This paper deals with a reentrant hybrid flow shop problem with sequence-dependent setup time and limited buffers where there are multiple unrelated parallel machines at each stage. A mathematical model with the minimization of total weighted completion time is constructed for this problem. Considering the complexity of the problem at hand, an effective cooperative adaptive genetic algorithm (CAGA) is proposed. In the algorithm, a dual chain coding scheme and a staged-hierarchical decoding approach are, respectively, designed to encode and decode each solution. Six dispatch heuristics and a dynamic adjustment method are introduced to define initial population. To balance the exploration and exploitation abilities, three effective operations are implemented: (1) two new crossover and mutation operators with collaborative mechanism are imposed on genetic algorithm; (2) an adaptive adjustment strategy is introduced to re-optimize better solutions after mutation operations, where ant colony search algorithm and modified greedy heuristic are intelligently switched; (3) a reset strategy with dynamic variable step strategy is embedded to re-generate some non-improved solutions. A Taguchi method of design of experiment is adopted to calibrate the parameter values in the presented algorithm. Comparison experiments are executed on test instances with different scale problems. Computational results show that the proposed CAGA is more effective and efficient than several well-known algorithms in solving the studied problem.

In this study, we address the integrated scheduling problem involving quay cranes and IGVs in automated terminals. We construct a mixed-integer planning model with the aim of minimizing the total energy consumption during quay crane and IGV operations, focusing on the loading-operation mode. The model considers the impact of the actual stowage of container ships on the loading order. We propose a dimension-by-dimension mutation sparrow search algorithm to optimize the model’s solution quality. Building upon the standard sparrow search algorithm, we incorporate cat mapping to enhance the diversity of the initial sparrow population. To improve global search in the early stage and local search in the later stage of the algorithm, we introduce an adaptive t-distribution mutation strategy. Finally, a total of 12 instances with container counts containing 30, 100, and 250 were designed for experiments to validate the effectiveness of the model and algorithm. The experiments demonstrate that, by appropriately increasing the number of quay cranes, configuring more than two or three IGVs can achieve optimal energy consumption for overall operations.

The relatively limited capacities of inland container liner shipping mean that, unlike in maritime container shipping, capacity utilisation is more important than scheduling. Capacity utilisation and stability must be considered in the stowage planning problem in inland container liner shipping. We adopt a multi-stage hierarchical decomposition approach to decompose the problem into multiple stages because a ship needs to visit multiple ports during its voyage. At each stage, the stowage planning problem of the current port is decomposed hierarchically into two sub-problems: the master bay planning problem (MBPP) and slot planning problem (SPP). The multi-port MBPP is first optimised to simultaneously generate the master bay plans for multiple ports over the full route. This approach incorporates two heuristic algorithms, a greedy randomised adaptive search procedure for the multi-port MBPP, and a heuristic evolutionary strategy algorithm for the SPP. Computational results for randomly generated data corresponding to real-size scenarios of inland container ships are presented validating the proposed algorithms.

The effective production scheduling of dry bulk ports is a challenging task that demands meticulous planning, task allocation based on customer requirements, as well as strategic route and timing scheduling. Dry bulk ports dedicated to handling commodities like coal and iron ore frequently engage in blending operations as a strategic imperative to gain market competitiveness. The process of blending coal and ore entails the timely arrival of the requisite raw materials at predetermined locations. Simultaneously, it necessitates the coordination of the sequencing of goods entering and departing the port to align with the operational demands associated with material stockpiles. This paper describes and analyzes an operational scheduling problem encountered by one of the largest coal blending sea ports in China. Specifically, a rich constraint programming model is presented to define operation sequences integrating daily inbound and outbound services provided by the port, minimizing the overall operation time. In order to enhance the practicality of the method, a CP-based adaptive simulated annealing local search algorithm has been designed and developed for the optimization problem. The empirical validation of the proposed method is conducted using both real production data and generated experimental data adhering to specific rules. The results conclusively demonstrate the efficacy and feasibility of the proposed method. This also substantiates its practicality and effectiveness in real-world applications, facilitating efficient production and energy-saving operations for the coal port.

Ultrashort baseline (USBL) positioning system is an important part of integrated navigation for underwater vehicles. The single USBL positioning system has problems such as reduced accuracy of azimuth measurement due to target motion and large impact by small angular variations, especially in the region where the target is close to the conical boundary, i.e., the low elevation region. To improve the positioning accuracy of the ULBS-based integrated navigation system, a tightly coupled USBL/DR(Dead Reckoning) integrated localization algorithm that considers the time varying characteristics of the measurement noise is proposed, and the filtering model of the algorithm is designed. The algorithm exploits the mechanism of the azimuth measurement covariance variation of the USBL system, constructs an adaptive observation noise estimator for the USBL system, and applies it to the USBL/DR data integration filtering. A nonlinear extended Kalman filtering (EKF) model is used to fuse the USBL positioning and dead reckoning trajectories. A number of simulation tests with different elevation angle settings have been carried out to compare the performance of the proposed algorithm with a conventional EKF for underwater localization. The test results reveal that the proposed algorithm can effectively reduce the positioning error caused by the change of the relative azimuth of the acoustic signal due to the motion of the mother ship and the motion of the underwater vehicle.