Research on flexible job-shop scheduling problem in green sustainable manufacturing based on learning effect

Journal of Intelligent Manufacturing - As one of the manufacturing industries with high energy consumption and high pollution, sand casting is facing major challenges in green manufacturing. In...     

制造网格资源调度及安全性管理

根据制造网格发展的现状和方向,分析了网格资源调度的主要方法,并对Globus环境下的GSI体系结构做了深入研究,分析讨论了其任务提交执行过程和架构特点,对现有的GSI架构基础做了相关改进,构建了一个满足制造网格下部分特性的安全体系结构M-GSI,实现了实时认证和强授权功能,满足了制造网格部分安全需求。     

基于多目标的半导体生产线满意调度

在分析生产线性能指标的基础上，选择出用于优化的目标，并针对基于单目标的调度策略所存在的局限性，采用满意的概念，根据模糊规则与比较法提出了基于多目标的满意调度，实验表明该调度方法较单目标调度策略有很大的优越性，从而为实现生产线的整体优化提供了解决的途径。     

基于改进蚁群算法的TT&C资源配置方法

研究了航天测控网资源配置问题,它是在航天器一地面站有限的时间窗口内对航天器的测控任务进行合理的配置,给每个任务分配一个开始执行时间,在给定的调度时间下,使得任务完成的目标值最大.将引入了杂交算子的蚁群算法来求解该问题,合理地构造了与问题相适应的杂交算子和杂交段.具体的仿真实例表明,在相同的约束条件下该算法总是能最大化地完成任务,并且求得的目标值也有很大的改进.     

基于机器学习的MapReduce资源调度算法

针对MapReduce中允许map和shuffle阶段重叠的优化模型需要自适应性的问题,提出了基于此模型的机器学习的资源调度算法,利用贝叶斯分类器依据作业对系统资源的需求和系统环境的匹配程度对作业进行调度,并不断更新分类器,使其具有自适应性,考虑了map和shuffle的重叠阶段。通过模拟实验验证,改进后的算法能够提高MapReduce系统的性能,获得更好的平均响应时间。     

基于制造网格的虚拟企业资源调度问题研究

为支持虚拟企业间资源的高效调度,在制造网格的基础上,提出了虚拟企业的资源调度平台。研究了基于GlobusToolkit 4和agent技术的平台实现方法和工作流程。在此基础上,提出了以T、C、Q、S为目标的多目标资源优化调度模型,结合制造网格与计算网格的差异,将网格底层的跨地域实体物流交互对资源调度的影响纳入优化调度模型,使虚拟企业资源调度方案更加合理。最后,以一个具体的实例,验证了多目标优化调度模型的有效性。     

An interoperable adaptive scheduling strategy for knowledgeable manufacturing based on SMGWQ-learning

To address the uncertainty of production environment in knowledgeable manufacturing system, an interoperable knowledgeable dynamic-scheduling system based on multi-agent is designed, wherein an adaptive scheduling mechanism based on the state membership grade weighted Q-learning (known as SMGWQ-learning) is proposed for guiding the equipment agent to select proper scheduling strategy in a dynamic environment. To avoid the side effect of large state space and minimize errors between the clustering and real states, the state membership grade, defined as weight coefficients, is incorporated into the weighted Q-value update so that several Q-values can be updated simultaneously in an iteration. Results from our convergence analysis and simulation experiments show the effectiveness of the proposed strategy that endows the scheduling system with higher intelligence, interoperability and adaptability to environmental changes by self-learning.     

An innovative approach for resource sharing and scheduling in a sustainable distributed manufacturing system

Secure, transparent, and sustainable distributed manufacturing system (DMS) is a pressing need for current Industry 4.0. In this paper, exchange of highly sensitive information in a more transparent and secure way and to avoid the misunderstandings and trust issues between the enterprises a smart contract based on blockchain technology has been proposed in case of a distributed manufacturing environment. Here, we used a public-permission less Ethereum platform to execute the smart contracts in the Blockchain to process the customer orders and to identify the right enterprise. Later, a multi-objective mixed-integer linear programming (MILP) model is formulated for optimal resource sharing and scheduling in a considered sustainable DMS. The objectives of the proposed model consist of simultaneously improvement of the performance measures such as makespan, machine utilization, energy consumption, and reliability. To solve this MILP model, a new Multi-objective-based Hybridized Moth Flame Evolutionary Optimization Algorithm (HMFEO) is developed and then the effectiveness of the proposed algorithm is validated with the Non-dominated Sorting Genetic Algorithm (NSGA-III). The results obtained from implementing the model using experimental data along with different cases show the efficiency and the validity of the proposed model and solution approach. Moreover, several performance indicators like hyper volume are increased by nearly 15–20 % that shows the superiority of the proposed algorithm with the NSGA-III.     

一种基于效用最优的计算网格资源调度算法

针对具有时间和费用约束的网格资源调度问题，通过分析任务的执行时间和执行费用与用户效用函数的关系，建立了效用函数模型，在此基础上提出了一种针对task farming应用程序模式的基于效用函数最优的启发式调度算法——效用最优调度算法。实验表明，与费用 时间均衡调度算法相比，本算法能给用户带来更高的效用。     

Reliability-aware scheduling for reducing system-wide energy consumption for weakly hard real-time systems

While energy consumption is the primary concern for the design of real-time embedded systems, reliability and Quality of Service (QoS) are becoming increasingly important in the development of today’s pervasive computing systems. In this paper, we present a reliability-aware energy management (RAEM) scheme for reducing the energy consumption for weakly hard real-time systems with (m, k)-constraints, which requires that at least m out of any k consecutive jobs of a task meet their deadlines. In order to ensure the (m, k)-constraints while preserving the system reliability, we propose to partition the real-time jobs into mandatory and optional ones as well as to reserve recovery space for mandatory ones in an adaptive way. Moreover, efficient on-line scheduling techniques are proposed to reduce the system-wide energy, which is consumed not only by the processor but also by other peripheral devices at run-time. Through extensive simulations, our experiment results demonstrate that the proposed techniques can significantly outperform the previous research in reducing system-wide energy consumption for weakly hard real-time systems while preserving the system reliability.     

