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In multi-manned assembly lines, more than one operator is allowed on the same workstation. Limited research has been carried out on sequence-dependent set-up times. Therefore, this article optimizes the multi-manned assembly line balancing problem with sequence-dependent set-up times using an enhanced migrating birds optimization (EMBO) algorithm. Sequence-dependent set-up times are embedded in two ways: forward and backward set-ups. EMBO designs a mixed neighbourhood search and acceptance criteria to help it escape from local optima, and a diversification mechanism to enhance the global exploration ability. Three types of experiment and two scenarios, with low and high set-up times, are conducted to test the performance of the mixed-integer linear programming (MILP) model and EMBO. The first experiment compares three MILP models (no, low and high set-up times) to illustrate the significance of set-up times. The final two experiments demonstrate the effectiveness of EMBO by comparison with the MILP model and six state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Owing to the different quantities and processing times of sub-lots, intermingling sub-lots with each other, instead of fixing the production sequence of sub-lots of a lot as in the existing studies, is a more practical approach to lot-streaming flow shops. Hence, a lot-streaming hybrid flow shop scheduling problem with consistent and intermingled sub-lots (LHFSP-CIS) was studied. A mixed integer linear programming (MILP) model was established, and a heuristic-based adaptive iterated greedy algorithm (HAIG) with three modifications was designed to solve the problem. Specifically, a two-layer encoding method was proposed to decouple the sub-lot-based connection. Two heuristics were embedded in the decoding process to reduce the manufacturing cycle. Based on this, a heuristic-based initialization is proposed to improve the performance of the initial solution; an adaptive local search with four specific neighborhoods and an adaptive strategy has been structured to improve the exploration and exploitation ability. Besides, an acceptance criterion of inferior solutions has been improved to promote global optimization ability. The experiment and the non-parametric Kruskal–Wallis test (p = 0) showed the significant advantages of HAIG in effectiveness and robustness compared with five state-of-the-art algorithms. An industrial case study verifies that intermingling sub-lots is an effective technique to enhance the utilization ratio of machines and shorten the manufacturing cycle. [ABSTRACT FROM AUTHOR]

Assembly scheduling problems have broad applications in discrete manufacturing enterprises and hence gained extensive attention in academics. However, some important and actual factors including lot streaming, batch transfer of components and mixed-model assembly are yet not well studied. To adapt to actual production and enhance competitiveness, a three-stage mixed-model assembly job shop scheduling with lot streaming and batch transfer is investigated in this work. To tackle this problem, spatial-temporal links among three stages with differentiated production lot size are established and a novel mathematical model is formulated to minimize total completion time and production costs including setup costs, transport costs, and inventory costs. Besides, an improved multi-objective coevolutionary simulated annealing algorithm is developed. To promote the coevolution of processing, transfer, and assembly sub-problems, the multi-attribute combination rule, similarity rule, and consistency rule are respectively designed for each sub-problem. An adaptive execution strategy of sub-problems is trained by Q-learning to maximize the overall benefits. Experimental results show that both the discovered rule and the adaptive strategy are effective, and the proposed algorithm significantly outperforms seven competitive multi-objective algorithms in fixing the studied problem. More importantly, the developed production mode of "processing with lot streaming-batch transfer-mixed-model assembly considering pull production" has a better comprehensive performance than the other three actual production modes. • Build a MILP model for the three-stage MAJSP with lot streaming and batch transfer • Propose a multi-attribute combination rule, similarity rule and consistency rule • Develop a Q-learning-based adaptive execution strategy to promote coevolution • Put a mode for the coordination and synchronization of production and logistics [ABSTRACT FROM AUTHOR]

In 2007, China discovered a hydrothermal anomaly in the Longqi hydrothermal area of the Southwest Indian Ridge. It was the first seabed hydrothermal area discovered in the ultraslow spreading ocean ridge in the world. Understanding the types of seabed sediments in this area is critical for studying the typical topography and geological characteristics of deep-sea seabed hydrothermal areas. The traditional classification of deep-seabed sediments adopts box sampling or gravity column sampling and identifies the types of seabed sediments through laboratory analysis. However, this classification method has some shortcomings, such as the presence of discrete sampling data points and the failure of full-coverage detection. The geological sampling in deep-sea areas is particularly inefficient. Hence, in this study, the EM122 multibeam sonar data collected in the Longqi hydrothermal area, Southwest Indian Ridge, in April 2019 are used to analyze multibeam backscatter intensity. Considering various errors in the complex deep-sea environment, obtaining backscatter intensity data can truly reflect seabed sediment types. Through unsupervised and supervised classification, the seabed sediment classification in the Longqi hydrothermal area was studied. The results showed that the accuracy of supervised classification is higher than that of unsupervised classification. Thus, unsupervised classification is primarily used for roughly classifying sediment types without on-site geological sampling. Combining the genetic algorithm (GA) and the support vector machine (SVM) neural network, deep-sea sediment types, such as deep-sea clay and calcareous ooze, can be identified rapidly and efficiently. Based on comparative analysis results, the classification accuracy of the GA-SVM neural network is higher than that of the SVM neural network, and it can be effectively applied to the high-precision classification and recognition of deep-sea sediments. In this paper, we demonstrate the fine-scale morphology and surface sediment structure characteristics of the deep-sea seafloor by finely processing high-precision deep-sea multibeam backscatter intensity data. This research can provide accurate seabed topography and sediment data for the exploration of deep-sea hydrothermal resources and the assessment of benthic habitats in deep-sea hydrothermal areas. [ABSTRACT FROM AUTHOR]

With the requirements of just-in-time and make-to-order, it is necessary to consider the batch delivery in distributed heterogeneous assembly flow shop scheduling as well as the minimization of inventory and tardiness costs. Hence, this work proposes a new mixed integer linear programming model and a self-learning knowledge-based multi-objective evolutionary algorithm based on decomposition. In this algorithm, 4 problem-specific properties are extracted and embedded in the designed solution representation, forward-reverse objective calculation and local search operators. A new hybrid initialization with six knowledge-based NEH heuristics and a random generation is developed to generate better initial solutions. A self-learning selection strategy is developed to coordinate the above knowledge-based local search operators. The experimental study, based on 900 small and 540 large instances, demonstrates that the designed components such as hybrid initialization and self-learning selection strategy are effective, and the proposed algorithm is superior to 6 existing algorithms in the literature in solving this new problem. • Formulate distributed heterogeneous assembly permutation flow shops with batch delivery. • Design a property-based solution representation and forward-reverse objective calculation. • Propose a hybrid initialization, self-learning selection strategy in MOEA/D. • Propose some knowledge-based local search operators in MOEA/D. [ABSTRACT FROM AUTHOR]

