相型同步启动时间的M／M／c排队系统

本文研究带有同步启动时间的Ｍ／Ｍ／ｃ系统，其中启动时间是相型变量，给出了稳态和等待时间分布等结果。     

具有多个制造商和分批配送的同类机排序问题

考虑了同类机环境下多个工件加工和配送的排序问题.有多个制造商分布在不同位置,每个制造商处有一台机器可以加工工件.不同的机器对应着不同的加工速度和加工费用.工件生产完后需要运输到客户处,每一批配送需要花费一定的时间和费用.研究了排序理论中主要的3个目标函数,分析了问题的复杂性,对于这些问题给出了它们的最优算法.     

Poincare‘—Cartan积分不变量的推广和Dirac猜想

该文将Poincare－Cartan积分不变量推广到显含时间的高阶微商奇异拉氏量系统，研究了该不变量与正则方程、正则变换之间的联系，讨论了广义Poincare－Cartan积分不变量与Dirac猜想的关系，以一个例子说明，对高阶微商奇异拉氏量系统，Dirac猜想是无效的．     

随机生产系统安全库存的JIT控制策略

本文研究了随机工件加工时间的生产系统在JIT（Just-In-Time）控制策略下的运行机制。基于文[1]所提出的看板控制生产系统的生产率与其马尔柯夫模型的状态空间相对应的理论，本文探讨了在JIT控制策略下生产系统的看板在安全库存受限的条件下的最优配置问题，并给出了工作站数目小于5的生产系统看板最优配置公式。     

考虑可拆分订单及加工类型匹配的平行机调度决策

介绍了制造资源共享环境下共享平台的生产和运作，以1688淘平台为例，将共享平台抽象刻画为考虑可拆分订单和加工类型匹配的平行机调度问题。客户将订单下达到共享平台上，供应商将闲置机器放在平台的资源池里。不同机器具有相同的加工速度但只能加工与其类型匹配的个性化订单，因此，需要决策使用哪些机器。一旦使用某台机器，会产生固定的加工或租赁成本。每个订单可以被拆分成整数长度的多个子订单，并在可用的机器上同时被加工。以最小化所使用机器的总加工成本和订单的总完工时间之和为优化目标，建立了一个整数线性规划模型。对于小规模实例，CPLEX可以求得最优解；对于中规模和大规模例子，提出了基于机器加工能力的贪婪算法和遗传算法。数据实验表明，基于机器加工能力的贪婪算法是一种高效且有效的算法。此外，尽量选择加工能力强的机器加工订单；将订单拆分在多台机器上并行加工可以缩短订单的完成时间。     

变速仿形系统中的模糊控制方法

本文介绍一种新型的活塞环非圆轮廓曲线的数控仿形系统，其基本原理是以某种凸轮曲线作为刀具进给运动的基准线，应用调速原理，独立调节刀具的进给速度，使之与机床主轴保持一定的关系，在电机。反转的前提下加工出不同型号和规格的活塞环零件。为提高系统的速度调节精度和系统的鲁棒性，本文着重阐述了智能决策方法和模糊控制技术在本系统中的应用。     

具有有限次休假的n部件串联系统可靠性分析

本文采用补充变量法,讨论了修理工带有有限次休假的n部件串联可修系统的可靠性.得到了系统的可用度、失效频度、等待修理的概率和平均更新时间等主要可靠性指标,并给出了模型的特例及系统的效益分析.     

数控插补中基于运动曲线的局部优化和基于前瞻控制的整体优化

数控加工在机械制造领域具有举足轻重的作用,如何在满足加工精度的前提下提高加工速度是数控加工的一个关键问题.据数学机械化方法,将插补问题分解为局部优化和整体优化两部分,其中局部优化是在线段连接处通过采用二次曲线(抛物线)和三次曲线族过渡的方法,提高线段连接处的通过速度,使得局部通过速度最优;同时根据加工误差确定线段连接处的插补时间,并以插补时间为参数对线段连接处的插补参数统一进行调整,提高了数控软件的智能性.在局郎优化的基础上进一步进行整体优化,即采用基于直线加减速和S型加减速控制方式的前瞻处理算法,对加工路径进行有效预测,避免了因突然加减速造成机床振动,从一定程度上保证整体加工速度的提高,采用符号与数值混合算法,算法简单,保证了计算精度和速度,满足实时加工的要求.算法在蓝天数控系统上进行了实际加工验证,结果表明该算法达到了预期效果,验证了算法的有效性.与当前已有算法相比,根据机床加工参数不同,加工速度提高了50%-170%.     

