The pig market had experienced a cycle of price rise and fall, also known as the “pig cycle.” This paper analyzes the fluctuation relationship between pig price, pig supply, and pork demand, constructs a system dynamics model of the pig industry by decomposing the structure of the pig supply chain, and then discusses the causes of “pig cycle,” as well as the supply chain management strategy and industrial policy, to stabilize the pig industry market. Research shows that reducing the cost of pig breeding, countercyclical adjustment, and government macrocontrol can effectively reduce the fluctuation of pig prices. Among them, reducing the pig breeding cost is the most effective long-term strategy to stabilize the pig price.

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 paper presents a simultaneous pickup and delivery route designing model, which considers the use of express lockers. Unlike the traditional traveling salesman problem (TSP), this model analyzes the scenario that a courier serves a neighborhood with multiple trips. Considering the locker and vehicle capacity, the total cost is constituted of back order, lost sale, and traveling time. We aim to minimize the total cost when satisfying all requests. A modified deep Q‐learning network is designed to get the optimal results from our model, leveraging masked multi‐head attention to select the courier paths. Our algorithm outperforms other stochastic optimization methods with better optimal solutions and O(n) computational time in evaluation processes. The experiment has shown that reinforcement learning is a better choice than traditional stochastic optimization methods, consuming less power and time during evaluation processes, which indicates that this approach fits better for large‐scale data and broad deployment.

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.

Retailers must reevaluate store positioning when implementing omnichannel strategies. This study examines retailers’ strategic preferences toward in-store services, including Buy-Online-Pickup-in-Store (BOPS), Buy-Online-Return-in-Store (BORS), and their combined offering. A stylized model incorporating capacity constraints and strategic consumers’ purchase behavior is developed to analyze omnichannel impacts on brick-and-mortar operations from an inventory perspective. Firstly, profit-maximizing retailers benefit from reducing online channel inventory when handling product returns. Under high online return rates or stringent capacity constraints, retailers prefer maintaining physical-only channels to mitigate returns and capture cross-selling opportunities. Secondly, offering BOPS services remains a strategic advantage during periods of moderate capacity utilization. When the online consumer market expands, spillover effects increase, return rates decrease, and capacity constraints ease, it becomes feasible to consider jointly providing BORS services. Finally, BORS may migrate offline shoppers online, making it unsuitable for high-return-rate products. For omnichannel retailers, this study offers valuable insights into implementing omnichannel strategies under capacity constraints, empowering practitioners to make more informed decisions and optimize operational tactics.

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.