基于贪心算法和遗传算法的仓储车辆调度算法

针对仓储车辆调度问题提出一种基于贪心算法与遗传算法的调度算法。它主要利用遗传算法为框架筛选、进化出高效的调度方案,算法又融合了贪心算法对调度中的任务排序进行了快速优化。此融合使得遗传算法的编码简便,排除了不可行解的可能,从而使得算法性能大大提高。算法已经C++语言编程实现,实验分析证明:算法有效地提升了调度方案的效率。     

基于启发式规则的混合遗传算法及其在生产计划优化中的应用

以最优或近似最优的作业顺序编制满足关键资源约束的生产计划优化问题一直是企业生产管理中重要的研究课题之一。文章提出了一种基于传统启发式规则的混合遗传算法。该算法将染色体分为两段,前段表示资源安排策略,后段表示为优先分配规则序列,并设计了一种新的交叉算子。最后,介绍了根据此算法编制的一个制造企业生产控制的软件系统。     

A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops

Obtaining an optimal solution for a permutation flowshop scheduling problem with the total flowtime criterion in a reasonable computational timeframe using traditional approaches and optimization tools has been a challenge. This paper presents a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime. Iterated greedy algorithms are comprised of local search procedures based on insertion and swap neighborhood structures. In the same context, we also consider a discrete differential evolution algorithm from our previous work. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the discrete artificial bee colony and hybrid differential evolution algorithms is compared against the best performing algorithms from the existing literature in terms of both solution quality and CPU times. Ultimately, 44 out of the 90 best known solutions provided very recently by the best performing estimation of distribution and genetic local search algorithms are further improved by the proposed algorithms with short-term searches. The solutions known to be the best to date are reported for the benchmark suite of Taillard with long-term searches, as well.     

基于云存储的二阶段动态优化调度机制

在分布式存储的研究中,如何高效地利用存储空间是个热点问题.存储集群中,每个数据节点存储容量不可能完全一致,由于主节点选择数据节点的随机性,被选中数据节点磁盘可能接近满额,此时主节点会自动做存储负载均衡,占用数据传输带宽,不仅影响数据传输的性能,而且会引起传输数据的不可靠.论文提出一种基于云存储的二阶段动态优化调度机制：第一阶段通过计算副本存储优选比率,采用基于贪心算法的局部优化存储方案,选择存储节点,均衡副本放置空间;第二阶段采用实时监控存储集群,动态调整副本放置节点,达到存储资源的高效利用.最后通过实验,验证了该调度机制可有效地放置副本,减少节点间的数据传输,并提高文件访问效率.     

A variable iterated greedy algorithm with differential evolution for the no-idle permutation flowshop scheduling problem

This paper presents a variable iterated greedy algorithm (IG) with differential evolution (vIG_DE), designed to solve the no-idle permutation flowshop scheduling problem. In an IG algorithm, size d of jobs are removed from a sequence and re-inserted into all possible positions of the remaining sequences of jobs, which affects the performance of the algorithm. The basic concept behind the proposed vIG_DE algorithm is to employ differential evolution (DE) to determine two important parameters for the IG algorithm, which are the destruction size and the probability of applying the IG algorithm to an individual. While DE optimizes the destruction size and the probability on a continuous domain by using DE mutation and crossover operators, these two parameters are used to generate a trial individual by directly applying the IG algorithm to each target individual depending on the probability. Next, the trial individual is replaced with the corresponding target individual if it is better in terms of fitness. A unique multi-vector chromosome representation is presented in such a way that the first vector represents the destruction size and the probability, which is a DE vector, whereas the second vector simply consists of a job permutation assigned to each individual in the target population. Furthermore, the traditional IG and a variable IG from the literature are re-implemented as well. The proposed algorithms are applied to the no-idle permutation flowshop scheduling (NIPFS) problem with the makespan and total flowtime criteria. The performances of the proposed algorithms are tested on the Ruben Ruiz benchmark suite and compared to the best-known solutions available at http://soa.iti.es/rruiz as well as to those from a recent discrete differential evolution algorithm (HDDE) from the literature. The computational results show that all three IG variants represent state-of-art methods for the NIPFS problem.     

Scheduling the truckload operations in automated warehouses with alternative aisles for pallets

In this study, the scheduling of truck load operations in automated storage and retrieval systems is investigated. The problem is an extension of previous ones such that a pallet can be retrieved from a set of alternative aisles. It is modelled as a flexible job shop scheduling problem where the loads are considered as jobs, the pallets of a load are regarded as the operations, and the forklifts used to remove the retrieving items to the trucks are seen as machines. Minimization of maximum loading time is used as the objective to minimize the throughput time of orders and maximize the efficiency of the warehouse. A priority based genetic algorithm is presented to sequence the retrieving pallets. Permutation coding is used for encoding and a constructive algorithm generating active schedules for flexible job shop scheduling problem is applied for decoding. The proposed methodology is applied to a real problem arising in a warehouse installed by a leading supplier of automated materials handling and storage systems.     

