集成预防性维护和流水线调度的鲁棒性优化研究

针对离散流水车间, 设备故障率函数服从威布尔分布, 在考虑维护策略的基础上, 以工件的最终完工时间期望值为质量鲁棒性指标、以所有工序的开始加工时间的延迟总和的期望值为解鲁棒性指标, 建立了不确定性环境下预防性维护(Preventive maintenance, PM)和生产调度的集成优化模型, 联合决策各工序的开始加工时间和预防性维护位置. 进一步, 设计了基于工件优先列表、有效代理指标、邻域搜索机制的三阶段启发式算法对模型进行求解. 最后, 数值实验与传统方法对比结果表明, 系统最优缓冲时间随着解鲁棒性权重的增大而逐渐增加, 且质量鲁棒性堕化速度远小于解鲁棒性提升的速度, 使得其与传统方法相比总体目标愈加优异.     

考虑资源置信度的跨企业项目鲁棒性调度算法

资源不确定性高和调度鲁棒性要求高是跨企业项目调度问题的重要特征,本文采用资源置信度度量资源的不确定性,建立了考虑资源置信度约束的跨企业项目鲁棒性优化调度模型,设计了路径重连求解算法.算法以路径重连机制搜索解空间,以嵌入 的启发式时间缓冲插入算法快速生成鲁棒性调度,并可通过局部增强搜索算法进一步优化调度的鲁棒性.本文应用项目调度标准问题 库PSPLIB中大量问题实例进行了仿真实验,同两个当前具有代表性的鲁棒性项目调度算法进行了比较,实验结果表明了文中算法的有 效性与优势.     

约束规划在原油调度中应用的探索

针对数学规划方法应用在原油调度中对非线性约束和连续变量处理的局限性,通过分析约束规划方法解决问题的原理、建模方式和搜索机制,指出了应用约束规划方法解决原油调度问题的可能性和优势;并针对实际问题,分别应用两种方法进行建模、求解．通过对结果的分析得出,约束规划方法建模简单,可直接处理非线性问题,对连续变量和整型变量都可以很快求得可行解。     

基于约束逻辑规划和遗传算法的柔性调度

根据柔性生产环境的特点，描述了约束逻辑规划（CLP）和遗传算法（GA）在解决调度问题中的应用框架。CLP的解决作为满足约束的调度问题的起始解，保证了初始解的合理性。把CLP用作计算每一代样本的约束检验手段，有利于在遗传算法的搜索中获得更好的解和更高的解算效率。最后对一个规模足够大的调度实例进行了计算。     

低碳环境下闭环供应链网络设计多目标鲁棒模糊优化问题

针对低碳环境下多级闭环供应链网络设计的战略定位和配置问题,综合考虑网络参数的模糊性以及多产品流,以供应链网络的总成本和总碳排放最小为目标,建立多目标鲁棒模糊优化模型.该模型将机会约束的最低置信水平作为决策变量,实现目标函数期望值、最优鲁棒性和可行鲁棒性之间的有效平衡.最后,设计一种基于必要性测度的交互式$\varepsilon$约束模糊求解方法,并通过算例验证所提模型和算法的有效性和可行性.     

基于粗糙规划的不确定加工时间的并行机调度

针对并行机调度中的不确定工件加工时间,提出用粗糙变量表示不确定量,并由此建立该问题的粗糙期望值规划模型.提出一种应用于调度问题的进化规划算法,改进了针对并行机问题的编码方式和变异方法.采用粗糙模拟的方法计算个体的适应值,即粗糙期望估计值,并加以不同规模的算例进行仿真实验.仿真结果表明,改进进化规划算法得到的解优于遗传算法得到的解.     

约束规划方法在原油混输调度中的应用

应用约束规划方法建立炼厂原油混输模型,并通过搜索求解获得可操作的调度方案。约束规划允许以逻辑约束表达混输过程中的操作规则,使模型简洁直观,而对整型变量及非线性约束不敏感,则降低了模型的求解难度。选取了一个时间跨度为300小时实际调度问题作为案例,模型中包含约8000个变量和14000条约束,在普通PC机上计算求解,能够在30秒内获得调度方案,方案具有可操作性且优于人工方案,表明以约束规划方法解决原油混输调度问题是可行的。     

