Lot-streaming enables a discrete supply of components to multiple assembled products, while the limited buffer controls the fluency of the production flow. Thus, integrating differentiation processing with assembly, this work addresses an assembly hybrid differentiation flowshop scheduling with variable sub-lot and limited buffer (AHDFSP-VS-LB). Focusing on makespan minimization, a mixed-integer linear programming model is established and an asynchronous multi-agent based differential evolution (AMDE) is developed. Constrained by the limited buffer capacity, the AMDE incorporates a deadlock pre-detection strategy to quickly identify infeasible solutions during encoding and a dynamic adjustment strategy to ensure a high-quality feasible solution during decoding. Further, derived from processing-assembly coordination and buffer constraint, four problem-specific properties are extracted and embedded into the initialization and improvement strategy to optimize the performance of the algorithm. To achieve fast and sufficient convergence of the algorithm, an asynchronous multi-agent cooperative learning mechanism is designed to dynamically control the evolution power and direction for each individual by identifying the critical encoding layer and its suitable parameters at different search phases. Comprehensive experiments demonstrate that the designed operators excel in efficiency and coordination, and the proposed algorithm is superior to five state-of-the-art algorithms in solving this new problem.

Collaborative robots (cobots) are increasingly used to help human workers perform assembly tasks or complete assembly tasks themselves in assembly lines. The ergonomic risks of human workers are a key factor influencing assembly line efficiency. Therefore, this study investigates the mixed-product-model assembly line balancing and sequencing problem (ALBSP) with cobots, considering ergonomic risks in cases where human workers and cobots can operate different tasks in parallel. A mixed-integer programming model is formulated to optimize the makespan and ergonomic risks; this model can solve small-scale instances optimally using the CPLEX solver. A Q-learning-based multiobjective coevolutionary algorithm (QMOCEA) is then developed to handle large-scale instances. This algorithm adopts five vectors for encoding: the task assignment vector handles the task allocation subproblem, the worker allocation vector handles the worker allocation subproblem, the cobot allocation vector handles the cobot allocation subproblem, the process alternative selection vector handles the process alternative selection subproblem, and the product model sequencing vector handles the product model sequencing subproblem. Additionally, this algorithm uses knowledge-based decoding and initialization to obtain high-quality initial solutions. A parameter self-update strategy is proposed to adjust algorithm parameters dynamically. Comparative analysis demonstrates that the proposed method outperforms the original version and exhibits promising performance in comparison with benchmark methods, achieving the highest average hypervolume (HV) ratio of 0.805 and the lowest inverted generational distance (IGD) of 0.028 across 22 instance groups.

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimanned assembly line balancing under uncertain demand, and presents a new robust mixed-integer linear programming model to minimize the production and penalty costs simultaneously. In addition, a reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed to tackle the problem. The algorithm includes a priority-based solution representation and a new task-worker-sequence decoding that considers robustness processing and idle time reductions. Five crossover and three mutation operators are proposed. The Q -learning-based strategy determines the crossover and mutation operator at each iteration to effectively obtain Pareto sets of solutions. Finally, a time-based probability-adaptive strategy is designed to effectively coordinate the crossover and mutation operators. The experimental study, based on 269 benchmark instances, demonstrates that the proposal outperforms 11 competitive MOEAs and a previous single-objective approach to the problem. The managerial insights from the results as well as the limitations of the algorithm are also highlighted.

