基于优化粒子数和分段权值的粒子群直线电机PID整定

以工程中直线电机伺服系统为研究对象,提出一种优化粒子数量加分段式惯性权重递减的粒子群PID控制器参数优化算法。优化粒子数量的方法可降低函数调用次数,通过对近两代的全局最优值进行比较,得到的误差值如果大于设定值,认为是在初始寻优阶段,保持粒子数量,否则在最终优化阶段,减少粒子数量,所减少的粒子特征是最接近最佳粒子的粒子,以保证在欧氏距离内实现粒子的分散性。最后再结合指数衰减曲线加线性递减曲线构成的分段式惯性权重递减策略提升算法的全局寻优和局部寻优能力。经数值验证分析,该优化算法在保证遍历性的同时,在一定程度上提高了算法的运行速度和寻优精度。实验仿真结果表明,该算法对PID控制器进行参数优化,直线电机系统响应速度快,超调量小,调节时间短。     

Integrated process design,scheduling, and model predictive control of batch processes with closed-loop implementation

Simultaneous evaluation of multiple time scale decisions has been regarded as a promising avenue to increase the process efficiency and profitability through leveraging their synergistic interactions. Feasibility of such an integral approach is essential to establish a guarantee for operability of the derived decisions. In this study, we present a modeling methodology to integrate process design, scheduling, and advanced control decisions with a single mixed-integer dynamic optimization (MIDO) formulation while providing certificates of operability for the closed-loop implementation. We use multi-parametric programming to derive explicit expressions for the model predictive control strategy, which is embedded into the MIDO using the base-2 numeral system that enhances the computational tractability of the integrated problem by exponentially reducing the required number of binary variables. Moreover, we apply the State Equipment Network representation within the MIDO to systematically evaluate the scheduling decisions. The proposed framework is illustrated with two batch processes with different complexities.     

A cross-layer design: energy efficient multilevel dynamic feedback scheduling in wireless sensor networks using deadline aware active time quantum for environmental monitoring

Recently, Packet scheduling plays a vital role in Wireless Sensor Networks (WSNs). The major key challenges include delay, packet dropping, energy consumption and lifetime due to constraints in energy and computing resources. All the research works on packet scheduling scheme in WSN uses only First Come First Served (FCFS) and Dynamic Multilevel Priority (DMP) schemes. FCFS works based on packet arrival time, it leads to starvation and high processing overhead for real-time packets. DMP works in multilevel with dynamic priority reduces the transmission overhead and bandwidth; it consumes more resources for real-time task leads to deadlock. To solve these problems, this work presents Multilevel Dynamic Feedback Scheduling (MDFS) algorithm. The sensor node classifies the emergency and normal data into three different ready queues named as high, medium and low priority, respectively. The queues are connected with a feedback mechanism; each packet from the sensor node has its own time quantum value based on the deadline. The updated time quantum value is compared with the boundary value of the queues, depends on the updated value the data packets are moved between queues with help of feedback mechanism. The simulation result proves that the projected MDFS outperforms in WSN environment.     

Integrated multi-factory production and distribution scheduling applying vehicle routing approach

This paper introduces a new integrated multi-factory production and distribution scheduling problem in supply chain management. This supply chain consists of a number of factories joined together in a network configuration. The factories produce intermediate or finished products and supply them to other factories or to end customers that are distributed in various geographical zones. The problem consists of finding a production schedule together with a vehicle routing solution simultaneously to minimise the sum of tardiness cost and transportation cost. A mixed-integer programming model is developed to tackle the small-sized problems using CPLEX, optimally. Due to the NP-hardness, to deal with medium- and large-sized instances, this paper develops a novel Improved Imperialist Competitive Algorithm (IICA) employing a local search based on simulated annealing algorithm. Performance of the proposed IICA is compared with the optimal solution and also with four variants of population-based metaheuristics: Imperialist Competitive Algorithm, Genetic Algorithm, Particle Swarm Optimisation (PSO), and Improved PSO. Based on the computational results, it is statistically shown that quality of the IICA’s solutions is the same as optimal ones solving small problems. It also outperforms other algorithms in finding near-optimal solutions dealing with medium and large instances in a reasonably short running time.     

