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.     

Decision-based process planning for wire and arc additively manufactured and machined parts

The conventional manufacturing of aircraft components is based on the machining from bulk material and the buy-to-fly ratio is high. This, in combination with the often low machinability of the materials in use, leads to high manufacturing costs. To reduce the production costs for these components, a process chain was developed, which consists of an additive manufacturing process and a machining process. To fully utilize the process chain’s capabilities, an integrated process planning approach is necessary. As a result, the work sequence can be optimized to achieve the economically most suitable sequence. In this paper, a method for a joint manufacturing cost calculation and subsequent decision-based cost minimization is proposed for the wire and arc additive manufacturing (WAAM) & milling process chain. Furthermore, the parameters’ influence on the results and the magnitude of their influence are determined. These results make it possible to design an economically optimal work sequence and to automate the process planning for this process chain.     

结合LSTM的强化学习动态环境路径规划算法

在路径规划领域已经涌现出了诸多的优秀的经典算法,但这些传统方法往往基于静态环境,对于动态可变环境缺乏处理能力.本文提出一种结合LSTM强化学习动态环境路径规划算法.首先,本文以环境图像作为输入,最大限度了保证了原始的信息来源.而后构建了自动编码器用来对环境图像进行特征降维,降低了整体模型的复杂程度.最后采用深度强化学习算法DDPG进行路径规划,其中Actor部分采用LSTM的网络构建,使Actor在决策时可以参考前序信息,做到有预测的避开动态障碍.最后通过实验证明了本文算法的可行性和高效性.     

基于SD*lite的月球车任务规划算法

月球车任务规划是在给定初始位置和目标位置,满足月面地形约束、工作模式约束以及能量约束的前提下,规划出月球车最优的路径。设计了一种月球车任务规划的模型,提出了一种基于SD*lite算法的规划器,在原有SD*lite算法的基础上进一步考虑了对可用资源的限制;它可以协助月球车在考虑太阳能帆板的能量约束的条件下,合理避障最终达到目标点,使规划的轨迹更符合实际。     

Heuristic collision-free path planning for an autonomous platform

In this paper, a heuristic and learning, algorithmic scheme for collision-free navigation is presented. This scheme determines an optimum collision-free navigation path of an autonomous platform by using a trial and error process, past navigation knowledge and current information extracted from the generated surrounding environment.     

避暑山庄的选址

造园首在选址。选址得当,可以事半功倍。计成在《园冶》中指出,只有“相地合宜”,才能“构园得体”。地址要好,清洁卫生,有丘有壑,草地和树林相间”。避暑山庄之所以能够在造园史上独树一帜,选址得当是其中一个重要原因。避暑山庄当时所以选择这块地方建造宫苑,除政治上的原因之外,主要是因为它具有优越的自然条件。避暑山庄的园林艺术成就,已得到世人的公认,造园家相地择址的匠心,也会给我们许多有益的启示。     

Commercial objectives,technology transfer,and systems analysis for fusion power development

Fusion is an essentially inexhaustible source of energy that has the potential for economically attractive commercial applications with excellent safety and environmental characteristics. The primary focus for the fusion-energy development program is the generation of centralstation electricity. Fusion has the potential, however, for many other applications. The fact that a large fraction of the energy released in a DT fusion reaction is carried by high-energy neutrons suggests potentially unique applications. These include breeding of fissile fuels, production of hydrogen and other chemical products, transmutation or burning of various nuclear or chemical wastes, radiation processing of materials, production of radioisotopes, food preservation, medical diagnosis and medical treatment, and space power and space propulsion. In addition, fusion R&D will lead to new products and new markets.Each fusion application must meet certain standards of economic and safety and environmental attractiveness. For this reason, economics on the one hand, and safety and environment and licensing on the other hand, are the two primary criteria for setting long-range commercial fusion objectives. A major function of systems analysis is to evaluate the potential of fusion against these objectives and to help guide the fusion R&D program toward practical applications. The transfer of fusion technology and skills from the national laboratories and universities to industry is the key to achieving the long-range objective of commercial fusion applications.     

无线技术在企业园区网中的应用

通过介绍企业园区网楼宇多、广场多、楼宇间距大的特点,针对性的详细介绍了公司新址园区网建设项目需使用无线网络接入才能实现在园区任何地方任何时间任何人都可以进行网上办公,详细介绍了公司园区网无线接入的规划和方案,并对园区网络项目实施过程中的技术难点进行了分析给出了解决方案。论证了此项迁移技术是值得推广能解决企业实际问题的成熟技术并预测无线终将大规模在企业网中使用。     

基于解析法和遗传算法的机械手运动学逆解

研究优化机械手轨迹规划问题,机械手运动时要具有稳定性避障性能。针对平面3自由度冗余机械手优化控制问题,建立机械手的结构模型。提出用解析法和遗传算法相结合满足具有计算量小和适应性强的特点。在给定机械手末端执行器的运动轨迹,按着机械手冗余自由度,运动轨迹上每个点对应的关节角有无穷多个解。而通过算法可以找到一组最优的关节角,可得到优化机械手运动过程中柔顺性和避障点。仿真结果表明,该算法可以快速收敛到全局最优解,可用于计算冗余机械手运动学逆解,并可实现机器人的轨迹规划和避障优化控制。