In a realistic two-sided assembly line, a preventive maintenance (PM) activity may cause a stoppage of the whole line and a waste of capacity in most stations. To promote production continuity, multiple interchangeable task assignment schemes are required, each targeting one of the regular and PM scenarios. Yet previous studies have not solved the resulting two-sided assembly line balancing problem considering PM scenarios (TALBP-PM), and the domain knowledge deserves extraction. Hence, a multiobjective mixed-integer linear programming model is formulated to minimize cycle times and total task adjustment simultaneously, and a knowledge-assisted variable neighborhood search (KVNS) is customized. Specifically, a decoding mechanism with idle time reduction is proposed to achieve schemes with the shortest cycle times. A rule-based initialization relying on the externalization of implicit relations among unique attributes is designed to derive a high-quality initial solution. Supported by the critical station and task knowledge, objective-oriented neighborhood structures are developed to generate neighbor solutions with increasingly better objectives. Besides, a restart operator adaptive to multidomain knowledge is refined to escape from local optima. Computational results show that the knowledge assistance is effective, and KVNS is superior to other state-of-the-art meta-heuristics in achieving well-converged and -distributed Pareto fronts of TALBP-PM.

Abstract The collaborative robots (cobots) are increasingly being utilized in industries due to the advancement in the field of robotic technology and also due to the increase in labor costs. The cobots on the assembly line can be utilized to complete the tasks independently or assist the workers to complete the tasks. This study considers the U-shaped assembly line balancing problem with cobots, where several cobots with different purchasing costs are selected under the budget constraint. Three mixed-integer programming models are formulated to optimize the cycle time, and the built models are capable of solving the small-sized instances optimally. Two algorithms, artificial bee colony algorithm and migrating bird optimization algorithm, are developed and improved to tackle the large-sized instances, where new encoding scheme and decoding procedure are developed for this new problem. The computational tests demonstrate that the utilization of collaborative robots reduces the cycle time effectively in the assembly line. The comparative study on a set of instances shows that the proposed methodologies obtain competing performance in comparison with other 12 implemented algorithms.

Global Navigation Satellite System-Acoustic (GNSS-A) technology has been widely used in ocean engineering and ocean environmental science. Accurate sound travel time modeling is essential for GNSS-A seafloor positioning. Currently, half of the two-way travel time (TWTT) has been used as an approximation for the one-way travel time (OWTT). In this work, the time error of the approximate OWTT is investigated under different survey configurations, and a sequential GNSS-A seafloor positioning method using the extended Kalman filter (EKF) is developed to investigate the impact of sound travel time modeling. Simulations show that the time error induced under the static survey configuration is less than 0.6 ms; the time error induced under the circle survey configuration with a stable inclination angle is stable, but the time error of the line survey configuration can reach 28 ms. As confirmed through field experiments, sequential GNSS-A seafloor positioning using TWTT modeling is more stable than OWTT modeling. The positioning residuals of TWTT modeling are similar to those of OWTT modeling under the circle configuration but at least 2 times less than those of OWTT modeling under the line survey configuration. Furthermore, the average positioning residuals of OWTT and TWTT modeling can be greatly reduced for a survey configuration combining circular and linear tracks. These findings provide a feasible method for improving the precision and efficiency of GNSS-A seafloor positioning.

Single-wavelength bathymetric LiDAR (532 nm) can provide seamless meter- and submeter-scale DEMs of both the terrestrial surface and seafloor. However, the mixed terrestrial and bathymetric surfaces obtained by this sensor are challenging for full-waveform (FW) signal detection. This study addresses the issues in two FW mixed surfaces: accurate classification of terrestrial and non-terrestrial waveforms from the original waveforms without auxiliary information, and flexible detection of peaks based on a new FW theoretical model. A novel FW signal-detection model (FWSD) for single-wavelength bathymetric LiDAR is proposed without complex feature extraction and iterative procedure through waveform classification and segmentation. The raw FW are divided into 5 categories for subsequent signal detection by utilizing a convolutional neural network that merges local descriptors with contextual information. The signal detection task is then split into FW segment recognition and peak extraction using a new FW model, which integrates a leapfrog sliding window FW segmentation, an improved extreme learning machine (ELM) algorithm for FW segment recognition and a flexible signal detection framework. In order to search for the optimal initial parameters for ELM, a self-annealing particle swarm optimization (SAPSO) algorithm is introduced, and the output weight is adjusted by online sequence to improve its generalization. When combined with the Richardson–Lucy deconvolution (RLD) algorithm, FWSD can be adapted to deal with shallow water waveforms. Finally, a test demonstration with an airborne dataset shows that FWSD has higher detection efficiency and higher accuracy than a generalized Gaussian model optimized using the Levenberg–Marquardt algorithm (LM-GGM) and RLD algorithm.

