Industrial Internet of Things applications like aircraft assembly impose stringent demands on Computing Power Networks. Existing deep reinforcement learning (DRL)-based schedulers not only operate under rigid time-step decision mechanisms but also inadequately handle multi-priority tasks owing to oversimplified queue modeling. To resolve these fundamental limitations, we propose an innovative integrated framework that synergistically combines three components. First, the framework establishes a pioneering formalization of preemptive priority scheduling problem, simultaneously optimizing task response time and violation rate. Second, it incorporates the Counterfactual-Aware Semi-Markov Decision Process (CA-SMDP), which employs counterfactual intervention to tackle temporal credit assignment under event-driven decision epochs. Third, we propose a novel Topology-context fusion Event-driven Scheduler (TESer) where specialized modules for latency minimization and SLA assurance collaboratively achieve optimization synergy. Experimental results demonstrate consistent superiority over state-of-the-art baselines across critical scheduling metrics.

The emergence of intelligent prostheses has facilitated the life and work of disabled patients. The interaction aspect of prostheses has become a highlight research topic in the field of rehabilitation robotics. However, most of the existing prosthetic interaction methods focus on the use of myoelectricity to classify finite gestures, rather than continuous (infinite) force detection, which greatly limits the use of prosthetic scenarios. In this study, a novel optical waveguide sensor was used to collect muscle deformation information from the human arm for continuous control of the prosthetic grip force. The optical waveguide sensor was embedded with carbon fiber to limit the stretching of the waveguide, which led to the optical waveguide sensor being sensitive to bending deformation. Compared with EMGs, the accuracy of continuous grip force control based on the optical waveguide sensor is higher. The R-Square for prosthetic grip force and hand grip force were 0.867 and 0.9724 in the periodic and sustaining grip force experiments, respectively. The results suggested that the proposed method could provide a new approach to the interaction of prostheses.

Stereoscopic warehouses improve logistics efficiency and reduce costs through automation, making it necessary to optimize their storage management systems. This paper focuses on the problem of stacker picking delay in the storage management system of a stereoscopic warehouse, aiming to improve storage and production efficiency through technology and process optimization. Firstly, a path planning model is established to optimize the path length and response speed. Then in order to enhance the global search capability and make the algorithm jump out of the local optimum, the MOEA/D algorithm is adapted to incorporate the adaptive Lévy flight operator into it. The superiority of the improved MOEA/D (IMOEA/D) is verified through a case study.

With the rapid development of vehicle edge offloading technology, smart device owners' quality of experience (QoE) requirements for computing offloading are gradually increasing. However, some dynamic or uncertain factors in the edge offloading scenario, such as the fluctuating network environment, battery energy consumption and other conditions, will affect users. Based on the multi-armed bandit theory, this paper proposes an adaptive learning algorithm that can dynamically sense environmental changes. Finally, the failure rate and offloading performance of large, medium and small tasks are simulated on the EdgeCloudSim simulation platform. The results show that the proposed algorithm has lower latency and energy consumption performance.

In the ramp merging area, the forced merging behavior of ramp vehicles will lead to the emergency deceleration of vehicles in the main lane, affecting the normal traffic flow. Based on the technical framework of the Internet of Vehicles and edge computing, this paper proposes a ramp merging algorithm for coordinated control of main lane change and speed planning of multilane highways to improve passenger comfort and reduce traffic congestion in the ramp merge area. The research results show that compared with the uncontrolled natural confluence, the algorithm proposed in this paper significantly improved stationary speed and traffic efficiency. At the same time, the vehicle track is optimized, and the ride comfort is enhanced by smoothing the speed curve.

Future smart factories need to use intelligent transport devices like automated guided vehicles (AGVs) for connecting intelligent production and logistics. To address the lack of edge side functions in the current AGV systems, this paper proposes an agent-based system architecture for AGVs in cloud-edge computing environments. The system is divided into three main components: the cloud center, edge nodes, and AGV agents. The cloud center is largely responsible for AGV transport route planning, while the edge nodes are in charge of AGV transport control and equipment management. The driving function and executing commands are handled by AGV agents. AGV agents can communicate and collaborate with each other to address emergent issues. The proposed approach has been validated through simulations.

