IET Collaborative Intelligent Manufacturing最新文献

Matheuristic is an optimisation methodology that integrates mathematical approaches and heuristics to address intractable combinatorial optimisation problems, where a common framework is to insert mixed integer linear programming (MILP) models as local search functions for evolutionary algorithms. However, since a mathematical programming formulation only tries to find the solution with the best objective value, matheuristics are rarely adopted to multi-objective scenarios asking for a set of Pareto optimal solutions, for example, vehicle routing problems and production scheduling problems. In this situation, the ɛ-constraint, which transforms multi-objective problems into single-objective formulations by considering selected objectives as constraints, seems to be a promising approach. First, an augmented ɛ-constraint-based matheuristic methodology (ɛ-MH) is proposed to apply the idea of ɛ-constraint to embedded MILP models, so that Pareto fronts obtained by meta-heuristics can be further improved by solving a set of MILP models. Afterwards, four speed-up strategies are developed to alleviate the computational burden resulting from repeatedly solving mathematical formulations, which also imply preferable scenarios for taking advantages of the ɛ-MH. Finally, several real-world bi-objective scheduling problems are discussed to present potential applications for the proposed methodology.

A flexible casting job shop scheduling problem (FCJSP) with batch processing machines is proposed based on the analysis of the flexible job shop scheduling problem (FJSP) and the study of the expendable casting process. Considering the makespan under the influence of the energy consumption, the authors apply the time execution window to the FCJSP model in conjunction with the characteristics of casting production. A hybrid particle swarm optimisation algorithm (HPSO) is developed to solve the FCJSP. The HPSO employs a block integration decoding rule to address scheduling integration. Particle swarm optimisation is used for global search, employing both discrete and continuous search strategies. Furthermore, the local search employs tabu search with neighbourhood operations based on knowledge-driven techniques. Simulation experiments demonstrate the feasibility of the proposed optimisation model. In the end, the HPSO algorithm has been successfully applied to the real expendable casting scheduling. The results demonstrate that it is more efficient and robust than previously reported algorithms.

The increasing need for safe and comfortable high-speed elevators due to the rise of super-tall buildings has led to a focus on vibration reduction modelling and optimisation. This article selects factors that have a significant impact on the vibration of high-speed elevator car systems through sensitivity evaluation to form a six-dimensional parameter space and establishes a multi-objective optimisation model for the car system. The Gibbis method and Radial Basis Function neural network are combined to sample and construct surrogate models, respectively. Meanwhile, a BA–EO algorithm that combines Bat algorithm and Extremal optimisation to adapt to a multidimensional parameter space is proposed here. In practical applications, the peak-to-peak value of vibration acceleration, which significantly affects human perception, is chosen as the objective function for vibration reduction optimisation. After optimisation, the vibrations of the car and car frame are decreased by 19% and 9%, respectively, which extend the service life of the high-speed elevator and enhance safety and comfort for passengers.

As the important resources and equipment of container terminals, berths and quay cranes (QCs) face various challenges in actual operations and their operation efficiency in turn affects the performance of the whole terminal. The authors investigate an integrated berth allocation and QC assignment and scheduling problem under the influence of various factors, including the two main factors of vessel arrival time uncertainty and tide, and the two secondary factors of berth deviation and interference between cranes. To formulate the problem, the authors develop a multi-factor robust scheduling model. A Genetic Algorithm (GA) with Brain Storm Optimisation based on the Contract Net Protocol (CNP) is designed to optimise the berth and QC scheduling scheme. Specifically, the authors use the GA for individual coding and population initialisation, use the brainstorming algorithm for clustering, and introduce the CNP for individual updating. The experimental results show that the designed algorithm can adapt the scheduling plan to complex environments and can improve the service level of terminals.

Prognostic and Health Management (PHM) is an emerging maintenance concept that is highly regarded by the scientific community and practitioners, as its adoption can bring economic, technical and environmental benefits to a company. PHM fully reflects the smart maintenance paradigm encompassing data collection, data manipulation, state detection, health assessment, prognostic assessment and advisory generation. Despite the undeniable benefits, there is still a large gap between the scientific and the real world. Several authors have investigated on the barriers to PHM implementation for companies, highlighting among them the lack of systematic approaches to its design and implementation. As a first contribution to this topic, the authors conducted a systematic literature review (SLR) to investigate the use of Decision Support Systems (DSSs) to support the PHM implementation. The SLR highlighted that few DSS had been developed and were limited to critical unit identification, maintenance strategy selection and data acquisition phase of PHM. Therefore, a conceptual framework for PHM implementation was provided as a second contribution. This framework summarises the decisions that should be addressed by a practitioner wishing to implement PHM services; moreover, it could lay the foundations for the development/improvement of the missing/existing DSSs for PHM implementation.

