用逐次最小权值轮询算法实现公平和低时延分组调度

分组调度算法是路由交换设备性能的重要保证,对基于轮询的分组调度进行了研究,提出了一种新的调度算法称为逐次最小权值轮询调度算法(successive minimal-weight round robin,SMRR),在每个轮次中为每个活动数据流提供与本轮次中的最小权值相当的服务机会.根据Latency-Rate(LR)Servers理论,证明了SMRR算法和WRR算法的时延上界,并对SMRR算法的公平性和实现复杂性进行了讨论,理论推导和性能分析表明SMRR算法具有比WRR算法更好的时延特性和公平性,同时具有O(1)的时间复杂度,具有良好的可扩展性.     

基于蝙蝠退火算法的无等待流水线调度问题研究

无等待流水线调度问题(no-wait flow shop scheduling problem,NWFSP)是一类比较重要的复杂生产调度问题,并已经被证明是典型的NP问题.蝙蝠算法(Bat algorithm,BA)是一种较新颖的群体智能算法.本文针对蝙蝠算法在求解无等待流水线调度问题上的不足,提出一种蝙蝠退火算法,它通过采用ROV的编码方式以实现离散问题的连续编码,同时为了避免算法早熟现象引入了模拟退火算法.算法采用基于NEH的局部搜索规则,在很大程度上提高了算法的性能.利用标准Car问题和Rec问题算例进行仿真实验,结果表明了改进算法的可行性和有效性.     

求解零等待流水车间调度问题的改进蝙蝠算法

针对零等待流水车间调度问题特性,设计了一种蝙蝠算法进行求解.算法模拟蝙蝠捕食搜索行为进行寻优,利用基于最小位置值规则的随机键编码方式来表示问题解,采用基于NEH方法的局部搜索策略和随机交换、插入、逆序操作的变邻域搜索策略来提高局部优化性能,进一步根据Metropolis概率准则接受劣解来避免早熟.通过典型算例对所提算法进行仿真测试并与粒子群算法和RAJ启发式算法进行对比,结果表明所设计算法求解零等待流水车间调度问题的有效性和优越性,是求解流水车间生产调度问题的一种有效工具.     

基于前摄性调度的电动汽车路径问题研究

针对车辆调度过程中资源不均衡的问题,利用需求的不确定性,将配送周期划分为初始配送阶段和补货阶段,建立多阶段电动汽车的两级车辆路径优化模型.根据需求的动态程度对配送区域进行划分,结合前摄性调度和反应性调度策略,提出了一种混合禁忌搜索算法(HTSA)来求解该模型.在真实的案例和多个基准评估算例上的实验结果表明:模型和算法的性能优于传统的启发式算法,具有一定的实用价值.     

改进鲸鱼优化算法在电力负荷调度中的应用

为了提高鲸鱼优化算法(WOA)的全局优化性能, 提出了一种基于黄金分割搜索的改进鲸鱼优化算法(GWOA)。首先利用黄金分割搜索对WOA的初始种群进行初始化, 使得初始种群能够尽可能的靠近全局最优解, 然后利用黄金分割搜索所形成的变区间, 进行变区间黄金分割非均匀变异操作, 以增加WOA的粒子多样性和提高粒子跳出局部最优陷阱的能力, 从而改善WOA的寻优性能。选取了15个大规模测试函数进行数值仿真测试, 仿真结果和统计分析表明GWOA的寻优性能要优于对比文献的改进鲸鱼优化算法(IWOA)。此外, 将GWOA用于对工程实际应用领域中的电力负荷优化调度问题进行实例分析, 实例应用结果表明, GWOA能有效对电力负荷优化调度问题进行寻优求解。     

一种求解延迟工件数最小的混合流水车间调度问题的模拟退火算法

针对延迟工件数最小的混合流水车间调度问题,给出了一种改进的模拟退火求解算法. 该算法首先给出一个启发式算法来获得初始解,然后用模拟退火算法对初始解改进. 通过交换工件在第一阶段的排序来获得一个新的解,采用最先空闲设备分配规则和先到先被加工规则,对工件在剩余各级的工序进行调度. 实验仿真表明算法是可行有效的.     

