Optimized allocation of defect inspection capacity with a dynamic sampling strategy

In semiconductor manufacturing, in-line inspections are necessary to monitor processes, products and tools in order to reduce excursions and achieve high yields of final products. However, capacity is limited and inspections directly impact the cycle times of products. Sampling strategies are used to improve product yields while limiting the number of inspections, and thus the impact on the cycle times of the inspected lots. Dynamic sampling has been recently introduced and new models are required to estimate the associated inspection capacity. In this paper, we focus on micro-defect inspections and the risk on process tools in terms of Wafers at Risk (W@R), which is the number of wafers processed on a tool since its latest defect inspection. A linear programming model that estimates the required defect inspection capacity to satisfy the W@R limits on process tools is proposed. Our model can be used at different decision levels. At the tactical level, it shows if W@R limits can be satisfied when the product mix changes and/or if planned W@R reductions can be met with the available inspection capacity. At the strategic level, the model helps to justify capacity investments if the objectives in terms of W@R reduction cannot be achieved with the available inspection capacity. Numerical experiments on industrial data are performed and discussed.     

A futures market-based model for dynamic network resource allocation

Management of telecommunication network requires quick, continuous and decentralized allocation of network bandwidth to various sorts of demands. So as to achieve the efficient network resource allocation, this paper describes a market-based model combining futures market with the agent-based approach. That is, utilization time is divided into many timeslots, and futures markets in hereafter use of bandwidth are opened. In our model, all market participants (software agents) observe only market prices and decide to buy or sell bandwidth trying to maximize their utilities over time so that they can secure enough network resources. The authors discuss network resource allocation through simulation using the proposed model. Masayuki Ishinishi, Ph.D.: He graduated from National Defense Academy in 1995 and 2000. He received the B.E. (1995) and M.E.(2000) degrees in computer science from National Institution for Academic Degrees (NIAD). He received his Ph.D. degree from Tokyo Institute of Technology in 2003. He has been a communications officer at Air Communications and Systems Wing in Japan Air Self-Defence Force (JASDF) since 2003. His research interests include information assurance, agent-based modeling and simulation, multi-agent system and market-based control. He is a member of IEEJ, IPSJ and JSAI. Yuhsuke Koyama, Ph.D.: He received the B.Econ., M.Econ., and Ph.D. degrees in economics from Kyoto University, in 1996, 1998, 2002, respectively. He has been a research associate of Tokyo Institute of Technology since 2002. His research field is evolutionary economics, mathematical sociology and experimental economics. He is a member of JAFEE, JAMS, JASESS and JASAG. Hiroshi Deguchi, Ph.D.: He received his Ph.D. degree in systems science from Tokyo Institute of Technology, in 1986. He also received the Dr. Econ. degree from Kyoto University in 2001. He has been a Professor of Tokyo Institute of Technology since 2002. His research field is evolutionary economics, computational organization theory, agent-based modeling, social system theory, gaming simulation, and philosophy of science. He is a member of SICE, JAMS, IPSJ, PHSC, JASAG and JAFEE. Hajime Kita, Ph.D.: He received the B.E., M.E., and Ph.D. degrees in electrical engineering from Kyoto University, in 1982, 1984, 1991, respectively. He has been a Professor of Kyoto University since 2003, His research field is systems science/engineering, and his research interests are evolutionary computation, neural networks and socio-economic analysis of energy systems, and agentbased modeling. He is a member of IEEJ, IEICE, ISCIE, JNNS, JSER, ORSJ and SICE.     

