Multi-space crowd sensing has emerged as a promising paradigm for 3D urban perception. However, it faces critical challenges including space coupling, task heterogeneity, and dynamic resource availability. To address these issues, the Multi-Space Fairness Task Allocation (MSFTA) problem is formulated, aiming to maximize task completion while ensuring fairness across spatial dimensions. The problem is proven to be NP-hard, and a dynamic collaborative optimization framework is proposed. Within this framework, a Multi-Space Clustering QuadTree Voronoi Partition (MCQVP) is developed for fine-grained multi-dimensional partitioning by leveraging DBSCAN and quadtree structures. In addition, a Group Urgency-Based Multi-Shortest Path (GUBMSP) scheduler is incorporated to prioritize time-sensitive task groups via urgency-aware critical paths. Furthermore, a Fairness-Aware Pareto Multi-Objective Ant-Q Learning (FA-PMOAQL) allocator is introduced to integrate Q-learning and ant-colony optimization under fairness-aware multi-objective guidance. These designs establish a unified framework that not only improves task allocation efficiency through multi-space partitioning and urgency-driven scheduling, but also ensures equitable resource utilization by embedding fairness into the learning process. Comparison experiments on Tokyo and New York datasets demonstrate that the proposed approach achieves up to 12.8% higher task completion rate compared with baseline algorithms, while maintaining relatively low runtime. In cross-layer scenarios, the completion rate improves by 20% when agent resources increase, and under heavy task loads it sustains competitive performance with only moderate decline.

With the popularity of smart mobile devices embedded with rich sensors, mobile crowdsensing (MCS) has gradually attracted the attention of researchers in recent years. Task allocation is a key research problem in MCS systems, where platforms recruit workers and assign them crowd tasks. While previous research has focused on the utility of recruiting workers, the location factor of workers has been ignored. Therefore, this paper proposes a two-stage worker recruitment framework named BW-Selector, which recruits workers in two stages. In the offline stage, this paper proposes an opportunity-crowd worker recruitment algorithm, which first divides the task area with a Voronoi diagram, and then builds a prediction model based on long short-term memory (LSTM) to predict the movement trajectory of workers and solve the cold start in the traditional MCS system. In the online stage, for maximizing the task space coverage under the premise of a limited task budget, this paper proposes a participatory-crowd worker recruitment algorithm based on adaptive threshold selection. Finally, through experiments on real datasets, it is verified that BW-Selector has better performance in terms of task space coverage and running time under the same constraints compared with other methods.

Unmanned agents represent a significant advancement in unmanned control and constitute an important element in the future agent warfare. Their autonomous decision-making capabilities are integral to accomplishing tasks independently. To address challenges inherent in multiparty game scenarios that traditional method struggle with and enhance the applicability and accuracy of game decision-making, this article proposes a novel multiagent confrontation method for unmanned vessels, tailored to a three-party dynamic multistrategy evolutionary game in incomplete information scenarios. The approach introduces a new incentive mechanism designed to enhance both individual and collective profits of agents. Using evolutionary game theory, a three-party model is developed, incorporating interactions among player, enemy, and neutral agents. The model tracks the evolution of strategies to identify stable equilibria across various perceptual conditions. Simulations validate the effectiveness of the proposed method in selecting optimal strategies for unmanned vessels in complex battlefield scenarios, demonstrating its potential for improving autonomous decision-making in multiparty confrontations.

The industrial internet of things (IIoT) plays a crucial role in manufacturing, logistics, and equipment management. Graph neural networks (GNNs) can effectively model graph-structured data and have received widespread attention in IIoT applications. However, existing methods face key challenges. First, IIoT data typically contains sensitive information, making it difficult to conduct centralized training on dispersed data. Second, the current model fails to fully capture the complex interrelationships between different devices. To address the above issues, this article proposes a federated learning-based graph neural network model FedMRGNN for joint analysis of distributed IIoT. This model performs federated learning through model aggregation and parameter exchange, while protecting privacy through differential privacy mechanisms. Meanwhile, to better capture the complex relationships between devices, this article integrates multiscale feature extraction and residual connections into the model. Multiscale feature extraction can process graph data in parallel through multiple branches, each branch using convolutional kernels of different scales to extract node features, and utilizing multiscale pooling operations for local aggregation and dimensionality reduction. Residual connections can enhance the fusion ability of multiscale features and alleviate the problem of gradient vanishing in deep network training. In order to further verify the effectiveness of FedMRGNN, experimental verification was conducted on different datasets. The results show that FedMRGNN improves classification accuracy by 2.21%–6.40% compared to other baseline algorithms in most scenarios. In practical IIoT applications, improved classification accuracy can help predictive maintenance systems detect potential device failures in advance, thereby improving overall device operational efficiency.