This paper presents a LiDAR-based end-to-end autonomous driving method with Vehicle-to-Everything (V2X) communication integration, termed V2X-Lead, to address the challenges of navigating unregulated urban scenarios under mixed-autonomy traffic conditions. The proposed method aims to handle imperfect partial observations by fusing the onboard LiDAR sensor and V2X communication data. A model-free and off-policy deep reinforcement learning (DRL) algorithm is employed to train the driving agent, which incorporates a carefully designed reward function and multi-task learning technique to enhance generalization across diverse driving tasks and scenarios. Experimental results demonstrate the effectiveness of the proposed approach in improving safety and efficiency in the task of traversing unsignalized intersections in mixed-autonomy traffic, and its generalizability to previously unseen scenarios, such as roundabouts. The integration of V2X communication offers a significant data source for autonomous vehicles (AVs) to perceive their surroundings beyond onboard sensors, resulting in a more accurate and comprehensive perception of the driving environment and more safe and robust driving behavior.

The process of recycling and remanufacturing begins with disassembly. Through disassembly, the components with recycling value are decomposed. However, with the rapid development of production automation, designers often ignore the fact that manual operation is flexible but fails to achieve maximum production efficiency and profit. Therefore, the consideration of human factors in disassembly lines holds significant importance. This study delves into the multi-objective optimization of a U-shaped disassembly line balancing problem involving multiple products. A comprehensive objective function is developed, taking into account various factors including employee fatigue and other factors. To address the aforementioned problem, this study uses a collaborative resource allocation strategy within a multi-objective evolutionary algorithm based on decomposition. By comparing the results of different experimental cases, this paper shows that the proposed algorithm is more competitive than the carnivorous plant algorithm, fruit fly optimization algorithm, and Pareto archiving evolutionary strategy.

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The constraint-coupled bi-level optimization is operated within a rolling horizon to balance traffic performance and computational efficiency. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established, demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios. Numerical experiments are conducted on extensive scenarios with different arrival flows, where the Constraint Programming technique is employed to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.

This paper investigates a longitudinal trajectory optimization problem of connected and automated vehicles with an energy-aware non-linear objective. In this paper, we first approximate each vehicle trajectory with a cubic spline function using the proposed solution representation scheme, while the curve shape can be controlled by the knot vectors. After that, we propose a new coevolutionary algorithm that decomposes the initially high-dimensional problem and performs as the optimizer for subproblems. In the local exploitation phase, a problem-specific steepest ascent hill-climbing algorithm is developed to escape from local minimum points and speed up convergences. This proposed approach is compared with several state-of-the-art algorithms in multiple scenarios with different traffic densities and platooning sizes. Simulation results indicate that it can yield near-optimal solutions with reasonable computation times for real-life applications.

The development of the intelligent transportation system has the potential to improve traffic management considerably. This paper research the car-following control of the autonomous vehicle in high way scenario. The given traditional car-following model does not consider the lateral lane change of the leader vehicle; Besides, the traditional control directly takes the related motion in the platoon of the leader vehicle as the system input of the following vehicle. But this paper combines the lateral and longitudinal movement of the vehicle as to the following control method. In addition, vehicle communication is taken as the way of platoon exchange between vehicles. Therefore, considering the influence of communication delay on the following control of vehicle platoon. The relevant simulation is carried out through Matlab/Simulink.

With the arrival of the Internet of Things, many fragmented intelligent terminals unload data to the edge cloud for computing. Based on the background of the vehicle platooning assisted by the edge cloud server, this paper conducted the following research on the problem of computing task offloading: First, considering the limited heterogeneous network resources of the Internet of Vehicles, the limited computing resources allocated by the edge cloud server for the vehicle platoon and member vehicle onboard computing unit, and the different delay constraints of the computing tasks, a computation offloading model was established with the optimization objective of reducing the total energy consumption of the platooning. Second, this paper used the proposed extended adaptive large neighbourhood search (EALNS) algorithm to optimize the offloading decision of computing tasks and used a method that enables computing tasks to be completed within the time delay constraint to optimize the allocation of computing resources. Finally, the EALNS and the generalized Benders decomposition algorithm were compared for energy optimization experiments. The experimental results verified the EALNS algorithm's effectiveness in optimizing the platooning's total energy consumption in the task offloading decision-making process.