突发事件下的医院应急资源动态分配模型研究

针对突发事件下医院里应急资源供需不平衡的问题,进行了医院应急资源动态分配模型研究。考虑到病人数量增多以及病人伤情演变导致医院应急资源供应相对紧缺的现象,基于序贯决策理论,将病人需求的变化设计成一个马尔可夫决策过程,建立了医院应急资源动态分配模型。使用基本粒子群算法求解,通过某次地震发生后医院的救援实例进行分析。案例分析表明,马尔可夫决策过程可以动态地满足伤情演变下不同状态病人的需求,使得应急救援中整体的资源利用达到最优。     

A novel model on dynamic resource allocation in optical networks

A novel model on dynamic resource allocation in the WDM optical networks is proposed, basing on the integrated considerations of the impacts of transmission impairments and service classification on dynamic resource allocation in the optical layer. In this model, the priorities of optical connection requests are mapped into different thresholds of transmission impairments, and a uniform method which is adopted to evaluate the virtual wavelength path (VWP) candidates is defined. The Advanced Preferred Wavelength Sets Algorithm (A-PWS) and the heuristic Dynamic Min-Cost & Optical Virtual Wavelength Path Algorithm (DMC-OVWP) are presented addressing the routing and wavelength assignment (RWA) problem based on dynamic traffic and multi priorities in wavelength-routed optical networks. For a received optical connection request, DMC-OVWP is employed to calculate a list of the VWP candidates, and an appropriate VWP which matches the request's priority is picked up to establish the lightpath by analyzing the tra     

Some new results on system identification with dynamic neuralnetworks

Nonlinear system online identification via dynamic neural networks is studied in this paper. The main contribution of the paper is that the passivity approach is applied to access several new stable properties of neuro identification. The conditions for passivity, stability, asymptotic stability, and input-to-state stability are established in certain senses. We conclude that the gradient descent algorithm for weight adjustment is stable in an L(infinity) sense and robust to any bounded uncertainties.     

基于遗传算法的动态飞机停机位分配模型研究

机场停机位作为机场保障服务工作中的重要设施,随着中国民航业逐步拓宽的规模,航班数量的不断增长给其分配带来巨大压力;迅速合理的机位分配方案可以最大化促进航班运行效率以及其它机场地面服务工作的开展;为了缓解机场停机位资源的紧张现象,同时解决飞机停机位到跑道出口或者入口的滑行时间最短,对到场飞机所使用的停机位最优化分配问题进行了研究,对飞机从跑道出口到停机位的最短路径进行了分析计算,采用了目前计算速度更快的遗传算法的关键技术,引入改进策略求解,对停机位最优分配进行迭代计算,不断计算出最合理的机位分配资源,根据机位分配问题的约束条件和主要优化目标分析,建立以机位空闲时间均匀化为目标的模型,经模型优化后,远机位使用空闲时间的平方差由156.89降低为75.69,近机位使用效率提高了12%～22%,实验验证满足了机场机位资源最优分配等工程应用。     

基于动态蚁群劳动分工模型的多AUV任务分配方法

多水下自主航行器(autonomous underwater vehicle,AUV)的动态任务分配问题具有高度非线性、动态不确定性以及多模态的特征,对多AUV任务分配方法的自组织性、鲁棒性以及快速性提出了更高的要求.动态蚁群劳动分工(dynamic ant colony''s labor division,DACLD)模型是一种采用分布式框架的群智能算法,众多行为简单的个体相互作用过程中涌现产生的整体智能行为能很好地适应复杂多变的环境,在解决任务分配问题上具有很好的柔性.引入动态蚁群劳动分工中的刺激-响应原理,建立动态蚁群劳动分工与多AUV任务分配问题之间的映射关系,将任务的状态预测纳入响应阈值,研究基于动态蚁群劳动分工模型的多AUV任务分配方法.同时,针对任务分配过程中可能出现的任务冲突现象,提出新的循环竞争方案以实现最大限度地利用AUV资源.仿真结果表明,所提出的方法能高效地完成任务分配过程,具有很好的自组织性、鲁棒性及快速性.     

