禁忌搜索方法解最小化拖期任务数的并行多机调度问题

禁忌搜索方法（ＴＳ）是一种将人工智能技术引入管理中的一种高于一般启发式算法的智能化“超启发式”算法，它能有效地解决大型组合优化问题。本文用ＴＳ方法解决最小化拖期任务数的并行多机调度问题，并同目前最好的启发式作了比较，大量实验表明了ＴＳ方法的有效性。     

基于遗传和禁忌搜索混合算法的预制生产调度的研究

遗传算法是一种全局搜索能力较强的元启发式算法,可通过不断进化种群得到最优或近优解;但是遗传算法的局部搜索能力较差,容易发生早熟收敛问题。因此为了克服遗传算法早熟收敛的问题,考虑到禁忌搜索算法的局部搜索能力较强的优势,提出了一种遗传和禁忌搜索的混合算法解决预制生产流水车间的提前和拖期惩罚问题。该混合算法是在遗传算法每次迭代后,通过禁忌搜索改进当前种群中的最好染色体,并替换种群中适应度值最差的染色体。经实验测试表明,所提出的混合算法的性能更优,更容易得到全局最优解或近优解。     

带返工的定点装配车间生产计划与调度集成优化

针对定点装配车间不合格品返工导致生产计划及调度不可行的情况,提出一种基于返工延后处理的定点装配车间生产计划与调度集成优化方法.首先制定返工延后处理的粗生产计划;下达计划生产后,将每周期产生的不合格品放入缓冲区,在下周期初时重新调整生产计划并求解新计划下的最优调度,判断其是否满足装配班组负载率要求,不断交替迭代生产计划与调度直至达到计划与调度的均衡和优化.最后通过算例验证了所提出方法的可行性和有效性.     

软计算求解并行多机成组工件提前/拖期惩罚调度问题

1　问题描述成组工件提前 /拖期惩罚调度在实际生产中普遍存在、急待解决又十分复杂 .目前在国内外相关杂志上 ,还未见报道能够有效解决实际规模问题的方法 ,本文提出了软计算方法( SC) ,实验结果证明了它可以有效地解决大规模实际问题 .N个工件 ( b组 ,每组 ni个工件 ) ,M台机     

基于交互式的并行蚁群优化算法

传统蚁群优化算法研究已经取得了很多重要的成果,但是在解决大规模组合优化问题时仍存在早熟收敛,搜索时间长等缺点.为此,将邻域搜索技术与蚁群优化算法进行融合,提出一种新的并行蚁群优化算法,实验结果表明,在解决大规模TSP问题时,该算法求解质量和稳定性更好,在短时间内即可得到较高质量的解.     

一种带禁忌搜索的粒子并行子群最小约简算法

为了提高基于群体智能的粗糙集最小属性约简算法的求解质量和计算效率,提出一个结合长期记忆禁忌搜索方法的粒子群并行子群优化算法.并行的各子群不仅具有禁忌约束,而且包含多样性和增强性策略.由于并行的子群共同陷入局部最优的概率小于一个粒子群陷入局部最优的概率,该算法可提高获得全局最优的可能性,并减少受初始粒子群体的影响.多个UC I数据集的实验计算表明,提出的算法相对于其他的属性约简算法具有更高的概率搜索到最小粗糙集约简.因此所提出的算法用于求解最小属性约简问题是可行和较为有效的.     

基于提前/拖期调度模型的电力检修企业生产管理系统设计

从当今电力检修企业面临的各种挑战出发，阐述了生产管理系统的特点和研究的必要性。使用面向对象编程语言Delphi和数据库系统SQL Server2000，按照企业设备检修流程设计开发了生产管理系统。系统运用提前／拖期调度模型，对企业生产作业流程进行计划、调度和控制，从而提高了生产效率，同时减少资源消耗和节约劳动力。     

串行生产线的集成最优生产控制与能力规划

研究了制造系统串行生产线生产控制和生产能力规划的集成优化方法。给出了串行生产线最优生产控制及生产能力规划问题的数学模型,通过粒子群优化算法来求解这一模型,通过Matlab进行了仿真,并对仿真结果进行了分析。     

项目调度与物料订购集成优化研究综述

在项目管理实践中,活动安排与物料供应相互影响、相互制约,需要对这两个决策事项进行协同管理和规划.项目调度与物料订购集成优化问题(project scheduling and material ordering problem, PSMOP)研究的是如何合理制定项目调度和物料采购协同计划,以实现项目总成本最低、净现值最大、总工期最短等目标. PSMOP因其重要的理论价值和应用前景,近年来受到学术界和产业界的广泛关注.鉴于此,对国内外项目调度与物料订购集成优化的相关研究成果进行系统性总结与梳理:首先,介绍PSMOP问题及其常见的目标函数;其次,针对确定和不确定环境,分别总结了考虑不同特征的集成优化问题研究进展;然后,归纳了PSMOP常用求解算法及其在实践领域的应用情况;最后,指出未来进一步的研究方向.     

