风光储制氢下多台制氢机组优化调度研究

以风光储制氢系统中多台制氢机组和储能电池的优化调度为研究对象,目标是制氢的经济效益最大化.根据调度对象和目标函数的特征分别采用改进时序差分算法(TDA)和多目标粒子群优化算法(MOPSO)进行优化调度,其中储能电池的调度起辅助作用,用来使风光出力曲线匹配制氢出力曲线.算例分析表明,文中所述改进时序差分算法在解决多台制氢...     

基于MPSO的有限缓冲区多产品厂间歇调度问题的研究

研究了以最小化最大完工时间为目标的有限缓冲区多产品厂间歇调度问题,提出了一种基于多种群粒子群优化(MPSO)的间歇调度算法.该算法采用多种群,增加了种群初始粒子的多样性,在每一代子种群并行进化的过程中引入移民粒子,使子种群之间相互影响和促进,避免算法过早地陷入局部最优,提高了算法的全局搜索能力;每代进化后选出子种群中的优秀粒子作为精华种群,并对其进行变邻域搜索(VNS),进一步提高了算法的收敛精度.通过对不同规模调度问题的仿真,以及与其它算法的对比,证明了该算法解决有限缓冲区多产品厂间歇调度问题的有效性和优越性.     

基于新型多传感器融合策略的移动端双目视觉惯性SLAM闭环算法研究

研究了复杂环境下基于视觉同时定位与地图构建(SLAM)算法的移动端实时定位问题。该问题有如下几个难点:首先是移动端设备的计算资源受限,这对算法的优化与解算效率提出了更严格的要求;其次是测试场景的复杂多变,使得算法在低纹理及快速运动等情况下容易丢失目标;最后是实际应用时对系统的可拓展性要求较高,需要具备复杂场景下的适应性。针对上述问题,本文提出了面向移动端的双目视觉惯性SLA M算法,采用新型多传感器融合策略,通过将双目视觉图像和惯性测量数据进行紧耦合优化,设计了移动端回环检测算法,显著提升了系统的鲁棒性和可靠性。通过实验验证了所提方法的有效性,其在定位精度上超过了当前同类方法的最好结果,并开发了移动端的增强现实(A R)应用,以展示系统在真实场景中的效果。     

基于多体系统传递矩阵法和遗传算法的一种物理参数识别方法

将多体系统传递矩阵法与遗传优化算法相结合,形成了一种新的基于多体系统传递矩阵法和遗传算法的物理参数识别方法(MS-TMM&GA).应用多体系统传递矩阵法进行动力学建模以及固有振动特性分析.将参数识别问题转化为优化问题,结合遗传算法,对由系统固有频率和增广特征矢量构造的目标函数全局最小值优化求解.给出了通过系统模态参数识别物理参数的计算步骤以及流程图.通过两个数值算例,表明了该方法的可行性及有效性.该方法对多体系统传递矩阵法和遗传算法进行了结合与拓展,无需建立复杂多体系统的总体动力学方程,涉及矩阵阶次低,即可快速获得高精度的优化计算结果.     

多车间协同调度平台的研究与应用

针对传统企业在分布式制造模式下实现多个车间之间生产计划与调度的协作问题,从企业全局出发,研究多车间生产计划调度方法,提出采用多代理和规则引擎技术在制造执行系统(MES)中构建调度协同平台的解决方案.在扩充MES系统协同调度功能的基础上,实现不同车间生产制造信息共享,提高了制造企业各车间协同生产的效率.     

Multi-radio packet scheduling for real-time packet traffic on general link layer

提出了一个能够为在通用链路层(GLL)上的实时分组业务提供QoS保障的多接入分组调度算法.首先设计了一个目标是在保障实时业务QoS的同时避免资源浪费的调度效用函数,该函数表示将一个用户数据包调度到一个无线接入链路上进行传输所带来的效用;其次在这个调度函数的基础之上兼顾了公平性,建立一个多接入分组调度模型,值得注意的是该模型是一个NP问题;最后利用Hopfield神经网络来快速有效地为这个调度模型找出优化解.仿真结果表明,与M-LWDF和PLR等典型算法相比,该算法在高系统负载的情况下能够满足实时分组的时延要求,同时提高了频谱效率并降低了丢包率及时延抖动.     

