Because they are environmentally friendly and safe, automated guided vehicles (AGVs) are increasingly used in newly constructed automated container terminals. However, their scheduling strategy is constrained by their limited battery capacity. When batteries reach their charging threshold, the AGVs need to be returned to battery-swapping stations. Moreover, the movement speed of AGVs has a significant impact on their energy consumption and operating times. Therefore, in this paper, a speed control strategy that considers the traffic environment of the terminal is proposed from the perspective of energy conservation and emission reduction. In addition, the charging capacity of the terminal is discretized to model its limited handling capacity to avoid congestion in the battery-swapping stations. To minimize the costs of delays and carbon emissions of AGV operations, a mixed integer programming model is established. It optimizes the efficiency and carbon emissions of the operations by assigning and prioritizing container transportation and AGV battery-swapping tasks. An improved genetic algorithm-based approach is designed where a better initial solution is obtained through a greedy strategy, while simulated annealing is adopted for population selection to prevent the algorithm from falling into local optima. Furthermore, an adaptive adjustment strategy for crossover and mutation probabilities is adopted to improve the algorithm’s convergence. Finally, a series of numerical experiments is conducted to verify the efficiency of the proposed method. The experimental results indicate that considering the variability of AGV speed can more accurately characterize their energy consumption, and increasing the number of AGVs and enhancing the battery-swapping capacity can effectively reduce the costs of delays and carbon emissions.

Automatic guided vehicles (AGVs) in the horizontal area play a crucial role in determining the operational efficiency of automated container terminals (ACTs). To improve the operational efficiency of an ACT, it is essential to decrease the impact of battery capacity limitations on AGV scheduling. To address this problem, this paper introduces battery swapping and opportunity charging modes into the AGV system and proposes a new AGV scheduling problem considering the hybrid mode. Firstly, this study describes the AGV scheduling problem of the automated container terminals considering both loading and unloading tasks under the hybrid mode of battery swapping and charging. Thereafter, a mixed-integer programming model is established to minimize the sum of energy costs and delay costs. Secondly, an effective adaptive large neighborhood search algorithm is proposed to solve the problem, in which the initial solution construction, destroy operators, and repair operators are designed according to the hybrid mode. Finally, numerical experiments are conducted to analyze the effectiveness of the model and the optimization performance of the algorithm. The results demonstrate that the hybrid mode of battery swapping and charging can effectively reduce the number of battery swapping times and scheduling costs compared to the existing mode.

Port operations have been suffering from hybrid uncertainty, leading to various disruptions in efficiency and tenacity. However, these essential uncertain factors are often considered separately in literature during berth and quay crane assignments, leading to defective, even infeasible schedules. This paper addressed the integrated berth allocation and quay crane assignment problem (BACAP) with stochastic vessel delays under different conditions. A novel approach that combines both proactive and reactive strategies is proposed. First, a mixed-integer programming model is formulated for BACAP with quay crane maintenance activities under the ideal state of no delay. Then, for minor delays, buffer time is added to absorb the uncertainty of the arrival time of vessels. Thus, a robust optimization model for minimizing the total service time of vessels and maximizing the buffer time is developed. Considering that the schedule is infeasible when a vessel is seriously delayed, a reactive model is built to minimize adjustment costs. According to the characteristics of the problem, this article combined local search with the genetic algorithm and proposed an improved genetic algorithm (IGA). Numerical experiments validate the efficiency of the proposed algorithm with CPLEX and Squeaky Wheel Optimization (SWO) in different delay conditions and problem scales. An in-depth analysis presents some management insights on the coefficient setting, uncertainty, and buffer time.

Given the swift expansion of fresh e-commerce, the front warehouse mode can respond quickly and ensure the quality of fresh products. However, the complexity of the supply chain structure under front warehouse mode poses a challenge in reducing logistics costs and improving distribution efficiency while meeting consumers’ immediate delivery demands. Therefore, this paper studies the vehicle routing problem of two-echelon fresh e-commerce under front warehouse mode. Considering trans-shipment time constraints between the two echelons and the characteristics of terminal distribution, this paper initially models the vehicle routing problem for front warehouses as a two-echelon multi-trip capacitated vehicle routing problem with time windows. A mixed-integer linear programming model is subsequently established. To solve the model, a hybrid genetic algorithm integrated with neighborhood search is developed. Matrix coding is employed to merge vehicle selection and route assignment decisions. Simultaneously, neighborhood search is applied to enhance the search capability of algorithms, thereby improving the quality of solutions. Furthermore, the effectiveness and efficiency of the model and algorithm are verified through experiments of varying scales. Finally, comparative strategies and sensitivity analysis highlight the advantages of multi-trip strategies and provide insights into the optimal vehicle capacity limit.

Inland shipping in the Yangtze River in China has become very prosperous, making feeder scheduling and container transportation increasingly difficult for feeder operators. This research analyzed the decision-making of container transportation businesses in feeder companies operating between Shanghai Port and inland ports along the Yangtze River in China. The research considered the complexity of the natural conditions and water channels, including the draught limitations and the height of the bridges over the river. To analyze ways to increase the effectiveness of shipping containers from Shanghai Port into inland river ports along the Yangtze River, we built a mixed integer nonlinear programming (MINLP) model to minimize the total operating cost and determine the most effective departure time of each feeder. After linearizing the model, we designed a particle swarm optimization (PSO) algorithm to increase solution efficiency and introduced a taboo list and aspiration criterion of a Taboo Search (TS) algorithm to improve the PSO algorithm. Finally, we verified the accuracy of the model and the efficiency of the algorithms using numerical experiments. The research provides theoretical guidance for feeder operators and inland river shipping companies.