With the acceleration of urbanization, the traffic congestion issue is becoming more and more prominent in large cities. The effective scheduling of urban traffic signals becomes critical. This study proposes three novel prediction and feedback assisted evolutionary algorithms (PFAEAs) to address the urban traffic signal scheduling problem (UTSSP) with minimizing vehicle delays. First, we construct a mathematical model of UTSSP and design an improved evolutionary algorithm (EA) framework that integrates an eight-phase control strategy based on a vehicle movement relationship graph. Then, by combining a back-propagation neural network (BPNN) and meta-heuristics, we improve the prediction accuracy of the vehicle turning rate for generating high-quality initial solutions. Further, 12 problem-specific search operators (PSSOs) are designed to enhance the exploration capability of EA. Reinforcement learning (RL) algorithms, especially the Q-learning and Sarsa algorithms, are employed to select premium PSSOs dynamically for guiding the search direction of EA. Finally, for 18 cases with different scales, the proposed PFAEAs show significant advantages in reducing vehicle delays compared with the state-of-the-art algorithms. The results validate the competitiveness and practicality of the PFAEAs for UTSSP.

Although an increasing number of studies have focused on the flexible job shop problem, there has been insufficient consideration of realistic constraints, such as the working hours of employees and the noninterruptible nature of certain operations. To address this issue, here an improved deep reinforcement learning (DRL) approach is presented that utilizes end-to-end multidecision-intelligent body proximal policy optimization (m-PPO). In the proposed framework, a heterogeneous graph self-attention neural network (HGAN) model is embedded, which efficiently extracts valuable features from the original state in heterogeneous graphs to capture intricate relationships. Within this framework, agents are divided into five rule-driven job decision agents and data-driven operation-machine ( $\mathcal {O}\text {-}\mathcal {M}$ ) pair decision agents, which incorporate problem-specific knowledge. To optimize the makespan, total costs, and total lateness concurrently, the weight parameters for the objectives are generated by the network and self-updated based on the current state. Numerical experiments demonstrate the effectiveness of the proposed method.

In response to escalating market demands, we extend the distributed assembly flowshop problems (DAFSPs) by incorporating batch delivery, optimizing both total energy consumption (TEC) and total completion time, simultaneously. First, a mathematical model for DAFSP with batch delivery is constructed. Second, the artificial bee colony (ABC) algorithm is enhanced to solve the concerned problems. Two dispatch rules are designed to enhance the quality and diversity of initial solutions. Third, seven local search operators tailored to problem characteristics and two objective-oriented machine speed adjustment strategies are designed for improving the performance of ABC. Two reinforcement learning (RL) algorithms, SARSA andQ-learning, are used to select the appropriate local search operators and speed adjustment strategies during iterations. Two pairs of state-action strategies are developed for local search selection and speed adjustment, respectively. Finally, extensive simulation experiments and detailed analysis demonstrate that the SARSA-assisted ABC has a better performance than its peers for DAFSP with batch delivery.