求解机器人制造单元调度问题的化学反应优化算法

针对多类型工件加工机器人制造单元调度NP难题,提出一种局部搜索的化学反应优化算法。该算法采用基于迭代次数的线性排序选择,维持解的多样性;构建紧后工件阻塞时间最小化交换的邻域结构加快收敛速度。此外,该算法主要参数由正交试验获得。通过求解随机产生的算例,仿真结果表明,化学反应优化算法优于遗传算法,提出算法较化学反应优化算法能更有效地搜索到更好解。     

云计算环境下人工蜂群作业调度算法设计

针对云计算环境下作业调度优化问题,提出了一种基于人工蜂群的调度算法.分析人工蜂群算法的求解组合优化问题过程,建立了收益度函数和蜜源位置更新公式,最后论述了利用该算法求解的具体步骤.并通过实验分析了该算法的性能.     

自适应蚁群算法及其在多约束多目标柔性Job-Shop调度中的应用

实际生产系统的车间作业调度一般是多约束多目标柔性Job-Shop调度,比经典的Job-Shop调度更复杂,存在多约束、多目标、动态柔性、建模复杂等特性.建立了多约束多目标柔性Job-Shop调度模型,提出了一种自适应蚁群算法,采用自适应机制和遗传原理防止算法过早停滞和加快收敛速度.西安航空发动机(集团)有限公司制造单元调度实例表明,提出的自适应蚁群算法是求解多约束多目标柔性Job-Shop调度的有效方法.     

求解带组换装时间单机调度问题的禁忌搜索算法

以包头某钢铁线材企业生产实际调度问题为背景,研究了一类带组换装时间的单机调度问题.由于该问题是NP难的,本文提出了一类适合该问题的禁忌搜索算法.此外,本文将问题性质引入了禁忌搜索算法以进一步提高算法寻优性能,降低算法运行时间.本文提出的算法在随机问题和实际问题上均进行了测试,实验结果表明,本文提出的算法能在不到10秒的时间内获得实际问题的一个近似最优解.     

单水库在线发电调度策略设计与分析

考察单水库电站的多时段发电调度问题,决策者在每个时段初决策该时段的发电量,目标是使得在整个调度期内总发电量最大。针对在每个时段决策时缺乏当前及后续时段来水信息的情形,运用在线理论建立在线发电调度模型,设计给出了竞争比为2/(2-β(1-Ф))的在线发电调度策略,其中,β∈(0,1)表示每个时段最大来水导致的水头最大增幅与水库有效水头最大落差的比值,Ф∈(0,1)表示最低与最高有效水头数值之比。针对各时段可获知当前时段来水信息的情形,给出了在线调度策略,并证明了其竞争比为1+(1-Ф)/(1+Ф)。     

Metaheuristics for scheduling jobs with incompatible families on parallel batching machines

In this paper, we discuss the scheduling of jobs with incompatible families on parallel batching machines. The performance measure is total weighted tardiness. This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication where the machines can be modelled as parallel batch processors. Given that this scheduling problem is NP-hard, we suggest an ant colony optimization (ACO) and a variable neighbourhood search (VNS) approach. Both metaheuristics are hybridized with a decomposition heuristic and a local search scheme. We compare the performance of the two algorithms with that of a genetic algorithm (GA) based on extensive computational experiments. The VNS approach outperforms the ACO and GA approach with respect to time and solution quality.     

Genetic algorithm-based subproblem solution procedures for a modified shifting bottleneck heuristic for complex job shops

In this paper, we consider a modified shifting bottleneck heuristic for complex job shops. The considered job shop environment contains parallel batching machines, machines with sequence-dependent setup times and reentrant process flows. Semiconductor wafer fabrication facilities (Wafer Fabs) are typical examples for manufacturing systems with these characteristics. Our primary performance measure is total weighted tardiness (TWT). The shifting bottleneck heuristic uses a disjunctive graph to decompose the overall scheduling into scheduling problems for single tool groups. The scheduling algorithms for these scheduling problems are called subproblem solution procedures (SSPs). In previous research, only subproblem solution procedures based on dispatching rules have been considered. In this paper, we are interested in how much we can gain in terms of TWT if we apply more sophisticated subproblem solution procedures like genetic algorithms for parallel machine scheduling. We conduct simulation experiments in a dynamic job shop environment in order to assess the performance of the suggested subproblem solution procedures. It turns out that using near to optimal subproblem solution procedures leads in many situations to improved results compared to dispatching-based subproblem solution procedures.     