A Bipartite Genetic Algorithm for Multi-processor Task Scheduling

Until now, several methods have been presented to optimally solve the multiprocessor task scheduling problem that is an NP-hard one. In this paper, a genetic-based algorithm has been presented to solve this problem with better results in comparison with related methods. The proposed method is a bipartite algorithm in a way that each part is based on different genetic schemes, such as genome presentation and genetic operators. In the first part, it uses a genetic method to find an adequate sequence of tasks and in the second one, it finds the best match processors. To evaluate the proposed method, we applied it on several benchmarks and the results were compared with well known algorithms. The experimental results were satisfactory and in most cases the presented method had a better makespan with at least 10% less iterations compared to related works.     

Minimizing makespan for flow shop scheduling problem with intermediate buffers by using hybrid approach of artificial immune system

In this study, three new meta-heuristic algorithms artificial immune system (AIS), iterated greedy algorithm (IG) and a hybrid approach of artificial immune system (AIS-IG) are proposed to minimize maximum completion time (makespan) for the permutation flow shop scheduling problem with the limited buffers between consecutive machines. As known, this category of scheduling problem has wide application in the manufacturing and has attracted much attention in academic fields. Different from basic artificial immune systems, the proposed AIS-IG algorithm is combined with destruction and construction phases of iterated greedy algorithm to improve the local search ability. The performances of these three approaches were evaluated over Taillard, Carlier and Reeves benchmark problems. It is shown that the AIS-IG and AIS algorithms not only generate better solutions than all of the well-known meta heuristic approaches but also can maintain their quality for large scale problems.     

An effective iterated greedy algorithm for reliability-oriented task allocation in distributed computing systems

This paper investigates the problem of allocating parallel application tasks to processors in heterogeneous distributed computing systems with the goal of maximizing the system reliability. The problem of finding an optimal task allocation for more than three processors is known to be NP-hard in the strong sense. To deal with this challenging problem, we propose a simple and effective iterative greedy algorithm to find the best possible solution within a reasonable amount of computation time. The algorithm first uses a constructive heuristic to obtain an initial assignment and iteratively improves it in a greedy way. We study the performance of the proposed algorithm over a wide range of parameters including problem size, the ratio of average communication time to average computation time, and task interaction density. The viability and effectiveness of our algorithm is demonstrated by comparing it with recently proposed task allocation algorithms for maximizing system reliability available in the literature.     

一种基于汽车CPS的服务调度算法

驾驶者通过路边基础设施感知外部环境并根据经验作出反应是汽车信息物理融合系统的一个最基本的特点,研究汽车与路边基础设施信息交互对建设汽车信息物理融合系统具有重要意义。基于汽车与路边基础设施通信的场景,提出一种新的服务消息调度模型,设计了基于优先级的调度算法,采用贪心思想,优先调度效用值大的消息,将效用值小的消息进行插空调度,最后通过实验证明了本文算法的有效性。     

遗传算法在公交调度中的应用研究

以乘客和公交公司总效益最大为调度目标,建立了公交运营参数优化模型,给出了用遗传算法求解该模型的步骤,用“青岛市公交示范线”501线路运营数据对提出的模型及算法进行了验证。     

基于任务调度的无线网贪婪信道分配算法

针对无线网络链路干扰问题,综合借鉴多处理器任务调度算法提出了一种贪婪信道分配算法,为所访问的无线网链路甄选出干扰最小的信道,并且证明了本算法的近似比率为2-1/k,其中为k为可用的正交信道数,算法复杂度为O（|E|2）。为了验证本文算法的可行性和有效性,将本文所提出的贪婪算法与随机信道分配算法和按序信道分配算法进行了实验对比。仿真结果表明：本文所提出的贪婪算法的整体性能优于其他两种算法,并且贪婪算法得到的最大干扰和平均干扰归一化值随着可用正交信道数的变化趋势较其他两种算法稳定。从而验证了本文算法能有效的降低链路干扰,一定程度上可以提升网络吞吐量。     

求解混合流水车间调度问题的一种遗传算法

由于高度的计算复杂性(NP-hard问题),混合流水车间调度问题很难求得最优解,启发式算法和智能优化算法(如遗传算法)求解此类问题的近优解的有效性和实用性已被证实。该文提出了一种基于遗传算法的求解方法,在由染色体转换成可行调度的过程中引入工件插入方法,同时设计了一种新的交叉算子。通过大量的数值计算表明,该算法的优化质量大大优于传统的遗传算法和NEH启发式算法。     

