Production control in a two-stage system

Consider a two-stage production system with a significant setup time at the upstream station. A threshold production control rule is used at the upstream station, with an objective of minimizing the work-in-process level while maintaining a required busy probability at the downstream station. To characterize the system performance under this rule, a Markov model is constructed and from which the analytical performance measures are obtained. The objective function for the decision rule is found to have good analytical properties and numerical optimization can thus be performed using a simple algorithm.     

Electric vehicles: effect of the availability threshold on the transportation cost

This paper addresses the transportation problem using public electric cars. The management of this new form of transportation means considering two issues: the redistribution of the cars among the stations (in this paper, we assume that this issue is perfectly settled, the number of cars needed at each station being available at any time), and the recharge problem that we describe in this paper. At each station, a decision concerning the cars which should become available to customers, has to be taken. We assume that a vehicle is available when its charge is greater than a given threshold. Our goal is to optimize this threshold. Copyright © 1999 John Wiley & Sons, Ltd.     

带随机启动时间与双阈值(m,N)-策略的M/G/1可修排队系统的最优控制策略

本文研究带随机启动时间与双阈值(m,N)-策略的M/G/1可修排队系统,首先讨论系统有关的排队指标,接着研究因为故障而产生的系统的下列可靠性指标,如:服务台首次失效前的寿命分布、不可用度和(0,t]时间内的平均故障次数。最后,在建立费用模型的基础上,结合实际中检测公司检测样品的这一现实情况,研究了双阈值最优控制策略(m^*,N^*),并在同一组参数下与服务台不发生故障时系统的双阈值最优控制策略进行了比较。     

Using information about machine failures to control flowlines

We study the problem of using information about the type of machine failures to control production. When each station in a two stage flowline has a single machine, the processing time distributions are Erlang, and the time to fail as well repair a machine are exponentially distributed; we show that the optimal policy for controlling the flowline is completely specified by selecting a produce up to level called threshold level corresponding to each type of machine failure. These threshold levels are shown to be ordered as per the rate at which the failed downstream station can be repaired. These results are partially extended to the case when there are multiple machines at the two stations of the flowline. We then summarize numerical results that illustrate when it is worth while to use information about failures in controlling inputs to the flowline.The work of C. Roger Glassey and Sridhar Seshadri was partly supported by grants to the University of California from the Semiconductor Research Corporation and the California State MICRO program.The work of J. G. Shanthikumar was partly supported by NSF Grant DDM-9113008 and the Sloan Foundation grants for the study on Competitive Semiconductor Manufacturing.     

Optimal assignment of servers to tasks when collaboration is inefficient

Consider a Markovian system of two stations in tandem with finite intermediate buffer and two servers. The servers are heterogeneous, flexible, and more efficient when they work on their own than when they collaborate. We determine how the servers should be assigned dynamically to the stations with the goal of maximizing the system throughput. We show that the optimal policy depends on whether or not one server is dominant (i.e., faster at both stations) and on the magnitude of the efficiency loss of collaborating servers. In particular, if one server is dominant then he must divide his time between the two stations, and we identify the threshold policy the dominant server should use; otherwise each server should focus on the station where he is the faster server. In all cases, servers only collaborate to avoid idleness when the first station is blocked or the second station is starved, and we determine when collaboration is preferable to idleness as a function of the efficiency loss of collaborating servers.     

Stability analysis of a two-station cascade queueing network

We consider a two-station cascade network, where the first station has Poisson input and the second station has renewal input, with i.i.d. service times at both stations. The following partial interaction exists between stations: whenever the second station becomes empty while customers are awaiting service at the first one, one customer can jump to the second station to be served there immediately. However, the first station cannot assist the second one in the opposite case. For this system, we establish necessary and sufficient stability conditions of the basic workload process, using a regenerative method. An extension of the basic model, including a multiserver first station, a different service time distribution for customers jumping from station 1 to station 2, and an arbitrary threshold d ₁≥1 on the queue-size at station 1 allowing jumps to station 2, are also treated.     

