On the efficiency of several VM provisioning strategies for workflows with multi-threaded tasks on clouds

Cloud computing promises the delivery of on-demand pay-per-use access to unlimited resources. Using these resources requires more than a simple access to them as most clients have certain constraints in terms of cost and time that need to be fulfilled. Therefore certain scheduling heuristics have been devised to optimize the placement of client tasks on allocated virtual machines. The applications can be roughly divided in two categories: independent bag-of-tasks and workflows. In this paper we focus on the latter and investigate a less studied problem, i.e., the effect the virtual machine allocation policy has on the scheduling outcome. For this we look at how workflow structure, execution time, virtual machine instance type affect the efficiency of the provisioning method when cost and makespan are considered. To aid our study we devised a mathematical model for cost and makespan in case single or multiple instance types are used. While the model allows us to determine the boundaries for two of our extreme methods, the complexity of workflow applications calls for a more experimental approach to determine the general relation. For this purpose we considered synthetically generated workflows that cover a wide range of possible cases. Results have shown the need for probabilistic selection methods in case small and heterogeneous execution times are used, while for large homogeneous ones the best algorithm is clearly noticed. Several other conclusions regarding the efficiency of powerful instance types as compared to weaker ones, and of dynamic methods against static ones are also made.     

The optimization of warehouse location and resources distribution for emergency rescue under uncertainty

China is one of the countries that suffer the most natural disasters in the world. The situation of emergency response and rescue is extremely tough. Establishing the emergency warehouse is one of the important ways to cope with rapid-onset disasters. In this paper, a mixed integer programming (MIP) model based on time cost under uncertainty is proposed, which help solve the emergency warehouse location and distribution problem. Comprehensive consideration of factors such as time cost, penalty cost for lack of resources, alternative origins of resources from both suppliers and emergency warehouses, different means of transportation and multiple resources types are involved in our study. We also introduce uncertain scenarios to describe the severity of the disaster. Particle swarm optimization (PSO) and variable neighborhood search (VNS) are designed to solve the MIP model of different scales of instances. Numerous examples have been tested to compare two heuristics with commercial solver (CPLEX). Both of two algorithms can obtain the exact solution same as CPLEX in small-scale instances while show great performance on larger instances with 10 candidate warehouses, 25 disasters and 50 scenarios.     

Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach

This paper presents an optimization via simulation approach to solve dynamic flexible job shop scheduling problems. In most real-life problems, certain operation of a part can be processed on more than one machine, which makes the considered system (i.e., job shops) flexible. On one hand, flexibility provides alternative part routings which most of the time relaxes shop floor operations. On the other hand, increased flexibility makes operation machine pairing decisions (i.e., the most suitable part routing) much more complex. This study deals with both determining the best process plan for each part and then finding the best machine for each operation in a dynamic flexible job shop scheduling environment. In this respect, a genetic algorithm approach is adapted to determine best part processing plan for each part and then select appropriate machines for each operation of each part according to the determined part processing plan. Genetic algorithm solves the optimization phase of solution methodology. Then, these machine-operation pairings are utilized by discrete-event system simulation model to estimate their performances. These two phases of the study follow each other iteratively. The goal of methodology is to find the solution that minimizes total of average flowtimes for all parts. The results reveal that optimization via simulation approach is a good way to cope with dynamic flexible job shop scheduling problems, which usually takes NP-Hard form.     

基于多灾点非合作博弈的资源调度建模与仿真

当突发事件发生后,在应急资源有限的情况下,对多个灾点进行合理的资源调度是一个非常现实而复杂的问题。从多灾点所需应急资源的角度出发,提出了基于非合作博弈的应急资源调度模型和算法。在该调度模型中,各个灾点被映射为博弈模型的局中人,可能的资源调度方案映射为策略集,资源调度成本的倒数映射为效用函数,将应急资源的调度问题转化为对非合作博弈调度模型的Nash均衡点求解问题,接着介绍了一种求解Nash均衡点的迭代算法。最后对模型的仿真测试验证了该模型的有效性和可行性。     

基于多模式分层网络的应急资源调度模型

分析对应急物流中救援资源流转过程,设计应急资源调度的多模式分层网络。根据突发事件对灾区路况的影响建立路网场景集,并以此来描述交通运输网络旅行时间的不确定性。运用最小最大理论提出绝对可靠路径模型和相对可靠路径模型及其相关算法,作为应急资源调度可靠路径的选择依据。应急资源调度的实例验证了该模型的可行性和有效性。     

战场环境中多无人机动态任务调度

采用了基于合同网的分布式规划方法,研究了战场环境中多无人机动态任务调度问题,并建立了数学模型,提出了分布式的任务调度体系结构,设计了一种基于代价变换的概率路标图路径规划算法,该算法能够在任务调度阶段,快速预估无人机执行不同任务的飞行航路,扩展了合同网协议,可在一次拍卖中“并发”进行多次交易,提高了任务调度的效率。通过多种合同类型的综合,解决了复杂战场态势下的任务调度问题。     

