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.     

Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services

As the keystones of the personalized manufacturing, the Industrial Internet of Things (IIoT) consolidated with 3D printing pave the path for the era of Industry 4.0 and smart manufacturing. By resembling the age of craft manufacturing, Industry 4.0 expedites the alteration from mass production to mass customization. When distributed 3D printers (3DPs) are shared and collaborated in the IIoT, a promising dynamic, globalized, economical, and time-effective manufacturing environment for customized products will appear. However, the optimum allocation and scheduling of the personalized 3D printing tasks (3DPTs) in the IIoT in a manner that respects the customized attributes submitted for each model while satisfying not only the real-time requirements but also the workload balancing between the distributed 3DPs is an inevitable research challenge that needs further in-depth investigations. Therefore, to address this issue, this paper proposes a real-time green-aware multi-task scheduling architecture for personalized 3DPTs in the IIoT. The proposed architecture is divided into two interconnected folds, namely, allocation and scheduling. A robust online allocation algorithm is proposed to generate the optimal allocation for the 3DPTs. This allocation algorithm takes into consideration meeting precisely the customized user-defined attributes for each submitted 3DPT in the IIoT as well as balancing the workload between the distributed 3DPs simultaneously with improving their energy efficiency. Moreover, meeting the predefined deadline for each submitted 3DPT is among the main objectives of the proposed architecture. Consequently, an adaptive real-time multi-task priority-based scheduling (ARMPS) algorithm has been developed. The built ARMPS algorithm respects both the dynamicity and the real-time requirements of the submitted 3DPTs. A set of performance evaluation tests has been performed to thoroughly investigate the robustness of the proposed algorithm. Simulation results proved the robustness and scalability of the proposed architecture that surpasses its counterpart state-of-the-art architectures, especially in high-load environments.     

Mathematical model and discrete artificial Bee Colony algorithm for distributed integrated process planning and scheduling

With the development of the globalization of economy and manufacturing industry, distributed manufacturing mode has become a hot topic in current production research. In the context of distributed manufacturing, one job has different process routes in different workshops because of heterogeneous manufacturing resources and manufacturing environments in each factory. Considering the heterogeneous process planning problems and shop scheduling problems simultaneously can take advantage of the characteristics of distributed factories to finish the processing task well. Thus, a novel network-based mixed-integer linear programming (MILP) model is established for distributed integrated process planning and scheduling problem (DIPPS). The paper designs a new encoding method based on the process network and its OR-nodes, and then proposes a discrete artificial bee colony algorithm (DABC) to solve the DIPPS problem. The proposed DABC can guarantee the feasibility of individuals via specially-designed mapping and switching operations, so that the process precedence constraints contained by the network graph can be satisfied in the entire procedure of the DABC algorithm. Finally, the proposed MILP model is verified and the proposed DABC is tested through some open benchmarks. By comparing with other powerful reported algorithms and obtaining new better solutions, the experiment results prove the effectiveness of the proposed model and DABC algorithm successfully.     

Bicriteria scheduling of a material handling robot in an m-machine cell to minimize the energy consumption of the robot and the cycle time

This study considers a flowshop type production system consisting of m machines. A material handling robot transports the parts between the machines and loads and unloads the machines. We consider the sequencing of the robot moves and determining the speeds of these moves simultaneously. These decisions affect both the robot’s energy consumption and the production speed of the system. In this study, these two objectives are considered simultaneously. We propose a second order cone programming formulation to find Pareto efficient solutions. We also develop a heuristic algorithm that finds a set of approximate Pareto efficient solutions. The conic formulation can find robot schedules for small cells with less number of machines in reasonable computation times. Our heuristic algorithm can generate a large set of approximate Pareto efficient solutions in a very short computational time. Proposed solution approaches help the decision-maker to achieve the best trade-off between the throughput of a cell and the energy efficiency of a material handling robot.     

煤矿带式输送机监控软件的设计

采用VB6.0设计了一套煤矿带式输送机监控软件,介绍了软件总体设计及实时监控主画面设计方案,阐述了采用MSComm控件、Winsock控件分别实现上下位机的串口通信、以太网通信的方法。该监控软件画面直观,控制灵活,较为实用。     

矿井主通风机不停风倒机控制系统改进方案

针对矿井主通风机不停风倒机控制系统中工控机使用一定年限后容易死机的问题,提出将传感器连接到交换机上,并采用多台工控机同时读取数据的解决方法。针对温度巡检仪及电参数采集模块输出的RS485信号无法直接接入交换机的问题,提出增加串口服务器和增加PLC的RS485模块两种改进方案;分析了两种方案的优缺点,并最终选择增加PLC的RS485模块的方案。实际应用表明,系统改进后运行良好,达到了预期的效果。     

成像卫星星地联合成像调度

成像卫星星地联合调度问题,涉及调度对象众多,约束条件复杂,需要考虑任务的观测、回传2个过程,是一个具有两层时间窗口约束的双层优化问题,统一建模困难。根据问题的特点,采用基于阶段优化的方式,降低了问题的复杂性。把问题分为观测调度阶段和数据回传调度阶段,分别给出了优化目标和约束条件,建立了基于阶段优化的成像卫星星地联合调度模型,实现了从任务观测到数据回传的全过程调度。仿真实例表明,该方法能够有效解决多星多站的协同任务调度问题。     

基于ENC424J600的以太网与串行接口转换技术

论述了在STM32系列的MCU上移植TCP/IP协议栈LwIP,并利用新型以太网控制器ENC424J600实现网络数据传输功能。该系统主要完成的功能是接收上位机发送的数据,并将这些数据转换成通用串行接口输出。ENC424J600是Microchip新推出的一款以太网控制器,其速度优于ENC28J60,同时为用户提供两种接口模式。本文介绍了其基于SPI接口模式下的实现过程,详细描述了该网卡驱动的硬件电路和软件流程。     

通用可靠的串口/以太网网关设计

介绍了一种串口/以太网嵌入式网关的设计方案,详细讨论了串口可靠通信的实现方法,并在开源操作系统RT-Thread和微处理器S3C2440开发板的软硬件平台上实现。网关实际应用于一个无线传感器网络项目,实验表明该网关能实现可靠的数据通信。     

AVR单片机的串口转FSK的通信模块设计

以AVR单片机ATmega48芯片及CMX865调制／解调芯片为硬件架构规范了对外串口通信协议,采用模拟口线的方式对CMX865寄存器进行操作,设计了一种串口转FSK的通信模块,实现数据传输方式的转换。硬件电路以AT—mega48微处理器为核心,并利用C／BUS总线控制CMX865芯片,实现FSK／DTMF收、发功能;...