Atmospheric water vapor plays an essential role in climate change and weather forecasting. However, monitoring water vapor with high spatial and temporal resolutions remains a challenge, especially over ocean regions where observations are insufficient. Shipborne global navigation satellite systems (GNSSs) contribute to enriching water vapor measurements over oceans and also can help validate satellite observations. Due to the lack of long-time serial observations, the performance of shipborne GNSS-derived precipitable water vapor (PWV) is inadequately evaluated on the global ocean scale. In this study, an overall assessment of shipborne GNSS PWV over global oceans is performed based on six voyages from 2014 to 2018. In coastal areas, the PWV differences of shipborne GNSS with respect to (w.r.t.) ground-based GNSS and ground-launched radiosonde data are 2.64 and 2.85 mm in the root mean square (rms), respectively. In open oceans, compared to ship-launched radiosonde profiles and satellite measurements, shipborne GNSS PWV shows the rms of differences of 2.54 and 2.53 mm, respectively. In addition, the rms of PWV differences between the whole track of shipborne GNSS PWV and National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) products is 2.96 mm. The intertechnique validations demonstrate that the accuracy of shipborne GNSS PWV is superior to 3 mm, which meets the requirements of climate research and numerical weather prediction (NWP).

The airborne light detection and ranging (LiDAR) bathymetry (ALB) system is an extension of the ubiquitous topographic LiDAR mapping system and has been most simply characterized as adding a green laser to the infrared laser of topo systems. Due to the low point cloud density and monotonous objects in the scene, it is difficult to mosaicing the ALB strips. Therefore, the existing airborne laser scanning strip stitching algorithm has poor performance for ALB strips. In this article, a coarse-to-fine strip mosaicing model for ALB is proposed. The framework is fast and efficient and can handle large ALB data. An improved alpha shapes algorithm can fast and accurately determine the overlap region of strip is applied. Due to different data accuracy and spatial characteristics, the water area and land area are processed separately. A weight distribution-based coarse-to-fine registration model is designed for underwater areas. The topological constraint term is added to the nonrigid iterative closest point (ICP) cost function to prevent excessive deformation caused by outliers. The implicit B-spline surface fitting algorithm using the 3L algorithm and the least-squares trend surface fitting algorithm are applied separately to assign weights for overlapping strips to solve the limitation of no control or less control. Moreover, a random sample consensus (RANSAC)-ICP registration model characterized by the normal vector and curvature is constructed for land area. Finally, the comparisons with ICP highlight the superiority of the proposed approach in flexibility and accuracy. The root-mean-square error (RMSE) is 0.12 m and the maximum error is 0.36 m.

In this paper the calibration methodology, data and models, and the absolute bias of HY-2A, Jason-2, and Saral/AltiKa based on the Qianliyan CAL/VAL site will be presented.

The absolute SSH (sea surface height) biases of three satellite altimeters Jason-2, Saral/AltiKa and HY-2 were determined using our GPS buoy at the Qian Li Yan Island of China, which are 9.3cm, 1.3cm and 66.8cm, respectively. In addition, the altimetry SWH (significant wave height) were assessed using GPS retrieved SWH, which shows good agreement between the GPS buoy and satellite altimeters. The detailed method and result are described in the paper.

Quantum genetic algorithm is a recently proposed new optimization algorithm combining quantum algorithm with genetic algorithm. It characterizes good population diversity, rapid convergence and good global search capability and so attracts serious and wide attentions. This paper proposes a novel quantum genetic algorithm called variable-boundary-coded quantum genetic algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. In this way we can obtain much shorter chromosome strings. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard genetic algorithm (sGA) and genetic quantum algorithm (GQA) proposed by Han in [6]. The results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.