Nowadays, manufacturing industry is under increasing pressure to save energy and reduce emissions, and thereby enhancing the energy efficiency of the machining system (MS) through operational methods on the system-level has attracted more attention. Energy-efficient scheduling (ES) has proved to be a typical measure suitable for all shop types, and an energy-efficient mechanism that a machine can be switched off and back on if it waits for a new job for a relatively long period is another proven effective energy-saving measure. Furthermore, their combination has been fully investigated in a single machine, flow shop and job shop, and the improvement in energy efficiency is significant compared with only applying ES for MS. However, whether such two energy-saving measures can be integrated in a flexible job shop environment is a gap in the existing study. To address this, a scheduling method applying an energy-efficient mechanism is proposed for a flexible job shop environment and the corresponding mathematical model, namely the energy-efficient flexible job shop scheduling (EFJSS) model, considering total production energy consumption (EC) and makespan is formulated. Besides, transportation as well as its impact on EC is taken into account in this model for practical application. Furthermore, a solution approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted, which can avoid the interference of subjective factors and help select a suitable machine for each operation and undertake rational operation sequencing simultaneously. Moreover, experimental results confirm the validity of the improved energy-efficient scheduling approach in a flexible job shop environment and the effectiveness of the solution.

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases.

Low-carbon sustainable development has become the consensus of manufacturing enterprises to fulfill their social responsibilities. Facility layout is an essential part of manufacturing system planning. Current research has demonstrated the advantages of energy saving on the manufacturing system level where operational methods (e.g., energy-efficient production scheduling and path planning) can be utilized and do not require massive investment in the existing legacy system. However, these efforts are mostly based on the existing fixed facility layout. Meanwhile, although facility layout problems have been extensively studied so far, the related work seldom involves the optimization of energy consumption (EC) or other EC-related environmental impact indicators, and does not clearly reveal if EC can be an independent optimization objective in facility layout. Accordingly, whether the energy-saving potential of a manufacturing system can be further tapped through rational facility layout is the gap of the current study. To address this, an investigation into energy-saving oriented manufacturing workshop facility layout is conducted. Correspondingly, an energy-efficient facility layout (EFL) model for the multi-objective optimization problem that minimizes total load transport distance and EC is formulated, and a multi-objective particle swarm optimization-based method is proposed as the solution. Furthermore, experimental studies verify the effectiveness of the presented model and its solution, indicating that EC can be regarded as an independent optimization objective during facility layout, and EFL is a feasible energy-saving approach for a manufacturing system.

Accurate die yield prediction is very useful for improving yield, decreasing cost and maintaining good relationships with customers in the semiconductor manufacturing industry. To improve prediction accuracy of die yield, a novel fuzzy neural networks based yield prediction model is proposed in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The mapping between these independent variables and yield is constructed in the fuzzy neural network (FNN). The lineal regression between FNN-based yield predicting output and actual yield demonstrates the effectiveness of the proposed approach by historical experimental data of semiconductor fabrication line in Shanghai. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy.