Bucket brigades with worker learning

The dynamics and throughput of a bucket brigade production system is studied when workers’ speeds increase due to learning. It is shown that, if the rules of the bucket brigade system allow a re-ordering of its workers then the bucket brigade production system is very robust and will typically rebalance to a self-organizing optimal production arrangement. As workers learn only those parts of the production line that they work on, the stationary velocity distribution for the workers of a stable bucket brigade is non-uniform over the production line. Hence, depending on the initial placement of the workers, there are many different stationary velocity distributions. It is shown that all the stationary distributions lead to the same throughput.     

The impact of mismeasurement in performance benchmarking: A Monte Carlo comparison of SFA and DEA with different multi-period budgeting strategies

Performance-based budgeting has received increasing attention from public and for-profit organizations in an effort to achieve a fair and balanced allocation of funds among their individual producers or operating units for overall system optimization. Although existing frontier estimation models can be used to measure and rank the performance of each producer, few studies have addressed how the mismeasurement by frontier estimation models affects the budget allocation and system performance. There is therefore a need for analysis of the accuracy of performance assessments in performance-based budgeting. This paper reports the results of a Monte Carlo analysis in which measurement errors are introduced and the system throughput in various experimental scenarios is compared. Each scenario assumes a different multi-period budgeting strategy and production frontier estimation model; the frontier estimation models considered are stochastic frontier analysis (SFA) and data envelopment analysis (DEA). The main results are as follows: (1) the selection of a proper budgeting strategy and benchmark model can lead to substantial improvement in the system throughput; (2) a “peanut butter” strategy outperforms a discriminative strategy in the presence of relatively high measurement errors, but a discriminative strategy is preferred for small measurement errors; (3) frontier estimation models outperform models with randomly-generated ranks even in cases with relatively high measurement errors; (4) SFA outperforms DEA for small measurement errors, but DEA becomes increasingly favorable relative to SFA as the measurement errors increase.     

金融危机对港口外贸吞吐量影响的干预分析

通过对我国外贸吞吐量2001年1月至2009年6月的时间序列数据进行分析,建立干预分析模型,研究金融危机对我国外贸的影响,结果显示该模型具有较好的模拟与预测效果.     

Multi-state throughput analysis of a two-stage manufacturing system with parallel unreliable machines and a finite buffer

This paper models and analyzes the throughput of a two-stage manufacturing system with multiple independent unreliable machines at each stage and one finite-sized buffer between the stages. The machines follow exponential operation, failure, and repair processes. Most of the literature uses binary random variables to model unreliable machines in transfer lines and other production lines. This paper first illustrates the importance of using more than two states to model parallel unreliable machines because of their independent and asynchronous operations in the parallel system. The system balance equations are then formulated based on a set of new notations of vector manipulations, and are transformed into a matrix form fitting the properties of the Quasi-Birth–Death (QBD) process. The Matrix-Analytic (MA) method for solving the generic QBD processes is used to calculate the system state probability and throughput. Numerical cases demonstrate that solution method is fast and accurate in analyzing parallel manufacturing systems, and thus prove the applicability of the new model and the effectiveness of the MA-based method. Such multi-state models and their solution techniques can be used as a building block for analyzing larger, more complex manufacturing systems.     

基于多元线性回归分析的民用运输机场旅客吞吐量预测

修建第二机场的必要性以及何时开始修建,取决于该地区的机场旅客吞吐量何时达到饱和.从机场旅客吞吐量的可能影响因素出发,分析各影响因素与旅客吞吐量之间的相关性,并以西南地区某枢纽运输机场为例,建立机场旅客吞吐量的多元线性回归预测模型,预测该机场未来年的旅客吞吐量,并利用时间序列法对所得的预测值进行验证.结果表明,该模型能够较准确的预测出机场未来年的旅客吞吐量,为机场扩建或新建第二机场的必要性提供科学依据.     

Dynamic server allocation for unstable queueing networks with flexible servers

This paper is concerned with the dynamic assignment of servers to tasks in queueing networks where demand may exceed the capacity for service. The objective is to maximize the system throughput. We use fluid limit analysis to show that several quantities of interest, namely the maximum possible throughput, the maximum throughput for a given arrival rate, the minimum arrival rate that will yield a desired feasible throughput, and the optimal allocations of servers to classes for a given arrival rate and desired throughput, can be computed by solving linear programming problems. We develop generalized round-robin policies for assigning servers to classes for a given arrival rate and desired throughput, and show that our policies achieve the desired throughput as long as this throughput is feasible for the arrival rate. We conclude with numerical examples that illustrate the points discussed and provide insights into the system behavior when the arrival rate deviates from the one the system is designed for.     