A network flow model for the dynamic and flexible berth allocation problem

This paper presents a new model for the dynamic berth allocation problem (BAP). The model is developed using a berth-flow network modeling approach and is formulated as an integer multi-commodity network flow problem. In addition, an innovative flexible berth-space utilization scheme, based on blocking plans, is incorporated into the proposed model. This is referred to as the dynamic (vessel arrivals) and flexible (berth space) BAP model (or DFBAP), and is designed to better utilize wharf space in a container port. Computational experiments conducted on an instance generated using actual data show that the DFBAP model is more effective and efficient than the method currently used by port authorities. A set of scenario analyses is also performed to obtain insights into important model parameters.     

Multi-robot, dynamic task allocation: a case study

This article presents a subgrouping approach to the multi-robot, dynamic multi-task allocation problem. It utilizes the percentile values of the distributional information of the tasks to reduce the task space into a number of subgroups that are equal to the number of robotic agents. The subgrouping procedure takes place at run-time and at every designated decision-cycle to update the elements of these subgroups using the relocation information of the elements of the task space. Furthermore, it reduces the complexity of the decision-making process proportional to the number of agents via introduction of the virtual representatives for these subgroups. The coordination strategy then uses the votes of the robotic agents for these virtual representatives to allocate the available subgroups. We use the elapsed time, the distance traveled, and the frequency of the decision-cycle as metrics to analyze the performance of this strategy in contrast to the prioritization, the instantaneous, and the time-extended coordination strategies.     

一种面向业务的动态带宽分配方法

在分析传统QoS业务模型以及传统动态带宽分配方法（DBA）的基础上,提出一种面向业务的动态带宽分配算法。该算法吸取了各种QoS业务模型及传统动态带宽分配方法的优点,充分考虑了不同业务服务质量对带宽分配要求的差别以及带宽资源的利用率,仿真结果表明这种方法更加有效。     

Solving a dynamic resource allocation problem through continuous optimization

A class of dynamic resource allocation problems with infinite planning horizon are studied. We observe special structures in the dynamic programming formulation of the problem, which enable us to convert it to continuous optimization problems that can be more easily solved. Structural properties of the problems are discussed, and explicit solutions are given for some special cases.     

Some resource allocation policies in a Multi Associative Processor

Summary The Multi Associative Processor is a hypothetical machine composed of eight control units and an arbitrary number of processing elements. Each control unit executes a single-instruction-stream multiple-data-stream program in conjunction with a subset of the dynamically allocatable processing elements. In this machine, the data bus interconnecting control units and processing elements is partitioned in order to decrease the hardware cost of the interconnection. In previous work, the degree of partitioning was investigated, and it was found that the processing element allocation algorithm was very critical to the performance of the system, [5]. In this paper, a basic allocation algorithm is investigated under various job loads, and then the performance is compared to three distinct algorithms that compact processing elements into smaller subsets of the partitions under varying conditions.This work was supported by the National Science Foundation under grant number GJ-42251. A preliminary version of this paper appeared as University of Colorado, Department of Computer Science, Technical Report No. CU-CS-069-75     

Multi-objective optimization for dynamic task allocation in a multi-robot system

In this paper, we propose a solution to the Multi-Robot Dynamic Task Allocation problem. We use Multi-Objective optimization in order to estimate, and subsequently, make an offer for its assignment. The motivation is to provide a generic solution, independent of the domain, with an aim to better utilize resources such as time or energy. The algorithm provides a significant degree of flexibility, and can be implemented in a number of diverse domains, provided the modeling of the parameters follows the convention presented. For this, we take into account – besides the distance traveled – the efficiency of a robot in a specific task type. The system has been shown to demonstrate scalability, as the experimental results indicate. It is also capable of responding to changes in the environment.     