基于PGA-ANFIS的露天矿山开采调度系统的实时优化与实践

矿山开采调度系统主要解决不同平台开采的穿孔爆破技术经济参数优化问题。本文以雪峰水泥原料矿山为例,运用自适应模糊推理系统方法构造露天矿山开采调度系统模型,用并行遗传算法解决了不确定环境条件下的复杂矿山开采调度系统模型的优化问题,取得了较好的经济效益。这不仅为生产调度系统的在线优化问题提供了新的思路,而且为穿孔爆破参数的优化提供了新的方法,对促进矿山生产系统的自动化、信息化、智能化、集约化具有重要的参考价值。     

基于PGA-ANFIS的露天矿山开采调度系统的实时优化与实践

矿山开采调度系统主要解决不同平台开采的穿孔爆破技术经济参数优化问题。本文以雪峰水泥原料矿山为例,运用自适应模糊推理系统方法构造露天矿山开采调度系统模型,用并行遗传算法解决了不确定环境条件下的复杂矿山开采调度系统模型的优化问题,取得了较好的经济效益。这不仅为生产调度系统的在线优化问题提供了新的思路,而且为穿孔爆破参数的优化提供了新的方法,对促进矿山生产系统的自动化、信息化、智能化、集约化具有重要的参考价值。     

基于模糊粗糙集和鲸鱼优化支持向量机的化工过程故障诊断

针对化工过程复杂,故障数据量大、属性多,难以保证故障诊断准确率和速度的问题,提出了一种基于模糊粗糙集(fuzzy rough sets,FRS)和鲸鱼优化的支持向量机(support vector machine,SVM)的化工过程故障诊断方法.通过对化工过程历史数据分析,判别故障类型.首先,利用模糊粗糙集对离散化后的...     

全复用多小区系统下行链路用户调度与功率分配

对全复用多小区系统下行链路用户调度与功率分配问题进行了研究,提出了一种半分布式的用户调度与功率分配方案.首先,各小区根据自身所辖用户的信道与干扰信息,采用最大信干噪比准则进行用户调度,然后各基站将所调度用户的信息上报给中央资源控制器,最后中央资源控制器基于最速下降法的思想,采用贪婪功率分配(GPA)算法为各用户分配相应的发射功率.所提方案利用小区之间的相互协作有效地弱化了小区间干扰,与现有方法相比获得了更好的吞吐量性能和更高的功率效率.     

Combined scheduling algorithm for re-entrant batch-processing machines in semiconductor wafer manufacturing

In this paper, a new combined scheduling algorithm is proposed to address the problem of minimising total weighted tardiness on re-entrant batch-processing machines (RBPMs) with incompatible job families in the semiconductor wafer fabrication system (SWFS). The general combined scheduling algorithm forms batches according to parameters from the real-time scheduling simulation platform (ReS²), and then sequences batches through slack-based mixed integer linear programming model (S-MILP), which is defined as batch-oriented combined scheduling algorithm. The new combined scheduling algorithm obtains families’ parameters from ReS² and then sequences these families through modified S-MILP, which is defined as family-oriented combined scheduling algorithm. With rolling horizon control strategy, two combined scheduling algorithms can update RBPMs scheduling continually. The experiments are implemented on ReS² of SWFS and ILOG CPLEX, respectively. The results demonstrate the effectiveness of our proposed methods.     

Closed loop control-based real-time dispatching heuristic on parallel batch machines with incompatible job families and dynamic arrivals

In this paper, a real-time closed loop control dispatching heuristic (RCLC) algorithm is proposed to address the scheduling problem of parallel batch machines with incompatible job families, limited waiting time constraints, re-entrant flow and dynamic arrivals in the diffusion and oxidation areas of a semiconductor wafer fabrication system (SWFS), which is known to be strongly NP-hard. The basis of this algorithm is the information of lots in the buffer when the parallel batch machines are idle and available. In RCLC, if the number of any family lots is less than the maximum batch size, the dispatching heuristic can be seen as a pull–pull–push–push (P⁴) strategy; otherwise, a genetic algorithm (GA). A look-itself strategy, P⁴ strategy and GA can build a closed loop control system. The experiments are implemented on the Petri nets-based real-time scheduling simulation platform of SWFS, and demonstrate the effectiveness of our proposed method.     

