In recent years, the literature has paid considerable attention to digital twin technology for the implementation of Industry 4.0 and intelligent manufacturing. Most of the literature argues that simulation models are a key platform for digital twins and considers discrete-event simulation to be a suitable method to model real dynamic manufacturing systems. However, the discrete-event simulation of complex manufacturing systems is a time-consuming process. Therefore, it is difficult to deal with the large-scale discrete optimisation problems in digital twin shop floors. To bridge this research gap, we propose an improved multi-fidelity simulation-based optimisation method based on multi-fidelity optimisation with ordinal transformation and optimal sampling (MO2TOS) in the current research. The proposed method embeds heuristic algorithms to accelerate the solution space search efficiency in MO2TOS. Moreover, we develop an improved multi-fidelity simulation-based optimisation system by integrating the proposed method with discrete-event simulation tools and apply this system to a digital twin-based aircraft parts production workshop. Based on this digital twin shop floor, we conduct different production planning experiments to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed improved multi-fidelity simulation-based optimisation method is well-applied in solving large-scale problems and outperforms other simulation-based optimisation methods.

Printed circuit board (PCB) assembly lines are significant to produce a variety of electronic products. PCB manufacturing industries tend to move towards automated and complex manufacturing system due to an increase in the customer demand for more sophisticated products. PCB assembly process involves highly interrelated levels of planning and scheduling problems. Therefore, the current research investigates multi-level planning and scheduling of PCB assembly lines, which include line assignment to the PCB models, component allocations to machines, and component placement sequencing by machines on PCB boards. A mixed integer-programming model is developed to integrate the planning and scheduling problem of parallel PCB assembly lines to maximize the net profit. A hybrid spider monkey optimization (HSMO) algorithm is proposed to solve the multi-level planning and scheduling problem. The performance of the proposed HSMO algorithm is compared to artificial bee colonial (ABC), genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms. Moreover, the proposed HSMO algorithm is validated against ABC, GA, PSO, and SA algorithms with the case problem taken from well-reputed PCB manufacturing industry in China. The computation experiments indicate that the proposed HSMO algorithm can achieve good near-optimal solutions when compared with the other-mentioned four algorithms based on performance and efficiency for benchmark problems and real case problem.

In recent years, more manufacturing enterprises are building automated flexible production lines (AFPLs) to satisfy the dynamic and diversified demand. Currently, static planning methods can hardly meet the requirements of the dynamic resource allocation for AFPLs. The technologies of the digital twin can help solve dynamic problems. Therefore, we propose a data-driven cloud simulation architecture for AFPLs in smart factories. First, we design a cloud simulation platform as the architecture foundation. Second, we use the data-driven modelling and simulation method to achieve automated modelling. Third, we implement the system on the cloud using Java, MySQL, and the Anylogic platform, and verify the efficiency of the proposed method by experiments in the real workshop of a 3C (Computer, Communication, Consumer electronics) company. The experimental results show the proposed architecture can support the real-time resource allocation decisions to maximise the throughput in AFPLs. This paper makes contributions by proposing an architecture realising automatic modelling and data-driven simulation first in the cloud simulation environment, and filling the gap of dynamic resource allocation in the research of AFPLs.

Today’s dynamic environment and increasing demand for highly customized products have significantly increased the number of companies operating in the project environment. Project planning and scheduling are one of the major problems faced by managers due to resource constraints. Enterprises have to execute several projects simultaneously while sharing limited resources (i.e., human resources, equipment, and budget) among the projects to effectively meet the deadlines. Therefore, this work investigates the integrated planning and scheduling problem of multiple projects with different release dates and execution modes while considering the renewable and non-renewable resource constraints. Moreover, the raccoon family optimization (RFO) algorithm is proposed to maximize the net profit while considering the early completion bonus, penalty cost, and resource costs. In the proposed RFO algorithm, greedy search and modified genetic operators are introduced to enhance the performance and efficiency. Effectiveness of the proposed RFO algorithm is compared with the genetic algorithm (GA), raccoon optimization algorithm (ROA), and artificial bee colonial (ABC) algorithm on test cases as well as an industrial case study. The results indicate that the proposed RFO algorithm outperforms the other compared algorithms, both in terms of effectiveness and efficiency.

Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to “learning effect”. The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

To address the printed circuit board shop scheduling problem, a dynamic based on multi-indicator bottleneck degree is proposed to formulate this complex flexible job shop scheduling problem (FJSP). Then, a bottleneck degree-based migrating birds optimization algorithm (MBO) is proposed to tackle this NP-hard problem. Specifically, a multi-indicator assessment model is first developed to obtain the bottleneck degree, and then a two-layer code-decoding structure based on the variable domain structure and the addition of the improved migratory bird algorithm with competitive mechanisms are employed to improve the effectiveness. Finally, the computational experiments demonstrate that the proposed dynamic scheduling approach based on bottleneck degrees and improved migrating bird algorithms can obtain promising results and it outperforms several other algorithms.