The cab is one of the most important parts of the vehicle, providing the driver’s space environment. Good driving comfort level plays a positive role in improving the driver’s driving state. This paper starts with the seat comfort analysis with fuzzy weight calculation, which is an important part of the overall comfort analysis of automobiles and calculates the total influence weight of each index. Then, through the investigation, the driving comfort of the road maintenance vehicle is selected as an example. And the simulation research of the cab is carried out by Jack simulation software, where the driving comfort was obtained, and the direction of cab improvement is preliminarily determined. Also, we improve the size of the relevant facilities in the cab with the simulation of Jack software again. Finally, the comfort of the driver and the size parameters of the improved cab are obtained by experiments. The experimental results verified the design of the cab space.

Most previous studies on on-ramp merging methods for connected and automated vehicles (CAVs) focused on single-vehicle merging algorithms with instantaneous traffic flow rather than continuous traffic stream. In this study, a cooperative control algorithm for on-ramp merging under continuous traffic flow is proposed to deal with real-time on-ramp merging problems. This algorithm includes: (1) determining the number of Roadside Units (RSUs) involved in the merging process, (2) selecting a cooperative vehicle and calculating the safety distance, and (3) calculating the merging speed. Also, a cooperative control strategy is provided to complete the merging process when the cooperative vehicle and the merging speed are determined. Finally, this paper employed SUMO and the traffic control interface (Traci) to interact with Python for simulational study. The simulational results verified the effectiveness of the proposed algorithm and strategy, especially in solving the vehicle phenomenon of the stop-and-go wave under on-ramp crowded traffic flow.

Vehicle platooning is a research focus on automated driving and an important means for realizing intelligent transportation. We propose a method of adaptive car-following strategy for model control of vehicle platooning. This method selects the changing trend of the vehicle's lateral position and longitudinal position deviation and adjusts the vehicle's own longitudinal, lateral speed, and front-wheel angle to control the leading and following vehicles. The stability of the system was judged via the Lyapunov method. Finally, a numerical simulation analysis was carried out using Matlab/Simulink to verify the stability of this method.

Large-scale Cranes often need to run in a long-term working time, and result in various potential structural health risks. To reduce these risks, we herein design structural health monitoring system of the crane (SHMC for short) with Narrow Band IoT (NB-IoT). Firstly, we design a monitoring terminal for the SHMC system, wherethe single-chip microcomputer controls the electronic device to collect external signals to monitor the crane information in real time. Secondly, we employ NB-IoT based the wireless transmission module for network bandwidth-saving and long-term running. Then, we design a core platform, which parses and calculates the information uploaded by the terminal, and stores it in the database. Our system can monitor the running status of cranes in real time, and timely diagnose the fault information of crane to ensure their safe operation. Also the implementation of our design with apache storm cluster, Kafka server and Alibaba EC2 web server is showed in this study.

Due to complexity of the train energy absorbers, multiple parameters must be considered in their structure optimization. However, the traditional optimization design methods lack an overall point of view for structure analysis and, therefore, cannot deal with multi-objective problems comprehensively. In this paper, a cooperative optimization platform is proposed to establish the design and optimization process, using a modular modeling system for the high speed train collision absorber. Playing the role of each module, the platform can have a real-time grasp of the entire optimization design process. In addition, with the help of the powerful file data processing system and driving engine of computer, the whole optimization design process is automated, which improves data analysis and post-processing ability. Therefore, the optimization platform can realize automation of optimization design process, shorten optimization time and reduce analysis costs. This platform can achieve the goal of collaborative optimization.

