Yu Wang

To address the efficiency and safety challenges arising from the mixed operation of autonomous and human-driven container trucks during the automation transformation of traditional container terminals, this study designed a simulation-based optimization framework for mixed vehicle scheduling. A spatio-temporal graph dynamic scheduling model was constructed, incorporating node capacity, arc capacity, and path constraints, to establish a multi-objective optimization model aimed at minimizing the maximum completion time of internal trucks and the average waiting time of external trucks. An improved NSGA-II algorithm was employed to generate task assignment solutions, which were evaluated using discrete-event simulation, integrating a dynamic programming-based yard block selection strategy for external trucks and a congestion-aware path planning algorithm. Experimental results demonstrate that the dynamic priority strategy effectively adapts to different traffic flow scenarios: under low external truck flow, the autonomous internal truck priority strategy reduces task completion time by 18% to 25%, while under high flow, the external truck priority strategy significantly decreases the average waiting time. The optimal configuration ratio between internal and external trucks was identified as approximately 1:2. This research provides a theoretical basis and decision support for enhancing terminal operational efficiency and automation transformation.

As global trade and container transportation continue to grow, port collection and distribution systems face increasing challenges, including congestion, inefficiency, and environmental impact. Traditional ground-based transportation methods often exacerbate these issues, especially under uncertain demand conditions. This study aims to optimize freight flow allocation in port collection and distribution networks by integrating traditional and innovative transportation modes, including underground container logistics systems, under demand uncertainty. A stochastic optimization model is developed, incorporating transportation, environmental, carbon tax and subsidy, and congestion costs while satisfying various constraints, such as capacity limits, time constraints, and low-carbon transport requirements. The model is solved using a hybrid algorithm combining an improved Genetic Algorithm and Simulated Annealing (GA-SA) with Deep Q-Learning (DQN). Numerical experiments and case studies, particularly focusing on A Port, demonstrate that the proposed approach significantly reduces total operational costs, congestion, and environmental impacts while enhancing system robustness under uncertain demand conditions. The findings highlight the potential of underground logistics systems to improve port logistics efficiency, providing valuable insights for future port management strategies and the integration of sustainable transportation modes.

Due to external environmental factors, the layout of compound channels in estuarine ports is restricted. Moreover, with the opposing distribution of anchorages and terminals within the port, vessels navigating between these areas must cross the channel, severely affecting channel navigation safety and efficiency. In order to improve the efficiency of vessel scheduling, we analyze the layout characteristics of an estuarine port and its compound channel, summarize vessel navigation modes and traffic flow conflicts, and identify five key conflict areas. On this basis, we develop a multi-objective optimization model aimed at minimizing vessel waiting times and the total channel occupancy time ratio. This model incorporates constraints such as navigation rules, traffic flow conflicts, tidal effects, and traffic control. To solve the model, we propose an adaptive non-dominated sorting genetic algorithm, ANSGA-NS-SA, which integrates neighborhood search (NS) and Simulated Annealing (SA). The entropy-weighted technique for order preference by similarity to ideal solution (TOPSIS) is used to calculate the objective weights of the Pareto frontier and identify the optimal solution. Experimental results show that the proposed model and algorithm yield optimal port entry and exit scheduling solutions. In terms of port scheduling performance, the proposed model and algorithm outperform the traditional First-Come-First-Served (FCFS) strategy and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), reducing total vessel waiting time by 33.8% and improving total channel occupancy ratio by 8.8%. This study provides a practical and effective decision support tool for estuarine port scheduling, enhancing overall port operational efficiency.

The underground logistics system is a relatively new concept for container transportation, which is designed to reduce the congestion and pollution on the road caused by the sharply growing number of collections and distributions of containers in the port cities. This paper considers a system where some underground logistics vehicles (ULVs) are marshaled and used to transport containers between two port terminals through a deep underground tunnel. Automated guided vehicles (AGVs) are used for horizontal transportation of containers in the above-ground yard of the terminals, and yard cranes (YCs) are used to transfer the containers vertically through a shaft linking the above-ground yard and the deep underground tunnel. To guarantee the efficiency of this system, a joint scheduling problem of the YCs and the ULVs is proposed and formulated as an integer programming model to minimize the total waiting time of the YCs and ULVs. Taking marshaling and congestion of the ULVs into consideration, a Genetic Algorithm is developed to solve the problem. Numerical experimental results prove the efficiency of the proposed algorithm, and different marshaling strategies are compared. Our research provides a scientific foundation for developing underground logistics systems in large port cities.

In recent years, underground logistics systems have attracted more and more attention from scholars and are considered to be a promising new green and intelligent transportation mode. This paper proposes a yard design problem considering an underground container logistics system. The structure and workflow of the underground container logistics system are analyzed, and key features are recognized for the yard design problem, such as the container block layout direction, the lane configuration in the yard, and the number of container blocks. We formulate the problem into mathematical models under different scenarios of the key features with the comprehensive objective of maximizing the total throughput and minimizing the total operation cost simultaneously. An improved tabu search algorithm is designed to solve the problem. Experimental results show that the proposed algorithm can generate a satisfactory layout design solution for a real-size instance. Our research studies different container yard design options for introducing the underground logistics system into port terminals, which provides an important scientific foundation for promoting the application of underground container logistics systems.