服务台可修的GI／M（M／PH）／1排队系统

本文首次讨论一个到达间隔为一般分布的可修排队系统。假定服务时间、忙期服务台寿命都服从指疏分布，修复时间是ＰＨ变量。首先证明该系统可转化为一个经典的ＧＩ／￣ＰＨ／１排队模型，然后给出系统在稳态下的各种排队论指标和可靠性指标。     

动态问题速度有限元法

分析了N.M.Newmark和E.L.Wilson等按位移作变量逐步积分法的主要特点.提出以速度为变量求解动力学问题的速度元法.针对无阻尼系统,构造了一种简化格式,讨论了稳定性.由于该格式在无阻尼和拟静力阻尼情况下为显式,每个时刻,不求解代数方程组,其计算量与Newmark等方法比较,显著减少.对非线性动态问题,该计算格式可作为取得较好迭代初值的一个办法.文中,就任意阻尼系统,列出了速度元法的推广形式.相应非线性情况,提供了速度增量迭代格式并证明了收敛性.文末,附录了典型问题的数值检验结果.     

Modelling complex assemblies as a queueing network for lead time control

In this paper we develop an open queueing network for optimal design of multi-stage assemblies, in which each service station represents a manufacturing or assembly operation. The arrival processes of the individual parts of the product are independent Poisson processes with equal rates. In each service station, there is a server with exponential distribution of processing time, in which the service rate is controllable. The transport times between the service stations are independent random variables with exponential distributions. By applying the longest path analysis in queueing networks, we obtain the distribution function of time spend by a product in the system or the manufacturing lead time. Then, we develop a multi-objective optimal control problem, in which the average lead time, the variance of the lead time and the total operating costs of the system per period are minimized. Finally, we use the goal attainment method to obtain the optimal service rates or the control vector of the problem.     

Optimal investment in setup reduction in manufacturing systems with WIP inventories

A number of models have been proposed to predict optimal setup times, or optimal investment in setup reduction, in manufacturing cells. These have been based on the economic order quantity (EOQ) or economic production quantity (EPQ) model formulation, and have a common limitation in that they neglect work-in-process (WIP) inventories, which can be substantial in manufacturing systems. In this paper a new model is developed that predicts optimal production batch sizes and investments in setup reduction. This model is based on queuing theory, which permits it to estimate WIP levels as a function of the decisions variables, batch size and setup time. Optimal values for batch size and setup time are found analytically, even though the total cost model was shown to be strictly non-convex.     

Asymptotics of the optimal time in a time-optimal control problem with a small parameter

A time-optimal control problem for a singularly perturbed linear autonomous system is considered. The main difference between this case and the case of systems with fast and slow variables studied earlier is that the eigenvalues of the matrix at the fast variables do not satisfy the standard requirement of negativity of the real part. We obtain and justify a complete power asymptotic expansion in the sense of Erdélyi of the optimal time and optimal control in a small parameter at the derivatives in the equations of the system.     

Setup cost reduction in (<Emphasis Type="Italic">Q,r</Emphasis>) policy with lot size,setup time and lead-time interactions

It is often assumed in most deterministic and stochastic inventory models that lead-time is a given parameter and the optimal operating policy is determined on the basis of this unrealistic assumption. However, the manufacturing lead-time is made up of several components (moving time, waiting time, setup time, lot size, and rework time) most of which should be treated as controllable variables. In this paper the effect of setup cost reduction is addressed in a stochastic continuous review inventory system with lead-time depending on lot size and setup time. An efficient iterative procedure is developed to determine the near optimal lot size, reorder point and setup time. Furthermore, a sensitivity analysis is carried out to assess the cost savings that can be realised by investing in setup.     