This study addresses an energy-efficient multiobjective distributed assembly permutation flowshop scheduling problem with sequence dependent setup time. The objectives are to minimize the maximum completion time (makespan), mean of earliness and tardiness, and total carbon emission, simultaneously. First, a mathematical model is established. Second, the double-learning-strategy-based Jaya algorithms are developed to address the problems. According to problem-specific nature, one Q-learning state-action strategy is designed to guide nondominated solutions choosing appropriate machine speed adjustment strategies for achieving a satisfactory tradeoff among the three objectives. Third, eight neighborhood structures are designed and embedded in the proposed Jaya algorithms to discover high-quality solutions in local spaces. Fourth, another three novel Q-learning state-action design strategies are proposed to dynamically select the appropriate neighborhood structures during iterations, which introduce the searching directions and improve the convergence of the proposed Jaya. Finally, 81 benchmark instances are solved and the effectiveness of improved strategies is demonstrated. The proposed Jaya algorithm with the best double Q-learning strategies is compared to a solver, Gurobi, to verify the developed mathematical model. The experimental analysis demonstrates that the improved Jaya algorithm with both the Q-learning based machine speed adjustment and the Q-learning-based neighborhood selection strategies shows the best performance.

With the rapid development of crowd sensing computing, Mobile crowdsourcing (MCS) has become an indispensable part of today’s society. While MCS brings convenience to people, it also exposes them to the risk of privacy leakage. In addition, the demand for data is increasing, and the personalized privacy requirements of crowd workers may affect the service quality. In order to address these problems, this paper proposes a personalized privacy protection incentive mechanism (PPPIM) for MCS based on homomorphic encryption and edge computing. Firstly, this paper designs a personalized privacy metric, using social attributes and private attributes of crowd workers to calculate the privacy level required by crowd workers. Then, based on homomorphic encryption and edge computing, a personalized residual federated security learning scheme (PRFSL) is proposed to ensure the security, timeliness, integrity of task data and the privacy of crowd workers’ needs to improve encryption efficiency. Finally, based on the evolutionary game, a personalized privacy incentive mechanism is proposed to improve the overall service utility. Experimental comparisons based on real datasets show that the proposed scheme can not only ensure the security, timeliness, and integrity of task data more effectively. It can also effectively reduce data processing time, improve the probability of crowd workers actively completing tasks and the overall service quality utility.

In Mobile Crowdsourcing (MCS), the task information released by task publishers and the sensed data submitted by workers may expose their privacy, while the rapid growth of MCS imposes increasing data processing pressure on cloud platforms and mobile devices. To address these challenges, a bilateral privacy protection scheme based on adaptive location generalization and grouping aggregation is presented in this paper. The scheme uses federated learning as a framework and utilizes edge computing to reduce the data processing burden on cloud platforms and mobile devices. This paper proposes the adaptive location generalization algorithm (KM-ALG) and a real task location release mechanism based on the RSA algorithm to protect the task location privacy of the task publisher. For workers’ privacy protection, the lightweight multiple perturbation algorithm based on localized differential privacy (LDP-MP) proposed in this paper is used to protect workers’ data privacy. Aiming at the problem of data quality loss caused by perturbation, a perturbation elimination mechanism based on homomorphic encryption technology is proposed. In order to prevent workers’ sensed data from leaking location information, a grouping aggregation mechanism is used to destroy the correspondence between workers and submitted data, thereby protecting workers’ location privacy. In addition, a task allocation scheme adapted to task location privacy protection is also proposed. Finally, the effectiveness of the proposed algorithm is verified through experiments on multiple real data sets.

Flexible job shop scheduling problem (FJSP) is one of the challenging issues in industrial systems. In this study, we propose a bi-population balancing multi-objective evolutionary algorithm, to solve the distributed FJSPs from a steelmaking system, with considering the fuzzy processing time and crane transportation processes. Two objectives are considered simultaneously, including minimization of the maximum fuzzy completion time and the energy consumption during machine processing and crane transportation. Firstly, the mathematical model is formulated for the considered problem. Then, an efficient problem-specific initialization heuristic is developed. To balance the convergence and diversity abilities, a novel crossover operator and two cooperative population environmental selection mechanisms are developed. In addition, an efficient population size adaptive adjustment mechanism is designed. Then, an enhanced local search heuristic is developed to further improve the searching abilities. Finally, a set of randomly generated instances based on realistic industrial processes are tested, and through comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several presented algorithms.Note to Practitioners—In practical manufacturing processes, the processing times for each job should not be considered as deterministic values because of the disruption events, such as machine breakdown, resource limitation, and machine maintenance. Therefore, the fuzzy scheduling should be considered in many industrial procedures. This study considered multi-objective optimization flexible job shop with energy and robotic transportations, where the fuzzy makespan and energy consumptions are minimized simultaneously. Two populations balancing the convergence and diversity abilities are developed. Efficient problem-specific heuristics are designed to enhance the searching performance. The proposed methods can be generalized and applied to many applications considering both the realistic constraints and objectives.