In the mixed-model assembly job-shops, all the components are transferred in batches from the processing stage to the assembly stage to reduce transportation costs and timely satisfy the assembly material requirements. However, batch transfer in such a workshop has not received due attention in the current literature. Thus, this work addresses the mixed-model assembly job-shop scheduling problem with batch transfer via a mathematical model and an augmented simulated annealing algorithm to minimize manufacturing and transportation costs. The mathematical model takes batch transfer constraints into account and constructs the temporal and spatial links among the processing, transfer, and assembly stages. Production sequencing knowledge and batch transfer knowledge are first extracted via gene expression programming algorithm and clustering method and then implanted into the algorithmic operators in the form of rules and strategies to directly identify the promising solution space. Besides, eleven neighborhood structures including the critical component-based and batch-based ones, are designed to enhance the exploitation ability. A restart mechanism considering crossover and tempering helps to get out of local optima. Experimental results indicate that by adopting the extracted knowledge, problem-specific neighborhood structures, and restart mechanism, the proposed algorithm significantly outperforms other comparison algorithms in fixing the studied problem. • A MAJSP with batch transfer is addressed via a MILP model and an augmented SA. • Sequence rules are created via empirical knowledge and gene expression programming. • Transfer batching knowledge is extracted and implemented by clustering. • 11 operators including critical job-based and batch-based operators are designed. • A restart mechanism considering crossover and tempering is designed. [ABSTRACT FROM AUTHOR]

U-shaped assembly lines have been popularly adopted in electronics and appliances to improve their flexibility and efficiency. However, most past studies assumed that the processing time of each task is fixed and hence just considered the task allocation but ignored worker assignment. In this paper, the processing time of each task depends on the workers and then the cooperative optimization of task allocation and workers assignment is considered in U-shaped assembly line balancing problems to optimize the cycle time. Later, an enhanced migrating birds optimization algorithm (EMBO) is proposed to solve it. In the EMBO algorithm, since this new problem has two subproblems: task allocation and worker assignment, the prevent work designs two neighborhood structures to improve the leader and following birds. Furthermore, the temperature acceptance criteria, to judge whether the neighbor replaces current following bird, are developed to ensure the diversity of population and avoid being trapped in the local optimum. And a competitive mechanism is introduced to increase the probability of the promising birds locating in the front of the line. The proposed algorithm is compared with other well-known algorithms in the literature, and the numerical results demonstrate that the proposed algorithm outperforms other algorithms. [ABSTRACT FROM AUTHOR]

Frequently occurring and unavoidable perturbations in railway systems cause a reduction in performance and even the infeasibility of the original schedule. In addition, existing related studies assume that the maintenance routes are fixed in a deterministic situation. Therefore, it is necessary to focus on robust scheduling of electric multiple units first-level maintenance with flexible maintenance routes to defend against stochastic uncertainties. Firstly, a mixed-integer linear programming model with uncertain parameters in objective functions and constraints is built for the first time. Multiple constraints are formulated to consider flexible maintenance routes, train shunting conflicts, and track occupation conflicts in a stub-end depot. A robust optimization approach is adopted to obtain a deterministic robust counterpart model with uncertainties in processing/arrival/departure times. In this model, uncertainty and reliability levels are introduced to describe and quantify the disturbance degree of processing/arrival/departure times and the allowable violation degree of resource constraints respectively. Moreover, an adaptive iterative local search is proposed by embedding heuristic rules for the initialization. The proposed algorithm includes problem-specific neighborhood structures to improve the evolutionary ability, a variable neighborhood descent method to guide and drive the search toward promising areas of the search space, and an adaptive perturbation mechanism to enable the algorithm to jump out of local optimum. Numerical results from China's railway system validate the proposed model and quantitatively demonstrate the merit of the proposed algorithm. Further analysis shows that our approach can achieve an appropriate trade-off between robustness and efficiency for various uncertainty and reliability levels. • The SSEFM problem under stochastic uncertainties is addressed. • A multi-objective MILP model with deterministic robust equivalent counterpart is defined. • An adaptive iterative local search is proposed to solve this problem. • The algorithm is enhanced with a variable neighborhood descent and adaptive perturbation. [ABSTRACT FROM AUTHOR]

Abstract Since the industrial robots are incrementally utilized in U-shaped assembly lines to replace operators, the focus of these lines is not only on productivity improvement, but also more on the carbon and noise emissions. Hence, this paper proposes a novel multi-objective mixed integer non-linear model to minimize carbon emission, noise emission and cycle time concurrently. In this model, quantifying carbon emission and noise emission is achieved respectively via presentations connected with processing time of tasks and robots. Existing general constraints of precedence relationship are readjusted into a novel integrated formula so as to remove worthless equations and improve computational efficiency. Besides, Hybrid Pareto Grey Wolf Optimization (HPGWO) is designed to solve the multi-objective balancing problems. This algorithm proposes a novel code to initialize the wolves and designs two new searching methods to update the position of wolf. Simultaneously, two crossover operators are designed to enhance the communication between the low-grade wolves. Finally, the proposed algorithm is compared with five other well-known multi-objective algorithms and the results indicate that the proposed algorithm outperforms these compared algorithms in the evaluation metrics of the inverse generation convergence (IGD), maximum spread (MS) and hypervolume ratio (HVR). That is, our proposed algorithm can achieve the trade-off in reducing carbon and noise emission and minimizing cycle time. Highlights • Carbon and noise emission are minimized in U-shaped robotic assembly line. • A mathematical model is developed and optimized within a multi-objective optimization framework. • The mathematical model utilizes binary variable to describe discrete decisions effectively. • A hybrid Pareto grey wolf optimization is employed to solve the multi-objective optimization problem. [ABSTRACT FROM AUTHOR]

As environmental awareness grows, sustainable scheduling is attracting increasing attention. The purposes of this paper are obtain the lower bound of energy-efficient flexible job shops with machine selection, job sequencing, and machine on-off decision making via a new mathematical model and to discover more energy-efficient rules with easy implementation in real practice via an efficient Gene Expression Programming (eGEP) algorithm. This paper first formulates a novel mixed-integer linear mathematical model to achieve effective machine selection, job sequencing, and machine off-on decision making. Then for the purpose of avoiding the empirical combination, five attributes exerting direct influence on the total energy consumption are extracted and consequently involved in the evolutionary process of eGEP. Furthermore, diversified rule mining operations with multi-gene representation and self-study are designed to enhance the search space and solutions quality. And, unsupervised learning is utilized in which global best and current worst are set to guide evolution direction since the learning progress has no prior knowledge. Experimental results show that machine off-on decisions efficiently reduce the total energy consumption; and, the discovered rules reach the lower bound calculated by GAMS/CPLEX in small problems and have significant superiority over other dispatching rules in energy saving. [ABSTRACT FROM AUTHOR]

