Optimal response against bioterror attack on airport terminal

We consider a potential bioterror attack on an airport. After the attack is identified, the government is faced with the problem of how to allocate limited emergency resources (human resources, vaccines, etc.) efficiently. The government is assumed to make a one-time resource allocation decision. The optimal allocation problem is discussed and it is shown how available information on the number of infected passengers can be incorporated into the model. Estimation for parameters of the cost function (number of deaths after the epidemic is over) is provided based on known epidemic models. The models proposed in the paper are demonstrated with a case study using real airport data.     

漂浮基空间机械臂关节轨迹跟踪的增广鲁棒控制方法

讨论了载体位置与姿态均不受控制的漂浮基空间机械臂系统的控制问题。基于增广变量法,解决了空间机械臂系统的控制方程关于系统惯性参数的非线性化问题;保持了控制方程关于系统惯性参数的线性关系。在此基础上,针对系统载荷参数不确定的情况下,提出了关节空间轨迹追踪 的增广鲁棒控制方法,并应用Lyapunov直接方法证明了 提到的控制方案能使系统满足渐近稳定性条件。通过仿真运算,证实了方法的有效性。     

基于DCGAN的拉曼光谱样本扩充及应用研究

拉曼光谱检测方法依赖于化学计量学算法,深度学习是当下最炙手可热的方向,可应用于拉曼光谱进行建模.但是深度学习需要大样本进行训练,而拉曼光谱采集受制于器材和人力成本,获取大批量的样本需要更大成本,且易受荧光等因素干扰,这些问题都制约了将深度学习应用于拉曼光谱.针对以上问题,通过引入深度卷积生成对抗网络(DCGAN)提取拉...     

A Memetic Algorithm for Minimum-Cost Vertex-Biconnectivity Augmentation of Graphs

This paper considers the problem of augmenting a given graph by a cheapest possible set of additional edges in order to make the graph vertex-biconnected. A real-world instance of this problem is the enhancement of an already established computer network to become robust against single node failures. The presented memetic algorithm includes effective preprocessing of problem data and a fast local improvement strategy which is applied before a solution is included into the population. In this way, the memetic algorithm's population consists always of only feasible, locally optimal solution candidates. Empirical results on two sets of test instances indicate the superiority of the new approach over two previous heuristics and an earlier genetic algorithm.     

Optimal procedures for the discrete time/cost trade-off problem in project networks

We describe two algorithms, based on dynamic programming logic, for optimally solving the discrete time/cost trade-off problem (DTCTP) in deterministic activity-on-arc networks of the CPM type, where the duration of each activity is a discrete, nonincreasing function of the amount of a single nonrenewable resource committed to it. The first algorithm is based on a procedure proposed by Bein, Kamburowski and Stallmann for finding the minimal number of reductions necessary to transform a general network to a series-parallel network. The second algorithm minimizes the estimated number of possibilities that need to be considered during the solution procedure. Both procedures have been programmed in C and tested on a large set of representative networks to give a good indication of their performance, and indicate the circumstances in which either algorithm performs best.     

A survey of scheduling with controllable processing times

In classical deterministic scheduling problems, the job processing times are assumed to be constant parameters. In many practical cases, however, processing times are controllable by allocating a resource (that may be continuous or discrete) to the job operations. In such cases, each processing time is a decision variable to be determined by the scheduler, who can take advantage of this flexibility to improve system performance. Since scheduling problems with controllable processing times are very interesting both from the practical and theoretical point of view, they have received a lot of attention from researchers over the last 25 years. This paper aims to give a unified framework for scheduling with controllable processing times by providing an up-to-date survey of the results in the field.     

Arc routing problems with time-dependent service costs

This paper studies an arc routing problem with capacity constraints and time-dependent service costs. This problem is motivated by winter gritting applications where the “timing” of each intervention is crucial. The exact problem-solving approach reported here first transforms the arc routing problem into an equivalent node routing problem. Then, a column generation scheme is used to solve the latter. The master problem is a classical set covering problem, while the subproblems are time-dependent shortest path problems with resource constraints. These subproblems are solved using an extension of a previously developed algorithm. Computational results are reported on problems derived from a set of classical instances of the vehicle routing problem with time windows.     

New and old bounds for standard quadratic optimization: dominance,equivalence and incomparability

A standard quadratic optimization problem (StQP) consists in minimizing a quadratic form over a simplex. Among the problems which can be transformed into a StQP are the general quadratic problem over a polytope, and the maximum clique problem in a graph. In this paper we present several new polynomial-time bounds for StQP ranging from very simple and cheap ones to more complex and tight constructions. The main tools employed in the conception and analysis of most bounds are Semidefinite Programming and decomposition of the objective function into a sum of two quadratic functions, each of which is easy to minimize. We provide a complete diagram of the dominance, incomparability, or equivalence relations among the bounds proposed in this and in previous works. In particular, we show that one of our new bounds dominates all the others. Furthermore, a specialization of such bound dominates Schrijver’s improvement of Lovász’s θ function bound for the maximum size of a clique in a graph.      

The single-machine earliness-tardiness scheduling problem with due date assignment and resource-dependent processing times

We study the earliness-tardiness scheduling problem on a single machine with due date assignment and controllable processing times. We analyze the problem with three different due date assignment methods and two different processing time functions. For each combination of these, we provide a polynomial-time algorithm to find the optimal job sequence, due date values and resource allocation minimizing an objective function which includes earliness, tardiness, due date assignment, makespan and total resource consumption costs.