fulfillment depends on coordinated inland container movements across factories, inland container depots (ICDs), and port gateways, yet many container trucking operations still follow synchronous one-truck-one-order execution. This study models the fulfillment network as a platform-enabled socio-technical transportation system in which the ICD acts as a digital–physical coordination node for spatiotemporal decoupling. A drop–buffer–pick task architecture is developed to represent direct execution, relay execution, and delayed dispatch, and a mixed-integer linear programming (MILP) model optimizes task assignment and tractor sequencing under loading-time, port cutoff, inventory, and working-time constraints. In the certified-optimal 10-order instance, gross positive cost decreases from CNY 27,540 to CNY 19,915 (−27.7%); after applying the same post hoc coordination-credit accounting rule, net total fulfillment cost decreases to CNY 18,734 (−32.0%). The 10 orders are served with five tractors under the tested platform configuration, compared with 10 tractors under the restricted benchmark. To address sustainability explicitly, the analysis also reports distance-based emissions and energy-use proxies; the proposed schedule lowers cost and fleet deployment but increases total mileage, showing that economic efficiency and emissions performance do not automatically move together. The evidence is a deterministic baseline for later stochastic, mixed import/export, and collaborative-platform extensions.

Inland container depot (ICD) truck schedules must absorb new orders, service delays, appointment changes, congestion, and port cut-offs without destabilizing an already executed plan. This study asks whether event-triggered local repair can be separated into an explicit business-rule audit and a learned ranking of feasible task–vehicle actions. The proposed decision-support framework connects a static baseline, candidate task chains, six modeled hard-feasibility predicates, a Transformer encoder trained with proximal policy optimization (Transformer-PPO), and discrete-event execution logs. A five-seed, 120-episode confirmation gave Transformer-PPO a held-out online completion proxy (α_online) of 0.3226 and reward of 110.58, compared with 0.2581 and 61.87 for the matched multilayer perceptron (MLP); deterministic rules and search remained competitive. An independent audit of 4,968,000 action cells across 552 decision states found no disagreement with an independently coded oracle for the implemented hard predicates, while a reward-weight screen exposed the expected efficiency-stability trade-off. Together with a rolling-horizon comparator and a three-scale by three-disturbance stress test, the evidence supports an auditable system-integration contribution, not a new generic reinforcement learning (RL) algorithm or universal performance superiority. Claims are limited to synthetic simulation-based decision support.