港口货物总吞吐量概率分布数学模型的建立与分析

为了研究港口货物总吞吐量随时间变化的规律,提出了港口货物总吞吐量的概率分布模型.由于货物总吞吐量的变化与到达港口的货运船数目以及装卸设备的工作效率有密切关系,构造一个关于到达港口的货运船数目以及装卸设备的工作能力组合而成的复合变量,货物总吞吐量是这些复合变量所表示的货物装卸量的和.应用wald方程,得到货物总吞吐量的概率分布.货物总吞吐量服从何种概率分布依赖于到达港口的货运船数目所服从的概率分布.克服了传统预测模型难于对未来货物吞吐量大小变化的可能性作出量化判断的缺点.同时,根据建立的数学模型,分析了影响货物总吞吐量变化的因素,以山东地区某港口的货物吞吐量变化规律进行了案例分析.实际结果与理论分析相符.     

Linear loss networks

This paper investigates theoretical properties of throughput and cost in linear loss networks. The maximum throughput of the network with exponential service times is derived and the arrival process that maximizes throughput, given a fixed arrival rate, is established. For general service times, an asymptotically critical loading regime is identified such that the probability of an arbitrary customer being lost is strictly within (0,1) as the network size increases. This regime delivers throughput comparable to the maximum at a relatively low network cost. The paper establishes the asymptotic throughput and network cost under this critical loading.     

Throughput and delay optimality of power-of-d choices in inhomogeneous load balancing systems

It is well-known that the power-of-d choices routing algorithm maximizes throughput and is heavy-traffic optimal in load balancing systems with homogeneous servers. However, if the servers are heterogeneous, throughput optimality does not hold in general. We find necessary and sufficient conditions for throughput optimality of power-of-d choices when the servers are heterogeneous, and we prove that almost the same conditions are sufficient to show heavy-traffic optimality. Additionally, we generalize the sufficient condition for throughput optimality to a larger class of routing policies.     

On the robustness of the bowl phenomenon

The bowl phenomenon provides a way of increasing the throughput of some production line systems with variable processing times by purposely unbalancing the line in a certain manner. However, achieving this increase in throughput depends on correctly identifying the values of the system parameters to estimate the optimal amount of unbalance and then actually being able to assign work to stations according to the optimal bowl allocation. In this paper we study the robustness of the bowl phenomenon by examining the effect of inaccurately estimating the optimal amount of unbalance and the effect of deviating from the optimal bowl allocation. Our results show that the bowl phenomenon is relatively robust in the sense that fairly large errors (even 50%) in the amount of unbalance still provide most of the potential improvement in throughput over a perfectly balanced line. Moreover, the throughput still exceeds that of a perfectly balanced line in most cases even when the work allocation to each station deviates from the optimal bowl allocation by as much as 10%. We also address the question of whether the optimal bowl allocation or the balanced line provides a more robust ‘target’ when assigning work to stations. When the deviations from these two targets are of the same magnitude, we found that the optimal bowl allocation target yields the larger throughput in most cases, where the average difference between their throughputs is roughly the same as the difference between the optimal throughput and the throughput of a balanced line. Furthermore, for the same magnitude of deviation, the throughput depends more heavily on the direction of the deviation from the balanced line than that from the optimal bowl allocation, so that the risk of a substantially reduced throughput is much larger when using the balanced line as the target. Therefore, the optimal bowl allocation provides a much more robust target than the balanced line.     

Analysis of flexible manufacturing systems with priority scheduling: PMVA

A new methodology for performance analysis of flexible manufacturing systems (FMSs) with priority scheduling is presented. The analytic model developed extends the mean value analysis of closed networks of queues with multiple product types, various non-preemptive priority service disciplines, and with parallel machine stations. Performance measures derived include the expected throughput per product and per station, utilization of machines and transporters, queuing times and queue length measures for various configurations. Extensive numerical calculations have shown that the algorithm used for solving the problem converges rapidly and retains numerical stability for large models. The paper also illustrates the application of the model to a system with a mixture of FCFS and HOL disciplines which gives insights into various priority assignment policies in FMSs. Special attention was given to the problem of scheduling the robot carriers (transporters).     

Performance of miniload systems with two-class storage

To increase throughput, the higher turnover items are often stored near the input/output point of a miniload system. We analyze the performance of such a miniload with a square-in-time rack containing two storage zones: high turnover and low turnover. First, we derive the distribution of the dual command travel time. System performance measures such as the throughput depend upon the distribution of the dual command travel time and the distribution of pick times. We work out the details and obtain closed-form expressions for throughput for two important families of pick time distributions: deterministic and exponential. Finally, we investigate how the size of the high turnover region affects throughput.     