Mobile crowdsourcing (MCS) is a new paradigm that uses various mobile devices to collect sensed data. Mobile edge computing (MEC) can effectively utilize the device resources of mobile edge, greatly relieve the pressure of network bandwidth and improve the response speed. In this article, we construct a four-party evolutionary game model consisting of the platform, crowd workers, task requesters, and edge servers. The computing tasks are conducted on edge servers, which greatly reduce remote data transmission and network operating costs and improve service quality. Taking into account the collusion between the platform and workers, and that between the platform and requesters, we analyze the stability of the strategic equilibrium in MCS using replicator dynamics methods. The optimal payoff strategies of the participants in different initial states are obtained. To prevent cheating and false-reporting problems, reward and punishment strategies are provided. Finally, the stability of the equilibrium of the four-party evolutionary game system is verified by simulation experiments, and an incentive strategy is designed to motivate all parties to choose the trust strategies.

Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by considering the workers' ability benefits, distance benefits, and trust benefits in this paper. The worker recruitment problem is modeled as an Undirected Complete Recruitment Graph (UCRG), for which a specific Tabu Search Recruitment (TSR) algorithm solution is proposed. An optimal execution team is recruited for each task by the TSR algorithm, and the collaboration team for the task is obtained under the constraint of privacy loss. To enhance the efficiency of the recruitment algorithm on a large scale and scope, the Mini-Batch K-Means clustering algorithm and edge computing technology are introduced, enabling distributed worker recruitment. Lastly, extensive experiments conducted on five real datasets validate that the recruitment algorithm proposed in this paper outperforms other baselines. Additionally, TREF proposed herein surpasses the performance of state-of-the-art trust evaluation methods in the literature.

Flexible job shop scheduling problem (FJSP) has attracted research interests as it can significantly improve the energy, cost, and time efficiency of production. As one type of reinforcement learning, deep Q-network (DQN) has been applied to solve numerous realistic optimization problems. In this study, a DQN model is proposed to solve a multiobjective FJSP with crane transportation and setup times (FJSP-CS). Two objectives, i.e., makespan and total energy consumption, are optimized simultaneously based on weighting approach. To better reflect the problem realities, eight different crane transportation stages and three typical machine states including processing, setup, and standby are investigated. Considering the complexity of FJSP-CS, an identification rule is designed to organize the crane transportation in solution decoding. As for the DQN model, 12 state features and seven actions are designed to describe the features in the scheduling process. A novel structure is applied in the DQN topology, saving the calculation resources and improving the performance. In DQN training, double deep Q-network technique and soft target weight update strategy are used. In addition, three reported improvement strategies are adopted to enhance the solution qualities by adjusting scheduling assignments. Extensive computational tests and comparisons demonstrate the effectiveness and advantages of the proposed method in solving FJSP-CS, where the DQN can choose appropriate dispatching rules at various scheduling situations.

Prefabricated construction has attracted research interest as it can significantly save energy consumption. In this study, a distributed hybrid flow shop with family setup time in a typical prefabricated system is investigated. A hybrid graph-based imitation learning from multiple experts (hereafter called IML) is developed to minimize the makespan. Efficient input features with operation processing times are presented. Next, to enhance the training speed of the network, a less parameter encoder mechanism is developed. Subsequently, a multiexpert learning method is proposed, in which the solutions obtained by these experts are used as the ground truth values to enhance the convergence and searching capabilities. Moreover, a variable neighborhood search (VNS)-based local search method is embedded to further improve the performance. Finally, based on a realistic prefabricated component production horizon, a set of instances is generated to test the performance of the proposed algorithm. The comprehensive computational comparison and statistical analysis reveal that the proposed IML algorithm, when compared to two recently published efficient algorithms, yields an average improvement of about 8.66% and 13.78%, respectively. This highlights the efficiency of the proposed algorithm to solve large-scale instances.