铁路应急资源动态多阶段调度决策模型及算法研究

近年来,铁路突发事件时有发生,严重影响铁路的正常运营,合理地进行应急资源的调度是提高铁路整体应急救援能力,减少突发事件所造成损失的有效途径。以博弈论为理论基础,将各应急点看作博弈局中人,考虑救援点到应急点的运力限制以及不同资源在不同应急点的重要度等因素,构建了资源动态需求函数,并用应急点对资源缺少量的时间累积来刻画系统损失。将多应急点的资源调度描述为一个多阶段非合作博弈过程,以系统总损失最小为目标,建立多应急点-多救援点-多种资源的动态多阶段资源调度模型,并设计了求解该模型Nash均衡的改进布谷鸟算法,从而得到最优的铁路应急资源调度方案。通过具体算例验证了模型的可行性与算法的优越性。结果表明该模型较为切近实际、适用性较强且改进后的算法更具高效性,可为铁路应急资源调度决策提供依据和支持。     

微电网耦合碳捕集封存与需求响应的多时间尺度鲁棒优化调度

随着可再生能源的普及,微电网成为了一个可持续能源供应的重要工具。然而,由于其源荷多不确定性和碳排放挑战,优化调度成为了一个难题。本文提出了一种基于储液式碳捕集封存和需求响应的多时间尺度鲁棒优化调度方法,旨在降低微电网的碳排放和提高其运行性能。该方法在日前阶段以1小时时间间隔进行调度,采用鲁棒优化处理不确定性中的低频分量,同时结合碳捕集技术和需求响应以促进调度计划的低碳经济性。日内阶段以15分钟时间间隔进行调度,采用模型预测控制跟踪并滚动修正日前鲁棒经济调度计划,以应对源荷预测误差和不确定性中的高频分量,确保微电网实际功率平衡。仿真分析表明,这种方法可以显著降低微电网的总成本和碳排放量,提高了微电网的运行性能和可持续性。     

面向突发事件的应急资源调度模型的构建和求解

针对突发事件发生后存在多个事故、应急资源有限的情况,提出了基于非合作博弈的应急资源调度模型和算法。在该调度模型中,各个事故点被视为博弈模型的局中人,可能的资源调度方案映射为策略集,将应急资源的调度问题转化为对非合作博弈调度模型的Nash均衡点求解问题,并对基本蚁群算法进行了改进,嵌入了遗传算法的交叉操作和变邻域搜索策略,将其应用于求解该博弈模型的Nash均衡解。最后通过算例测试验证了模型和算法的有效性和可行性。     

遗传算法在刹车片生产计划排产中的应用与实践

生产计划的排产问题是影响车间生产效率的主要因素之一.合理的排产计划能够高效地使用车间现有的生产资源,提高车间生产能力,降低生产成本.本文首先分析了刹车片生产运行状况,针对热压成型生产车间的多品种变批次生产模式,建立了排产数学模型;其次,设计了一种联合均值-遗传算法,对热压成型车间的排产数学模型进行求解;最后通过实验仿真,对本文设计的算法进行验证,实验结果表明联合均值-遗传算法能够很好的解决当前刹车片生产计划的排产问题.     

多灾害点应急资源调度研究与实现

针对多灾害点、多点出救、多目标应急调度问题,建立一种以时间最短、成本最低为目标的数学模型。考虑到调度过程中各灾害点对资源的竞争,提出利用表上作业算法对该模型进行优化求解,实现了在整体的优化方案中时间和成本总体消耗最少。提出的方法简单、实用、易用,并成功应用在省级应急管理信息示范平台上。最后,通过一个实例验证该方法的有效性。     

An optimal dynamic threat evaluation and weapon scheduling technique

Real time scheduling problems demand high level of flexibility and robustness under complex dynamic scenarios. Threat Evaluation (TE) and Weapon Assignment (WA), together TEWA is one such complex dynamic system that has optimal or near optimal utilization of scarce defensive resources, a supreme priority. Several static solutions of TEWA have been proposed. This paper discusses an optimal dynamic multi-air threat evaluation and weapon allocation algorithm using a variant of Stable Marriage Algorithm (SMA). WA uses a new dynamic weapon scheduling algorithm, allowing multiple engagements using shoot-look-shoot strategy, to compute near-optimal solution. For optimality different types of constraints are identified and defined. Testing part of this paper shows feasibility of this approach for a range of scenarios.     

A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem

To improve capital effectiveness in light of demand fluctuation, it is increasingly important for high-tech companies to develop effective solutions for managing multiple resources involved in the production. To model and solve the simultaneous multiple resources scheduling problem in general, this study aims to develop a genetic algorithm (bvGA) incorporating with a novel bi-vector encoding method representing the chromosomes of operation sequence and seizing rules for resource assignment in tandem. The proposed model captured the crucial characteristics that the machines were dynamic configuration among multiple resources with limited availability and sequence-dependent setup times of machine configurations between operations would eventually affect performance of a scheduling plan. With the flexibility and computational intelligence that GA empowers, schedule planners can make advanced decisions on integrated machine configuration and job scheduling. According to a number of experiments with simulated data on the basis of a real semiconductor final testing facility, the proposed bvGA has shown practical viability in terms of solution quality as well as computation time.     

