Performance evaluation of an automated material handling system for a wafer fab

Discrete-event simulation model was developed to evaluate the performance of an automated material handling system (AMHS) for a wafer fab with a zone control scheme avoiding all vehicle collision. The layout of this AMHS is a custom configuration. The track option contains turntables, turnouts and high-speed express lanes. The behavior of the interarrival for all stockers from the real data set was analyzed to verify the assumption of the simulation model. The results show that the underlying distributions of most stockers for interarrival times belong to the exponential or Weibull distribution. The simulation results show that the number of vehicles significantly affects the average delivery time and the average throughput. A simple one-factor response surface model is used to determine the appropriate vehicle numbers. This study was also investigated to determine the vehicle numbers in an automated guided vehicle-based intrabay material handling system.     

A novel approach to determine a tactical and operational decision for dynamic appointment scheduling at nuclear medical center

In this research, appointment scheduling is addressed in a nuclear medical center. A finite-horizon Markov Decision Process as dynamic programming is applied to formulate the problem by considering the patients' choice behavior, and different no-show rate for patients. The proposed model determines a tactical and operational decision for patient appointments. Based on the tactical decision; How many patients request for hospitalization as they call in and to what slot should they be assigned? According to the operational decision, should a walk-in patient hospitalization request be accepted? Also, this decision determines which patients must receive the services for each slot. One of the distinguishing contributions of this research is that two algorithms and one mathematical programming are developed hierarchically to solve exactly and deal with an intractable dimension of the Markov Decision Process model. Simulation tools are applied to compare the performance of optimal policies with First-Come-First-Serve policy based on a real case. The results show that the proposed model presents a more effective and efficient scheduling compared with current policies for scheduling. More revenue, lower patients waiting during the working day, and lower postponed patients are the results of the proposed model rather than the current policies for scheduling. Then, the impact of revenues, waiting costs, penalty costs, and center’s capacity on the results has been investigated. By increasing revenue and capacity and decreasing waiting costs and penalty costs, the total net revenue is increased.     

基于深度强化学习的车辆边缘计算任务卸载方法

车辆边缘计算允许车辆将计算任务卸载到边缘服务器,从而满足车辆爆炸式增长的计算资源需求。但是如何进行卸载决策与计算资源分配仍然是亟待解决的关键问题。并且,运动车辆在连续时间内进行任务卸载很少被提及,尤其对车辆任务到达随机性考虑不足。针对上述问题,建立动态车辆边缘计算模型,描述为7状态2动作空间的Markov决策过程,并建立一个分布式深度强化学习模型来解决问题。另外,针对离散—连续混合决策问题导致的效果欠佳,将输入层与一阶决策网络嵌套,提出一种分阶决策的深度强化学习算法。仿真结果表明,所提算法相较于对比算法,在能耗上保持了较低水平,并且在任务完成率、时延和奖励方面都具备明显优势,这为车辆边缘计算中的卸载决策与计算资源分配问题提供了一种有效的解决方案。     

A hybrid push/pull-dispatching rule for a photobay in a 300 mm wafer fab

In this paper, the simulation analysis of an automated material handling system (AMHS) for a photobay in a 300 mm wafer fab was analyzed, considering the effects of the dispatching rules. Discrete-event simulation models were developed in an e-M Plant to study this system. Currently, the combination of the shortest distance with the nearest vehicle (SD_NV) and the first-encounter first-served (FEFS) dispatching rule was used in this system. In order to improve the system performance, a hybrid push/pull (PP) dispatching rule was proposed. The simulation results reveal a substantial improvement of the AMHS performance and reduced the WIP and cycle time as a consequence of implementing a PP dispatching rule.     

燃料电池汽车最优氢耗马尔科夫决策控制

本文基于马尔科夫决策过程提出一种燃料电池汽车最优等效氢燃料消耗控制策略.控制策略以部分观测量为基础,以马尔科夫转移概率矩阵为条件,采用基于蒙特卡洛马尔科夫(MCMC)算法的Metropolis-Hastings采样方法,获得平均奖励输出,进而通过最优氢燃料消耗代价函数的优化以控制在氢燃料电池系统和动力电池系统间进行能量...     