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.     

A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility

The traditional flexible job shop scheduling problem (FJSP) considers machine flexibility but not worker flexibility. Given the influence and potential of human factors in improving production efficiency and decreasing the cost in practical production systems, we propose a mathematical model of an extended FJSP with worker flexibility (FJSPW). A hybrid artificial bee colony algorithm (HABCA) is presented to solve the proposed FJSPW. For the HABCA, effective encoding, decoding, crossover and mutation operators are designed, and a new effective local search method is developed to improve the speed and exploitation ability of the algorithm. The Taguchi method of Design of Experiments is used to obtain the best combination of key parameters of the HABCA. Extensive computational experiments carried out to compare the HABCA with some well-performing algorithms from the literature confirm that the proposed HABCA is more effective than these algorithms, especially on large-scale FJSPW instances.     

Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain

Recently, the applications of Blockchain technology have begun to revolutionise different aspects of supply chain (SC) management. Among others, Blockchain is a platform to execute the smart contracts in the SC as transactions. We develop and test a new model for smart contract design in the SC with multiple logistics service providers and show that this problem can be presented as a multi-processor flexible flow shop scheduling. A distinctive feature of our approach is that the execution of physical operations is modelled inside the start and completion of cyber information services. We name this modelling concept ‘virtual operation’. The constructed model and the developed experimental environment constitute an event-driven dynamic approach to task and service composition when designing the smart contract. Our approach is also of value when considering the contract execution stage. The use of state control variables in our model allows for operations status updates in the Blockchain that in turn, feeds automated information feedbacks, disruption detection and control of contract execution. The latter launches the re-scheduling procedure, comprehensively combining planning and adaptation decisions within a unified methodological framework of dynamic control theory. The modelling complex developed can be used to design and control smart contracts in the SC.     

模型驱动的大数据流水线框架PiFlow

复杂流程的大数据处理多依托于流水线系统，但大数据处理的流水线系统在易用性、功能复用性、扩展性以及处理性能等方面存在不足。针对上述问题，为提高大数据处理环境的构建与开发效率,优化处理流程，提出了一种模型驱动的大数据流水线框架PiFlow。首先，将大数据处理过程抽象为有向无环图；然后，开发一系列组件用于构建数据处理流水线，并设计了流水线任务执行机制。同时，为规范和简化流水线框架的描述，设计了基于模型驱动的大数据流水线描述语言——PiFlowDL，该语言以模块化、层次化的方式对大数据处理任务进行描述。PiFlow以所见即所得(WYSIWYG)的方式配置流水线，集成了状态监控、模板配置、组件集成等功能，与Apache NiFi相比有2~7倍的性能提升。     

三电平CLLC谐振型DC/DC变换器参数优化设计

针对直流配网高低压母线间互联的应用场景,双向三电平CLLC谐振型DC/DC变换器具有良好的应用前景。本文首先采用基波分析法对该变换器拓扑进行了简化和等效,并通过详细的理论推导研究了电路各参数对变换器增益以及软开关实现条件的影响。在此基础上提出了变换器各元件参数设计的完备的流程,并通过功能校验实现优化参数的目的。按照此设计和校验流程,基于PSCAD仿真平台搭建了双向三电平CLLC谐振型DC/DC变换器的模型,仿真结果表明该变换器不仅可以满足额定电压和额定功率的设计规格要求,还可以在从轻载到满载的全负载范围内实现软开关,且具有在负载变动时的良好的动态调节性能。本文的工作能够为双向三电平CLLC谐振型直流变换器的设计和应用提供扎实的理论依据。