This article is concerned with the bipartite synchronization problem of coupled switching neural networks with cooperative–competitive interactions and reaction–diffusion terms. Different from the existing literature, the networked systems under investigation possess the relationship of cooperation and competition among nodes. Notably, the switching topology is described by a signed graph subject to the Markov jump process with the coexistence of positive and negative interaction weights. Specifically, a positive weight indicates an alliance relationship between two nodes and a negative one shows an adversary relationship. This article aims to design a bipartite synchronization controller for the aforementioned networks with the switching topology such that a prescribed$\mathcal {H}_{\infty }$bipartite synchronization is satisfied. Then, some sufficient criteria to ensure the stochastic stability of bipartite synchronization error systems are established in view of an appropriate Lyapunov function. Finally, two simulation examples are presented to verify the validity of the proposed bipartite synchronization control method.

The present work expects to explore the application effect of biologically inspired Plasticity Neural Network in the industrial intelligent dispatching energy storage system, and highlight the intelligence and fault detection performance of the control system. To address the faults in intelligent dispatching energy storage system, the present work implements a fault diagnosis model of intelligent dispatching energy storage system based on Deep Belief Network (DBN), and simulates and analyzes the model. The results show that the transmission probability of the fault diagnosis model of the constructed intelligent energy storage scheduling system is 100% and when the parameters$\lambda $is between 0.01 and 0.05, the real-time performance of data transmission is the highest. Compared with other classical algorithm models, the success rate and detection accuracy of the proposed algorithm are about 85%, the energy consumption is lower, and the detection effect is more obvious. Therefore, the constructed system obviously has higher real-time performance and more accurate fault detection performance, and significantly better system detection and protection performance. The results provide an experimental basis for the operation and fault detection of intelligent dispatching energy storage system.

This paper provides a novel intelligent scheduling strategy for a real-world transportation dynamic scheduling case from an engine workshop of general motor company (GMEW), which is a key production line throughout the manufacturing process. In order to reduce the carbon emission in the scheduling process and make up for ignoring the energy consumption of each part in the scheduling when optimizing the carbon emission of the workshop and the factory. This paper first formulates a fuzzy random chance-constrained programming model of inverse scheduling problem (ISP) with energy consumption. A multi-strategy parallel genetic algorithm based on machine learning (RL-MSPGA) is proposed, which uses machine learning to improve the genetic algorithm. First, the parallel idea is developed to accelerate the process of evolution of genetic algorithm, and the initial population is divided into clusters by$k$-means clustering algorithm. Second, similar individuals are evenly distributed to different sub-populations to ensure the diversity and uniformity of sub-populations. Third, in the process of evolution, the sub-populations communicate with each other, and extend the excellent individuals to replace the poor ones in other populations, so as to improve the overall quality of the population. Fourth, the self-learning of the crossover probability is realized by the self-learning of the self-sensing environment, which makes the crossover probability adapt to the evolutionary process according to experience. Finally, the real instance is used to validate the different algorithms. It can effectively adjust the completion time and the proportion of energy consumption, thus providing the possibility for the production of energy-saving enterprises. This implies that the suggested model is reasonable and the provided algorithm can effectively solve the inverse shop scheduling problem.

With the rapid development of the Internet of Things (IoT) and the rapid popularization of 5 G networks, the data that needs to be processed in Mobile Crowdsourcing (MCS) system is increasing every day. Traditional cloud computing can no longer meet the needs of crowdsourcing for real-time data and processing efficiency, thus, edge computing was born. Edge computing can be calculated at the edge of network so that greatly improve the efficiency and real-time performance of data processing. In addition, most of the existing privacy protection technologies are based on the trusted third parties. Therefore, in view of the semi-trustworthiness of edge servers and the transparency of blockchain, this paper proposes a triple real-time trajectory privacy protection mechanism (T-LGEB) based on edge computing and blockchain. Through combining the localized differential privacy and multiple probability extension mechanism, the T-LGEB mechanism is proposed to send the requests and data to the edge server in this paper. Then, through the spatio-temporal dynamic pseudonym mechanism proposed in the paper, the entire trajectory of task participants is divided into multiple unrelated trajectory segments with different pseudonymous identities in order to protect the trajectory privacy of task participants while ensuring high data availability and real-time data. Through a large number of experiments and comparative analysis on multiple real data sets, the proposed T-LGEB has extremely high privacy protection capabilities and data availability, and the resource consumption caused is relatively low.