Simulation study for a proposed segmented automated material handling system design for 300-mm semiconductor fabs

With the increase in the size and weight of 300-mm wafers, the factory area must be enlarged accordingly. Due to the flow of material over long distances, the elimination of manual wafer handling has become necessary. Consequently, an automated material handling system (AMHS) is required for 300-mm semiconductor manufacturing facilities. The design of an AMHS must not only be capable of meeting numerous complex material handling requirements, but it must also simplify control and reduce capacity loss. In this study, a segmented dual-track bidirectional loop (SDTBL) design for an AMHS is proposed. The configuration is based on a double-loop flow path structure that is divided into non-overlapping segments, each containing a certain number of vehicles operating in bidirectional mode. A transfer buffer is set to enable conversion between segments and connect each independent zone. This structure eliminates congestion and blocking without requiring additional investment by operating vehicles on mutually exclusive tracks. The segmentation strategies and steps for two scenarios are developed in this research, and a simulation is performed to evaluate the performance of each segmented strategy. The simulation results show that the proposed strategies can reduce the cycle time and increase stocker utilization by up to 55.55% and 39.39%, respectively, while the throughput remains the same. The proposed design has great potential for practical application.     

多时空配送任务驱动的无人车队车辆数优化方法

为解决快递终端配送多时空任务驱动下的最小无人车队车辆数配置问题,提出一种随机优化方法。首先,分析服务时长和等待时长对无人车队行驶路线规划的影响,从而构建最短路径模型;然后,基于二维时空网络构造服务序列网络;其次,通过网络转换将最小无人车队车辆数配置问题转化为网络最大流问题,并建立以车队车辆数最小为目标的最小车队模型;最后,针对模型特征设计一种融合Dijkstra算法和Dinic算法的Dijkstra-Dinic算法来对最小无人车队车辆数配置问题进行求解。在四种不同规模的服务网络中进行仿真实验,实验结果表明：在不同成功服务率下,最小无人车队车辆数与服务网络规模呈正相关,但随等待时长的增加而减少并趋向于稳定;所提算法中所引入的One-stop算子大大提高了搜索效率,所提模型和算法适用于大规模服务网络中的最小车队计算。     

Expected Response Times for Closed-Loop Multivehicle AMHS

We present an analytical approach for estimating the expected time for an automated material handling system (AMHS) to respond to move requests at loading stations in a vehicle-based, unidirectional, closed-loop AMHS. The expected response times are important for estimating the expected work-in-process (WIP) levels at the loading stations for design purposes, and for evaluating the performance of the AMHS as delayed response can impact the production cycle times. The expected response time approximation is validated by comparing the analytical model to the simulation results using a SEMATECH 300 mm hypothetical fab data set. Note to Practitioners - This paper describes an analytic method for estimating the average time for the AMHS to respond to lots ready for movement in a 300 mm wafer fab. The analysis is based on a large-scale model, requires standard solvers, and provides a very fast and reasonably accurate alternative to high-fidelity simulation. It is intended to support the early stage of fab design/redesign, allowing engineers to examine many different options before committing to the time and expense of simulation.     

Two-machine no-wait flowshop scheduling with learning effect and convex resource-dependent processing times

Two-machine no-wait flowshop scheduling problems in which the processing time of a job is a function of its position in the sequence and its resource allocation are considered in the study. The primary objective is to find the optimal sequence of jobs and the optimal resource allocation separately. Here we propose two separate models: minimizing a cost function of makespan, total completion time, total absolute differences in completion times and total resource cost; minimizing a cost function of makespan, total waiting time, total absolute differences in waiting times and total resource cost. Since each model is strongly NP-hard, we solve both models by breaking them down to two sub-problems, the optimal resource allocation problem for any job sequence and the optimal sequence problem with its optimal resource allocation. Specially, we transform the second sub-problem into the minimum of the bipartite graph optimal matching problem (NP-hard), and solve it by using the classic KM (Kuhn–Munkres) algorithm. The solutions of the two sub-problems demonstrate that the target problems remain polynomial solvable under the proposed model.     

