高层电梯系统运行方案的建模与求解

随着社会的发展,运用垂直交通系统的高层建筑和智能化建筑不断出现。而有效的电梯交通配置,是垂直交通系统高效运行的基本保证。本文针对高层商务建筑中的电梯运行管理方案设计问题,分析了影响电梯耗能和用户满意度的主要因素。分别建立了电梯数目已知和电梯数目未知情况下的电梯调度优化模型,并设计相应动态规划算法和遗传算法。结合算例,求解算例中的电梯优化调度方案,以验证模型的合理性。最后根据我们建立的电梯调度模型,借助VC++作出可视化的电梯调度示意界面,将本文的研究结果用于实际的电梯调度中。     

基于实际路网情境的配送车辆调度优化

在实际路网情境下结合车道数、车道宽度、路口信号灯设置等路网物理特性,构建了考虑综合交通阻抗的多车型车辆调度模型,提出了两阶段求解策略:第1阶段设计了改进A-star精确解算法用于计算客户时间距离矩阵;第2阶段针对实际路网的特征设计了混合模拟退火算法求解调度方案。以大连市某配送中心运营实例进行路网情境仿真试验,结果表明:改进A-star算法较改进Dijkstra算法具有更短的路径搜索时间;混合模拟退火算法求解结果较实际调度方案优化了13.1% 的综合成本;路网增流、区域拥堵和路段禁行三类路网情境均能对配送方案的车辆配置、路径选择、客户服务次序、作业时间和违约费用等5方面内容产生干扰,调度计划的制定需要详细考虑这些因素的变化。     

粒子群遗传混合算法求解考虑传输时间的FJSP

在某些生产制造场景中,工件在不同机器间的传输时间对车间调度的总拖期具有重要影响,本文基于此扩展了总拖期最小的柔性作业车间调度模型。针对问题模型的复杂性,采用粒子群优化算法和遗传算法的混合算法进行求解。在初始化过程以一定概率优选加工时间和传输时间短的机器并排除调度频繁的机器,使种群在保持多样性的前提下尽量选择优化结果好的个体;采用线性调整的方式动态改变交叉概率和变异概率的值,使种群在遗传算法的不同阶段具有不同的搜索强度;采用粒子群优化算法进行局部搜索,弥补了遗传算法局部搜索能力的不足。最后采用本文方法和其他方法求解柔性作业车间调度问题实例,并对比不同水平层次传输时间下的总拖期,验证了本文方法的有效性。     

基于混沌量子算法和MAGTD的多目标FJSP求解策略

针对多目标环境下柔性作业车间的调度问题,以最小化最大完工时间和惩罚值为目标,建立调度问题的数学模型,提出了基于混沌理论的量子粒子群算法。针对实际生产交货期不确定的特点,在量子粒子群算法基础上,提出引入混沌机制建立初始群的方法;利用混沌机制的遍历性,提出混沌局部优化策略;为获取最优调度方案提出了引入多指标加权灰靶选择策略。通过典型基准算例和对比测试,验证了所提出的算法获得最满意调度方案的可行性和求解多目标柔性作业车间调度问题的有效性。     

煤炭码头卸车调度问题多目标优化模型及算法

本文针对煤炭码头卸车调度问题,提出了相应的多约束多目标优化模型,并设计了采用仿真推演策略解码的遗传算法求解。首先,本文考虑列车、煤种、场存、设备、翻堆线和卸车作业过程等约束条件,以卸车效率最大和列车在港时间最短为目标,构建了煤炭码头卸车调度问题多目标数学模型。然后,综合运筹学、遗传算法以及仿真技术,给出了煤炭码头卸车调度问题遗传算法详细设计,包括组合式编码和仿真推演解码方法,染色体生成算法,适应度函数设计,以及采用多种策略的遗传操作及修正等,并列出了算法步骤。实例测试表明,本算法的执行效率高而且优化效果好,结果适用。     

具有学习-遗忘效应的半导体批调度问题研究

半导体生产制造系统具有大规模、工艺繁杂、随机性大、可重入等显著特点。以半导体最终测试阶段批处理调度为基础,把学习-遗忘效应应用到典型半导体批调度问题中,构建基于学习-遗忘效应的批调度模型。分别结合调度问题和调度模型对双层算法(粒子群算法&萤火虫算法)进行设计,通过仿真实验检验了双层算法在求解具有学习遗忘效应的批调度模型方面的可行性和有效性,并对比分析以最大完工时间为优化目标的实验结果,探讨学习遗忘效应对半导体批调度问题的影响程度,对实际半导体生产具有重要指导意义。     

具有模糊行程的城市物流动态费用多车型车辆调度问题研究

针对城市物流配送中广泛存在的多车型问题,以及由于交通路况等因素导致的配送行程模糊化现象,给出了一种基于梯形模糊数的,以最小化行程费用为目标的具有模糊行程的动态费用多车型车辆调度问题模型.在问题求解方面,针对基本粒子群算法容易陷入局部最优的情况,引入混沌局部搜索策略,给出了一种基于混沌优化技术的混合粒子群算法.仿真实验表明,该算法具有可行性和有效性.     