Emergency resources scheduling based on adaptively mutate genetic algorithm

The emergency resources dispatch is critical in emergency relief, while it is quite difficult to achieve an optimized scheduling, adjusting to a practical situation. In this paper, an emergency resources scheduling model is built, which simulates realistic problems, this model includes multiple suppliers with a variety of resources, a single accident site and some restrictions, all these elements closing to a practical event. Then we applied an adaptively mutate genetic algorithm to figure out a superior solution, which adopts the Binary Space Partitioning tree for heuristic searching and adaptive mutation. Finally, we compare the experimental results obtained by canonical genetic algorithm and the adaptively mutate genetic algorithm, respectively. As is observed, this novel method proposed in our work has acquired better solutions than canonical genetic algorithm.     

A taboo search approach for deadlock-free scheduling of automated manufacturing systems

This paper addresses the scheduling problem of a class of automated manufacturing systems. A new manufacturing system model is proposed. In this model, a set of jobs is to be processed and each job requires a sequence of operations. Each operation may need more than one resource. Upon the completion of an operation, resources needed in the next operation of the same job cannot be released and the remaining resources cannot be released until the start of the next operation. The scheduling problem consists in sequencing the operations on the resources in order to avoid deadlocks and to minimize the makespan. The classical disjunctive graph representation is extended to model the scheduling problem. A taboo search algorithm is then proposed using an original neighborhood structure defined by two basic moves: the permutation of disjunctive arcs of critical paths and a deadlock recovery move if the former fails. Numerical results presented in the paper show the efficiency of the proposed algorithm.     

Optimal booking and scheduling in outpatient procedure centers

Patient appointment booking, sequencing, and scheduling decisions are challenging for outpatient procedure centers due to uncertainty in procedure times and patient attendance. We extend a previously developed appointment scheduling model to formulate a model based on a two-stage stochastic mixed integer program for optimizing booking and appointment times in the presence of uncertainty. The objective is to maximize expected profit. Analytical insights are reported for special cases and experimental results show that they provide useful rules of thumb for more general problems. Three solution methods are described which take advantage of the underlying structure of the stochastic program, and a series of experiments are performed to determine the best method. A case study based on an endoscopy suite at a large medical center is used to draw a number of useful managerial insights for procedure center managers.     

救灾物资多阶段分配与调度问题建模与求解

救灾物资多阶段分配与调度问题是灾害应急决策中的一个难点问题.首先,基于三角模糊数描述发放点物资需求的不确定性, 并基于路段历史行程时间函数刻画交通路网的动态性,构建应急救援物资多阶段分配与调度模型;然后,基于蚁群优化搜索各储备点到各发放点的通行时间矩阵,引入NSGA-II搜索救灾物资分配和调度方案,设计救灾物资多阶段分配与调度集成优化算法和编码调整策略;最后,通过仿真实验验证所提方法的有效性.实验结果表明,所提方法可为决策者提供多种不同偏好的方案,从而为决策者提供更多的选择空间,契合应急场景.     

Yard crane scheduling problem in a container terminal considering risk caused by uncertainty

In a container terminal, the arriving times and handling volumes of the vessels are uncertain. The arriving times of the external trucks and the number of containers which are needed to be brought into or retrieved from a container terminal by external trucks within a period are also uncertain. Yard crane (YC) scheduling is under uncertainty. This paper addresses a YC scheduling problem with uncertainty of the task groups' arriving times and handling volumes. We do not only optimize the efficiency of YC operations, but also optimize the extra loss caused by uncertainty for reducing risk of adjusting schedule as the result of the task groups' arriving times and handling volumes deviating from their plan. A mathematical model is proposed for optimizing the total delay to the estimated ending time of all task groups without uncertainty and the extra loss under all uncertain scenarios. Furthermore, a GA-based framework combined with three-stage algorithm is proposed to solve the problem. Finally, the proposed mathematical model and approach are validated by numerical experiments.     

基于遗传算法的应急物资分层联动调度研究

本文提出了在多物资、多车型特征的应急物资分层调度情况下求解调度系统中各运输工具具体调度方案的算法。该算法以系统调度任务完成时间最小为目标,基于遗传算法采用整体联动的求解思想。实际问题中的调度问题往往具有本文所提出的层次性,针对物资分层联动调度问题,本文给出了物资两层调度的算例,并建立了相应的数学模型,算例中第一层调度系统由一级仓库、二级仓库、一级运输工具和一级路网构成;第二层调度系统由灾害点、二级仓库、二级运输工具和二级路网构成,将两层调度系统视作整体,采用基于遗传算法的整体联动求解方法对算例进行求解得出结果,并对结果进行分析论证,验证算法的可行性与有效性。     

卫星的智能规划与调度

以对地观测卫星为例，分析卫星的结构功能和飞行任务的特点，并在此基础上建立卫星智能规划与调度系统。规划与调度系统由卫星模型和推理机组成，其中模型描述卫星结构功能和各种约束条件，推理机分析并解决这些约束条件，形成一个没有冲突的飞行计划。规划与调度系统还具有修正计划的能力，能满足任务删除、更改和新任务插入等需求。