As consumers’ green awareness continues to grow, the level of a product’s eco-friendliness and the quality of its marketing have become significant factors in shaping consumers’ purchasing decisions. The power structures within the supply chain, as well as corresponding government subsidy policies, are also key elements influencing sustainable strategies for the green supply chain. In a green supply chain comprising one manufacturer and one e-commerce platform, two sales models exist within the online e-commerce platforms: reselling and agency selling. This paper establishes and analyzes three distinct Stackelberg game models, namely: manufacturer-led model without subsidy (bm), manufacturer-led model with subsidy (sm), and platform-led model with subsidy (sp). The results are shown as follows, with the rise of consumers’ environmental awareness, more consumers opt for green products, inspiring the manufacturer to increase its optimal greenness and platform to enhance its optimal marketing efforts level. Notably, government subsidies provide a significant stimulus. An increase in the green technology cost coefficient leads to a decline in the manufacturer’s profits across all three modes. Intriguingly, the manufacturer’s profits are always highest in the sp mode. As the marketing efforts cost coefficient increases, the platform’s profits decrease in the bm and sm modes. In contrast, in the sp mode, the platform’s profits increase rather than a decrease. The choice of mode primarily depends on the platform’s marketing efforts cost coefficient. When this coefficient exceeds a threshold, the platform chooses the sp mode. However, due to the relatively low marketing efficiency in this scenario, the manufacturer prefers the sm mode. For the government, the sp mode involves agency selling, serves as an effective mechanism to redistribute subsidies, thereby yielding the maximum social welfare benefits. Management insights are provided for the manufacturer and platform managers to make decisions about the degree of greenness and marketing efforts level, along with insights for governments to optimize subsidy policies.

This paper considers a pickup and delivery problem in automobile logistics. In the daily operations of a third-party logistics company (3PL), decisions must be made for two kinds of demands: delivering finished automobiles from an outbound warehouse to distribution centers (DCs) and transferring automobiles among the DCs according to specific customer orders. The problem is to assign a set of automobiles to a set of heterogeneous auto-carriers and deliver them to their destinations considering the outbound and transfer demands. Each automobile is assigned a value indicating its urgency level to be handled and a car type: small, medium, or large. Each of the auto-carriers has a specific number of slots with different types indicating the largest size of an automobile that can be loaded into the slot. An integer programming (IP) model is formulated for the problem to maximize the total loaded value and minimize the total transportation cost depending on the routing of the carriers. An improved adaptive large neighborhood search algorithm is developed to solve the problem efficiently, where a heuristic generates an initial solution, and a series of operators update the solution iteratively. Experimental results based on multi-scale instances show that the proposed algorithm can generate near-optimal solutions in an acceptable amount of time, and outperforms solving the IP model directly by CPLEX to a large extent. The algorithm can help 3PL companies make efficient and economical decisions in daily operations.

E-commerce companies generate massive orders daily, and efficiently fulfilling them is a critical challenge. In the “parts-to-picker” order fulfillment system, the joint optimization of order allocation and rack selection is a crucial problem. Previous research has primarily focused on these two aspects separately and has yet to consider the issue of workload balancing across multiple picking stations, which can significantly impact picking efficiency. Therefore, this paper studies a joint optimization problem of order allocation and rack selection for a “parts-to-picker” order picking system with multiple picking stations to improve order picking efficiency and avoid uneven workload distribution. An integer programming model of order allocation and rack selection joint optimization is formulated to minimize the racks’ total moving distance and to balance the orders allocated to each picking station. The problem is decomposed into three sub-problems: order batching, batch allocation, and rack selection, and an improved simulated annealing (SA) algorithm is designed to solve the problem. Two workload comparing operators and two random operators are developed and introduced to the SA iterations. Random instances of different scales are generated for experiments. The algorithm solutions are compared with those generated by solving the IP model directly in a commercial solver, CPLEX, and applying the first-come-first-serve strategy (FCFS), respectively. The numerical results show that the proposed algorithm can generate order allocation and rack selection solutions much more efficiently, where the moving distances of the racks are effectively reduced and the workloads are balanced among the picking stations simultaneously. The model and algorithm proposed in this paper can provide a scientific decision-making basis for e-commerce companies to improve their picking efficiency.

With the rapid development of global trade, ports and terminals are playing an increasingly important role, and automatic guided vehicles (AGVs) have been used as the main carriers performing the loading/unloading operations in automated container terminals. In this paper, we investigate a multi-AGV dynamic scheduling problem to improve the terminal operational efficiency, considering the sophisticated complexity and uncertainty involved in the port terminal operation. We propose to model the dynamic scheduling of AGVs as a Markov decision process (MDP) with mixed decision rules. Then, we develop a novel adaptive learning algorithm based on a deep Q-network (DQN) to generate the optimal policy. The proposed algorithm is trained based on data obtained from interactions with a simulation environment that reflects the real-world operation of an automated in Shanghai, China. The simulation studies show that, compared with conventional scheduling methods using a heuristic algorithm, i.e., genetic algorithm (GA) and rule-based scheduling, terminal the proposed approach performs better in terms of effectiveness and efficiency.