The type-2 U-shaped assembly line balancing problem is important for many just-in-time manufactures, but an efficient algorithm is not available at present. Thus, in this study, a novel heuristic approach based on multiple rules and an integer programming model is proposed to address this problem. In the proposed approach, three rules are systematically grouped together, i.e., task selection, task assignment, and task exchange rules. The sufficient conditions for implementing the exchange rules are proposed and proved. Thirteen small or medium scale benchmark issues comprising 63 instances were solved, where the computational results demonstrate the efficiency and effectiveness of the proposed method compared with integer programming. The computational results obtained for 18 examples comprising 121 instances demonstrate that the task exchange rules significantly improve the computational accuracy compared with the traditional heuristic. Finally, 30 new standard instances produced by a systematic data generation process were also solved effectively by the proposed approach. The proposed heuristic approach with multiple rules can provide a theoretical basis for other local search algorithms, especially for addressing issues such as the U-Shaped assembly line balancing problem. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Two-sided assembly lines are usually utilized to produce large-sized high-volume products. Recently robots are widely utilized in this line to replace the manual handling and manage the allocated tasks. For a robotic assembly line, the energy consumption is a major expense and the increased energy cost draws much more attentions from manufacturing enterprises. To the best knowledge of the authors, there is no research reported on the energy consumption of two-sided robotic assembly line. This paper presents a new mixed-integer programming model to minimize the energy consumption and cycle time simultaneously. A restarted simulated annealing algorithm is developed to deal with the complexity of the model, which utilizes new local search with three neighbor structures and restart phase based on the crowding distance assignment procedure to obtain well-spread Pareto-optimal set. Testing cases are designed to measure the performance of the proposed method and the restarted simulated annealing algorithm is compared with the fast elitist non-dominated sorting genetic algorithm. The computational results demonstrate that the proposed model is useful to reduce the total energy consumption and the restarted simulated annealing algorithm outperforms the non-dominated sorting genetic algorithm in both convergence and spread criteria. [ABSTRACT FROM AUTHOR]

Considering preventive maintenance (PM) scenarios in the assembly line balancing problem (ALBP) have been proved effective and can be used to increase production efficiency. All these studies assumed that the processing time of tasks was fixed. However, many factors can affect the processing time in the actual production process and make the original task allocation plans infeasible. The robust method is introduced to solve the uncertain processing time, and the robust ALBP considering PM scenarios (RALBP_PMs) is studied in this paper. In this problem, three objectives are optimized: the cycle time under the regular scenario, the maximum cycle time, and the maximum task alteration under all PM scenarios. A robust mixed-integer optimization model is proposed to hedge against uncertainty, and then it is linearized by duality to make it solvable. A Q-learning-based variable neighborhood search (QVNS) algorithm with several improvements is designed to solve this problem. Several heuristic rules are designed to improve the performance of the initial population. Roulette wheel selection based on crowding distance is designed to select a solution with more potential. Eleven problem-specific neighborhood structures are designed to improve search efficiency. These structures are sorted by the Q-learning-based sorting method to find a good solution quickly. Experiment results demonstrate that considering PM scenarios in RALBP is necessary. The proposed mathematical model can obtain the Pareto solutions of small-scale instances, and the proposed QVNS algorithm is more suitable for solving large-scale instances. • RALBP considering PM scenarios promotes productivity and smoothness. • A mathematical model is designed to generate allocation plans for scenarios. • Eleven neighborhood structures are designed to generate a new solution. • Q-learning-based sorting method is proposed to sort neighborhood structures. • Results indicate that QVNS outperforms the other six algorithms. [ABSTRACT FROM AUTHOR]

The two-sided assembly line balancing problem type-II (TALBP-II) is of major importance for the reconfiguration of the two-sided assembly lines which are widely utilized to assemble large-size high-volume products. The TALBP-II is NP-hard, and some assignment restrictions in real applications make this problem much more complex. This paper provides an integer programming model for solving the TALBP-II with assignment restrictions optimally and utilizes a simple and effective iterated greedy (IG) algorithm to address large-size problems. This algorithm utilizes a new local search by considering precedence relationships between tasks in order to reduce the computational time. In particular, a priority-based decoding scheme is developed to handle these assignment restrictions and reduce sequence-dependent idle times by adjusting the priority values. Experimental comparison among the proposed decoding scheme and other published ones demonstrates the efficiency of the priority-based decoding. A comprehensive computational comparison among the IG algorithm and other eight recent algorithms proves effectiveness of the proposed IG algorithm. [ABSTRACT FROM AUTHOR]

The multi-beam echo sounder system can not only obtain high-precision seabed bathymetry data, but also obtain high-resolution seabed backscatter strength data. A number of studies have applied acoustic remote sensing method to classify seabed sediment type with multi-beam backscatter strength data, and obtained better classification results than the traditional sediment sampling method. However, these studies mainly focus on the single type sediment classification or seabed mixed sediment classification using single beam data, not multi-beam echo sounder data. Based on backscatter strength data by a high-frequency (300 kHz) Simrad EM3000 multi-beam echo sounder and seabed sediment sampling data of Jiaozhou Bay in Qingdao, China, we establish the relation model between seabed backscatter strength and sediment type characteristics after data processing and corrections. The purpose of data processing is to diminish or weaken the influence of local bottom slope and near nadir reflection on backscatter strength data. Processed backscatter strength data through corrections and compensations can better characterize the features of the seabed sediments. Applying the back propagation neural network method based on genetic algorithm, we present a fast and accurate seabed classification method in this paper which could identify not just a single type of sediment-like rock, sand but also mixed types of sediment like sand gravel, clayey silt and sand-silt-clay. [ABSTRACT FROM PUBLISHER]