Communication based dynamic role allocation in a group of homogeneous robots

The field of collective robotics has been raising increasing interest in the last few years. In the vast majority of works devoted to collective robotics all interacting robots play always the same function, while less attention has been paid to groups of collaborating robots in which different robots play different roles. In this paper we evolve a population of homogeneous robots for dynamically allocating roles through communicative interactions. In particular, we focus on the development of a team of robots in which one and only one individual (the ’leader’) must differentiate its communicative behaviour from that of all the others (’non-leaders’). Evolved solutions prove to be very robust with respect to changes in the size of the group. Furthermore, both behavioural analyses and a comparison with a control condition in which robots are not allowed to move demonstrate the importance of co-adapting communicative and non-communicative behaviours, and, in particular, of being allowed to dynamically change the topology of communicative interactions. Finally, we show how the same method can be used for solving other kinds of role-allocation tasks. The general idea proposed in this paper might be used in the future for evolving general, robust, and scalable role differentiation mechanisms which can be exploited to develop non-communicative collaborative behaviours that require specialisation of roles within groups of homogeneous individuals.     

Optimization of lift-gas allocation using dynamic programming

The continuous gas-lift method is one of the most used artificial lifting techniques in which the allocation of gas injection rates is a very important optimization problem. In this work, we develop a dynamic programming (DP) algorithm that solves the profit maximization problem for a cluster of oil wells producing via gas lift, with multiple well performance curves (WPCs) and constrained by the amount of lift gas available for injection. The algorithm is a low-cost and high-efficiency decision support tool that outperforms alternative methods found in the literature.     

System model and performance estimation of dynamic spectrum allocation strategy with multi-channel and imperfect sensing

The concept of cognitive radio networks (CRNs) is a promising candidate for enhancing the utilization of existing radio spectrum. In CRNs, secondary users (SUs) are allowed to use the spectrum unused by primary users (PUs). In order to mathematically estimate the system performance of dynamic spectrum allocation strategy with multi-channel and imperfect sensing, we propose a novel preemptive priority queueing model. We establish a discrete-time Markov chain in line with the stochastic behaviour of SU and PU packets. Then, we derive some performance measures, such as the interference rate of PU packets, the normal throughput and the average delay of SU packets. Moreover, we provide theoretical and simulation experiments to investigate the system performance. Numerical experiments show that there is a tradeoff between different performance measures when imperfect sensing is considered. Finally, we present an optimal design for setting the number of the channels in a spectrum.     

关于新型冠状病毒肺炎一类基于CCDC统计数据的随机时滞动力学模型

科学地预测新型冠状病毒肺炎疫情发展趋势对疫情防控至关重要.本文对中国疾病预防控制中心(CCDC)发布^[1]的数据进行了分析,给出了关于新型冠状病毒肺炎的一些可能的统计模型:传播链中连续病例的发病时间间隔分布、感染至发病的时间间隔分布和发病至住院的间隔时间3个分布,并形成了感染至确诊的时间间隔分布表达.结合CCDC统计数据和程晋团队的时滞动力学模型(TDD–NCP模型),本文发展了新的随机时滞动力学模型(Fudan–CCDC模型),并给出了参数反演结果与疫情分析.     

Optimal allocation with costly inspection and discrete types under ambiguity

We consider the following problem: a principal has a good to allocate among a collection of agents who attach a private value to receiving the good. The principal, instead of using monetary transfers (i.e. charging the agents) to allocate the good, can check the truthfulness of the agents' value declaration at a cost. Under the assumption that the agents' valuations are drawn from a discrete set of values at random, we characterize the class of optimal Bayesian mechanisms which are symmetric, direct and maximizing the expected value of assigning the good to the principal minus the cost of verification using such standard finite-dimensional optimization tools as linear programming and submodular functions, thus extending the work of [R.V. Vohra, Optimization and mechanism design, Math. Program. 134 (2012), pp. 283–303]. Our results are discrete-type analogs of those of [E. Ben-Porath, E. Dekel, and B.L. LipmanBen-Porath, Optimal allocation with costly verification, Amer. Econ. Rev. 104 (2014), pp. 3779–3813]. When the distribution of valuations is not known but can be one of a set of distributions (the case referred to as ambiguity), we compute a robust allocation mechanism by maximizing the worst-case expected value of the principal in two cases amenable to solution with two suitable assumptions on the set of distributions.