In this work, a neural-networks (NNs)-based adaptive asymptotic tracking control scheme is presented for a class of uncertain nonstrict feedback nonlinear systems with time-varying full-state constraints. First, we construct a novel exponentially decaying nonlinear mapping to map the constrained system states to new system states without constraints. Instead of the traditional barrier Lyapunov function methods, the feasible conditions which require the virtual control signals satisfying the constraint requirements are removed. By employing the Nussbaum design method to eliminate the effect of unknown control gains, the general assumption about the signs of the unknown control gains is relaxed. Then, the nonstrict feedback form of the system can be pulled back to the strict feedback form through the basic properties of radial basis function NNs. Simultaneously, the intermediate control signals and the desired controller are constructed by the backstepping process and the Nussbaum design method. The designed controller can ensure that all signals in the whole closed-loop system are bounded without the violation of the constraints and hold the asymptotic tracking performance. In the end, a practical example about a brush dc motor driving a one-link robot manipulator is given to illustrate the effectiveness of the proposed design scheme.

This article presents a global adaptive neural-network-based control algorithm for disturbed pure-feedback nonlinear systems to achieve zero tracking error in a predefined time. Different from the traditional works that only solve the semiglobal bounded tracking problem for pure-feedback systems, this work not only achieves that the tracking error globally converges to zero but also guarantees that the convergence time can be predefined according to the user specification. In order to get the desired predefined-time controller, first, a mild semibound assumption for nonaffine functions is skillfully proposed so that the design difficulty caused by the structure of pure feedback can be easily solved. Then, we apply the property of radial basis function (RBF) neural networks (NNs) and Young's inequality to derive the upper bound of the term that contains the unknown nonlinear function and external disturbances, and the designed adaptive parameters decide the derived upper and robust control gain. Finally, the predefined-time virtual control inputs are presented whose derivatives are further estimated by utilizing finite-time differentiators. It is strictly proved that the proposed novel predefined-time controller can guarantee that the tracking error globally converges to zero within predefined time and a practical example is shown to verify the effectiveness and practicability of the proposed predefined-time control method.

A permutation flow-shop scheduling problem (PFSP) has been studied for a long time due to its significance in real-life applications. This work proposes an improved artificial bee colony (ABC) algorithm with$Q$-learning, named QABC, for solving it with minimizing the maximum completion time (makespan). First, the Nawaz–Enscore–Ham (NEH) heuristic is employed to initialize the population of ABC. Second, a set of problem-specific and knowledge-based neighborhood structures are designed in the employ bee phase.$Q$-learning is employed to favorably choose the premium neighborhood structures. Next, an all-round search strategy is proposed to further enhance the quality of individuals in the onlooker bee phase. Moreover, an insert-based method is applied to avoid local optima. Finally, QABC is used to solve 151 well-known benchmark instances. Its performance is verified by comparing it with the state-of-the-art algorithms. Experimental and statistical results demonstrate its superiority over its peers in solving the concerned problems.

This article focuses on input-to-state stability (ISS) of impulsive time-delay systems where the hybrid effect of impulses with time-dependent multiple jump maps in different subsystems is fully considered. By virtue of M-matrix and vector Lyapunov function, some theorems for ISS are established for Krasovskii-type conditions which avoid the common threshold of impulses in subsystems and allow the simultaneous existence of stable and unstable continuous subdynamics. If the time intervals between impulses are bounded, the ISS of time-delay systems can be ensured even though hybrid impulses exist in each subsystem, which determines the robustness of stable time-delay systems with respect to hybrid impulses. The unstable time-delay systems with different subdynamics can be stabilized in input-to-state-stability sense by involving hybrid impulses. In addition, several extended criteria are presented to bridge the results derived via vector Lyapunov function, scalar Lyapunov function, and comparison principle. At last, the theoretical results are validated by numerical example and a simplified model of sewage treatment tank.