For assembly line balancing problem considering preventive maintenance scenarios (ALBP-PM), the production managers' preferences should be concerned and hence the derived Pareto-optimal solution set (POS) should be advanced towards the corresponding region-of-interest. Otherwise, production managers might select a final solution far from their interests, and then obviously reducing the quality and efficiency of decision-making. Thus, a preference-based multi-objective optimization problem is formulated to simultaneously minimize the cycle times under different scenarios and total task adjustments among scenarios. An improved variable neighborhood search algorithm (IVNS) with novel ar -dominance and three modifications is proposed to obtain a preferred POS. Specifically, ar -dominance is integrated into it to guide the advancement direction of the preferred POS. Enhanced neighborhood structures based on critical stations are proposed to generate better-quality neighbor solutions whose cycle times are mathematically proven smaller. On this basis, a local search strategy is designed to randomly select a neighbor solution to guarantee quality and decrease the computational cost. A problem-specific adaptive restart mechanism is developed to escape from the local optimum. Computational results suggest that the IVNS obtains a better preferred POS than other eight state-of-the-art algorithms in terms of convergence, distribution and closeness to the preferences, and furthermore, incorporating preferences into the ALBP-PM helps production managers make final decisions in a more precise and faster way. • A novel ALBP-PM concerning production managers' preferences is tackled. • The ar -dominance advances Pareto-optimal solutions towards the region-of-interest. • Enhanced neighborhood structures generate neighbor solutions with smaller cycle time. • Problem-specific restart mechanism sets threshold adaptively to escape local optimum. • Statistical results indicate IVNS outperforms all other comparison algorithms. [ABSTRACT FROM AUTHOR]

Owing to the rapid exhaustion of energy in the manufacturing sector, energy-efficient scheduling has become a research hotspot. This work addresses an energy-efficient manufacturing cell scheduling problem, in which three major energy-saving strategies, machine off/on criterion, speed-scaling policy and transportation optimization strategy, are simultaneously utilized to save energy comprehensively. A mixed-integer linear programming model is hereafter developed to reduce the transportation energy, processing energy and standby energy via cell formation, cell scheduling and machine off/on decision respectively. To solve this problem efficiently, an enhanced cooperative co-evolutionary algorithm (ECCA) with two improvements is developed. Specifically, four energy-oriented heuristic rules are designed to create initial sub-swarms with lower energy consumption; a Q-learning-based sub-swarm size adjustment mechanism is developed to maximize the exploration efficiency across all sub-problem domains and escape from local optima. Experimental results indicate that the two improvements in ECCA are effective, and the proposed ECCA significantly outperforms six state-of-the-art algorithms with a p -value much less than 0.0001. More importantly, by simultaneously employing the three major energy-saving strategies in manufacturing cells, the resulted energy-saving ratio is above 5%. • Address a manufacturing cell scheduling problem with three energy-saving policies. • Formulate a MILP model for the energy-efficient cell scheduling problem. • Design four energy-oriented rules and adjust adaptively the swarm size in ECCA. • ECCA outperforms other algorithms significantly with a p -value less than 0.0001. • This method achieves a 5% energy-saving ratio in cell manufacturing. [ABSTRACT FROM AUTHOR]

Probabilistic guarantees on constraint satisfaction forrobustcounterpart optimization are studied in this paper. The robust counterpartoptimization formulations studied are derived from box, ellipsoidal,polyhedral, “interval穙抦쨢疩”, and “intervalꚉ犅”uncertainty sets (Li, Z.; Ding, R.; Floudas, C.A.A Comparative Theoretical andComputational Study on Robust Counterpart Optimization: I. RobustLinear and Robust Mixed Integer Linear Optimization. Ind. Eng. Chem. Res.2011, 50, 10567). For those robust counterpartoptimization formulations, their corresponding probability boundson constraint satisfaction are derived for different types of uncertaintycharacteristic (i.e., bounded or unbounded uncertainty, with or withoutdetailed probability distribution information). The findings of thiswork extend the results in the literature and provide greater flexibilityfor robust optimization practitioners in choosing tighter probabilitybounds so as to find less conservative robust solutions. Extensivenumerical studies are performed to compare the tightness of the differentprobability bounds and the conservatism of different robust counterpartoptimization formulations. Guiding rules for the selection of robustcounterpart optimization models and for the determination of the sizeof the uncertainty set are discussed. Applications in production planningand process scheduling problems are presented. [ABSTRACT FROM AUTHOR]

The current assembly line balancing studies ignore the preventive maintenance (PM) of machines in some workstations, implying that the already-known PM information has been completely missed. Moreover, PM may bring about a production stoppage for a considerable time. Hence, this paper considers PM scenarios into the assembly line balancing problem to improve the production efficiency and smoothness simultaneously. For this multi-objective problem, a heuristic rule relying on the tacit knowledge is dug up via gene expression programming to obtain an acceptable solution quickly. Then, an enhanced grey wolf optimizer algorithm with two improvements is proposed to achieve Pareto front solutions. Specifically, a variable step-size decoding mechanism accelerates the speed of the algorithm; the specially-designed neighbor operators prevent the algorithm from trapping in local optima. Experiment results demonstrate that the discovered heuristic rule outperforms other existing rules; the joint of improvements endows the proposed meta-heuristic with significant superiority over three variants and other six well-known algorithms. Besides, a real-world case study is conducted to validate the discovered rule and the proposed meta-heuristic. • ALBP considering PM scenarios promotes the productivity and smoothness. • The heuristic rule mined by GEP can obtain an acceptable solution quickly. • The variable step-size decoding method can speed up the solution procedure. • Specially-designed neighbor operators can balance exploration and exploitation. • Statistical results indicate EGWO with two improvements outperforms others. [ABSTRACT FROM AUTHOR]

Disassembly lines are widely utilized to disassemble end-of-life products. Most of the research focuses on the complete disassembly of obsolete products. However, there is a lack of studies on profit and on carbon emission saved. Hence, this study considers the multi-objective partial disassembly line balancing problem with AND/OR precedence relations to optimize profit, saved carbon emission and line balance simultaneously. Firstly, a multi-objective mixed-integer programming model is formulated, which could optimally solve the small number of instances with a single objective. Meanwhile, an improved multi-objective artificial bee colony algorithm is developed to generate a set of high-quality Pareto solutions. This algorithm utilizes two-layer encoding of the task permutation vector and the number of selected parts, and develops two-phase decoding to handle the precedence relation constraint and cycle time constraint. In addition, the modified employed bee phase utilizes the neighborhood operation, and the onlooker phase utilizes the crossover operator to achieve a diverse population. The modified scout phase selects a solution from the Pareto front to replace the abandoned individual to obtain a new high-quality solution. To test the performance of the proposed algorithm, the algorithm is compared with the multi-objective simulated annealing algorithm, the original multi-objective artificial bee colony algorithm and the well-known fast non-dominated genetic algorithm. The comparative study demonstrates that the proposed improvements enhance the performance of the method presented, and the proposed methodology outperforms all the compared algorithms. [ABSTRACT FROM AUTHOR]