考虑交付期限的订单配送策略

近年来,一种新兴的高效同城配送服务模式迅速发展,即针对随机到达的订单实现当天从点到点送货上门具有交付期限的订单配送服务.针对具有交付期限的、随机到达的订单配送问题,考虑平台可通过自有车辆服务订单,同时也可委托第3方物流服务订单的特点,提出一种决定是否接受该订单和确定车辆服务订单集合的订单配送策略.首先,以平台的运营成本最小为目标,建立订单配送的马尔可夫决策模型;然后,分析最优策略的性质;最后,与FCFS策略和按时交付策略进行对比分析.实验结果表明:所提出订单配送策略相比于FCFS策略与按时交付策略,不仅能够降低平台的总运营成本,且能够提高顾客满意度,保证服务质量,并通过数值分析验证了所建立的模型和求解算法的有效性.     

信号灯控制的多阶段决策模型及其前向动态规划算法

为研究路口交通信号灯的实时最优控制问题,提出一种以最小化等待时间为目标的多阶段决策模型.该模型利用最短绿灯和红灯时间的结构特征,通过合理选择系统状态和控制变量压缩了模型规模,进而提出了前向动态规划算法以高效得到最优解.数值实验显示,对比于固定时长的周期性控制可以节省路口车辆的等待时间;对比基于混合整数规划的求解方法,可以提高求解效率,满足实时控制的要求.     

A heuristic method for the vehicle routing problem with backhauls and inventory

The purpose of this paper is to determine the route of the vehicle routing problem with backhauls (VRPB), delivering new items and picking up the reused items or wastes, and resolve the inventory control decision problem simultaneously since the regular VRPB does not. Both the vehicle routing decision for delivery and pickup, and the inventory control decision affect each other and must be considered together. Hence, a mathematical model of vehicle routing problem with backhauls and inventory (VRPBI) is proposed. Since finding the optimal solution(s) for VRPBI is a NP-hard problem, this paper proposes a heuristic method, variable neighborhood tabu search (VNTS), adopting six neighborhood searching approaches to obtain the optimal solution. Moreover, this paper compares the proposed heuristic method with two other existing heuristic methods. The experimental results indicate that the proposed method is better than the two other methods in terms of average logistic cost (transportation cost and inventory cost).     

无线多媒体通信网适应带宽配置在线优化算法

基于强化学习的方法,提出一种无线多媒体通信网适应带宽配置在线优化算法,在满足多类业务不同QoS(quality of service)要求的同时,提高网络资源的利用率.建立事件驱动的随机切换分析模型,将无线多媒体通信网中的适应带宽配置问题转化为带约束的连续时间Markov决策问题.利用此模型的动态结构特性,结合在线学习估计梯度与随机逼近改进策略,提出适应带宽配置在线优化算法.该算法不依赖于系统参数,如呼叫到达率、呼叫持续时间等,自适应性强,计算量小,能够收敛到全局最优,适用于复杂应用环境中无线多媒体通信网适应带宽配置的在线优化.仿真实验结果验证了算法的有效性.     

Multi-objective real-time dispatching for integrated delivery in a Fab using GA based simulation optimization

In a wafer fabrication Fab, the “integrated delivery”, which integrates the automated material handling system (AMHS) with processing tools to automate the material flow, is difficult to implement due to the system complexity and uncertainty. The previous dispatching studies in semiconductor manufacturing have mainly focused on the tool dispatching. Few studies have been done for analyzing combinatorial dispatching rules including lot dispatching, batch dispatching and automated guided vehicle (AGV) dispatching. To handle this problem, a GA (genetic algorithm) based simulation optimization methodology, which consists of the on-line scheduler and the off-line scheduler, is presented in this paper. The on-line scheduler is used to monitor and implement optimal combinatorial dispatching rules to the semiconductor wafer fabrication system. The off-line scheduler is employed to search for optimal combinatorial dispatching rules. In this study, the response surface methodology is adopted to optimize the GA parameters. Finally, an experimental bay of wafer fabrication Fab is constructed and numerical experiments show that the proposed approach can significantly improve the performance of the “integrated delivery system” compared with the traditional single dispatching rule approach.     