基于约束规划的岸桥与集卡集成调度

针对进口集装箱卸船的岸桥与集卡集成调度问题,分别提出混合整数规划(MIP)模型和约束规划(CP)模型,目标是使得卸船完工时间最短,该问题是NP难题。通过OPL语言设计约束规划模型,利用其为调度问题提供的特殊构造,如区间变量、序列变量等进行建模,并采用"扩展操作任务"的概念来定义区间变量以提升求解效率。为评价解的质量,设计一个新的下界求解方法。使用不同规模的实例对约束规划模型和MIP模型进行测试,结果表明,在小规模实例中,CP模型求解性能略差于MIP模型,但对于中大规模实例,MIP模型无法在设定时限内找到解,而CP模型则能以较快的收敛速度得到高质量的解,目标距离下界的差距控制在2.19%~8.28%。     

基于遗传算法的空中突击编队攻击路径优化问题研究

在有时间约束的情况下,空中突击编队完成攻击任务,寻找使资源消耗最低的攻击路径,一直以来是航空兵作战指挥部门关注的问题之一。通过约束转换,确立了采用遗传算法进行优化调度的模型,并详细的说明了遗传操作方法的设计,实例验证了遗传算法在调度问题处理过程中的有效性。     

用于炼油厂原油库存调度的混合模拟退火算法

现有的数学规划法在解决原油库存调度优化问题时存在着组合爆炸的问题,是阻碍调度优化实用化的主要原因。由于实践中往往只要求快速地获得一个较好解,因此作为启发式算法之一的模拟退火法,在解决调度问题的实用化方面具有很大的优越性。但由于模拟退火法较适于处理无约束的整数规划问题,而在原油库存调度优化模型中却存在着大量的实数约束,所以在其中直接应用模拟退火法比较困难。该文将模拟退火法与线性规划法相结合,以前者调动后者,后者为前者提供可行解判据,构成了一种优化混合算法。在将混合算法应用于原油库存调度问题时,该文采用了特定的编码方式,使各控制变量在随机变化时尽量满足相关的约束条件,从而避免了许多无效解的产生。实例计算结果表明,同传统的混合整数线性规划方法相比,这种混合算法可以快速地给出优化解,其优化值与全局最优值差别不大,表明混合算法可以更好地解决实际原油调度问题。     

Multi-objective solid transportation problems with budget constraint in uncertain environment

This paper investigates multi-objective solid transportation problems (MOSTP) under various uncertain environments. The unit transportation penalties/costs are taken as random, fuzzy and hybrid variables respectively, in three different uncertain multi-objective solid transportation models and in each case, the supplies, demands and conveyance capacities are fuzzy. Also, apart from source, demand and capacity constraints, an extra constraint on the total budget at each destination is imposed. Chance-constrained programming technique has been used for the first two models to obtain crisp equivalent forms, whereas expected value model is formulated for the last. We provide an another approach using the interval approximation of fuzzy numbers for the first model to obtain its crisp form and compare numerically two approaches for this model. Fuzzy programming technique and a gradient based optimisation - generalised reduced gradient (GRG) method are applied to beget the optimal solutions. Three numerical examples are provided to illustrate the models and programming.     

Inherent robustness properties of quasi-infinite horizon nonlinear model predictive control

We consider inherent robustness properties of model predictive control (MPC) for continuous-time nonlinear systems with input constraints and terminal constraints. We show that MPC with a nominal prediction model and persistent but bounded disturbances has some degree of inherent robustness when the terminal control law and the terminal penalty matrix are chosen as the linear quadratic control law and the related Lyapunov matrix, respectively. We emphasize that the input constraint sets can be any compact set rather than convex sets, and our results do not depend on the continuity of the optimal cost function or of the control law in the interior of the feasible region.     

Modelling linear fractional transportation problem in belief degree—based uncertain environment

A linear fractional transportation problem in uncertain environment is studied in this paper where the uncertain parameters of the problem are of belief degree—based uncertainty. For the first time, this type of uncertainty is considered for the linear fractional transportation problem. Belief degree—based uncertainty is useful for the cases that no historical information of an uncertain event is available. Zigzag type uncertainty distribution is used to show the belief degree—based uncertainty of the parameters of the problem. As solution methodology, the uncertain linear fractional transportation problem is converted to a crisp form using three approaches of expected value model, expected value and chance-constrained model, and chance-constrained model, separately. An extensive computational study on a real illustrative example shows the efficiency of the proposed formulation and the conversion approaches. The sensitivity analysis over the example illustrates the high dependency of the objective function value to the changes of the confidence level values of the chance constraints in the expected value and chance-constrained programming approach and the chance-constrained programming approach.     

