Optimal service and arrival rates in Jackson queueing networks

In this paper we present an algorithm for solving a class of queueing network design problems. Specifically, we focus on determining both service and arrival rates in an open Jackson network of queueing stations. This class of problems has been widely studied and used in a variety of applications, but not well solved due to the difficulty of the resulting optimization problems. As an example, consider the classic application in computer network design which involves determining the minimum cost line capacities and flow assignments while satisfying a queueing performance measure such as an upper limit on transmission delay. Other application areas requiring the selection of both service and arrival rates in a network of queues include the design of communication, manufacturing, and health care systems. These applications yield optimization problems that are difficult to solve because typically they are nonconvex, which means they may have many locally optimal solutions that are not necessarily globally optimal. Therefore, to obtain a globally optimal solution, we develop an efficient branch and bound algorithm that takes advantage of the problem structure. Computational testing on randomly generated problems and actual problems from a health care organization indicate that the algorithm is able to solve realistic sized problems in reasonable computing time on a laptop computer. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 1–17, 2000     

几类特殊多叶函数的极值点和支撑点

设S(p,α)、C(p,α)及K(p,α)分别表示α阶的p 叶星像、凸像和近于凸函数族。本文对这些函数族的支撑点和S(p,α)的从属函数族的极值点和支撑点进行了研究,推广了Grassmann 和Perera 等人的结果。     

Scheduling to minimize the total compression and late costs

We consider a single-machine scheduling model in which the job processing times are controllable variables with linear costs. The objective is to minimize the sum of the cost incurred in compressing job processing times and the cost associated with the number of late jobs. The problem is shown to be NP-hard even when the due dates of all jobs are identical. We present a dynamic programming solution algorithm and a fully polynomial approximation scheme for the problem. Several efficient heuristics are proposed for solving the problem. Computational experiments demonstrate that the heuristics are capable of producing near-optimal solutions quickly. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 67–82, 1998     

Scheduling a production–distribution system to optimize the tradeoff between delivery tardiness and distribution cost

We consider a make‐to‐order production–distribution system with one supplier and one or more customers. A set of orders with due dates needs to be processed by the supplier and delivered to the customers upon completion. The supplier can process one order at a time without preemption. Each customer is at a distinct location and only orders from the same customer can be batched together for delivery. Each delivery shipment has a capacity limit and incurs a distribution cost. The problem is to find a joint schedule of order processing at the supplier and order delivery from the supplier to the customers that optimizes an objective function involving the maximum delivery tardiness and the total distribution cost. We first study the solvability of various cases of the problem by either providing an efficient algorithm or proving the intractability of the problem. We then develop a fast heuristic for the general problem. We show that the heuristic is asymptotically optimal as the number of orders goes to infinity. We also evaluate the performance of the heuristic computationally by using lower bounds obtained by a column generation approach. Our results indicate that the heuristic is capable of generating near optimal solutions quickly. Finally, we study the value of production–distribution integration by comparing our integrated approach with two sequential approaches where scheduling decisions for order processing are made first, followed by order delivery decisions, with no or only partial integration of the two decisions. We show that in many cases, the integrated approach performs significantly better than the sequential approaches. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

地形通视性算法改进

为提高地形上两点间的通视性查询效率,满足仿真系统实时性要求,对通视性算法进行改进,并将改进算法与现有算法进行试验比较,结果表明改进算法的精度和计算效率是满足仿真需要的。     

Scheduling with centralized and decentralized batching policies in concurrent open shops

This article considers batch scheduling with centralized and decentralized decisions. The context of our study is concurrent open shop scheduling where the jobs are to be processed on a set of independent dedicated machines, which process designated operations of the jobs in batches. The batching policy across the machines can be centralized or decentralized. We study such scheduling problems with the objectives of minimizing the maximum lateness, weighted number of tardy jobs, and total weighted completion time, when the job sequence is determined in advance. We present polynomial time dynamic programming algorithms for some cases of these problems and pseudo‐polynomial time algorithms for some problems that are NP‐hard in the ordinary sense. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 58: 17–27, 2011     

微量闪点自动快速测定仪的研制

闪点是表征石油产品火灾危险性的安全指标.常规闪点测定法存在对操作者专业技能要求高,劳动强度大,无法进行连续不间断测定的缺点,影响了测定,结果的准确度和实验效率.将高压电弧、压力传感等现代分析检测技术引入石油产品闪点测定,研制了一台微量闪点自动快速测定仪.实验表明,微量闪点自动快速测定仪操作简单,试样量仅1 mL,减少了...     

Hilbert空间中拟非扩张映像族公共不动点的迭代算法

给出了Hilbert空间中拟非扩张映像族公共不动点的一个杂交投影算法,使用修正的杂交投影迭代算法,证明了一个强收敛定理,扩展了文献的结果。     

脉冲末修弹落点预测导引方法研究

在分析剩余射程影响因素的基础上,以弹道特征参数弹道高y及速度矢量分量Vx和Vy为解释变量,以回归分析方法为数学工具,建立脉冲末修弹剩余射程预测模型。以目标点与弹丸预测落点之间的差值为广义弹道偏差,结合数值仿真,对落点预测导引进行深入研究,验证了回归分析模型的可行性。     

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.