Backlog Control Design for a Closed Loop PPC System

In this paper a discrete dynamic model of a single workstation is used to design and analyse control algorithms for closed-loop PPC that improve performance, especially response to disturbances such as rush orders and periodic fluctuations in capacity, while ensuring that dynamic behavior remains favorable and robust. The presence of delays in adjusting capacity presents challenges in both dynamic analysis and control algorithm design that are addressed in the paper. Methods of control engineering, such as transfer function and frequency response analysis, are used to make analysis of fundamental system properties tractable and to improve control of dynamic behavior.     

在役生产装置安全评价存在的职业卫生问题

分析了在役生产装置安全评价存在的职业卫生问题,提出了如何做好重点生产装置职业病预防工作的建议。     

产品电磁兼容设计和生产可靠性探讨

本文对产品的电磁兼容设计和生产的可靠性作了较深入的分析。     

Energy and resources as production factors in process industries

It is shown that, when the mean productivity of some factors, such as energy or materials, is upperly bounded (a condition usually arising in energy production or in the process industries where energy takes part in physico-chemical processes), the production function may not be convex and, under some further conditions, is concave. Consequences of this model on the economics of energy savings are examined, and it is shown that energy savings may be considered at three levels: in the short run, by action on labour factors: in the medium run, by investment and action on labour factors; in the long run, by technological innovation, investment in new technologies and action on labour factors. Empirical findings along these lines are thus confirmed.     

Groupwork and computers: task type and children's performance

Abstract In the majority of British primary schools, children use the computer in groups of two or three. This is partly due to the lack of resources, that is, most classrooms still only have one or two computers to share between around 30 children. Groupwork on computers is also justified for pedagogic reasons. Previous research comparing children working in groups or alone has revealed an advantage for children working in groups although the work undertaken has generally been problem solving tasks. This investigation examined the performance of pairs and individual 6-year old children on a drill and practice program using a pre-test, intervention, post-test design. In comparison to the problem solving evidence, individuals were found to have a significant advantage over pairs during the computer-based task.     

Human-centred appraoches to control and information technology: European experiences

In this paper, the concept of Human-Centred Technology will be described with regard to the different dimensions of workplace, groupwork and networks and in terms of the frameworks of both society and the natural environment. These different aspects of Human-Centred Systems will be illustrated by a series of case studies representing several European countries. The report covers a wide range of research fields. The emphasis is on technology: the roles of control and information technology in enterprises today — including issues of applying AI — and the strategies of designing and implementing technology taking into account the specific aspects which characterize human-centred systems.     

《电路》课程教学模式改革探索与实践

针对传统教学模式下《电路》课程教学中学生积极性和主动性不强,缺少综合能力训练内容等问题,对教学方法和教学实践环节进行改革。提出基于任务驱动课堂教学的方法和通过科研小课题初步培养大学生创新学习能力的方法。教学实践证明,本校学生较为适应改革后的教学模式,改进后的教学效果良好。此项研究对于高校教学改革,尤其是培养大学生创新学习能力具有一定的价值。     

Scheduling Optimization Modelling: A Case Study of a Woven Label Manufacturing Company

Production scheduling involves all activities of building production schedules, including coordinating and assigning activities to each person, group of people, or machine and arranging work orders in each workplace. Production scheduling must solve all problems such as minimizing customer wait time, storage costs, and production time; and effectively using the enterprise’s human resources. This paper studies the application of flexible job shop modelling on scheduling a woven labelling process. The labelling process includes several steps which are handled in different work-stations. Each workstation is also comprised of several identical parallel machines. In this study, job splitting is allowed so that the power of work stations can be utilized better. The final objective is to minimize the total completion time of all jobs. The results show a significant improvement since the new planning may save more than 60% of lead time compared to the current schedule. The contribution of this research is to propose a flexible job shop model for scheduling a woven labelling process. The proposed approach can also be applied to support complex production scheduling processes under fuzzy environments in different industries. A practical case study demonstrates the effectiveness of the proposed model.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.     

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.