We herein proposed a collaborative planning scheme for an unmanned parking system in large scale underground parking place. Our system didn't make a brand new parking building, but reused the existing parking lot to meet the requirements: (1) maximizing the utilization rate of parking space (called by compatibilization rate herein) and (2) minimizing the waiting time of users to deposit and pick up the parked car. Our system cannot perform best on these two goals simultaneously. Meanwhile, our parking place has complex ground conditions such as many pillars, which causes difficulties in obtaining the optimized layout design of parking space and path planning of AGVs. Therefore, we proposed a multi-objective collaborative planning scheme to tackle the difficulties. This scheme employed the layout subsystem and path planning subsystem, attempting to obtain a better layout of parking lot and path planning of AGVs. Also, we discussed how to balance maximizing the number of parking spaces and minimizing the waiting time. Finally, the simulation of a real-world case showed the effectiveness of our collaborative planning scheme.

We herein build a smart parking system for large-scale underground parking lot in China, and deal with scheduling problems for multiple automatic guided vehicles (AGVs). In previous studies, multiple AGVs usually were scheduled in warehouse system, workshop system, etc. There was little report about AGVs in smart parking. In underground parking lot, designing maximum layout of parking space led to limited and double pathway for AGVs. Therefore, we proposed a dynamic time-window based on scheduling method (DTWS for short). In our DTWS, each AGV can dynamically adjust the path according to a time window, and thus implement the conflict-free pathway of AGVs. Finally, the experimental results showed that the our method solved the scheduling problems such as deadlock of multiple AGVs, and our parking system had an improved performance.

Self-driving and connected vehicles, communicating with one another and with the road infrastructure are expected to revolutionize the automotive industry and our life in the future. We propose a distributed heuristic algorithm based on 5G-V2X technology to solve the motion planning problem of industrial vehicles, especially passing through intersections in industrial parks. Autonomous industrial vehicles must not only ensure that vehicles do not collide with each other through intersections, but also ensure the safety of pedestrians. So this case demands highly on the communication and mutual cooperation among vehicles. To solve this problem, we employ 5G-V2X technology to ensure low delay and highly reliable communications. Then, we propose a distributed heuristic algorithm to solve the mutual cooperation problem among vehicles. Specifically speaking, intersection safety information system will download LDM (Local Dynamic Map) information to vehicle closest to the intersection, and then our solution will give higher priority to paths that have more vehicles and no pedestrians. Starting with highest priority approach, our solution sets a time period for the vehicle to establish a timetable for it to cross the intersection. Preliminary experiments results showed that on the premise of ensuring the safety of pedestrians, the industrial vehicles can pass through the intersection smoothly and have the lowest delay at the same time.

We herein propose an evolutionary multi-agent system (EMAS for short) to build an ensemble of surrogates for prediction. In our EMAS, we employ six kinds of basic surrogates, including Gaussian process, Kriging model, polynomial response surface, radial basis function, radial basis function neural network, and support vector regression machine. We define each surrogate as one agent and co-evolve parameters of basic surrogates to obtain the evolutionary weighted average surrogate, where sample cross-validation errors evaluate an ensemble of surrogates. The preliminary results from predicting the benchmark function with high dimension showed the effectiveness of our EMAS for an ensemble of surrogates.

We herein tackled concurrent design of machines layout and automated guided vehicle (AGV) planning in a workshop, and proposed a co-evolutionary framework for the whole design with tandem AGVs system. Our objective is to reduce the material transporting costs in the manufacturing process and realize the flexible reconfigurable. Firstly, we divide the whole workshop into several regions (or groups), and each region is a subsystem of cooperative co-evolutionary (CC) framework (Potter's CC model). Then, we employ a typical evolutionary algorithm, such as genetic algorithm, to solve the machine layout with AGV planning in each region. Also, we use a group generator to regroup the regions for improving the design. The preliminary experimental results from an example with ten parts, 30 machines and three loops in a workshop showed the effectiveness of our framework compared with noncoevolutionary framework.