货物吞吐量预测的改进ARIMAX方法——以天津港为例

为提高港口货物吞吐量预测精度,建立了基于ARIMAX-SVR的组合预测模型。以天津港为例,选取1999～2018年货物吞吐量数据进行分析,首先运用BP神经网络补插缺失数据,然后通过Pearson相关分析筛选出影响货物吞吐量的主要因素;再在ARIMA模型的基础上建立了ARIMAX模型,为进一步提高模型精度,最后建立了SVR模型修正的ARIMAX模型。实证分析结果表明组合模型拟合精度更高,预测效果更好,适用于港口吞吐量预测并且模型具有一定的先进性。     

A cooperative differential game model based on transmission rate in wireless networks

Network throughput and energy efficiency are paramount for network performance in an energy-constrained wireless network. However, it is difficult to achieve optimal objectives simultaneously. Therefore, it is necessary to find a rate control solution based on tradeoff between network throughput and energy efficiency. In this paper, we propose a cooperative differential game model and find an optimal rate control of each player to get the total minimal cost with tradeoff between network throughput and energy efficiency of the networks.     

Bounds and heuristics for assembly-like queues

We consider an assembly system with exponential service times, and derive bounds for its average throughput and inventories. We also present an easily computed approximation for the throughput, and compare it to an existing approximation.     

A comparison of univariate methods for forecasting container throughput volumes

In this paper, six univariate forecasting models for the container throughput volumes in Taiwan’s three major ports are presented. The six univariate models include the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey model, the hybrid grey model, and the SARIMA model. The purpose of this paper is to search for a model that can provide the most accurate prediction of container throughput. By applying monthly data to these models and comparing the prediction results based on mean absolute error, mean absolute percent error and root mean squared error, we find that in general the classical decomposition model appears to be the best model for forecasting container throughput with seasonal variations. The result of this study may be helpful for predicting the short-term variation in demand for the container throughput of other international ports.     

Maximum-throughput dynamic network flows

This paper presents and solves the maximum throughput dynamic network flow problem, an infinite horizon integer programming problem which involves network flows evolving over time. The model is a finite network in which the flow on each arc not only has an associated upper and lower bound but also an associated transit time. Flow is to be sent through the network in each period so as to satisfy the upper and lower bounds and conservation of flow at each node from some fixed period on. The objective is to maximize the throughput, the net flow circulating in the network in a given period, and this throughput is shown to be the same in each period. We demonstrate that among those flows with maximum throughput there is a flow which repeats every period. Moreover, a duality result shows the maximum throughput equals the minimum capacity of an appropriately defined cut. A special case of the maximum dynamic network flow problem is the problem of minimizing the number of vehicles to meet a fixed periodic schedule. Moreover, the elegantsolution derived by Ford and Fulkerson for the finite horizon maximum dynamic flow problem may be viewed as a special case of the infinite horizon maximum dynamic flow problem and the optimality of solutions which repeat every period.     

Near-optimal analysis of homogeneous central-server queueing networks

The problem of achieving near-optimality in homogeneous central-server queueing networks is investigated by means of a composite approach based on approximate operational analysis and goal programming procedures. A near-optimal solution is shown to exist: this includes the expected overall waiting time, the overall throughput rate, as well as the distribution of queue length values. The need to maintain a balanced network flow and the desire to minimize the overall waiting time are expressed as complimentary objectives. Numerical results, based on past measurements from a multi-server computing facility, indicate that the performance gains obtained by the application of the present methodology are quite significant throughout the network's feasible population scale. © 1997 by John Wiley & Sons, Ltd.     

Throughput optimization in robotic cells with input and output machine buffers: A comparative study of two key models

We consider the problem of scheduling operations in a robotic cell processing a single part type. Each machine in the cell has a one-unit input buffer and a one-unit output buffer. The machines and buffers are served by one single gripper robot. The domain considered is free-pickup cells with additive inter-machine travel time. The processing constraints specify the cell to be a flow shop. The objective is to find a cyclic sequence of robot moves that minimizes the long-run average time to produce a part or, equivalently, maximizes throughput. Bufferless robotic cells have been studied extensively in the literature. However, the few studies of robotic cells with output buffers at each machine have shown that the throughput can be improved by such a configuration. We show that there is no throughput advantage in providing machine input buffers in addition to output buffers. The equivalence in throughput between the two models has significant practical implications, since the cost of providing additional buffers at each machine is substantial.     

Solving a multi-objective simulation model using a hybrid response surface method and lexicographical goal programming approach—a case study on integrated circuit ink-marking machines

In an integrated circuit (IC) packaging plant, the ink-marking machine has a significantly higher throughput than the other processing machines. When periodic demand surges result in backlog orders or in lost customers, there is a need to increase system throughput. To resolve this problem, the purchase of a new machine often results in excess capacity in addition to added operation and acquisition costs. Therefore, the productivity improvement effort has priority over the machine purchase decision. This paper seeks to optimize both throughput and cycle time performance for IC ink-marking machines. While throughput increase is the primary objective, there is an acceptable cycle time limit for a feasible solution. It is a multi-objective problem. The proposed solution methodology constructed a simulation metamodel for the ink-marking operation by using a fractional factorial experimental design and regression analysis. It is then solved by a hybrid response surface method and lexicographical goal programming approach. Solution results illustrated a successful application.