A Heuristics-Based Cost Model for Scientific Workflow Scheduling in Cloud

Scientific Workflow Applications (SWFAs) can deliver collaborative tools useful to researchers in executing large and complex scientific processes. Particularly, Scientific Workflow Scheduling (SWFS) accelerates the computational procedures between the available computational resources and the dependent workflow jobs based on the researchers’ requirements. However, cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate (near-optimal) solution within polynomial computational time. Motivated by this, current work proposes a novel SWFS cost optimization model effective in solving this challenge. The proposed model contains three main stages: (i) scientific workflow application, (ii) targeted computational environment, and (iii) cost optimization criteria. The model has been used to optimize completion time (makespan) and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context. This will ultimately reduce the cost for service consumers. At the same time, reducing the cost has a positive impact on the profitability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers. To evaluate the effectiveness of this proposed model, an empirical comparison was conducted by employing three core types of heuristic approaches, including Single-based (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Invasive Weed Optimization (IWO)), Hybrid-based (i.e., Hybrid-based Heuristics Algorithms (HIWO)), and Hyper-based (i.e., Dynamic Hyper-Heuristic Algorithm (DHHA)). Additionally, a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets. The proposed model provides an efficient platform to optimally schedule workflow tasks by handling data-intensiveness and computational-intensiveness of SWFAs. The results reveal that the proposed cost optimization model attained an optimal Job completion time (makespan) and total computational cost for small and large sizes of the considered dataset. In contrast, hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.     

A two-layer dynamic scheduling method for minimising the earliness and tardiness of a re-entrant production line

This study focuses on the dynamic scheduling problem of a re-entrant production line, in which all of the parts are assumed to have the same processing routes and be processed on every machine. A two-layer dynamic scheduling method is proposed for the dynamic scheduling of the re-entrant line with the objective of minimising total earliness and tardiness. This method consists of two layers. The top layer is to select the appropriate scheduling policy, and the bottom layer is to generate the scheduling by using the policy selected in the top layer. In the top layer, three different rules are constructed for selecting scheduling policies, namely the lateness comparison rule, the lateness variation comparison rule, and the equal interval switching rule. In the bottom layer, three different scheduling policies are proposed to generate the real-time scheduling for manufacturing, namely the FCFS (first come first service) scheduling policy, the PI (proportional-integral) scheduling policy, and the fuzzy PI scheduling policy. Considering that the real-time status of manufacturing changes constantly, it is necessary to switch among different scheduling policies to adapt to this change. Numerical experiments are performed in the situations with and without urgent jobs. The results show that the proposed two-layer dynamic scheduling method outperforms any single scheduling policy (e.g., the FCFS policy, the PI policy and the fuzzy PI policy) for the dynamic scheduling of a re-entrant production line.     

An effective estimation of distribution algorithm for the flexible job-shop scheduling problem with fuzzy processing time

Considering the fuzzy nature of the data in real-world scheduling, an effective estimation of distribution algorithm (EDA) is proposed to solve the flexible job-shop scheduling problem with fuzzy processing time. A probability model is presented to describe the probability distribution of the solution space. A mechanism is provided to update the probability model with the elite individuals. By sampling the probability model, new individuals can be generated among the search region with promising solutions. Moreover, a left-shift scheme is employed for improving schedule solution when idle time exists on the machine. In addition, some fuzzy number operations are used to calculate scheduling objective value. The influence of parameter setting is investigated based on the Taguchi method of design of experiment, and a suitable parameter setting is suggested. Numerical testing results and comparisons with some existing algorithms are provided, which demonstrate the effectiveness of the proposed EDA.     