Centrifugal compressors present very complex unsteady characteristics under running. The influence of unsteady aerodynamic load on blades surface may be related to the blade fracture. This issue involves aerodynamics, engineering thermodynamics, structural mechanics, computational fluid dynamics, mathematics, etc. A multidisciplinary analysis method based on CFD software has been applied to predict the flow field in a semi-open impeller stage of a centrifugal compressor, to analyze 3D flow characteristics in the transient flow field and aerodynamic load on the blade surfaces. Mechanism with a high amplitude frequency was focused on. Combined with entropy distribution diagrams, the wake vortex shedding frequency and the interference frequency generated by low-energy groups were captured. Results indicate that the wake vortex shedding and the low-energy groups are the main factors causing high aerodynamic load on the impeller blade. The large pressure pulsation generated by wake vortex shedding and low-energy group may greatly threaten the blade safety. This study provides beneficial references for the analysis of blade fracture causes in a semi-open impeller stage of a centrifugal compressor.

We herein propose a new collaborative framework, called cooperative co-evolutionary multi-agent system(CCEMAS), for solving multi-objective layout optimization problems. Every agent in CCEMAS is encoded as a multi-objective cooperative co-evolutionary strategy, including an algorithm and its setting. In the iterative procedure, the strategies will evolve along with the evolution of the agent team, and use different algorithms and settings during different stages of problem solving. A multi-objective optimization of satellite module is solved to validate the method and obtain the Pareto optimal solutions. Finally, the evolution process of multi-objective cooperative co-evolutionary strategies in agents is analyzed for constructing new multi-objective cooperative co-evolutionary algorithms for this problem in the future.

This paper proposed a co-evolutionary methodology to optimize the layout of a practical tandem automated guided vehicles (AGV) workshop aiming to reduce the costs for material transporting in manufacturing process. This methodology provided a fresh line to address the machine assignment, internal and external loop layout, as well as the loops arrangement on the floor synthetically, the transfer station setting was also considered. This methodology is not like the previous method that solved these contents in sequence. Genetic algorithm (GA) was herein applied for the iteration after these contents were ascertained. A mathematical model was also established for this methodology. The optimization result illustrated the efficiency of proposed co-evolutionary methodology in decreasing the material transporting costs comparing with the non co-evolutionary method. The workshop layout design can apply our methodology, which involved an overall consideration, as guidance.

We herein try to schedule multiple AGVs (Automated Guided Vehicles) in real time with two-phase strategy in the flexible manufacturing workshops. This study considers the impact of running time, vehicle stopping and turning of AGVs, and deals with static workshop scheduling, real-time workshop scheduling with time-window based micro genetic algorithm. And we present a two-stage scheduling strategy for offline shortest path library generation and online optimal scheduling scheme generation. The preliminary experimental results showed the efficiency and stability of the proposed strategy and algorithm for Multiple AGVs system.

We herein model workshop layout problem as quadratic assignment problem (QAP), which is an important NP-hard problem in logistics system. Moreover, we proposed an effective algorithm hybridizing invasive weed optimization (IWO for short) with the simulated annealing (SA) for solving this problem. Our basic idea is to employ IWO for providing diversity to explore solution, and use metropolis criterion of SA to provide a better direction. In our algorithm, we employed an offspring generation rule with disturbance, and used random-keys encoding to produce new solution for solving QAP. We also designed a harmonic coefficient to improve the fluctuation problem effectively. The computational results from equipment layout problems validated our algorithm.

We herein present a tabu search algorithm with variable cluster grouping (TSVCG for short) to deal with Multi-depot vehicle routing problem (MDVRP for short). In TSVCG, we firstly adopt variable cluster grouping to convert a complicated MDVRP to typical single depot vehicle routing problem (SDVRP for short). And then we apply a tabu search algorithm to solve each SDVRP. In the grouping process, we discuss how to find a scale factor and minimum geometric semicircle correction factor to improve the customer points' grouping, and thus get the different groups for further problem-solving. The experimental results shown that the proposed variable cluster grouping can reduce grouping blindness and improve the efficiency of grouping and viability of group results. The results also shown that the proposed TSVCG performed well compared with the previous work with the geometric grouping.