A multi-objective lead time control problem in multi-stage assembly systems using genetic algorithms

In this paper, we develop a multi-objective model to optimally control the lead time of a multi-stage assembly system, using genetic algorithms. The multi-stage assembly system is modelled as an open queueing network. It is assumed that the product order arrives according to a Poisson process. In each service station, there is either one or infinite number of servers (machines) with exponentially distributed processing time, in which the service rate (capacity) is controllable. The optimal service control is decided at the beginning of the time horizon. The transport times between the service stations are independent random variables with generalized Erlang distributions. The problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. The objective functions are the total operating costs of the system per period (to be minimized), the average lead time (min), the variance of the lead time (min) and the probability that the manufacturing lead time does not exceed a certain threshold (max). Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed genetic algorithm approach.     

Dynamic control of a flexible server in an assembly-type queue with setup costs

We consider a queueing system with multiple stations attended by a single flexible server. An order arriving at this system needs to go through each station only once but there is no particular precedence relationship among these stations. One can also think of this system as an assembly system where each station processes a different component of an order and once all the components associated with an order are processed they are assembled instantaneously. A holding cost is charged for keeping the orders in the system and there is a penalty associated with the switches of the server between stations. Our objective is to minimize the long-run average costs by dynamically assigning the server to stations based on the system state. Using sample-path arguments, we provide partial characterizations of the optimal policy and provide sufficient conditions under which a simple state-independent policy that works on one order at a time is optimal. We also propose three simple threshold policies and present a numerical study that provides supporting evidence for the superior performance of our double-threshold heuristic.     

Scheduling networks of queues: Heavy traffic analysis of a simple open network

We consider a queueing network with two single-server stations and two types of customers. Customers of type A require service only at station 1 and customers of type B require service first at station 1 and then at station 2. Each server has a different general service time distribution, and each customer type has a different general interarrival time distribution. The problem is to find a dynamic sequencing policy at station 1 that minimizes the long-run average expected number of customers in the system.The scheduling problem is approximated by a dynamic control problem involving Brownian motion. A reformulation of this control problem is solved, and the solution is interpreted in terms of the queueing system in order to obtain an effective sequencing policy. Also, a pathwise lower bound (for any sequencing policy) is obtained for the total number of customers in the network. We show via simulation that the relative difference between the performance of the proposed policy and the pathwise lower bound becomes small as the load on the network is increased toward the heavy traffic limit.     

Admission control strategies for tandem Markovian loss systems

Motivated by communication networks, we study an admission control problem for a Markovian loss system comprised of two finite capacity service stations in tandem. Customers arrive to station 1 according to a Poisson process, and a gatekeeper, who has complete knowledge of the number of customers at both stations, decides whether to accept or reject each arriving customer. If a customer is rejected, a rejection cost is incurred. If an admitted customer finds that station 2 is full at the time of his service completion at station 1, he leaves the system and a loss cost is incurred. The goal is to find easy-to-implement policies that minimize long-run average cost per unit time. We formulate two intuitive, extremal policies and provide analytical results on their performances. We also present necessary and/or sufficient conditions under which each of these policies is optimal. Next, we show that for some states of the system it is always optimal to admit new arrivals. We also fully characterize the optimal policy when the capacity of each station is two and discuss some characteristics of optimal policies in general. Finally, we design heuristic admission control policies using these insights. Numerical experiments indicate that these heuristic policies yield near-optimal long-run average cost performance.     

Analysis of an HIV model with post-treatment control

Recent investigation indicated that latent reservoir and immune impairment are responsible for the post-treatment control of HIV infection. In this paper, we simplify the disease model with latent reservoir and immune impairment and perform a series of mathematical analysis. We obtain the basic infection reproductive number $R_{0}$ to characterize the viral dynamics. We prove that when $R_{0}<1$, the uninfected equilibrium of the proposed model is globally asymptotically stable. When $R_{0}>1$, we obtain two thresholds, the post-treatment immune control threshold and the elite control threshold. The model has bistable behaviors in the interval between the two thresholds. If the proliferation rate of CTLs is less than the post-treatment immune control threshold, the model does not have positive equilibria. In this case, the immune free equilibrium is stable and the system will have virus rebound. On the other hand, when the proliferation rate of CTLs is greater than the elite control threshold, the system has stable positive immune equilibrium and unstable immune free equilibrium. Thus, the system is under elite control.     