Pockmarks are circular or elliptical depressions in the seabed that are geomorphologically similar but otherwise distinct from volcanic craters. Pockmarks are found at various depths in the ocean including on the continental shelf, the continental slope and the deep-sea plain. This paper presents data covering a large-scale pockmark group in the middle part of the shallow sea sedimentary plain of the North Yellow Sea at a water depth of 51–58 m. The distribution of pockmarks within this group is very dense, and pockmark diameters range from several hundred meters to several kilometers. Based on multibeam echo sounder data, a high-resolution image of the pockmark area is obtained by efficient image processing methods. The textural features of the seabed surface layer are then described and analyzed. Multibeam echo sounder backscatter data are combined with seabed sediment sampling data to develop a statistical model linking seabed backscatter sonar strength and sediment grain-size characteristics. Additionally, an improved BP neural network is used to realize rapid and automatic classification and identification of seabed sediment types. Fine-scale processing analysis of sonar images and acoustic classification of seabed sediments as presented in this paper are useful for determining seabed surface textures and local sediment distribution laws. This study thus provides a basis for further study of the formation and evolution of seabed pockmarks. [ABSTRACT FROM AUTHOR]

Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study. [ABSTRACT FROM AUTHOR]

Energy-saving, one of the most significant strategies for green manufacturing, has become the focus of more and more scholars and enterprise managers. Hence, this work addresses the minimization of energy and cost requirements on mixed-model multi-manned assembly line balancing with multi-skilled workers. A new mixed-integer linear programming model is proposed to define the problem. Additionally, a novel multi-objective co-evolutionary algorithm is designed to achieve the trade-off between energy and cost requirements. This algorithm includes a two-layer solution representation to achieve full coverage of the solution space and a new decoding mechanism with idle time reduction. A collaborative initialization as the first stage of the algorithm is extended to get high-quality and great-diversity initial solutions. A self-learning evolution for each sub-population with four problem-specific evolutionary operators is developed to explore task or worker assignment sequences, and a dual-cooperation strategy is proposed to enhance the interaction between sub-populations. The final experiments, based on 269 instances, demonstrate that the improvement components are effective and the proposed algorithm is superior to seven latest multi-objective evolutionary algorithms from numerical, statistical and differential analyses. • Define the MALBP-MW by a MILP model to minimize the energy and cost requirements. • Design a new decoding with idle time reduction and a collaborative initialization. • Propose four problem-specific evolutionary operators for MOCEA. • Develop MOCEA with a self-learning selection and dual-cooperation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Industries are increasingly looking for opportunities at utilizing collaborative robots in assembly lines to perform the tasks independently or assist the human workers due to the advancement of industry 4.0 technologies. Purchasing cost is one of the important factors to be considered by production managers, while designing or redesigning assembly line when collaborative robots are being utilized. Several objectives are to be optimized in an assembly line balancing problem and optimizing line efficiency along with purchasing cost sometimes results in conflicting situation. This paper presents the first study to tackle the cost-oriented assembly line balancing problem with collaborative robots, where several different types of collaborative robots with different purchasing costs are available and selected. A multi-objective mixed-integer programming model is developed to minimize the cycle time and the total collaborative robot purchasing cost. The multi-objective migrating bird optimization algorithm is developed to obtain a set of high-quality Pareto solutions. This algorithm utilizes the fast non-dominated sorting approach to update the population and develops a restart mechanism to select one solution in the permanent Pareto archive to replace the abandoned solution which remains unchanged for several iterations. The computational study validates that the utilization of the multi-objective model is reasonable and developed algorithm produces competing performance in comparison with multi-objective non-dominated sorting genetic algorithm II, multi-objective simulated annealing algorithm and two multi-objective artificial bee colony algorithms. [ABSTRACT FROM AUTHOR]

Seafloor real-time positioning is important for the instantaneous detection of seafloor crustal motion, seismic activities, hydrological rapid variations and rapid geodetic datum updates. Current GNSS-Acoustic (GNSS-A) seafloor positioning usually utilizes batch processing of long-term observations in the postprocessing mode. Seafloor real-time positioning can be achieved using sequential processing of the epoch-wise observations. We propose the sequential GNSS-A seafloor point positioning method with modeling of the sound speed variation in the kinematic survey. We propose real-time modeling of the sound speed variation using oceanography analysis data and we then calculate the random walk (RW) process noise of the acoustic nadir total delay (NTD) caused by the sound speed variation. The experiments conducted in the South China Sea and Japan Trench validate the method performance in terms of epoch-wise positioning accuracy, high-resolution sound speed variation, and filter convergence time. The difference between the estimated sound speed and the in situ sound velocity profiles was 0.128 m/s root mean square. The vessel track of the line and circle combination performs best with a high positioning accuracy and a short convergence time. The position in real-time sequential processing with the modeled NTD RW process noise converged to a 3D range of 0.125 m from the static post-determined position. The a posteriori residual of the acoustic travel time observations was equivalently 0.270 m in range. These findings can improve the temporal resolution of the GNSS-A positioning and oceanography. [ABSTRACT FROM AUTHOR]

Exact methods have shown advanced and promising performance in solving the simple assembly line balancing problem, known as NP-hard. This research investigates the impact of various structural parameters on the performance of exact methods, including branching methods, search direction, method to achieve upper bounds, utilized lower bounds, utilized dominance rules and search strategy. In accordance with the structural parameter evaluation, utilized dominance rules and search strategy have shown the most important effect on the exact methods' performance. This research also improves and re-implements three well-known exact methods [i.e., SALOME, bounded dynamic programming (BDP) heuristic and branch, bound and remember (BBR) algorithm] using effective parameters. Computational study demonstrates that the utilization of high-performance structural parameters enhances the performance of exact methods by a significant margin. The re-implemented BBR method with proper parameters shows clear superiority over all the published exact methods and might be regarded as the state-of-the-art exact methodology. [ABSTRACT FROM AUTHOR]

This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently. [ABSTRACT FROM AUTHOR]

Methods for reducing the carbon footprint is receiving increasing attention from industry as they work to create sustainable products. Assembly line systems are widely utilized to assemble different types of products and in recent years, robots have become extensively utilized, replacing manual labor. This paper focuses on minimizing the carbon footprint for robotic assembly line systems, a topic that has received limited attention in academia. This paper is primarily focused on developing a mathematical model to simultaneously minimize the total carbon footprint and maximize the efficiency of robotic assembly line systems. Due to the NP-hard nature of the considered problem, a multi-objective co-operative co-evolutionary (MOCC) algorithm is developed to solve it. Several improvements are applied to enhance the performance of the MOCC for obtaining a strong local search capacity and faster search speed. The performance of the proposed MOCC algorithm is compared with three other high-performing multi-objective methods. Computational and statistical results from the set of benchmark problems show that the proposed model can reduce the carbon footprint effectively. The proposed MOCC outperforms the other three methods by a significant margin as shown by utilizing one graphical and two quantitative Pareto compliant indicators. [ABSTRACT FROM AUTHOR]

