Performance analysis of test scheduling schemes for a multi-access communications network

In this paper, we investigate the performance of two implementable test scheduling schemes for a multi-access communication channel whose components are subject to failure or malfunction. We relate the reliability of the system design, as reflected by system failure rate parameters, and the frequency at which the system (or nodal subsystem) is tested for failure detection, to the underlying key message delay and throughput performance. We derive queue-size distribution results for a discreteGeom ^(X)/D/1 system, representing the operation of the multi-access channel, or of a network node operating as a communications or queueing processor, which is maintained by a periodic or near periodic test scheduling scheme. Explicit formulas are presented for the system behavior as exhibited by the generating functions of the system queue-size distributions. The mean message delay is then calculated. The mean delay (or mean system size/workload performance index) can then be optimized by selecting the proper scheme parameters, under specified system (and component) failure conditions, noting that performing a test at too high a rate leads to inefficient system bandwidth utilization, while if tests are not carried out often enough, excessive message (or task) retransmissions and delays ensue.     

Market Power Issues in Bid-Based Hydrothermal Dispatch

The objective of this work is to investigate market power issues in bid-based hydrothermal scheduling. Initially, market power was simulated with a single stage Cournot–Nash equilibrium model. In this static model the equilibrium was calculated analytically. It was shown that the total production of N strategic agents is smaller than the least-cost solution by a factor of (N/(N+1)). Market power analysis for multiple stages was then carried through a stochastic dynamic programming scheme, where the decision in each stage and state is the Cournot–Nash equilibrium of a multi-agent game. Case studies with data taken from the Brazilian system are presented.     

面向绿色智能制造的高维多目标动态作业车间调度优化

在当前环境问题日益严峻情况下,绿色智能制造受到广泛关注。在动态柔性作业车间基础上考虑不同机器状态下的能耗情况、机器使用节能方法,构建以极小化总能耗、最大完工时间、机器总负荷和产品质量稳定性为目标的高维多目标绿色动态柔性作业车间调度模型,并设计改进的灰狼优化IMOGWO算法求解该问题。首先,采用反向学习初始化种群策略,以扩大种群多样性;然后,依据多目标问题和标准GWO算法的特点提出多级官员领导机制,并引入POX交叉和逆序变异算子;最后,改进精英保留策略用于多目标优化算法。为证明算法的有效性,设计两组仿真实验分别对三种算法进行比较。实验结果表明,运用本文改进的IMOGWO算法求解多目标问题有更好的收敛性和分布性。     

Proportional fair scheduling using water-filling technique for SC-FDMA based D2D communication

The resource allocation in SC-FDMA is constrained by the condition that multiple subchannels should be allocated to a single user only if they are adjacent. Therefore, the scheduling scheme of a D2D-cellular system that uses SC-FDMA must also conform to the so-called adjacency constraint. This paper proposes a heuristic algorithm with low computational complexity that applies proportional fair (PF) scheduling in the D2D-cellular system. The proposed algorithm consists of two main phases: (i) subchannel allocation and (ii) adjustment of data rates, which are executed for both CUEs and DUEs. In the subchannel allocation phase for CUEs (or D2D pairs), the users’ data rates are maximized via optimal power allocation to frequency-contiguous subchannels. In the second phase, a PF scheduling problem is solved to decide the modulation and coding scheme (MCS) of both CUEs and D2D pairs. Both phases of the proposed algorithm benefit from the Water-Filling (WF) technique. The simulation results suggest that the proposed scheme performs similar to optimal PF scheduling from the perspective of users’ data rate and their logarithmic sum. An additional benefit of the proposed scheme is its low computational overhead.     

Stochastic optimization of an urban rail timetable under time‐dependent and uncertain demand

Urban rail planning is extremely complex, mainly because it is a decision problem under different uncertainties. In practice, travel demand is generally uncertain, and therefore, the timetabling decisions must be based on accurate estimation. This research addresses the optimization of train timetable at public transit terminals of an urban rail in a stochastic setting. To cope with stochastic fluctuation of arrival rates, a two‐stage stochastic programming model is developed. The objective is to construct a daily train schedule that minimizes the expected waiting time of passengers. Due to the high computational cost of evaluating the expected value objective, the sample average approximation method is applied. The method provided statistical estimations of the optimality gap as well as lower and upper bounds and the associated confidence intervals. Numerical experiments are performed to evaluate the performance of the proposed model and the solution method.     

变速转子准稳态主动平衡系统的增益调度控制

在现代高速旋转机械中,不平衡引起的振动是机器性能降低甚至损坏的重要原因。对变速转子准稳态主动平衡系统,可按一定时间步长把变转速离散成有限个转动角速度。针对每个离散角速度,依据影响系数法,采用包含残余振动值和校正不平衡的广义线性二次型目标性能函数推导出控制律。通过每个离散转速下影响系数的估计和增益矩阵的计算,形成对应于各离散转速的增益表,从而实现变速转子准稳态主动平衡系统的增益调度控制。数值模拟验证了增益调度控制能很好地抑制不平衡振动。与传统的加权二乘法(WLS)相比,数值模拟显示,该法对校正不平衡的惩罚可提高系统的稳定性,也可增强控制的鲁棒性。     

Using genetic algorithms for the coordinated scheduling problem of a batching machine and two-stage transportation

This paper considers a coordinated scheduling problem. For the first-stage transportation there is a crane available to transport the product from the warehouse to a batching machine. For the second-stage transportation there is a vehicle available to deliver the completed jobs from the machine shop floor to the customer. The coordinated scheduling problem of production and transportation deals with sequencing the transportation of the jobs and combining them into batches to be processed. The problem of minimizing the sum of the makespan and the total setup cost was proven by Tang and Gong [1] to be strongly NP-hard. This paper proposes two genetic algorithm (GA) approaches for this scheduling problem, with different result representations. The experimental results demonstrate that a regular GA and a modified GA (MGA) can find near-optimal solutions within an acceptable amount of computational time. Among the two proposed metaheuristic approaches, the MGA is superior to the GA both in terms of computing time and the quality of the solution.     

Resource constraint scheduling with a fractional shared resource

We consider a scheduling problem motivated by mining in remote off-grid areas. In this model, mines have pre-assigned mineral processing jobs and their own machine for executing these jobs. Each job also needs a certain amount of electricity in order to get completed. The electricity, on the other hand, is of limited supply and must be shared between the mines. We present a mathematical formulation of the problem and a Lagrangian relaxation based heuristic. Computational results which compares our heuristic with genetic algorithm and simulated annealing are also presented.     

A solution approach for optimizing long- and short-term production scheduling at LKAB’s Kiruna mine

We present a mixed-integer program to schedule long- and short-term production at LKAB’s Kiruna mine, an underground sublevel caving mine located in northern Sweden. The model minimizes deviations from monthly preplanned production quantities while adhering to operational constraints. Because of the mathematical structure of the model and its moderately large size, instances spanning a time horizon of more than a year or two tend to be intractable. We develop an optimization-based decomposition heuristic that, on average, obtains better solutions faster than solving the model directly. We show that for realistic data sets, we can generate solutions with deviations that comprise about 3-6% of total demand in about a third of an hour.