含有限缓冲区的上游生产线协调生产调度

通过对制造企业生产现场的调研,发现在实际的生产过程中存在仓库(有限缓冲区)问题,而且是多条生产线生产不同的成品.针对这一问题,文中研究了一类带有限缓冲区的上游生产线协调的生产调度问题,把生产线简化为一个Flow shop问题,给出了单条生产线输入输出结构图,建立了上下游生产线之间部件成品关联结构,依据该关联结构建立了实用的数学模型,给出Tabu搜索算法.     

An architecture for workflow scheduling under resource allocation constraints

Research on specification and scheduling of workflows has concentrated on temporal and causality constraints, which specify existence and order dependencies among tasks. However, another set of constraints that specify resource allocation is also equally important. The resources in a workflow environment are agents such as person, machine, software, etc. that execute the task. Execution of a task has a cost and this may vary depending on the resources allocated in order to execute that task. Resource allocation constraints define restrictions on how to allocate resources, and scheduling under resource allocation constraints provide proper resource allocation to tasks. In this work, we provide an architecture to specify and to schedule workflows under resource allocation constraints as well as under the temporal and causality constraints. A specification language with the ability to express resources and resource allocation constraints and a scheduler module that contains a constraint solver in order to find correct resource assignments are core and novel parts of this architecture.     

On cluster resource allocation for multiple parallel task graphs

Many scientific applications can be structured as parallel task graphs (PTGs), that is, graphs of data-parallel tasks. Adding data parallelism to a task-parallel application provides opportunities for higher performance and scalability, but poses additional scheduling challenges. In this paper, we study the off-line scheduling of multiple PTGs on a single, homogeneous cluster. The objective is to optimize performance without compromising fairness among the PTGs. We consider the range of previously proposed scheduling algorithms applicable to this problem, from both the applied and the theoretical literature, and we propose minor improvements when possible. Our main contribution is an extensive evaluation of these algorithms in simulation, using both synthetic and real-world application configurations, using two different metrics for performance and one metric for fairness. We identify a handful of algorithms that provide good trade-offs when considering all these metrics. The best algorithm overall is one that structures the schedule as a sequence of phases of increasing duration based on a makespan guarantee produced by an approximation algorithm.     

A parallel hypercube algorithm for discrete resource allocation problems

It has been suggested that parallel processing helps in the solution of difficult discrete optimization problems, in particular, those problems that exhibit combinatorial search and require large-scale computations. By using a number of processors that are connected, coordinated and operating simultaneously, the solutions to such problems can be obtained much more quickly. The purpose of this paper is to propose an efficient parallel hypercube algorithm for the discrete resource allocation problem (DRAP). A sequential divide-and-conquer algorithm is first proposed. The algorithm is then modified for a parallel hypercube machine by exploiting its inherent parallelism. To allocate N units of discrete resources to n agents using a d-dimensional hypercube of p=2/sup d/ nodes, this parallel algorithm solves the DRAP in O((n/p+log/sub 2/p)N/sup 2/) time. A simulation study is conducted on a 32-node nCUBE/2 hypercube computer to present the experimental results. The speedup factor of the parallel hypercube algorithm is found to be more significant when the number of agents in the DRAP is much greater than the number of processing nodes on the hypercube. Some issues related to load balancing, routing, scalability, and mappings of the parallel hypercube algorithm are also discussed.     

Evolutionary resource assignment for workload-based production scheduling

In this paper, we propose an evolutionary method with a simulation model for scheduling jobs including operations specified in terms of workload rather than processing time. It is suggested that processing times should be determined according to the number of assigned resources rather than the workload. The simulation model is used to estimate the result of resource allocation in a time horizon based on preselected rules. The evolutionary methods improve a production schedule in terms of compliance with due dates by selecting an alternative resource allocation rule and changing timing constraints. The results of computational experiments show that compliance with due dates improved by as much as 30 % under the modified production schedule over the initial schedule.     