Control and scheduling in a two-station queueing network: Optimal policies and heuristics

Consider a two-station queueing network with two types of jobs: type 1 jobs visit station 1 only, while type 2 jobs visit both stations in sequence. Each station has a single server. Arrival and service processes are modeled as counting processes with controllable stochastic intensities. The problem is to control the arrival and service processes, and in particular to schedule the server in station 1 among the two job types, in order to minimize a discounted cost function over an infinite time horizon. Using a stochastic intensity control approach, we establish the optimality of a specific stationary policy, and show that its value function satisfies certain properties, which lead to a switching-curve structure. We further classify the problem into six parametric cases. Based on the structural properties of the stationary policy, we establish the optimality of some simple priority rules for three of the six cases, and develop heuristic policies for the other three cases.     

煤粉起动速度与粒径及湿度的关系

在煤码头和车站的煤堆积场上,煤粉的扬起是污染环境的主要原因之一.作者根据力学的基本原理,考虑颗粒和液滴重力、煤粉颗粒尺寸和湿度的影响,利用煤粉颗粒起动时的力平衡条件,推导出在风的作用下,具有一定湿度的煤粉颗粒开始扬起的起动风速的理论表达式.最后.根据煤粉起动速度与煤粉颗粒粒径及湿度的关系,将理论公式和风洞实验结果进行了比较,理论和实验结果符合得相当好.     

Production planning and pricing policy in a make-to-stock system with uncertain demand subject to machine breakdowns

We consider a make-to-stock system served by an unreliable machine that produces one type of product, which is sold to customers at one of two possible prices depending on the inventory level at the time when a customer arrives (i.e., the decision point). The system manager must determine the production level and selling price at each decision point. We first show that the optimal production and pricing policy is a threshold control, which is characterized by three threshold parameters under both the long-run discounted profit and long-run average profit criteria. We then establish the structural relationships among the three threshold parameters that production is off when inventory is above the threshold, and that the optimal selling price should be low when inventory is above the threshold under the scenario where the machine is down or up. Finally we provide some numerical examples to illustrate the analytical results and gain additional insights.     

The effect of variability in a closed-loop conveyor system

A production system consisting of a work station, a loading and unloading stations linked by a closed-loop material-handling system is considered. The material-handling system consists of two continuous line conveyors. Each conveyor is assumed to have a specified constant velocity, length and capacity. The work station is assumed to have a single machine, an unloading station and no local storage. In this production system the work pieces, which at their instant of arrival find the work station busy, are blocked. Those work pieces bypass the work station and are transported by the conveyor to the loading station to merge with the incoming work pieces to be transported to the work station again. The above production system is modeled by a G/G/1/0 queueing loss system with retrials, stationary counting arrival process, generally distributed service times, a single server and no waiting room. The flow of work pieces inside the system is modeled by a point process and is approximated by a renewal process. To analyze the asymptotic performance of the above system, a recursive procedure is developed. Furthermore, an expression for the asymptotic distribution of the number of work pieces along each conveyor is derived and is used to control the congestion along the material handling system. Finally numerical results are provided and compared against those from a simulation study.     

Optimal control and the simple logistic prototype: Three cases

A discrete dynamics optimum control problem is stated, which utilizes the simple logistic equation as its deterministic underlying equation of motion. Three specific cases are studied, simple enough to afford analytical treatment. It is shown that when aversion to oscillations in both the state variable and the controlling parameter are assumed, then over a time horizon involving three time periods, the optimum solution may involve thresholds in the relative weight of these two types of oscillations. Up to that threshold, the control problem may be simple; whereas, beyond that threshold, management may become complicated.     