Opportunistic scheduling for streaming multimedia users in high-speed downlink packet access (HSDPA)

High-speed downlink packet access (HSDPA) achieves high data rates and high spectral efficiency by using adaptive modulation and coding schemes and employing multicode CDMA. In this paper, we present opportunistic algorithms for scheduling HSDPA users and selecting modulation/coding and multicode schemes that exploit channel and buffer variations to increase the probability of uninterrupted media play-out. First, we introduce a stochastic discrete event model for a HSDPA system. By employing the discrete event model, we transform the scheduling problem of providing uninterrupted play-out to a feasibility problem that considers two sets of stochastic quality-of-service (QoS) constraints: stability constraints and robustness constraints. A methodology for obtaining a feasible solution is then proposed by starting with a so-called stable algorithm that satisfies the stability QoS constraints. Next, we present stochastic approximation algorithms that adapt the parameters of the stable algorithm in a way that a feasible point for the robustness QoS is reached within the feasibility region of the stability QoS.     

Examples when nonlinear model predictive control is nonrobust

We consider nominal robustness of model predictive control for discrete-time nonlinear systems. We show, by examples, that when the optimization problem involves state constraints, or terminal constraints coupled with short optimization horizons, the asymptotic stability of the closed loop may have absolutely no robustness. That is to say, it is possible for arbitrarily small disturbances to keep the closed loop strictly inside the interior of the feasibility region of the optimization problem and, at the same time, far from the desired set point. This phenomenon does not occur when using model predictive control for linear systems with convex constraint sets. We emphasize that a necessary condition for the absence of nominal robustness in nonlinear model predictive control is that the value function and feedback law are discontinuous at some point(s) in the interior of the feasibility region.     

基于不确定作业时间的FSM P 调度问题研究

针对一类加工时间不确定的FSMP调度问题，建立了基于扩展期望区间数近似不确定参数的优化模型．提出了利用扩展期望区间数构造参数近似边界的取极大运算方法，给出了包含不确定度控制指标的多目标优化模型．基于算例，讨论了决策因子和不确定度控制指标对调度性能的影响，仿真结果及分析表明了该模型和算法的有效性与鲁棒性．     

Design optimization of resource combination for collaborative logistics network under uncertainty

Collaborative logistics networks (CLNs) are considered to be an effective organizational form for business cooperation that provides high stability and low cost. One common key issue regarding CLN resource combination is the network design optimization problem under discrete uncertainty (DU-CLNDOP). Operational environment changes and information uncertainty in network designs, due to partner selection, resource constrains and network robustness, must be effectively controlled from the system perspective. Therefore, a general two-stage quantitative framework that enables decision makers to select the optimal network design scheme for CLNs under uncertainty is proposed in this paper. Phase 1 calculates the simulation result of each hypothetical scenario of CLN resource combination using the expected value model with robust constraints. Phase 2 selects the optimal network design scheme for DU-CLNDOP using the orthogonal experiment design method. The validity of the model and method are verified via an illustrative example.     

Expected model for portfolio selection with random fuzzy returns

This paper researches portfolio selection problem in combined uncertain environment of randomness and fuzziness. Due to the complexity of the security market, expected values of the security returns may not be predicted accurately. In the paper, expected returns of securities are assumed to be given by fuzzy variables. Security returns are regarded as random fuzzy variables, i.e. random returns with fuzzy expected values. Following Markowitz's idea of quantifying investment return by the expected value of the portfolio and risk by the variance, a new type of mean–variance model is proposed. In addition, a hybrid intelligent algorithm is provided to solve the new model problem. A numeral example is also presented to illustrate the optimization idea and the effectiveness of the proposed algorithm.     

Robust control strategies facing disturbances in manufacturing workshops with time constraints

Developments presented in this paper are devoted to the robustness control of manufacturing job-shops with time constraints and without assembling tasks. Such systems have robustness properties which allow them to face time disturbances. Three robust control strategies towards time disturbances are presented. The first one consists of generating by the control a temporal shift similar to the disturbance, in order to avoid the death of marks on the levels of synchronization transitions of the P-time Petri net model. The second one rejects the disturbance as soon as it is observed, using the control. The third strategy represents the main contribution of this paper. It is a combination of the two preceding approaches in order to face time disturbances. Based on this last approach, an algorithm allowing computing the robustness margin at various graph nodes is established, providing a value integrating the constraints of the whole system.