Fibre-reinforced composites manufacturing is a large and growing industry, with much of the work carried out manually by skilled human laminators. The physical nature of the work can be significantly deleterious to these workers' health, while growing demand requires increased rates of manufacture. Human–robot collaborative manufacturing offers a potential solution but requires the human to feel confident working with the robot and trust that they will be safe. Successful human trials of two different approaches to collaborative lay-up of fibre-reinforced plastic composites are presented, with tasks representative of manufacturing challenges in industry. Volunteer responses are measured by questionnaires, with users reporting the processes to be safe, simple to use and allowing greater ease of manufacturing than manual-only lay-up.

To expedite the modernisation of equipment construction and address practical challenges, such as low efficiency in armoured vehicle passenger information retrieval, diverse perception channels, and inadequate combat effectiveness in traditional vehicle-integrated electronic information systems, the authors aim to transition to a helmet-mounted display system (HMD). On the basis of the target mission stage of military vehicles, the authors have organised the required information items for the vehicle HMD, integrated the hierarchical relationships of interaction interface design elements, and formulated design strategies using the Garrett user experience element model. We have constructed a vehicle HMD interaction interface design model and conducted comparative experiments with typical vehicle electronic display system interfaces. The usability of the model has been verified through eye-tracking experiments and reaction time analysis. Experimental data indicates that the vehicle HMD interactive interface system, guided by the user experience element model, effectively enhances operational performance for passengers, demonstrating superior recognition, search ability, comprehensibility, and rationality. In conclusion, the vehicle HMD interaction interface design model, guided by the user experience element model, meets the requirements of vehicle HMD interaction interface design. It validates the effectiveness and feasibility of transitioning from a traditional vehicle-integrated electronic information system to a vehicle HMD, providing technical support for enhancing display efficiency in future prototype platforms on the prototype platform digital warfare.

A hybrid flow shop is pivotal in modern manufacturing systems, where various emergencies and disturbances occur within the smart manufacturing context. Efficiently solving the dynamic hybrid flow shop scheduling problem (HFSP), characterised by dynamic release times, uncertain job processing times, and flexible machine maintenance has become a significant research focus. A NeuroEvolution of Augmenting Topologies (NEAT) algorithm is proposed to minimise the maximum completion time. To improve the NEAT algorithm's efficiency and effectiveness, several features were integrated: a multi-agent system with autonomous interaction and centralised training to develop the parallel machine scheduling policy, a maintenance-related scheduling action for optimal maintenance decision learning, and a proactive scheduling action to avoid waiting for jobs at decision moments, thereby exploring a broader solution space. The performance of the trained NEAT model was experimentally compared with the Deep Q-Network (DQN) and five classical priority dispatching rules (PDRs) across various problem scales. The results show that the NEAT algorithm achieves better solutions and responds more quickly to dynamic changes than DQN and PDRs. Furthermore, generalisation test results demonstrate NEAT's rapid problem-solving ability on test instances different from the training set.

Distributed assembly permutation flow shop scheduling problem is the hot spot of distributed pipeline scheduling research; however, parallel assembly machines are often in the assembly stage. Therefore, we propose and study distributed parallel assembly permutation flow shop scheduling problem (DPAPFSP). This aims to enhance the efficiency of multi-factory collaborative production in a networked environment. Initially, a corresponding mathematical model was established. Then, an improved hybrid distribution estimation algorithm was proposed to minimize the makespan. The algorithm adopts a single-layer permutation encoding and decoding strategy based on the rule of the Earliest Finished Time. A local neighbourhood search based on critical paths is performed for the optimal solution using five types of neighborhood design. A dual sampling strategy based on repetition rates was introduced to ensure the diversity of the population in the later periods of iteration. Simulated annealing searching was applied to accelerate the decline of optimal value. Finally, we conduct simulation experiments using 900 arithmetic cases and compare the simulation experimental data of this algorithm with the other four existing algorithms. The analysis results demonstrate this improved algorithm is very effective and competitive in solving the considered DPAPFSP.

In industrial and real-world systems, recognising errors and adopting the best approaches are gaining relevance. The authors’ goal is to identify artificial intelligence apps that provide the most reliable and valuable data-based fault detection techniques. A system for fault identification is presented based on reinforcement learning and proximal policy optimisation (PPO). Due to the lack of fault data, one of the key issues with the standard policy is its inability to recognise fault classes; this issue was resolved by modifying the cost equation. Using improved PPO, the authors can improve performance, address data imbalances, and forecast possible failures more accurately. The approach utilises policy-based optimisation, which offers several advantages. Firstly, it directly optimises the advantage quantity, and secondly, it ensures the stability of function approximation. The authors have studied two different turbines in Iran and collected data from them separately when a fault occurred. To demonstrate the efficiency of our algorithm, the authors have included the third and fourth datasets as cyber attack benchmarks. When the authors’ proposed policy is adopted, all evaluation metrics will improve by 3%–4% as compared to the previous policy in the first benchmark, between 20% and 55% in the second benchmark, between 6% and 14% in the third benchmark, and between 4% and 5% in the fourth benchmark, with improved results and prediction times compared to existing studies.