Optimal production control of a failure-prone machine

We consider a problem of optimal production control of a single unreliable machine. The objective is to minimize a discounted convex inventory/backlog cost over an infinite horizon. Using the variational analysis methodology, we develop the necessary conditions of optimality in terms of the co-state dynamics. We show that an inventory-threshold control policy is optimal when the work and repair times are exponentially distributed, and demonstrate how to find the value of the threshold in this case. We consider also a class of distributions concentrated on finite intervals and prove properties of the optimal trajectories, as well as properties of an optimal inventory threshold that is time dependent in this case.     

Admission control for a multi-server queue with abandonment

In a M/M/N+M queue, when there are many customers waiting, it may be preferable to reject a new arrival rather than risk that arrival later abandoning without receiving service. On the other hand, rejecting new arrivals increases the percentage of time servers are idle, which also may not be desirable. We address these trade-offs by considering an admission control problem for a M/M/N+M queue when there are costs associated with customer abandonment, server idleness, and turning away customers. First, we formulate the relevant Markov decision process (MDP), show that the optimal policy is of threshold form, and provide a simple and efficient iterative algorithm that does not presuppose a bounded state space to compute the minimum infinite horizon expected average cost and associated threshold level. Under certain conditions we can guarantee that the algorithm provides an exact optimal solution when it stops; otherwise, the algorithm stops when a provided bound on the optimality gap is reached. Next, we solve the approximating diffusion control problem (DCP) that arises in the Halfin–Whitt many-server limit regime. This allows us to establish that the parameter space has a sharp division. Specifically, there is an optimal solution with a finite threshold level when the cost of an abandonment exceeds the cost of rejecting a customer; otherwise, there is an optimal solution that exercises no control. This analysis also yields a convenient analytic expression for the infinite horizon expected average cost as a function of the threshold level. Finally, we propose a policy for the original system that is based on the DCP solution, and show that this policy is asymptotically optimal. Our extensive numerical study shows that the control that arises from solving the DCP achieves a very similar cost to the control that arises from solving the MDP, even when the number of servers is small.     

Passenger flow control strategies for urban rail transit networks

Urban rail traffic congestion is becoming increasingly serious due to the large traffic demands in modern cities. In order to ensure the safety and quality of station services in peak hours, it's necessary to adopt some reasonable and effective passenger flow control strategies. In this study, through considering the time-dependent passenger demands, a passenger flow control model based on the network-level system is explicitly developed. The passenger successive motion process is discretized by the modeling method. Systematically considering the coordinated relationship between traffic demands and strict capacity constraints (including station passing capacity, platform load capacity and train transport capacity), we establish a mixed integer linear programming model to minimize the total passenger waiting time (including passengers outside stations and on the platforms). The optimization software Cplex is adopted to solve the developed model, and a real network of Beijing urban railway is calibrated to verify the effectiveness of the suggested model. As a result, the proposed flow control strategies can provide detailed information about control stations, control durations and control intensities, and can effectively reduce the total waiting time and relieve the number of stranded passengers in the urban rail transit network.     

The threshold infection level for Wolbachia invasion in random environments

Dengue fever and Zika are mosquito-borne diseases threatening human health. A novel strategy for mosquito-borne disease control uses the bacterium Wolbachia to block virus transmission. It requires releasing Wolbachia infected mosquitoes to exceed a threshold level. Since an accurate forecast for temperature and rainfall, the major environmental conditions regulating the mosquito dynamics, is often not available over a long time period, it is important to explore how the threshold releasing level changes in random environments. In this work, we estimate the threshold level in a stochastic system of differential equations where the reproduction rates of mosquitoes change randomly. We prove that the threshold level is, surprisingly, defined by a deterministic curve that does not fluctuate with environmental conditions. The major difficulty in the proof is to construct various auxiliary curves to limit the dynamic behaviors of the whole family of innumerable solutions satisfying a given initial condition.