In this study, we propose an efficient optimization algorithm that is a hybrid of the iterated greedy and simulated annealing algorithms (hereinafter, referred to as IGSA) to solve the flexible job shop scheduling problem with crane transportation processes (CFJSP). Two objectives are simultaneously considered, namely, the minimization of the maximum completion time and the energy consumptions during machine processing and crane transportation. Different from the methods in the literature, crane lift operations have been investigated for the first time to consider the processing time and energy consumptions involved during the crane lift process. The IGSA algorithm is then developed to solve the CFJSPs considered. In the proposed IGSA algorithm, first, each solution is represented by a 2-D vector, where one vector represents the scheduling sequence and the other vector shows the assignment of machines. Subsequently, an improved construction heuristic considering the problem features is proposed, which can decrease the number of replicated insertion positions for the destruction operations. Furthermore, to balance the exploration abilities and time complexity of the proposed algorithm, a problem-specific exploration heuristic is developed. Finally, a set of randomly generated instances based on realistic industrial processes is tested. Through comprehensive computational comparisons and statistical analyses, the highly effective performance of the proposed algorithm is favorably compared against several efficient algorithms.Note to Practitioners—The flexible job shop scheduling problem (FJSP) can be extended and applied to many types of practical manufacturing processes. Many realistic production processes should consider the transportation procedures, especially for the limited crane resources and energy consumptions during the transportation operations. This study models a realistic production process as an FJSP with crane transportation, wherein two objectives, namely, the makespan and energy consumptions, are to be simultaneously minimized. This study first considers the height of the processing machines, and therefore, the crane lift operations and lift energy consumptions are investigated. A hybrid iterated greedy algorithm is proposed for solving the problem considered, and several problem-specific heuristics are embedded to balance the exploration and exploitation abilities of the proposed algorithm. In addition, the proposed algorithm can be generalized to solve other types of scheduling problems with crane transportations.

In this article, a distributed hybrid flow shop scheduling problem with variable speed constraints is considered. To solve it, a knowledge-based adaptive reference points multiobjective algorithm (KMOEA) is developed. In the proposed algorithm, each solution is represented with a 3-D vector, where the factory assignment, machine assignment, operation scheduling, and speed setting are encoded. Then, four problem-specific lemmas are proposed, which are used as the knowledge to guide the main components of the algorithm, including the initialization, global, and local search procedures. Next, an efficient initialization approach is presented, which is embedded with several problem-related initialization rules. Furthermore, a novel Pareto-based crossover heuristic is designed to learn from more promising solutions. To enhance the local search abilities, a speed adjustment local search method is investigated. Finally, a set of instances generated based on the realistic prefabricated production system is tested to verify the efficiency and effectiveness of the proposed algorithm.

Visual tracking is a fundamental capability for robots tasked with humans and environment interaction. However, state-of-the-art visual tracking methods are still prone to failures and are imprecise when applied to challenging stereos, and their results are generally confidence agonistic. These methods depend on an embedded deep learning model to provide deterministic features or regression maps. A deterministic output with low confidence can result in disastrous consequences and lacks evidence needed for subsequent operations. Moreover, training data ambiguities or noise in the observations (so-called data uncertainty) can also lead to inherent uncertainty. In this paper, we focus on exploiting probabilistic Siamese visual tracking with a conditional variational autoencoder (CVAE). First, we build a bridge between the Siamese architecture and the CVAE and propose a novel Bayesian visual tracking method. Second, the proposed method generates a complete probability distribution that enables the production of multiple plausible tracking outputs. Third, CVAE conditioned by ground truth data encodes a low-dimensional latent space and conducts noise-injection training to prevent overfitting. Our proposed tracking method outperformed the state-of-the-art trackers on the VOT2016, VOT2018 and TColor-128 datasets.

In this paper, we address the adaptive neural tracking control problem for a class of uncertain switched stochastic nonlinear pure-feedback systems with nonlower triangular form. The significant design difficulty is the completely unknown nonlinear functions with all state variables that can neither be directly estimated by radial basis function (RBF) neural networks (NNs) nor be eliminated by the traditional backstepping technique. To achieve the control objective of this paper, a common state-feedback controller for all subsystems is first systematically constructed by using the common coordinate transformation, the variable separation technique, and the universal approximation capability of RBF NNs. Then the stability analysis shows that the semi-global bounded in probability of the whole closed-loop switched system can be obtained and the desired tracking performance can also be insured under a class of switching signals with the average dwell time property. Finally, simulation results are given to demonstrate the effectiveness of the obtained control scheme.