Multilevel intelligent scheduling and control system for an automated flow shop manufacturing environment

Current manufacturing scheduling and control systems are incapable of coping with complex system dynamics inherent in real-world situations and, hence, human intervention is required to maintain real-time adaptation and optimization. A unique feature of biological intelligent systems is that they build and improve over their communication, decision-making and control structures in real time autonomously. A challenge is now emerging in the design of manufacturing systems where on-line adaptation and optimization become increasingly important. This paper reports on the development of a new integrated intelligent scheduling and control system for an automated manufacturing environment using a multilevel approach. At the first level, a conventional scheduling and control system is considered, then at the second level, a new fuzzy logic mechanism is developed to enable the conventional system to improve and perceive the changes of system parameters adaptively. A new perturbation mechanism is embedded in the third level to implement on-line optimization for coping with the more complex structural changes of system dynamics. The final level is composed of artificial neural networks that can learn from experiences provided by the perturbation mechanism. The approach is designed to improve system intelligence gradually to cope with various forms of systems dynamics. A fully automated flow shop manufacturing system is taken to demonstrate this approach.     

Integrated optimisation of scheduling and ordering based on semi-finished goods delayed differentiation

The implementation of dynamic ordering policies is becoming increasingly important for the competitiveness of modern manufacturing systems. However, existing models on dynamic ordering pay little attention to production scheduling, which greatly affects the fulfilment of dynamic ordering, especially in complex manufacturing systems. Therefore, it is imperative to establish a new model which integrates both dynamic ordering and production scheduling. Accordingly, a quantitative measurement method for integration is needed. To this end, this paper proposes a semi-finished goods delayed differentiation (SFGDD) model by taking into account integration of the scheduling inventory control and dynamic ordering simultaneously. The objective of this model is to study the relationship between the shop floor inventory and the ordering control based on the semi-finished goods dynamic dispatching mechanism. In addition, the days of inventory (DOI) and a backorder penalty exponential function are developed to quantitatively measure such a relationship. To obtain the optimal results, this paper employs a heuristic genetic algorithm (HGA) with a heuristic encoding scheme to synchronise the generation and selection of inventory variables coherently. A case study on a semiconductor assembly and test manufacturing (ATM) is presented, and a significant revenue enhancement and inventory reduction are achieved accordingly.     

An effective discrete harmony search algorithm for flexible job shop scheduling problem with fuzzy processing time

This study addresses flexible job shop scheduling problem (FJSP) with fuzzy processing time. The fuzzy or uncertainty of processing time is one of seven characteristics in remanufacturing. A discrete harmony search (DHS) algorithm is proposed for FJSP with fuzzy processing time. The objective is to minimise maximum fuzzy completion time. A simple and effective heuristic rule is proposed to initialise harmony population. Extensive computational experiments are carried out using five benchmark cases with eight instances from remanufacturing. The proposed heuristic rule is evaluated using five benchmark cases. The proposed DHS algorithm is compared to six metaheuristics. The results and comparisons show the effectiveness and efficiency of DHS for solving FJSP with fuzzy processing time.     

Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects

This paper investigates a multi-objective parallel machine scheduling problem under fully fuzzy environment with fuzzy job deterioration effect, fuzzy learning effect and fuzzy processing times. Due dates are decision variables for the problem and objective functions are to minimise total tardiness penalty cost, to minimise earliness penalty cost and to minimise cost of setting due dates. Due date assignment problems are significant for Just-in-Time (JIT) thought. A JIT company may want to have optimum schedule by minimising cost combination of earliness, tardiness and setting due dates. In this paper, we compare different approaches for modelling fuzzy mathematical programming models with a local search algorithm based on expected values of fuzzy parameters such as job deterioration effect, learning effect and processing times.     

Research on Fuzzy Decision of Resources Selection in Job-shop Scheduling for a One-of-a-Kind and Order-Oriented Production System

In a one-of-a-kind and order-oriented production corporation, job shop scheduling plays an important role in the production planning system and production process control. Since resource selection in job shop scheduling directly influences the qualities and due dates of products and production cost, it is indispensable to take resource selection into account during job shop scheduling. By analyzing the relative characteristics of resources, an approach of fuzzy decision is proposed for resource selection. Finally, issues in the application of the approach are discussed.