大规模伤亡事件应对流程的前摄性调度优化

以死亡人数最少化为目标,研究大规模伤亡事件应对流程的前摄性调度优化问题。首先,使用伤情等级和伤情随机转化的马尔可夫链,建立伤员死亡概率与伤员处置时间的函数关系。随后,将研究问题转化为柔性作业车间静态调度问题,并设计遗传算法求解。最后用一个随机算例对算法进行仿真,结果表明：该算法可行有效;与现有研究中救援时间最短化的调度方法相比,伴随着可接受的救援时间跨度增加,该方法可大幅降低救援过程中的死亡人数。本文研究有助于决策者优化应急救援过程,有效减少死亡人员数量。     

混合无等待流水线干扰管理调度方法研究

针对带分批约束的混合无等待流水加工环境中干扰事件的出现导致初始调度计划发生偏离的问题,研究如何运用干扰管理理论来应对工件变更扰动情况,建立了兼顾最小化工件完工时间加权和指标(初始调度目标)和最小化工件完工滞后时间加权和指标(偏离校正目标)的干扰管理调度模型,提出了双层微粒群优化策略与随机多邻域搜索机制相结合的混合求解算法。数值算例仿真实验结果表明,包含“插入-交换”大概率邻域搜索算子的混合微粒群优化算法求解本文所构建的干扰管理调度模型是有效的。     

双机成比例无等待流水线重调度干扰管理研究

针对由异速机构成的双机成比例无等待流水线的加工特点,研究了机器扰动工况下的生产重调度问题,提出了兼顾初始调度目标(最小化制造期)和扰动修复目标(最小化工件滞后时间和)的干扰管理方法。在最短加工时间优先(SPT)排序规则的最优解特性分析基础上,证明了右移初始加工时间表是事后干扰管理的最优调度方案,建立了基于SPT规则的事前干扰管理模型,设计了基于理想点趋近的多目标处理策略,提出了离散量子微粒群优化与局部搜索机制相结合的启发式模型求解算法。算例实验结果表明,本文提出的干扰管理模型和算法是有效的。     

Evolutionary bi-objective optimisation in the elevator car routing problem

The paper introduces a genetic algorithms based elevator group control system utilising new approaches to multi-objective optimisation in a dynamically changing process control environment. The problem of controlling a group of elevators as well as the basic principles of the existing single-objective genetic elevator group control method are described. The foundations of the developed multi-objective approach, Evolutionary Standardised-Objective Weighted Aggregation Method, with a PI-controller operating as an interactive Decision Maker, are introduced. Their operation as a part of bi-objective genetic elevator group control is presented together with the performance results obtained from simulations concerning a high-rise office building. The results show that with this approach it is possible to regulate the service level of an elevator system, in terms of average passenger waiting time, so as to bring it to a desired level and to produce that service with minimum energy consumption. This has not been seen before in the elevator industry.     

时延网络控制系统的协同设计方法研究

在搭建的虚拟平台上对多任务的网络控制系统调度和嵌入LQG控制算法进行了仿真.探索了网络控制系统中调度与控制协同设计方法,讨论系统采样周期对网络控制系统的影响.以优化控制系统的性能为目标,以网络的可调度性为条件,结合系统控制和调度算法,对网络控制系统进行静态性能指标估计和动态调度仿真相结合.结果表明该方法既满足了控制系统的性能,又优化了网络的调度,提高了网络的资源率.     

Optimization of setup times in the furniture industry

The aim of this paper is to develop a scheduling policy oriented towards minimizing setup times in the made-to-order furniture industry. The task is treated as a dynamic job shop scheduling problem, with the exception that customers?? orders collected over a?specified period of time are combined into a?production plan and released together. A?simulation of a production flow based on technological routes of real subassemblies was performed. The proposed method of calculating a setup time eliminates the need to determine machine setup time matrices. Among the tested priority rules the best performance was observed in the case of the hierarchical rule that combines similar setup, the earliest due date and the shortest processing time. This rule allowed the setup time per operation to be reduced by 58?% compared to a combination of the earliest due date with the shortest setup and processing time rule and by over 70?% compared to the single shortest processing time rule.     