In this article, an adaptive approximation-based output-feedback tracking control scheme is presented for a class of stochastic switched lower-triangular nonlinear systems with input saturation and unmeasurable state variables. First, to overcome the design obstacle caused by the nondifferential saturation nonlinearity, a carefully selected nonlinear function of the control input signal is applied to estimate the saturation function. Then, a reduced-order state observer is designed to model the unmeasured system states, which also means the error system can be established. Furthermore, the fuzzy-logic systems are utilized to approximate the unknown system nonlinearities in the adaptive backstepping-based controller design procedure. It is ensured that all the closed-loop system variables are bounded in probability and the error signal belongs to a compact set in the mean square sense. Finally, the effectiveness and the practicability of the proposed control scheme are shown by two examples.

In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

In this paper, the problem of adaptive neural tracking control for a type of uncertain switched nonlinear nonlower-triangular system is considered. The innovations of this paper are summarized as follows: 1) input to state stability of unmodeled dynamics is removed, which is an indispensable assumption for the design of nonswitched unmodeled dynamic systems; 2) the design difficulties caused by the nonlower-triangular structure is handled by applying the universal approximation ability of radial basis function neural networks and the inherent properties of Gaussian functions, which avoids the restriction that the monotonously increasing bounding functions of the nonlower-triangular system functions must exist; and 3) multiple Lyapunov functions are utilized to develop a backstepping-like recursive design procedure such that the solvability of the adaptive neural tracking control issue of all subsystems is unnecessary. Based on the proposed controller design methods, it can be obtained that all signals in the closed-loop switched system remain bounded and the tracking error can eventually converge to a small neighborhood of the origin. In the simulation study, two examples are supplied to prove the practicability and feasibility of the developed design schemes.

In this article, we study the issue of adaptive neural output-feedback controller design for a class of uncertain switched time-delay nonlinear systems with nonlower triangular structure. The prominent contribution of this article is that the delay-dependent stability criterion of nonswitched nonlinear systems is successfully extended to that of switched nonlower triangular nonlinear systems. The design algorithm is listed as follows. First, a switched state observer is designed such that the error dynamic system can be generated. Second, neural networks, adaptive backstepping technique, and variable separation method are, respectively, applied to construct a common controller for all subsystems, in which the Lyapunov-Krasovskii functionals are deliberately constructed such that the average dwell-time scheme can be employed to guarantee the stability and performance of the closed-loop system, despite the existence of time delays. Third, the stability analysis process confirms in detail that all the variables of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation study is given to show the validity of the proposed control approach.

In this paper, we propose a deep propagation based image matting framework by introducing deep learning into learning an alpha matte propagation principal. Our deep learning architecture is a concatenation of a deep feature extraction module, an affinity learning module and a matte propagation module. These three modules are all differentiable and can be optimized jointly via an end-to-end training process. Our framework results in a semantic-level pairwise similarity of pixels for propagation by learning deep image representations adapted to matte propagation. It combines the power of deep learning and matte propagation and can therefore surpass prior state-of-the-art matting techniques in terms of both accuracy and training complexity, as validated by our experimental results from 243K images created based on two benchmark matting databases.

In this paper, we propose an improved discrete artificial bee colony (DABC) algorithm to solve the hybrid flexible flowshop scheduling problem with dynamic operation skipping features in molten iron systems. First, each solution is represented by a two-vector-based solution representation, and a dynamic encoding mechanism is developed. Second, a flexible decoding strategy is designed. Next, a right-shift strategy considering the problem characteristics is developed, which can clearly improve the solution quality. In addition, several skipping and scheduling neighborhood structures are presented to balance the exploration and exploitation ability. Finally, an enhanced local search is embedded in the proposed algorithm to further improve the exploitation ability. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed DABC algorithm is favorably compared against several presented algorithms, both in solution quality and efficiency.