Digital Twin Enhanced Dynamic Job-Shop Scheduling

For dynamic scheduling, which is daily decision-making in a job-shop, machine availability prediction, disturbance detection and performance evaluation are always common bottlenecks. Previous research efforts on addressing the bottlenecks primarily emphasize on the analysis of data from the physical job-shop, but with little connection and convergence with its virtual models and simulated data. By introducing digital twin (DT), further convergence between physical and virtual spaces of the job-shop can be achieved, which greatly enables dynamic scheduling. DT fuses both real and simulated data to provide more information for the prediction of machine availability on one hand; and on the other hand, it helps to detect disturbances through comparing the physical machine with its continuously updated digital counterpart in real time, triggering timely rescheduling when needed. It also enables comprehensive performance evaluation for rescheduling using multiple-dimension models, which can describe geometric properties, physics parameters and behaviors of the machines. In the paper, a five-dimension DT for a machine in the job-shop is introduced first, then the DT-based machine availability prediction, disturbance detection and performance evaluation methods are explored. Based on this, a DT-enhanced dynamic scheduling methodology is proposed. A scheduling process of making hydraulic valves in a machining job-shop is taken as a case study to illustrate the effectiveness and advantages of the proposed method.     

基于互触发的Oracle细粒度数据保护和恢复的研究及实现

本系统的主要思路是利用Oracle的触发器、Flash Recovery Area等技术,结合J2EE技术,实现一套基于B/S架构的Oracle数据保护和恢复系统,包括权限管理、数据恢复、系统管理三大模块,旨在解决以下问题:(1)建立新的权限控制。Oracle自带的权限控制不够安全,本系统拟创建一套新的权限体系,作为对Oracle的权限的补充,使业务系统的数据使用起来更加安全;(2)建立新的数据恢复。用软件实现Oracle的数据实时恢复,在真正执行危险操作以后,对数据进行针对性地、迅速恢复;(3)实现互触发保护,避免"通过人为的方式直接去drop触发器"之类操作的实现。     

A new hybrid approach for dynamic continuous optimization problems

A new hybrid approach for dynamic optimization problems with continuous search spaces is presented. The proposed approach hybridizes efficient features of the particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. In the proposed dynamic hybrid PSO (DHPSO) algorithm, the swarm size is varied in a self-regulatory manner. Inspired from the microbial life, the particles can reproduce infants and the old ones die. The infants are especially reproduced by high potential particles and located near the local optimum points, using the quadratic interpolation method. The algorithm is adapted to perform in continuous search spaces, utilizing continuous movement of the particles and using Euclidian norm to define the neighborhood in the reproduction procedure. The performance of the new proposed approach is tested against various benchmark problems and compared with those of some other heuristic optimization algorithms. In this regard, different types of dynamic environments including periodic, linear and random changes are taken with different performance metrics such as real-time error, offline performance and offline error. The results indicate a desirable better efficiency of the new algorithm over the existing ones.     

一种面向SOA业务恢复的服务选择算法

针对SOA业务恢复的服务选择问题,本文提出了一种QoS近似全局最优的服务选择算法GALSA:首先利用GA算法将全局QoS约束分解为最优的局部QoS约束,然后利用LSA算法选取出满足局部QoS约束的最优服务。算法不仅能作用于服务重构的服务选择过程中,而且在单个服务节点失效时亦能选取出满足全局QoS约束的服务进行替换。理论分析与实验结果说明了算法的可行性和有效性。     

计算机硬盘坏磁道修复的思路与技巧

本文通过隐藏坏磁道分区的方法,着重介绍了硬盘坏磁道的修复技术及注意事项,同时还介绍了逻辑坏磁道、0磁道损坏的修复方法。     

含钼废渣冶炼钼铁的优化试验

利用含钼废料钼酸钙为原料,采用碳还原法冶炼钼铁,通过单因素分析试验及正交试验优化得出最佳的试验条件,钼酸钙加入量应低于10%,熔炼温度为1 525 ℃,保温时间为25 min,碱度为1.3。通过XRD、SEM、EDS分析得出用此方法生产得到的钼铁合金中,钼以Mo₂C、Fe₃Mo以及Fe₃Mo₃C的形式存在,渣系为二元玻璃渣系,具有与普通玻璃相似的成分系统。经测定合金中钼的回收率大于99%,钼资源回收效益可观。     

PRO/Ⅱ软件在硫磺回收中的二次开发应用

硫磺回收装置的设备需要有较大的操作弹性。针对其过程气进口压力低，手工计算难于进行严格的全程压降计算，现以PRO／Ⅱ软件为开发平台，通过扩充数据库和建立模拟流程，成功地将其用于硫磺回收装置的设备选型和核算，为工程设计提供了先进的计算手段。     

Simultaneous recovery of copper and surfactant by an electrolytic process from synthetic solution prepared to simulate a concentrate waste stream of a micellar-enhanced ultrafiltration process

Electroplating method was employed to recover copper and surfactant simultaneously from synthetic solutions prepared to simulate concentrated waste stream generated from micellar-enhanced ultrafiltration. Effects of surfactant and copper (II) concentrations, surfactant to copper (II) molar ratio (S/M), electroplating voltage and time, solution pH, and ionic strength on metal recovery and electrical current efficiency were investigated. Results show that at a fixed S/M ratio of 5, the first-order kinetic constant for Cu (II) removal by electroplating increases with decreasing SDS concentration. At fixed SDS concentration of 8.5 mM, increasing initial Cu (II) concentration increases both Cu (II) recovery and current efficiency. Electrolyte pH has profound effects on the metal recovery and current efficiency due to the difference in solution conductivity after pH adjustment and the extent of Cu (II) adsorption onto SDS at different pHs.     

Nonextensive lattice gauge theories: Algorithms and methods

High-energy phenomena presenting strong dynamical correlations, long-range interactions and microscopic memory effects are well described by nonextensive versions of the canonical Boltzmann–Gibbs statistical mechanics. After a brief theoretical review, we introduce a class of generalized heat-bath algorithms that enable Monte Carlo lattice simulations of gauge fields on the nonextensive statistical ensemble of Tsallis. The algorithmic performance is evaluated as a function of the Tsallis parameter q$q$ in equilibrium and nonequilibrium setups. Then, we revisit short-time dynamic techniques, which in contrast to usual simulations in equilibrium present negligible finite-size effects and no critical slowing down. As an application, we investigate the short-time critical behaviour of the nonextensive hot Yang–Mills theory at q$q$-values obtained from heavy-ion collision experiments. Our results imply that, when the equivalence of statistical ensembles is obeyed, the long-standing universality arguments relating gauge theories and spin systems hold also for the nonextensive framework.     

Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi–Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in ‘time to delivery’ by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.