Using a risk-based approach to project scheduling: A case illustration from semiconductor manufacturing

This paper introduces a risk-based optimization method to schedule projects. The method uses risk mitigation and optimal control techniques to minimize variables such as the project duration or the cost estimate at completion. Mitigation actions reduce the risk impacts that may affect the system. A model predictive control approach is used to determine the set of mitigation actions to be executed and the time in which they are taken. A real-life project in the field of semiconductor manufacturing has been taken as an example to show the benefits of the method in a deterministic case and a Monte Carlo simulation has also been carried out.     

柔性制造系统的载荷模型和对偶分解算法

本文研究柔性制造系统最优排序问题的载荷模型，通过优化系统的最优利用率并考虑系统各机器的工作平衡，本文给出了载荷问题三个新的优化模型，这些模型形成具有０－１变量和一般整型变量的大规模整数规划问题，根据分解理论，考虑到问题的变量特性，这些大规模问题可被分解成若干维数较低的子问题求解，文章还给出了一个对偶分解算法。     

A multi-objective lead time control problem in multi-stage assembly systems using genetic algorithms

In this paper, we develop a multi-objective model to optimally control the lead time of a multi-stage assembly system, using genetic algorithms. The multi-stage assembly system is modelled as an open queueing network. It is assumed that the product order arrives according to a Poisson process. In each service station, there is either one or infinite number of servers (machines) with exponentially distributed processing time, in which the service rate (capacity) is controllable. The optimal service control is decided at the beginning of the time horizon. The transport times between the service stations are independent random variables with generalized Erlang distributions. The problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. The objective functions are the total operating costs of the system per period (to be minimized), the average lead time (min), the variance of the lead time (min) and the probability that the manufacturing lead time does not exceed a certain threshold (max). Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed genetic algorithm approach.     

Due date assignment for multistage assembly systems

This paper is concerned with the study of the constant due-date assignment policy in a multistage assembly system. The multistage assembly system is modeled as an open queueing network. It is assumed that the product order arrives according to a Poisson process. In each service station, there is either one or infinite machine with exponentially distributed processing time. The transport times between every pair of service stations are independent random variables with generalized Erlang distributions. It is assumed that each product has a penalty cost that is some linear function of its due-date and its actual completion time. The due date is found by adding a constant to the time that the order arrives. This constant value is the constant lead time that a product might expect between time of placing the order and time of delivery. By applying the longest path analysis in queueing networks, we obtain the distribution function of manufacturing lead time. Then, the optimal constant lead time is computed by minimizing the expected aggregate cost per product. Finally, the results are verified by Monte Carlo simulation.     

On the optimal control of manufacturing and remanufacturing activities with a single shared server

We consider a single-product make-to-stock manufacturing–remanufacturing system. Returned products require remanufacturing before they can be sold. The manufacturing and remanufacturing operations are executed by the same single server, where switching from one activity to another does not involve time or cost and can be done at an arbitrary moment in time. Customer demand can be fulfilled by either newly manufactured or remanufactured products. The times for manufacturing and remanufacturing a product are exponentially distributed. Demand and used products arrive via mutually independent Poisson processes. Disposal of products is not allowed and all used products that are returned have to be accepted. Using Markov decision processes, we investigate the optimal manufacture–remanufacture policy that minimizes holding, backorder, manufacturing and remanufacturing costs per unit of time over an infinite horizon. For a subset of system parameter values we are able to completely characterize the optimal continuous-review dynamic preemptive policy. We provide an efficient algorithm based on quasi-birth–death processes to compute the optimal policy parameter values. For other sets of system parameter values, we present some structural properties and insights related to the optimal policy and the performance of some simple threshold policies.     

Numerical solutions for optimal control of monodomain equations

We present the numerical solutions of optimality systems corresponding to optimal control problems governed by the mono-domain equations which are widely used for describing the electrical activity of the cardiac tissue. This mono-domain model is based on a parabolic equation and a system of stiff ordinary differential equations. The space discretization of the state variables and dual variables is done using piecewise linear finite elements and the time discretization is based on linearly implicit Runge-Kutta methods. The main goal of this work is to study the optimal control behavior of the electrical potentials under the influence of extracellular ionic currents as control variables. The numerical results presented are based on first and second order optimization methods. (© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)