基于BS-GA的资源约束多项目调度问题研究

针对资源约束的多项目调度问题(RCMPSP),考虑到项目、项目任务和资源各自之间的差异性,引入项目权重系数、活动质量因子和资源能力系数3个概念,提出了一个工期与质量的均衡优化模型.该模型根据资源的配置计划,确定了项目任务的资源平均能力系数,然后用项目权重系数和活动质量因子计算出多项目的单位工期时间内资源平均能力系数,利...     

A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction

Manufacturing scheduling strategies have historically emphasized cycle time; in almost all cases, energy and environmental factors have not been considered in scheduling. This paper presents a new mathematical programming model of the flow shop scheduling problem that considers peak power load, energy consumption, and associated carbon footprint in addition to cycle time. The new model is demonstrated using a simple case study: a flow shop where two machines are employed to produce a variety of parts. In addition to the processing order of the jobs, the proposed scheduling problem considers the operation speed as an independent variable, which can be changed to affect the peak load and energy consumption. Even with a single objective, finding an optimal schedule is notoriously difficult, so directly applying commercial software to this multi-objective scheduling problem requires significant computation time. This paper calls for the development of more specialized algorithms for this new scheduling problem and examines computationally tractable approaches for finding near-optimal schedules.     

Maintenance scheduling in manufacturing systems based on predicted machine degradation

In this paper, we propose a new method for scheduling of maintenance operations in a manufacturing system using the continuous assessment and prediction of the level of performance degradation of manufacturing equipment, as well as the complex interaction between the production process and maintenance operations. Effects of any maintenance schedule are evaluated through a discrete-event simulation that utilizes predicted probabilities of machine failures in the manufacturing system, where predicted probabilities of failure are assumed to be available either from historical equipment reliability information or based on the newly available predictive algorithms. A Genetic Algorithm based optimization procedure is used to search for the most cost-effective maintenance schedule, considering both production gains and maintenance expenses. The algorithm is implemented in a simulated environment and benchmarked against several traditional maintenance strategies, such as corrective maintenance, scheduled maintenance and condition-based maintenance. In all cases that were studied, application of the newly proposed maintenance scheduling tool resulted in a noticeable increase in the cost-benefits, which indicates that the use of predictive information about equipment performance through the newly proposed maintenance scheduling method could result in significant gains obtained by optimal maintenance scheduling.     

Deadlock-free genetic scheduling algorithm for automated manufacturing systems based on deadlock control policy

Deadlock-free control and scheduling are vital for optimizing the performance of automated manufacturing systems (AMSs) with shared resources and route flexibility. Based on the Petri net models of AMSs, this paper embeds the optimal deadlock avoidance policy into the genetic algorithm and develops a novel deadlock-free genetic scheduling algorithm for AMSs. A possible solution of the scheduling problem is coded as a chromosome representation that is a permutation with repetition of parts. By using the one-step look-ahead method in the optimal deadlock control policy, the feasibility of a chromosome is checked, and infeasible chromosomes are amended into feasible ones, which can be easily decoded into a feasible deadlock-free schedule. The chromosome representation and polynomial complexity of checking and amending procedures together support the cooperative aspect of genetic search for scheduling problems strongly.     

云制造环境下基于关联度的资源组合方法

云制造环境中资源数量巨大,且广泛分布,对于业务过程而言,合理的资源组合可提高资源的选取效率以及利用率。以工作流模型为基础,根据资源之间的依赖关系构造关联度矩阵,进而采用键能算法对关联矩阵做二分变换,将依赖关系强的资源聚集到一起,形成若干个资源集。以产品设计、加工和组装为例,通过实验以及对资源选取效率的对比,验证该方法的有效性。     

网格经济模型下基于信誉度的资源调度策略

基于资源的信誉度,提出一种经济模型下的网格资源调度策略.由用户自己设定各因素的重要性,调度器根据用户提交任务的需求不同,将任务放到对应的任务队列中,各队列调用不同的调度算法,以得出最优资源分配.同时每次任务完成后,用户的满意度又直接影响到资源的信誉度.理论上能够实现资源和消费者的有效匹配,使消费者请求达到快速响应,让资源提供者和消费者双方获得更高的满意度.     

基于区块链的云制造系统内可信资源调度方案

针对目前云制造系统中存在的各参与主体间信任问题以及资源调度效率问题,研究了将区块链技术应用于云制造系统中.首先,阐述了区块链技术应用于云制造系统的意义,提出了一种基于区块链技术的云制造系统;其次,设计了基于智能合约的制造资源调度方式,构建制造成本最小、时间最短、合格率最高的资源调度模型并用差分进化算法进行求解;最后,进行实验仿真.结果表明,基于区块链技术的智能合约内进行资源调度方法在保证了系统内各参与主体间相互信任的同时,有效地提高了云制造系统的资源调度效率和资源调度方案的优越性.