Flexible Services and Manufacturing Journal最新文献

Within an uncertain environment and following carbon trade policies, this study uses the Extended Exergy Accounting (EEA) method for coal supply chains (SCs) in eight of the world's most significant coal consuming countries. The purpose is to improve the sustainability of coal SCs in terms of Joules rather than money while considering economic, environmental, and social aspects. This model is a multi-product economic production quantity (EPQ) with a single-vendor multi-buyer with shortage as a backorder. Within the SC, there are some real constraints, such as inventory turnover ratio, waste disposal to the environment, carbon dioxide emissions, and available budgets for customers. For optimization purposes, three recent metaheuristic algorithms, including Ant Lion Optimizer, Lion Optimization Algorithm, and Whale Optimization Algorithm, are suggested to determine a near-optimum solution to an "exergy fuzzy nonlinear integer-programming (EFNIP)." Moreover, an exact method (GAMS) is employed to validate the results of the suggested algorithms. Additionally, sensitivity analyses with different percentages of exergy parameters, such as capital, labor, and environmental remediation, are done to gain a deeper understanding of sustainability improvement in coal SCs. The results showed that sustainable coal SC in the USA has the lowest fuzzy total exergy, while Poland and China have the highest.

Under the influence of the global COVID-19 pandemic, the demand for medical equipment and epidemic prevention materials has increased significantly, but the existing production lines are not flexible and efficient enough to dynamically adapt to market demand. The human-machine collaboration system combines the advantages of humans and machines, and provides feasibility for implementing different manufacturing tasks. With dynamic adjustment of robots and operators in the production line, the flexibility of the human-machine collaborative production line can be further improved. Therefore, a parallel production line is set up as a parallel community, and the digital twin community model of the intelligent workshop is constructed. The fusion and interaction between the production communities enhance the production flexibility of the manufacturing shop. Aiming at the overall production efficiency and load balancing state, a digital twin-driven intra-community process optimization algorithm based on hierarchical reinforcement learning is proposed, and as a key framework to improve the production performance of production communities, which is used to optimize the proportion of human and machine involvement in work. Finally, taking the assembly process of ventilators as an example, it is proved that the intelligent scheduling strategy proposed in this paper shows stronger adjustment ability in response to dynamic demand as well as production line changes.

In this study, the shift scheduling problem of the personnel who maintain the 324 trains operating on the lines of the Ankara Metro, which carry approximately 10 million passengers per month, is discussed. In the problem, daily personnel needs and personnel qualifications are evaluated together according to the fault prediction. Firstly, a total of 1721 fault data for 181 days is analyzed, and the prediction is made according to 8 prediction methods. Then, a 30-day fault prediction is made with the prediction method, which has the least error rate. According to the number of faults obtained as a result of the prediction, the daily personnel requirement is determined. Then, according to the type of fault, the personnel are classified as electronics, electromechanics, and mechanics. It is aimed at distributing the personnel evenly to the shifts. The goal programming model is used to solve the problem, allowing us to add more than one goal to the model. When the solution results of the problem are compared with the current schedule, it is seen that the monthly total working hours are distributed equally, and the shifts are distributed fairly, and a necessary balance is achieved in the shifts according to the personnel qualifications. It is thought that this study, in which personnel qualifications and fault prediction are evaluated together, will contribute to the literature on railway maintenance personnel scheduling.