一类柔性机器人装配系统的优化调度

研究了印刷电路板柔性机器人装配系统的中的优化调度问题,该系统中的机器人都具有一定的柔性,因而某些装配操作可由不同的机器人来完成,且所需装配时间也可以不同。本文针对这类机器人装配系统,采用一种新算法,以解决此类系统的优化调度问题,并通过算例验证了该逄法的有效性。     

飞机移动生产线作业调度问题的启发式算法

飞机移动生产线作业调度问题是一种具有特殊约束的项目调度问题。综合分析了飞机移动生产线的作业顺序关序、资源约束、空间限制的多重特点,建立了以最小装配完工时间为目标的模型。将问题分为确定作业的优先顺序和选择作业人数的两个阶段进行决策,设计了由不同规则组合成27种启发式方法。通过比较启发式方法在250个测试问题集的表现,确定最有效的解决此问题的方法。     

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.     

具有成组约束的柔性同序加工车间的排序算法

针对轮胎模具的生产调度特点,研究了具有成组约束的两阶段柔性同序加工车间(FF2)的调度问题,建立了目标函数为最小化最大完成时间的调度数学模型,基于Johnson准则提出一种新的启发式算法,并应用企业的实际算例,说明了数学模型和求解方法的可靠性和有效性。     

基于增强学习的半导体测试调度研究

采用Sarsa(λ,k)学习算法求解、产品、测试机、测试工具包、使能器部件对应关系非常复杂的半导体测试调度问题.针对测试调度,通过定义系统状态的表示方式、构造行为和报酬函数把调度问题转化为增强学习问题,并把Sarsa(λ,k)算法和梯度下降径向基神经网络函数泛化器结合使用.实验验证了Sarsa(λ,k)算法解决半导体测试调度问题的有效性.Sarsa(λ,k)算法通过反复解决调度问题来调整调度策略,能克服单个行为策略短视的缺点,综合利用各个行为策略的优点,从而找到较优的调度方案.     

工件带到达时间并具有成组约束的车间排序算法

针对模具制造过程的特点,在工件不同时到达的情况下,研究了前阶段带有成组约束的两阶段柔性同序加工车间的调度问题,建立了目标函数为最小化最大完成时间的调度数学模型．基于Potts的RJ’算法提出解决此类问题的启发式算法,并将该算法应用到轮胎模具企业的生产实例中,通过仿真说明数学模型和求解方法的可靠性和有效性．     

一类多资源调度问题的优化算法

若调度系统中的作业或任务需要一个或多个资源共同完成， 则这一类调度问题被称为多资源（或多处理机）调度问题。本文针对Jobshop中的这一调度问题，提出了基于遗传的优化调度算法，并用实例证实了该算法的有效性。     

Optimal Coverage Multi-Path Scheduling Scheme with Multiple Mobile Sinks for WSNs

Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method.     

关键链上可被部分替代的受限资源动态调度

关键链上受限资源的优化调度,能有效提高系统运作效率,是关键链项目管理的重要研究方向之一.但现有研究主要针对资源能否更新,较少考虑资源的可替代性.针对此,研究了关键链上可被部分替代的受限资源调度问题,以达到提高资源利用率、降低成本等目的.采用α/β/γ三元组方法,将研究的问题描述为以最小化滞后时间和为目标,具有机器适用限制的并行异速机调度问题,并建立对应的数学模型;运用混合重调度策略和改进粒子群算法进行了算法设计;设计了仿真算例,并通过算法比较表明该方法在求解结果和问题求解规模上显著优于传统PSO、GA算法.     

基于优先规则的船舶分段空间调度算法

为有效解决船舶分段的空间调度问题,提出了一种基于优先规则的求解算法。首先利用优先规则和禁忌搜索算法产生可行的分段调度序列,再采用一种启发式定位策略——最下最左填满策略对产生的调度序列进行解码,以评估调度序列的优劣。算法不断迭代,最终可得到近似最优解。对船厂的实际生产数据进行了实证分析,并与现有的算法进行了对比,验证了所提出的算法在空间调度问题上的有效性和优越性。     

原油处理过程短期生产计划优化

在原油处理过程短期生产计划的递阶求解方法中,原油处理短期生产计划问题分为上下两层,上层根据市场需求产生一个目标炼油计划;在此基础上,下层得到一个详细生产计划以实现目标炼油计划。研究了在上层目标炼油计划已知的情况下,下层详细生产计划的求解问题。为该问题建立了基于离散时间表示的混合整数线性规划模型,分析了问题的特点并将其进行转化,给出了基于启发式的求解方法,在保证目标炼油计划实现的前提下,对原油转运过程中油品切换及不同油品的罐底混合进行了优化,取得了一定的成果。用一个工业实例验证了启发式规则的可行性和有效性。     

新型集装箱码头堆场混合装卸计划方法研究

对一种新型的集装箱装卸系统在给定集装箱装卸作业任务前提的单装/卸和装卸混合模式调度问题进行了探索研究.针对这种自动化码头装卸作业的特征,将此集装箱装卸作业调度问题定义为特殊的三阶段柔性Job shop调度问题,以完成给定装卸作业任务的最短时间为优化目标,设计了以低架桥分配小车作为调度中心,对这一三阶段集成优化调度问题求...     

