基于粒子群算法的资源受限项目扩展调度方法

针对资源受限的项目调度问题,提出了一种离散粒子群算法与扩展调度机制相结合的优化方法.离散粒子群算法中每个粒子的位置代表一组项目任务的优先权,迭代中通过交叉策略和局部搜索策略来更新粒子的位置,这既保持了粒子位置的离散性,又增加了粒子的多样性,避免早熟收敛.每个粒子的位置通过扩展串行调度机制转换成可行的调度方案.实算表明,扩展调度机制的引入显著地加速了收敛的进程,提高了解的精度.这种基于粒子群算法的扩展调度优化方法是求解资源受限项目调度问题的有效方法.     

针对作业调度问题的遗传选择算子性能分析

针对作业调度问题,通过变形遗传算法实验,对轮盘赌、随机联赛、随机遍历抽样和确定式采样等选择算子进行了比较分析.以FT06典型车间作业调度问题为实例,比较了这几种常用选择算子在解决车间作业调度问题时的性能优劣程度;从全局收敛性和收敛速度两个方面,分析总结了这些选择算子对算法的全局搜索能力的影响程度.实验结果表明随机遍历抽样算子的整体性能要优于其他几种选择算子.     

批量生产作业车间的双资源多目标调度问题研究

对于批量生产调度问题,根据批次判定公式确定每种工件能划分的批数及每批的工件数,然后把每个子批当作一个新的工件进行调度。采用遗传算法和禁忌搜索算法相结合的混合优化调度算法,用于解决同时考虑设备和工人的双资源问题,以及综合考虑生产周期、工件总延误时间、设备闲置时间和工人闲置时间的多目标综合优化问题。其中采用多目标决策理论用于确定遗传算法中的目标函数,以及由非劣解集合获得较优解。调度算例表明本研究能获得很好的调度效果。     

基于改进粒子群算法的车间作业排序的优化设计

兼顾车间作业排序中的制造周期和机器利用率,建立了以最小化最大完工时间为主目标、以最大化机器利用率为从目标的优化模型。设计了引入自适应技术的惯性权重,使基本粒子群算法的学习因子可动态变化地改进粒子群算法,并用该改进后的算法对车间作业排序进行了优化设计。实例研究表明：改进后的粒子群算法在收敛速度和收敛可靠性上均优于未改进的粒子群算法,在求解车间作业排序问题的应用中具有更高的求解质量。     

基于客流量变化的智能公交调度问题研究

为解决传统的公交调度系统由于采用固定发车间隔的刚性发车模式,引起的乘客群体满意度和公交公司满意度之间的矛盾,建立了以乘客群体和公交公司满意度之和为优化目标的数学模型,采用基于引入免疫浓度机制的免疫遗传算法来寻找各个时段的最优发车间隔.经改进后,公交车辆发车时间间隔随着客流量波动而柔性地变化,可以使乘客群体和公交公司的满意度最大.     

A multiscale decomposition method for the optimal planning and scheduling of multi-site continuous multiproduct plants

This paper addresses the solution of simultaneous scheduling and planning problems in a production–distribution network of continuous multiproduct plants that involves different temporal and spatial scales. Production planning results in medium and long-term decisions, whereas production scheduling determines the timing and sequence of operations in the short-term. The production–distribution network is made up of several production sites distributing to different markets. The planning and scheduling model has to include spatial scales that go from a single production unit within a site, to a geographically distributed network. We propose to use two decomposition methods to solve this type of problems. One method corresponds to the extension of the bi-level decomposition of Erdirik-Dogan and Grossmann (2008) to multi-site, multi-market networks. A second method is a novel hybrid decomposition method that combines bi-level and spatial Lagrangean decomposition methods. We present four case studies to study the performance of the full space planning and scheduling model, the bi-level decomposition, and the bi-level Lagrangean method in profit maximization problems. Numerical results indicate that in large-scale problems, decomposition methods outperform the full space solution and that as problem size increases the hybrid decomposition method becomes faster than the bi-level decomposition alone.     

制造系统中多智能体运输子系统的调度与监控

在论述制造控制系统常用体系结构的基础上,提出AGV运输子系统的体系结构,详细阐述AGV运输子系统中的运输调度、路径规划和冲突解决等关键技术;并开发了该AGV运输子系统的仿真系统。     

基于改进人工免疫算法的云计算任务调度

为了提高云计算任务调度效率,提出一种改进人工免疫算法的云计算任务调度方法。首先建立云计算任务调度的数学模型,并以任务总时间最短作为目标函数,然后采用人工免疫算法进行求解,并将粒子群优化算法作为算子嵌入人工免疫算法中,保持种群的多样性,防止局部最优解的出现,最后采用仿真实验对算法的性能进行测试。结果表明,相对于其它算法,改进人工免疫算法减少了任务的完成时间,提高了用户满意度。     

A linguistic approach to non-identical parallel processor scheduling with fuzzy processing times

This study presents an application of non-identical parallel processor scheduling under uncertain operation times. We have been motivated from a real case scheduling problem that contains some uncommon welding operations to be processed by workers in an automotive subcontract company. Here each operator may weld each job but in different processing times depending on learning effect because of operator’s ability and experience, and batch sizes. To determine the crisp operation times in such a fuzzy environment, a linguistic reasoning approach (with a 75-“If- Then” rules) considering the learning effect is proposed in the study. Since the fuzzy linguistic approach allows the representation of expert information more directly and adequately, it can be more possible to make realistic schedules under uncertainty. With the objective to balance the workload among all operators, the longest processing time heuristic algorithm is been used and measured average makespan. For evaluating the effectiveness of this approach, it is compared with the scheduling method that use the random operation times generated from a uniform distribution. Results showed that the proposed fuzzy linguistic scheduling approach has balanced the workload of operators with a standard deviation of 0.37 and improved the Cmax value as 16%. A general conclusion can be drawn the proposed approach is able to generate realistic schedules and especially useful to solve non-identical parallel processor scheduling problem under uncertainty. An important contribution of this study is that Mamdani inference method with learning effect is the first time used to obtain the crisp processing times of non-identical processors by the help of a rule base with expert knowledge.     

Hierarchical Particle Swarm Optimization with Ortho-Cyclic Circles

Cloud computing is an emerging technology which deals with real world problems that changes dynamically. The users of dynamically changing applications in cloud demand for rapid and efficient service at any instance of time. To deal with this paper proposes a new modified Particle Swarm Optimization (PSO) algorithm that work efficiently in dynamic environments. The proposed Hierarchical Particle Swarm Optimization with Ortho Cyclic Circles (HPSO-OCC) receives the request in cloud from various resources, employs multiple swarm interaction and implements cyclic and orthogonal properties in a hierarchical manner to provide the near optimal solution. HPSO-OCC is tested and analysed in both static and dynamic environments using seven benchmark optimization functions. The proposed algorithm gives the best solution and outperforms in terms of accuracy and convergence speed when compared with the performance of existing PSO algorithms in dynamic scenarios. As a case study, HPSO-OCC is implemented in remote health monitoring application for optimal service scheduling in cloud. The near optimal solution from HPSO-OCC and Dynamic Round Robin Scheduling algorithm is implemented to schedule the services in healthcare.