Single-wavelength bathymetric light detection and ranging (LiDAR) (532 nm) can provide seamless meter- and submeter-scale digital elevation model (DEMs) of both the terrestrial surface and seafloor. However, 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 nonterrestrial 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 FWs are divided into five categories for subsequent signal detection using 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. 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 Levenberg–Marquardt algorithm optimized generalized Gaussian model (LM-GGM) and RLD algorithm. [ABSTRACT FROM AUTHOR]

Scientific management of coral reefs is a global research topic due to their latent economic value and ecological significance. This research benefits from optical and acoustic remote sensing technology with high spatial resolution, sufficient coverage, and stable repeatability. However, each sensor with its own set of limitations, such as insufficient penetration ability, limited sensor resolution and range, which often hinder their applications in the precise classification of coral reef habitats. To address these issues, a novel scheme integrating airborne laser bathymetry (ALB), multibeam echo sounder (MBES) and the multispectral image is designed to serve large-scale and high-precision coral reef habitat sediments research. This scheme focuses on valuable attribute mining, inconsistent coverage, feature default, and redundancy according to multi-source data used in engineering demands. After per-system pretreating, numerous properties, including band, waveform, intensity, terrain, and derivative characteristics, are extracted and quantitatively analyzed to be responsible for habitat description. Subsequently, a complete seabed terrain is generated using the LightGBM regression model to establish the conversion relationship between satellite images and depth. Eventually, a new benthic habitat sediments classification method called RF-SAPSO-LightGBM is introduced with a powerful generalization ability. The unique functions of automatic default feature completion and optimal parameter search improve the discrimination ability of complex sediments. The results indicate that the new method outperforms five other approaches in sediment classification accuracy, and the combination of multi-source data provides more extensive coverage and higher accuracy with an overall of 87. 9%. In comparison, the accuracy values for multispectral image and terrain data alone are 74.5% and 54.7% respectively. • Optical-acoustic sensors were more effective when used together rather than separately for mapping coral reef habitat. • The SAPSO algorithm for LightGBM parameter optimization improved the sediment classification accuracy by 7.4%. • Feature completion effectively improved the accuracy of coral reef habitat mapping. • The RMSE of the derived depth by the LightGMB regression model reached 0.89 m. [ABSTRACT FROM AUTHOR]

The construction of underwater geodetic networks (UGN) is crucial in marine geodesy. To provide high-precision kinematic positioning for underwater submersibles, an underwater acoustic geodetic network configuration of three seafloor base stations, one subsurface buoy, and one sea surface buoy is proposed. The simulation results show that, for a 3 km-deep sea, based on the proposed UGN, the submersible positioning range and positioning accuracy are primarily affected by the size of the seafloor base station array, while the height of the subsurface buoy has a greater impact on the submersible positioning accuracy than the positioning range. Considering current acoustic ranging technology, the kinematic positioning performance of the UGN is optimal when the seafloor base stations are 9~13 km apart and the subsurface buoy is less than 2.5 km above the seafloor, which can achieve a submersible positioning accuracy of less than 30 m within an underwater space of 25 km × 25 km × 3 km. The proposed cost-effective UGN configuration can provide high-precision submersible kinematic positioning performance for seafloor surveying and ocean precision engineering. The impact of the underwater environment on the acoustic transmission characteristics should be further investigated. [ABSTRACT FROM AUTHOR]

Object detection is a key technique for automatic information extraction, analysis and understanding of high spatial resolution remote-sensing images (HSRIs). Object anchor scales are a critical factor for object detection of HSRIs based on convolutional neural networks (CNNs). With respect to adaptively obtaining optimal object anchor scales for object detection of HSRIs, this paper proposes a novel object detection method for HSRIs based on CNNs with optimal object anchor scales. First, optimal object anchor scales for object detection of HSRIs are obtained using an adaptive object-scale learning operator. Then, a CNN object detection framework for HSRIs is designed based on optimal object anchor scales. Using multiple-object detection datasets, the proposed method is compared with some state-of-the-art object detection algorithms. Experimental results show that the proposed method can achieve 90.42%, 91.98% and 85.07% mAP on WHU-RSONE, UCAS-AOD and HSRC2016, respectively, and outperforms state-of-the-art object detection algorithms. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

The scheduling of steelmaking continuous-casting (SCC) processes is of major importance in iron and steel operations, since it is often a bottleneck in iron and steel production. Optimal scheduling of SCC processes can increase profit, minimize production cost, reduce material and energy consumption, and improve customer satisfaction. Scheduling of SCC processes is challenging, because of its combinatorial nature, complex practical constraints, and strict requirements on material continuity and flow time, as well as the technological requirements to ensure practical feasibility of the resulting scheduling. In this paper, we first develop a novel unit-specific event-based continuous-time mixed-integer linear optimization (MILP) model for this problem and incorporate several realistic operational features. Then, we extend the rolling horizon approach proposed by Lin et al. [Lin et al. Ind. Eng. Chem. Res.2002, 41, 3884–3906] and Janak et al. [Janak et al. Ind. Eng. Chem. Res.2006, 45, 8234–8252] to solve this large-scale and complex optimization problem. Four large-scale industrial problems are used to illustrate the efficiency and effectiveness of the proposed formulation and rolling horizon approach. The computational results show that the extended rolling horizon approach successfully solves the large-scale case studies and results in the same or better integer solution than that obtained from directly solving the entire scheduling model. [ABSTRACT FROM AUTHOR]

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers' expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness. [ABSTRACT FROM AUTHOR]

The Wide Field of View (WFV) imaging system equipped on the GaoFen-6 (GF-6) optical remote sensing satellite can acquire an image with a swath width 800 km and a resolution of 16 m, which is the largest Earth observation swath width among similar satellites in the world. With the advantages of the high spatial resolution and the wide field of view, GF-6 WFV images are widely used in agricultural resources monitoring, forestry resources investigation, and disaster relief. However, the existence of clouds is inevitable problem in GF-6 WFV images, which influences their availability. To quickly and accurately detect cloud areas in GF-6 WFV images, a cloud detection method for GF-6 WFV images based on the spectrum and variance of superpixels is proposed in the paper. First, the GF-6 WFV image is down-sampled. The simple linear iterative clustering algorithm is used to segment down-sampled images to obtain superpixels. The initial cloud detection result is obtained based on the spectrum of superpixels. Second, the initial cloud detection result is refined based on the variance of superpixels to eliminate the influence of cloud-like ground objects. Finally, the refined cloud detection result is post-processed using the region growing algorithm and expansion algorithm. The post-processed cloud detection result is up-sampled to obtain cloud detection result of the GF-6 WFV image. The experimental results show that the recall and precision of the proposed method are 84.61% and 88.46%, respectively, providing good cloud detection results for GF-6 WFV images. [ABSTRACT FROM AUTHOR]