Centrifugal compressor, as the significant equipment in the energy field, is of very complexity in its structure. Good collaboration of individual components determines the good aerodynamic performance and running security of the compressor. Unsteady simulations were performed, based on the platform of CFX and through the UDF interface program, to evaluate the 3D collaborative aerodynamic characteristics of a semi-open impeller and diffuser in a large centrifugal compressor, and the numerical study was focused on the impeller-diffuser interaction. The result obtained from flow structures shows the development of low-velocity vortexes in the diffuser flow passage is affected by impeller-diffuser interaction. The low-velocity vortex may increase flow resistance of the flow passage, however, improving at some extent the flow condition of the neighbor one, which generates a kind of low-frequency interference. In addition, the strongest interface effect is located on the rotor blade trailing edge and the diffuser vane leading edge. Amplitude-frequency characteristics converted from the pressure pulsation on monitor points indicate that the rotor wake flow and potential repercussion of diffuser work together to generate strong vibration and excitation force. The impeller-diffuser interaction is stronger under winter operating condition than it is under design condition. Meanwhile, the impact of diffuser exerts dominating effects on the excitation force near the impeller, which should be considered when exploring the impeller damage accident causes under winter operating condition.

We deal with multi-objective Flexible Job Shop Scheduling Problem (FJSSP) with transportation resources constraints herein, where the cost time of loaded and empty Automatic Guided Vehicle (AGV) cannot be neglected. This problem is a NP-hard problem, whose optimization objectives are to minimize the makespan and total workload of machines. We proposed a multi-objective micro artificial bee colony algorithm (MMABC) to tackle this problem. In MMABC, each solution corresponds to a food source, which is encoded to reflect the assignment of AGV tasks, machine operations, and operation sequence; the smaller bee population is divided in two parts: a replaceable bee part and non-replaceable bee part. We also employed the crossover operator to the employed bee for exchanging the good scheduling. Experimental results on larger examples and comparisons with multi-objective micro genetic algorithm showed the effectiveness of the proposed algorithm.

The layout design of complex engineering systems (such as satellite-module layout design) is very difficult to solve in polynomial time. This is not only a complex coupled system design problem but also a special combinatorial problem. The fitness function for this problem is characterized as multimodal because of interference constraints among layout components (objects), etc. This characteristic can easily result in premature convergence when solving this problem using evolutionary algorithms. To deal with the above two problems simultaneously, we propose a dual-system framework based on the cooperative coevolutionary algorithm (CCEA, e.g., cooperative coevolutionary genetic algorithm) like multidisciplinary design optimization. The proposed algorithm has the characteristic of solving the complex coupled system problem, increasing the diversity of population, and decreasing the premature convergence. The basis for the proposed algorithm is as follows. The original coupled system P is decomposed into several subsystems according to its physical structure. The system P is duplicated as systems A and B, respectively. The A system is solved on a global level (all-in-one), whereas the solving of B system is realized through the computation of its subsystems in parallel. The individual migration between A and B is implemented through the individual migration between their corresponding subsystems. To reduce the computational complexity produced additionally by the dual-systems A and B, we employ a variable-grain model of design variables. During the process of optimization, the two systems A and B gradually approximate to the original system P, respectively. The above-proposed algorithm is called the dual-system variable-grain cooperative coevolution algorithm (DVGCCEA) or Oboe-CCEA. The numerical experimental results of a simplified satellite-module layout design case show that the proposed algorithm can obtain better robustness and trade-off between computational precision and computational efficiency.

This work presents a hybrid peer-to-peer-based architecture for collaborative engineering design. Many current Internet-enabled environments for distributed engineering design have been proposed based on the centralized architecture. However, as complexity of design tasks increase, the centralized architecture has some potential drawbacks such as communicating bottlenecks and guarantee scalability. Therefore, we propose a hybrid peer-to-peer (P2P) network model, and describe a CSCW architecture using multi-agent techniques. Treating an agent as a peer in this study, we also describe its structure, the interaction between design agents, and implementation of a prototype system. A case study on collaborative layout design of satellite modules illustrates the proposed architecture