The decision model of task allocation for constrained stochastic distributed systems

In distributed systems, an application program is divided into several software modules, which need to be allocated to processors connected by communication links. The distributed system reliability (DSR) could be defined as the probability of successfully completing the distributed program. Previous studies about optimal task allocation with respect to DSR focused on the effects of the inter-connectivity of processors, the failure rates of the processors, and the failure rates of the communication links. We are the first to study the effects of module software reliabilities and module execution frequencies on the optimal task allocation. By viewing each module as a state in the Markov process, we build a task allocation decision model to maximize DSR for distributed systems with 100% reliable network. In this model, the DSR is derived from the module software reliabilities, the processor hardware reliabilities, the transition probabilities between modules, and the task allocation matrix. Resource constraints of memory space limitation and computation load limitation on each processor are considered. The constraint of total system cost, including the execution cost, the communication cost, and the failure cost, is also considered. We solve the problem by Constraint Programming using the ILOG SOLVER library. We then apply the proposed model to a case extended from previous studies. Finally, a sensitivity analysis is performed to verify the effects of module software reliabilities and processor hardware reliabilities on the DSR and on the task allocation decision.     

基于干扰精细估计与神经网络推力分配的载人潜水器控制

针对潜水器在水下运行时会受到洋流、参数摄动等多种干扰因素影响和潜水器的过驱动问题,设计一种基于干扰观测的反步控制器和基于神经网络二次规划的推力分配器的双层控制结构.首先,建立潜水器系统在洋流影响下的动力学模型;其次,将潜水器受到的干扰分为由洋流产生的干扰和由其他因素引起的干扰两部分,分别使用洋流观测器和非线性干扰观测器进行估计,并基于干扰观测信息利用反步法设计运动控制器;然后,针对潜水器的过驱动特性以及推进器的推力受限问题,提出一种基于神经网络二次规划的推力分配方法;最后,使用Matlab进行数值仿真,验证所提控制方法的有效性和优越性.结果表明,基于干扰精细估计与神经网络推力分配的潜水器运动控制系统具有干扰估计更加准确、推进系统的耗能最优,以及避免推进器的推力超限等优势.     

一种混合智能的Interbay系统调度方法

为提高晶圆制造Interbay物料运输系统的多目标调度性能,提出一种混合智能的Interbay系统调度方法。采用Takagi-Sugeno模糊逻辑方法对晶圆卡运输成本模型的参数权重进行自适应调节,用匈牙利方法对Interbay系统的运输小车和晶圆工件进行优化指派。实验结果表明,该方法在Interbay系统和晶圆加工系统的多目标综合性能方面优于传统方法。     

智能电网中两阶段网络切片资源分配技术

为满足网络切片在智能电网中的多样化需求,提出了一个在智能电网中基于云-边协同的切片资源分配模型。为优化网络切片分配,提出一种两阶段的切片分配模型：在第一阶段中,以用户体验最优为目标,建立了本地边缘网络的资源分配问题的优化模型,并采用拉格朗日乘子法对此最优问题进行了求解;在第二阶段中,首先将网络切片资源分配系统建模成Markov决策过程,然后提出使用深度增强学习方法对核心云的切片自适应地进行资源分配。实验结果表明所提的两阶段切片资源优化分配模型可有效减少网络延迟,提高用户满意度。     

Reinforcement learning based resource allocation in business process management

Efficient resource allocation is a complex and dynamic task in business process management. Although a wide variety of mechanisms are emerging to support resource allocation in business process execution, these approaches do not consider performance optimization. This paper introduces a mechanism in which the resource allocation optimization problem is modeled as Markov decision processes and solved using reinforcement learning. The proposed mechanism observes its environment to learn appropriate policies which optimize resource allocation in business process execution. The experimental results indicate that the proposed approach outperforms well known heuristic or hand-coded strategies, and may improve the current state of business process management.     

基于双层规划的高超声速飞行器预警资源分配方法

针对高超声速飞行器预警系统中资源难以合理利用的问题,提出一种基于双层规划的预警资源分配方法.首先,建立高超声速飞行器运动状态的马尔可夫模型,提出威胁评估的方法;其次,基于隐马尔可夫模型和卡尔曼滤波,提出双层规划的高超声速飞行器预警资源分配模型,下层规划以单位资源损耗下信息增益为目标函数,上层规划以风险的降低为目标函数;...