Seabed sediment classification using acoustic remote sensing technique is an attractive approach due to its high coverage capabilities and limited costs compared to taking samples of the seafloor. This paper focuses on backscatter intensity correction, sonar image quality improvement, and classifier construction, which aims to improve the accuracy of seabed sediment classification. The details are as follows. 1) A series of multibeam echosounder backscatter intensity correction model is constructed, including time-varying gains (TVG), transmission loss, actual area of insonification, source level, transmitting and receiving beam patterns, specular area correction, etc., to obtain accurate intensity values that accurately reflect seabed sediment types. 2) The pulse coupled neural network (PCNN) image enhancement model is established to improve the quality of sonar images, and 40 dimensional features are included to enrich the intensity description. 3) Selecting optimal random forest (SORF) seabed sediment automatic classification models that can select the input feature vectors and optimize the model parameters automatically are established. 4) Taking multibeam backscatter intensity data collected in Jiaozhou Bay as an example, the effectiveness and advantages of SORF are verified by comparing with support vector machine (SVM) and random forest (RF) classifiers. [ABSTRACT FROM AUTHOR]

Sea ice type is one of the most sensitive variables in Arctic sea ice monitoring, and it is important for the retrieval of ice thickness. In this study, we analyzed various waveform features that characterize the echo waveform shape and Sigma0 (i.e., backscatter coefficient) of CryoSat-2 synthetic aperture radar altimeter data over different sea ice types. Arctic and Antarctic Research Institute operational ice charts were input as reference. An object-based random forest (ORF) classification method is proposed with overall classification accuracy of 90.1%. Accuracy of 92.7% was achieved for first-year ice (FYI), which is the domain ice type in the Arctic. Accuracy of 76.7% was achieved at the border of FYI and multiyear ice (MYI), which is better than current state-of-the-art methods. Accuracy of 83.8% was achieved for MYI. Results showed the overall accuracy of the ORF method was increased by ∼8% in comparison with other methods, and the classification accuracy at the border of FYI and MYI was increased by ∼10.5%. Nevertheless, ORF classification performance might be influenced by the selected waveform features, snow loading, and the ability to distinguish sea ice from leads. [ABSTRACT FROM AUTHOR]

The atmospheric water vapour is an important indicator of the climate state and evolution, and also the key parameter affecting the hydrological cycle, atmospheric convection, and the weather. This study validated the accuracy of surface pressure and temperature data interpolated from European Centre for Medium-Range Weather Forecasts (ECMWF) Interim ReAnalysis (ERA-Interim) and ECMWF 5th ReAnalysis (ERA5) datasets, and evaluated the performance of the interpolation meteorological data for precipitable water vapour (PWV) retrieval using the measured meteorological data and Global Positioning System (GPS) data of the year 2014 obtained from 25 stations of the Chinese Coastal GPS Observation Network. We then analysed the temporal and spatial distribution of water vapour in the coastal regions of China using a 3-year GPS PWV time series calculated with the interpolated reanalysis meteorological data at 25 stations from 2014 to 2016. Evaluation of the reanalysis meteorological data results showed that the accuracies of the interpolated pressure and temperature data from ERA5 were slightly superior than those from ERA-Interim. The root-mean-square errors (RMSEs) of the surface temperature and pressure interpolated from two reanalysis datasets were less than 2.4 K and 1.6 hPa, respectively. Data based on GPS PWV products that used interpolated parameters were very close to those of meteorological observations, with biases within ±0.4 mm and RMSEs below 0.5 mm in most areas; these data also strongly agreed with radiosonde observations. However, the interpolated meteorological data from reanalysis datasets could not reflect the true change during typhoon events, which could not be used in GPS PWV retrieval. The analysis of the temporal and spatial distribution showed that the distribution of water vapour was mainly affected by latitude, land-sea distribution, and water vapour advection. The variations in water vapour were mainly seasonal with the highest PWV occurring in the summer and the lowest always occurring in the winter. Significant diurnal variations of water vapour varied with season and latitude, with an amplitude of about 0.9–3.5 mm; the peak value occurred in the afternoon or early morning, and was affected by surface evaporation, large-scale land–sea breeze circulation, and valley-wind circulation. [ABSTRACT FROM AUTHOR]

In this study, to meet the need for the accurate prediction of sea level anomaly (SLA), a hybrid model is proposed. In this model, empirical mode decomposition is combined with singular spectrum analysis and least-squares extrapolation to predict satellite-derived SLA. Each intrinsic mode function series of an empirical mode decomposition is decomposed and reconstructed using singular spectrum analysis. The reconstructed components and the residual series are predicted using least-squares extrapolation. This hybrid model was used for satellite-derived SLAs that were obtained using multi-mission along-track satellite altimetry data from September 1992 to January 2018, and the prediction errors for 3 years lead times were analysed. The observations and predictions of the principal components for annual or interannual periods correlated well, and the proposed hybrid model effectively predicted the SLAs. For the 3 years lead time predictions, the mean absolute error and root-mean-square error were 1.03 and 1.32 cm, respectively, which were less than those reported for existing methods. [ABSTRACT FROM AUTHOR]