调度和拥塞控制相结合的无线网络资源分配模型

研究了基于WLAN访问Internet的网络基站处流,提出了一种基于队列长度的调度方法和基于信道容量的拥塞控制模式,以达到网络资源的公平分配,并解决由于不恰当处理基站处堆积数据包而引起的弊端。在提出的资源分配模型中,调度算法根据各条流堆积的队列长度来随机地选择将要发送的数据分组;而拥塞控制模式中,将链路使用率作为拥塞指示,通过计算,平等地反馈给每一条流的发送端。发送端根据反馈到的拥塞信息来调整发送速率,以达到资源分配的公平性。仿真的结果表明：各条流能公平地共享无线网络的带宽。此算法的最大的优点在于基站不需要按照某种特定的公平性定义来选择数据包却能达到很高的公平性。     

电力线通信系统中跨层的用户调度和资源分配

针对多用户多业务基于正交频分多址的电力线通信系统,提出一种在数据链路控制层进行用户调度和在物理层进行资源分配的多层多目标最优的跨层资源分配算法,其用户调度根据所有用户的服务质量(QoS)满意程度、QoS要求、业务包模型、信道状态信息和队列状态信息,从所有用户中选出要服务的用户和确定这些用户的最优跨层参数;其资源分配则根据所有调度用户的QoS要求、最优跨层参数和信道状态信息,先把功率按地窖注水原理分给每个子载波,再把每个子载波最优地分给调度用户并采用逐比特加载查表算法调整其上分配的功率和比特。最后在典型的电力线信道环境下对算法进行仿真,结果表明新算法在系统资源大范围变化时也能保障用户的服务质量,同时有效地提高系统资源的利用。     

Optimal methods for resource allocation and scheduling: a cross-disciplinary survey

Classical scheduling formulations typically assume static resource requirements and focus on deciding when to start the problem activities, so as to optimize some performance metric. In many practical cases, however, the decision maker has the ability to choose the resource assignment as well as the starting times: this is a far-from-trivial task, with deep implications on the quality of the final schedule. Joint resource assignment and scheduling problems are incredibly challenging from a computational perspective. They have been subject of active research in Constraint Programming (CP) and in Operations Research (OR) for a few decades, with quite difference techniques. Both the approaches report individual successes, but they overall perform equally well or (from a different perspective) equally poorly. In particular, despite the well known effectiveness of global constraints for scheduling, comparable results for joint filtering of assignment and scheduling variables have not yet been achieved. Recently, hybrid approaches have been applied to this class of problems: most of them work by splitting the overall problem into an assignment and a scheduling subparts; those are solved in an iterative and interactive fashion with a mix of CP and OR techniques, often reporting impressive speed-ups compared to pure CP and OR methods. Motivated by the success of hybrid algorithms on resource assignment and scheduling, we provide a cross-disciplinary survey on such problems, including CP, OR and hybrid approaches. The main effort is to identify key modeling and solution techniques: they may then be applied in the construction of new hybrid algorithms, or they may provide ideas for novel filtering methods (possibly based on decomposition, or on alternative representations of the domain store). In detail, we take a constraint-based perspective and, according to the equation CP = model + propagation + search, we give an overview of state-of-art models, propagation/bounding techniques and search strategies.     

Novel multi-objective resource allocation and activity scheduling for fourth party logistics

In order to reduce logistic costs, the scheduling of logistic tasks and resources for fourth party logistics (4PL) is studied. Current scheduling models only consider costs and finish times of each logistic resource or task. Not generally considered are the joint cost and time between two adjacent activities for a resource to process and two sequential activities of a task for two different resources to process are ignored. Therefore, a multi-objective scheduling model aiming at minimizing total operation costs, finishing time and tardiness of all logistic tasks in a 4PL is proposed. Not only are the joint cost and time of logistic activities between two adjacent activities and two sequential activities included but the constraints of resource time windows and due date of tasks are also considered. An improved nondominated sorting genetic algorithm (NSGA-II) is presented to solve the model. The validity of the proposed model and algorithm are verified by a corresponding case study.     

Single-machine group scheduling with resource allocation and learning effect

This paper addresses single-machine scheduling problems under the consideration of learning effect and resource allocation in a group technology environment. In the proposed model of this paper the actual processing times of jobs depend on the job position, the group position, and the amount of resource allocated to them concurrently. Learning effect and two resource allocation functions are examined for minimizing the weighted sum of makespan and total resource cost, and the weighted sum of total completion time and total resource cost. We show that the problems for minimizing the weighted sum of makespan and total resource cost remain polynomially solvable. We also prove that the problems for minimizing the weighted sum of total completion time and total resource cost have polynomial solutions under certain conditions.     

Multi-objective scheduling and a resource allocation problem in hospitals

This study addresses the issue of scheduling medical treatments for resident patients in a hospital. Schedules are made daily according to the restrictions on medical equipment and physicians who are being assigned at the same time. The problem is formulated as a multi-objective binary integer programming (BIP) model. Three types of metaheuristics are proposed and implemented to deal with the discrete search space, numerous variables, constraints and multiple objectives: a variable neighborhood search (VNS)-based method, scatter search (SS)-based methods and a non-dominated sorting genetic algorithm (NSGA-II). This paper also provides the results of computational experiments and compares their ability to find efficient solutions to the multi-objective scheduling problem.