基于约束满足问题的中继卫星调度问题研究

中继卫星任务规划与调度是中继卫星系统应用中的重要问题。根据航天器的空间轨道参数，得到中继卫星与用户航天器之间的可见时间窗口。在此基础上，通过分析中继卫星系统中各种资源之间的约束关系、任务优先级与调度准则，建立中继卫星系统的任务调度模型。仿真结果表明，基于约束规划理论建立中继卫星调度模型是解决中继卫星调度问题的有效方法。     

A hybrid genetic algorithm with dominance properties for single machine scheduling with dependent penalties

In this paper, a hybrid genetic algorithm is developed to solve the single machine scheduling problem with the objective to minimize the weighted sum of earliness and tardiness costs. First, dominance properties of (the conditions on) the optimal schedule are developed based on the switching of two adjacent jobs i and j. These dominance properties are only necessary conditions and not sufficient conditions for any given schedule to be optimal. Therefore, these dominance properties are further embedded in the genetic algorithm and we call it genetic algorithm with dominance properties (GADP). This GADP is a hybrid genetic algorithm. The initial populations of schedules in the genetic algorithm are generated using these dominance properties. GA can further improve the performance of these initial solutions after the evolving procedures. The performances of hybrid genetic algorithm (GADP) have been compared with simple genetic algorithm (SGA) using benchmark instances. It is shown that this hybrid genetic algorithm (GADP) performs very well when compared with DP or SGA alone.     

A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan

It is well known that the flow-shop scheduling problem (FSSP) is a branch of production scheduling and is NP-hard. Now, many different approaches have been applied for permutation flow-shop scheduling to minimize makespan, but current algorithms even for moderate size problems cannot be solved to guarantee optimality. Some literatures searching PSO for continuous optimization problems are reported, but papers searching PSO for discrete scheduling problems are few. In this paper, according to the discrete characteristic of FSSP, a novel particle swarm optimization (NPSO) algorithm is presented and successfully applied to permutation flow-shop scheduling to minimize makespan. Computation experiments of seven representative instances (Taillard) based on practical data were made, and comparing the NPSO with standard GA, we obtain that the NPSO is clearly more efficacious than standard GA for FSSP to minimize makespan.     

Analysis of a multicast switch via the arrival modulation technique

Multicast switching is emerging as a new switching technology that can provide efficient transport in a broadband network for video and other multipoint communication services. In this paper, we analyze the call blocking probability in a multicast switch via thearrival modulation technique. Our study shows that multicast traffic encounters higher blocking probability than point-to-point traffic because of simultaneous output port contentions. To ensure adequate performance for multicast traffic, we develop and analyze a simple call scheduling algorithm calledFixed Splitting. The results show that the fixed splitting algorithm reduces the call blocking probability significantly when it is applied with appropriately selected splitting parameters.     

Dynamic scheduling for switched processing systems with substantial service-mode switching times

Switched Processing Systems (SPS) represent canonical models for many communication and computer systems. Over the years, much research has been devoted to developing the best scheduling policies to optimize the various performance metrics of interest. These policies have mostly originated from the well-known MaxWeight discipline, which at any point in time switches the system into the service mode possessing “maximal matching” with the system state (e.g., queue-length, workload, etc.). However, for simplicity it is often assumed that the switching times between service modes are “negligible”—but this proves to be impractical in some applications. In this study, we propose a new scheduling strategy (called the Dynamic Cone Policy) for SPS, which includes substantial service-mode switching times. The goal is to maximize throughput and maintain system stability under fairly mild stochastic assumptions. For practical purposes, an extended scheduling strategy (called the Practical Dynamic Cone Policy) is developed to reduce the computational complexity of the Dynamic Cone Policy and at the same time mitigate job delay. A simulation study shows that the proposed practical policy clearly outperforms another throughput-maximizing policy called BatchAdapt, both in terms of the average and the 95th percentile of job delay for various types of input traffic.     

A state-of-the-art review on scheduling with learning effects

Recently learning effects in scheduling have received considerable attention in the literature. All but one paper are based on the learning-by-doing (or autonomous learning) assumption, even though proactive investments in know how (induced learning) are very important from a practical point of view. In this review we first discuss the questions why and when learning effects in scheduling environments might occur and should be regarded from a planning perspective. Afterwards we give a concise overview on the literature on scheduling with learning effects.