High resolution multibeam bathymetry revealed the occurrence of numerous craterlike depressions, so-called pockmarks, in the sea floor of the North Yellow Sea Basin (NYSB). To investigate whether these pockmarks are related to any type of fluid flow or gas seepage from subsurface stratum, acoustic survey and box samplings were conducted in a selected area containing several typical pockmarks. This allowed for an integrated analysis of the morphology, distribution, and subsurface strata as well as a direct comparison of seafloor characteristics among different areas inside and outside of a pockmark. Additionally, surface sediments were analyzed for particle structure and grain size. Based on the combination of multibeam bathymetry and backscatter intensity data, 282 seafloor depressions were accurately identified. Moreover, the formation of pockmark can be attributed to existence of gas chimneys and other shallow gases revealed by shallow seismic profile records in the subsurface strata. Sediments inside and outside the pockmark showed remarkable discrepancies in terms of grain size, and benthic fauna. Additionally, strong internal reflection area was observed inside the depressions where benthic habitats are abundant and sediments contain strawberry pyrite and carbonate cementation. Side scan sonar also recorded sediment plumes in the water column, which indicates that the pockmarks are still active. Several "pearl-string-like" accumulations of pockmark were found in the study area, which well agree with the distribution of buried channels and lakes. These results thus provide new insights in interpreting shallow water fluid seepage, with implications for our current knowledge of overall sedimentation process in offshore continental shelves. • Active fluid seepage in shallow water continental shelf environment • Shallow-gas related acoustic features recorded on sub-bottom acoustic data • Benthic fauna anomalies, and strawberry pyrite associated with seafloor seeps • The large but shallow pockmarks can be ascribed to low fluid pressure. • String like distribution of pockmarks controlled by subsurface buried channels. [ABSTRACT FROM AUTHOR]

This paper investigated the quality of site-specific surface temperature and surface pressure data in the coastal regions of China, which were interpolated from the European Centre for Medium-Range Weather Forecast Interim reanalysis (ERA-Interim), Japanese 55-year Reanalysis Project (JRA-55), and the National Centers for Environmental Prediction Final (NCEP FNL) reanalysis surface meteorological datasets as well as from the new Global Pressure and Temperature (GPT2) model data. The measured temperature and pressure along with the collocated GPS data from 2014 were collected from 25 observation stations evenly located in the region. Compared with the actual meteorological observations, the performances of the interpolated data from three reanalysis datasets differ marginally, with the root mean square errors (RMSEs) of the interpolated surface temperature and pressure less than 2.4 K and 1.6 hPa, respectively; however, the RMSEs of the surface temperature and pressure interpolated from the GPT2 model were 3.0 K and 4.2 hPa, respectively. Data based on GPS PWV products that used the meteorological parameters interpolated from three reanalysis data were very close to those of meteorological observations, with biases within ± 0.4 mm and RMSEs below 0.5 mm in most areas, and the RMSE of PWV using the GPT2 model interpolation data was superior by 2 mm. The measurement of GPS PWV using the interpolated reanalysis meteorological data also compared well with radiosonde observations, with RMSE between them tending to increase with a decrease of the GPS station's latitude. However, the GPS PWV based on the interpolated data could not reflect the true change in water vapor during typhoon events. [ABSTRACT FROM AUTHOR]

Robotics are extensively utilized in modern industry to replace human labor and achieve high automation and flexibility. In order to produce large-size products, two-sided assembly lines are widely applied, where robotics can be employed to operate tasks on workstations. Since the applied traditional optimization methods are limited, the current work presented a new discrete cuckoo search algorithm to solve the two-sided robotic assembly line balancing problem. The original cuckoo search algorithm was modified by employing neighbor operations. Furthermore, a new procedure to generate individuals to replace the abandoned nests was developed to enhance the intensification. Since the considered problem has two subproblems, namely the robot allocation and assembly line balancing, the present work extended the cuckoo search algorithm to cooperative coevolutionary paradigm by dividing the cuckoos into two sub-swarms, each addressing a subproblem. In order to emphasize the exploration, a restart mechanism was employed. The proposed discrete algorithm’s evolution process and convergence were compared with another two popular optimization algorithms, namely the genetic algorithm and particle swarm optimization algorithm. Computational study on the proposed algorithms and other five recent algorithms along with statistical analysis demonstrated that the proposed methods yielded promising results. [ABSTRACT FROM AUTHOR]

Highlights: [•] A deterministic robust counterpart is introduced to guarantee the robustness of the generated production schedule. [•] A two-stage scenario-based stochastic programming framework is investigated for the SCC process under demand uncertainty. [•] A scenario reduction method is applied to reduce the number of scenarios to a small set of representative realizations. [•] Computational results demonstrate robustness under demand uncertainty. [•] The robust optimization-based solution is of comparable quality to the two-stage stochastic programming based solution. [ABSTRACT FROM AUTHOR]

Shallow-water depth information is essential for ship navigation and fishery farming. However, the accurate acquisition of shallow-water depth has been a challenge for marine mapping. Combining Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data with multispectral data, satellite-derived bathymetry is a promising solution through which to obtain bathymetric information quickly and accurately. This study proposes a photon refraction correction method considering sea-surface undulations to address errors in the underwater photons obtained by the ICESat-2. First, the instantaneous sea surface and beam emission angle are integrated to determine the sea-surface incidence angle. Next, the distance of photon propagation in water is determined using sea-surface undulation and Snell's law. Finally, position correction is performed through geometric relationships. The corrected photons were combined with the multispectral data for bathymetric inversion, and a bathymetric map of the Yongle Atoll area was obtained. A bathymetric chart was created using the corrected photons and the multispectral data in the Yongle Atoll. Comparing the results of different refraction correction methods with the data measured shows that the refraction correction method proposed in this paper can effectively correct bathymetry errors: the root mean square error is 1.48 m and the R2 is 0.86. [ABSTRACT FROM AUTHOR]

An improved ant colony optimization (ACO), namely, station ant colony optimization (SACO), is proposed to solve the type 2 assembly line balancing problem (ALBP-2). In the algorithm, ACO is employed to search different better combinations of tasks (component solutions) for each station; an iteration compress mechanism is proposed to reduce the searching space of feasible solutions of ALBP-2. Three heuristic factors [i.e., (1) task time, (2) number of successors, and (3) number of releasable successors], two pheromones, and a task assignment mechanism are proposed to search better component solutions for every station. Finally, the effectiveness and stability of SACO are confirmed through comparison with literatures in 23 instances included in nine examples. [ABSTRACT FROM AUTHOR]

Monitoring of CO2 column concentrations is valuable for atmospheric research. A mobile open-path system was developed based on tunable diode laser absorption spectroscopy (TDLAS) to measure atmospheric CO2 column concentrations. A laser beam was emitted downward from a distributed feedback diode laser at 2 μm and then reflected by the retroreflector array on the ground. We measured the CO2 column concentrations over the 20 and 110 m long vertical path. Several single-point sensors were distributed at different heights to provide comparative measurements for the open-path TDLAS system. The results showed that the minimum detection limit of system was 0.52 ppm. Some similarities were observed in trends from the open-path TDLAS system and these sensors, but the average of these sensors was more consistent with the open-path TDLAS system values than the single-point measurement. These field measurements demonstrate the feasibility of open-path TDLAS for measuring the CO2 column concentration and monitoring carbon emission over large areas. [ABSTRACT FROM AUTHOR]