A New Task Scheduling Scheme Based on Genetic Algorithm for Edge Computing

With the continuous evolution of smart grid and global energy interconnection technology, amount of intelligent terminals have been connected to power grid, which can be used for providing resource services as edge nodes. Traditional cloud computing can be used to provide storage services and task computing services in the power grid, but it faces challenges such as resource bottlenecks, time delays, and limited network bandwidth resources. Edge computing is an effective supplement for cloud computing, because it can provide users with local computing services with lower latency. However, because the resources in a single edge node are limited, resource-intensive tasks need to be divided into many subtasks and then assigned to different edge nodes by resource cooperation. Making task scheduling more efficient is an important issue. In this paper, a two-layer resource management scheme is proposed based on the concept of edge computing. In addition, a new task scheduling algorithm named GA-EC(Genetic Algorithm for Edge Computing) is put forth, based on a genetic algorithm, that can dynamically schedule tasks according to different scheduling goals. The simulation shows that the proposed algorithm has a beneficial effect on energy consumption and load balancing, and reduces time delay.     

An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes

Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes. “Straggling” tasks, however, have a serious impact on task allocation and scheduling in a Hadoop system. Speculative Execution (SE) is an efficient method of processing “Straggling” Tasks by monitoring real-time running status of tasks and then selectively backing up “Stragglers” in another node to increase the chance to complete the entire mission early. Present speculative execution strategies meet challenges on misjudgement of “Straggling” tasks and improper selection of backup nodes, which leads to inefficient implementation of speculative executive processes. This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution (ORSE) by introducing non-cooperative game schemes. The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem, where the tasks are regarded as game participants, whilst total task execution time of the entire cluster as the utility function. In that case, the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point, i.e., the final resource scheduling scheme to be obtained. The strategy has been implemented in Hadoop-2.x. Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load, Busy Load and Busy Load with Skewed Data.     

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.     

基于HTCPN的分段生产物流系统建模与仿真:案例研究

船舶分段生产物流系统是一个典型的资源约束下的多项目调度复杂系统。根据S企业分段生产流程和资源的配置情况,利用层次赋时有色佩特里网(HTCP-net),结合提出的一种适合多项目调度的启发式规则,建立了分段生产物流系统模型。通过CPN tools进行仿真,验证了模型的正确性,发现了流程中的瓶颈,提出了合理的资源配置数量,通过对比证明了本启发式规则较适合船厂的多项目调度。文中的研究方法对复杂的造船流程的建模、仿真和分析具有一定参考价值。     

一类资源负荷均衡问题的优化调度算法

针对一类n个独立任务在m个不完全同等的处理机上处理，使处理机的最大负荷为最小的非抢先调度问题，提出了一种启发式算法－－最小平衡算法，并分析了它的时间复杂性，在此基础上，又将最小平衡算法和遗传算法结合起来，提出了基于遗传的最小平衡算法，并用实例证实了该算法的有效性。     

面向混合负载的集群资源弹性调度

针对目前集群资源调度方法难以适应互联网业务多样化、定制化特征的问题,提出了一种面向混合负载的集群资源弹性调度方法.该方法通过构建作业约束描述语言,允许作业基于自身负载特征提出多维度的资源申请和具有负载意识的资源调度算法,实现在同一集群内各类业务统一部署与管理,及时匹配资源需求的变化;通过建立作业的软约束与硬约束之间的转化机制,满足作业在不同执行阶段对资源的定制化需求.实验表明,该方法相比于Hadoop,可允许作业利用较少资源获得更优性能,在实际生产系统中,基于该方法可将集群资源利用率由62％提升到75％.     

考虑公有与私有资源约束的多项目调度规则对比研究

为了识别出适用于具有公有资源与私有资源约束的多项目调度问题的优先规则,基于标准测试集MPSPLIB进行计算实验,并基于相对偏差指标对比分析25种经典优先规则在最小化最大完工时间、最小化总拖期、最小化加权总拖期3种决策目标下的表现。实验结果表明,优先规则的表现与决策目标、单项目任务数、并行项目数、资源利用系数等因素之间具有显著相关性。在最小化总拖期目标下,尽管大多数规则的表现与现有文献总体一致,但是MAXTWK和MINSLK规则的表现有着显著差异。所得实验结论对于工程实际多项目调度决策具有指导意义。     

序时多指标决策的夹角模糊优选法

针对有时序的多指标决策问题,提出一种新的决策方法－夹角模糊优选法,并给出该方法的原理与步骤,最后以实例说明此方法的应用,为决策者提供了一条新思路。