基于人机交互—蚁群算法的港口疏船调度优化

为解决因港口无法正常作业导致大量船舶压港后的疏船调度问题,从同时兼顾船公司和港口方利益出发,建立了船舶平均在港时间最短、额外作业成本最低、生产秩序恢复最快的调度生产多目标优化模型。利用多属性效用理论将多目标转换为单目标,并构建了相应的评价函数,采用改进的蚁群算法并结合人机交互以及邻域搜索方法求解,最后以大连港集装箱码头实际案例进行验证。结果表明,与通常调度方法相比,文中建立的优化模型能够更好地解决疏船问题;对比常规的蚁群算法,改进后的算法搜索效率更高。上述模型和算法为集装箱码头的生产组织调度提供了新的优化思路和方法。     

Optimal size-based opportunistic scheduler for wireless systems

Modern wireless cellular systems are able to utilize the opportunistic scheduling gain originating from the variability in the users’ channel conditions. By favoring users with good instantaneous channel conditions, the service capacity of the system can be increased with the number of users. On the other hand, for service systems with fixed service capacity, the system performance can be optimized by utilizing the size information. Combining the advantages of size-based scheduling with opportunistic scheduling gain has proven to be a challenging task. In this paper, we consider scheduling of data traffic (finite-size elastic flows) in wireless cellular systems. Assuming that the channel conditions for different users are independent and identically distributed, we show how to optimally combine opportunistic and size-based scheduling in the transient setting with all flows available at time 0. More specifically, by utilizing the time scale separation assumption, we develop a recursive algorithm that produces the optimal long-run service rate vectors within the corresponding capacity regions. We also prove that the optimal operating policy applies the SRPT-FM principle, i.e., the shortest flow is served with the highest rate of the optimal rate vector, the second shortest with the second highest rate, etc. Moreover, we determine explicitly how to implement the optimal rate vectors in the actual time slot level opportunistic scheduler. In addition to the transient setting, we explore the dynamic case with randomly arriving flows under illustrative channel scenarios by simulations. Interestingly, the scheduling policy that is optimal for the transient setting can be improved in the dynamic case under high traffic load by applying a rate-based priority scheduler that breaks the ties based on the SRPT principle.     

On reducing time spent in M/G/1 systems

In this paper we define a new ‘truncated shortest processing time’ scheduling discipline and present the first two moments of the time spent in a single server queuing system (M/G/1) with Poisson arrivals and truncated shortest processing time scheduling discipline. Also for quadratic cost functions, the mean cost of time spent in an M/G/1 system under (1) first come first served. (2) shortest processing time. (3) two level shortest processing time, (4) two class non-preemptive priority, and (5) truncated shortest processing time scheduling disciplines are compared.     

超市模型的实时动态控制及其数值分析

超市模型是针对大型并行排队网络所进行的 实时动态控制的随机负载平衡策略, 它在计算机网络、云计算、制造系统、交通网络等领域有着重要的实际应用价值. 本文考虑了超市模型中的若干重要问题: 实时动态控制模式; 效率比较; 平均场黑洞; 马氏变动环境; 稳定性; 固定点; 系统性能评价等等. 同时, 本文也通过数值算例研究了上述重要问题, 包括对顾客加入最短队列的超市模型与服务台服务最长队列的超市模型进行了性能比较, 给出了他们效率的优劣分析; 在超市模型中对控制到达过程机制进行了三种情况的对比; 对马氏变动环境下的超市模型进行了性能评价.     

Multi-layered round robin routing for parallel servers

We study a system of several identical servers in parallel, where a routing decision must be made immediately on a job’s arrival. Jobs arrive according to a Poisson process, with their processing times following a discrete distribution with finite support. The processing time of a job is known on arrival and may be used in the routing decision. We propose a policy consisting of multi-layered round robin routing followed by shortest remaining processing time scheduling at the servers. This policy is shown to have a heavy traffic limit that is identical to one in which there is a single queue (no routing) and optimal heavy traffic scheduling. In light traffic, we show that the performance of this policy is worse than round robin routing followed by shortest remaining processing time scheduling. We also quantify the difference between round robin and multi-layered round robin routing, which in turn yields insights on the relative importance of routing versus (local) scheduling in such systems. AMS subject classifications: 68M20 · 60K25 (Work done while both authors were visitors at EURANDOM, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.)     

基于物流AGV的“货到人”订单拣选系统任务调度研究

基于物流AGV的“货到人”订单拣选模式由于其高效率和灵活性,逐渐成为电商物流配送中心订单拣选系统发展趋势。本文通过对基于物流AGV的电商物流配送中心订单拣选作业流程分析,提出多拣选台同步拣选和多拣选台异步拣选两种作业模式。然后对基于物流AGV的订单拣选任务调度问题进行描述,以物流AGV完成所有任务的时间最短为目标,分别建立同步和异步两种拣选模式下物流AGV任务调度模型;针对物流AGV任务调度问题特性,对共同进化遗传算法粗粒度模型进行改进用于模型求解。最后,通过改进前后算法的对比,验证了改进共同进化遗传算法在求解物流AGV任务调度问题中的有效性;通过在求解速度和优化结果上对多拣选台同步拣选和异步拣选两种作业模式进行比较,得出同步拣选优于异步拣选的结果。