An Improved Genetic Algorithm for Berth Scheduling at Bulk Terminal

Berth and loading and unloading machinery are not only the main factors that affecting the terminal operation, but also the main starting point of energy saving and emission reduction. In this paper, a genetic Algorithm Framework is designed for the berth allocation with low carbon and high efficiency at bulk terminal. In solving the problem, the scheduler’s experience is transformed into a regular way to obtain the initial solution. The individual is represented as a chromosome, and the sub-chromosomes are encoded as integers, the roulette wheel method is used for selection, the two-point crossing method is used for cross, and the exchange variation method is used for variation in the procedure of designing the Algorithm. Considering the complexity of berth scheduling problem and the diversity of constraints and boundary conditions, the genetic algorithm combines with system simulation to get the final scheme of berth allocation. This model and algorithm are verified to be practical by analyzing multiple sets of examples of shorelines with different lengths. When compared with the traditional algorithms in three aspects which includes berth offset distance, departure delay cost and energy consumption of portal crane, the result indicates that the improved algorithm is more effective and feasible. The study will help to lower energy consumption and resource waste, reduce environmental pollution, and provide a reference for low-carbon, green and sustainable development of the terminal.     

高校排课系统的算法研究

阐述了排课系统的问题及需求,介绍了排课问题中必须遵循的相关约束条件,分析了基于回溯算法、贪心算法和遗传算法的排课原理及特点。     

面向移动机器人分布式计算的任务调度方法

针对移动机器人导航控制中信息处理量大、任务多的情况,提出了一个适用于移动机器人的分布式计算框架,并在此框架的基础上设计了一种任务调度方法——GMBSA,该方法以资源代理为基础,首先对任务执行时间进行预测,然后运用遗传算法结合多队列Backfilling方法进行任务调度,达到最小化任务执行时间的要求,最终实现资源的优化分配,满足了机器人导航控制中的实时性要求。该文采用实验室构建的分布式计算环境对GMBSA的性能进行了测试,并比较了轻重负载情况下GMBSA,多队列Backfilling和FCFS 3种调度方案的性能差异。     

A Hybrid Estimation of Distribution Algorithm for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times

A hybrid estimation of distribution algorithm (EDA) with iterated greedy (IG) search (EDA-IG) is proposed for solving the unrelated parallel machine scheduling problem with sequence-dependent setup times (UPMSP-SDST). For makespan criterion, some properties about neighborhood search operators to avoid invalid search are derived. A probability model based on neighbor relations of jobs is built in the EDA-based exploration phase to generate new solutions by sampling the promising search region. Two types of deconstruction and reconstruction as well as an IG search are designed in the IG-based exploitation phase. Computational complexity of the algorithm is analyzed, and the effect of parameters is investigated by using the Taguchi method of design-of-experiment. Numerical tests on 1640 benchmark instances are carried out. The results and comparisons demonstrate the effectiveness of the EDA-IG. Especially, the bestknown solutions of 531 instances are updated. In addition, the effectiveness of the properties is also demonstrated by numerical comparisons.      

求解一类并行多机调度问题的混合启发式算法

该文研究了一类工件具有不同释放时间的并行多机调度问题,调度目标为使总流程时间最小。针对该类调度问题具有强NP—hard的特点,首先构造了的一种启发式算法,该算法能够在很短的时间内找到次优解。由于通常启发式算法会随着问题规模的扩大导致求解的质量有所下降,结合遗传算法的全局搜索能力,提出了一种混合启发式算法进一步改善解的质量。仿真结果表明该算法很好地结合了启发式算法和遗传算法的特点,能够在较短的时间内求解较大规模的调度问题,算法的计算量小,鲁棒性好。     

基于改进迭代贪婪算法的预制构件调度研究

迭代贪婪算法是一种具有较强局部搜索能力的元启发式算法,但由于传统迭代贪婪算法搜索范围过大,搜索效率有限,为了进一步提升传统迭代贪婪算法的搜索能力,考虑到阈值接受算法具有能缩小搜索范围的特点,提出了一种改进的迭代贪婪算法解决流水车间预制生产的订单接受与调度问题。该改进算法是在破坏原调度序列后加入一种基于构造启发式规则的重建策略,并结合阈值接受算法的自适应接受准则用以跳出局部最优。经大量仿真实验结果显示,与传统迭代贪婪算法、禁忌搜索算法以及遗传算法对比,改进的迭代贪婪算法具有更好的求解质量和鲁棒性。     

基于改进遗传算法的资源优化配置研究

当前在解决资源优化配置问题时往往使用贪婪算法、遗传算法等.但贪婪算法只能选择一个最优度量标准,所以只能获得度量意义下的最优解而不是该问题的最优解,而如果直接使用遗传算法又存在搜索空间过大、耗时过长的问题.提出了一种新的算法.先基于贪婪算法获得问题的初始解空间,然后对初始解空间进行冲突检测与消解,最后运用改进的遗传算法进行优化获得最优方案.测试算例表明大大缩小了遗传算法的搜索空间,在保证获得最优解的条件下加快了收敛速度并有效防止了种群的退化.提出的算法在突发事务的处理方面具有一定的意义.