Hayriye Ayhan

Server Assignment Policies for Maximizing the Steady-State Throughput of Finite Queueing Systems

For a system of finite queues, we study how servers should be assigned dynamically to stations in order to obtain optimal (or near-optimal) long-run average throughput. We assume that travel times between different service facilities are negligible, that each server can work on only one job at a time, and that several servers can work together on one job. We show that when the service rates depend only on either the server or the station (and not both), then all nonidling server assignment policies are optimal. Moreover, for a Markovian system with two stations in tandem and two servers, we show that the optimal policy assigns one server to each station unless that station is blocked or starved (in which case the server helps at the other station), and we specify the criterion used for assigning servers to stations. Finally, we propose a simple server assignment policy for tandem systems in which the number of stations equals the number of servers, and we present numerical results that show that our policy appears to yield near-optimal throughput under general conditions.

Dynamic Server Allocation for Queueing Networks with Flexible Servers

This paper is concerned with the design of dynamic server assignment policies that maximize the capacity of queueing networks with flexible servers. Flexibility here means that each server may be capable of performing service at several different classes in the network. We assume that the interarrival times and the service times are independent and identically distributed, and that routing is probabilistic. We also allow for server switching times, which we assume to be independent and identically distributed. We deduce the value of a tight upper bound on the achievable capacity by equating the capacity of the queueing network model with that of a limiting deterministic fluid model. The maximal capacity of the deterministic model is given by the solution to a linear programming problem that also provides optimal allocations of servers to classes. We construct particular server assignment policies, called generalized round-robin policies, that guarantee that the capacity of the queueing network will be arbitrarily close to the computed upper bound. The performance of such policies is studied using numerical examples.

A maintenance strategy for systems subjected to deterioration governed by random shocks

The authors examine the time-stationary availability of maintained systems that deteriorate according to a random-shock process. System failures are not self-announcing; hence, failures must be detected via inspection. The approach considers randomly occurring shocks that cumulatively damage the system; shock magnitudes are taken as random. The authors develop an expression for computing system availability when inspections follow a renewal process. This expression leads to a proved proposition showing that, for any specified mean inspection rate, system availability is maximized by choosing deterministic inter-inspection times. >

Throughput Maximization for Tandem Lines with Two Stations and Flexible Servers

For a Markovian queueing network with two stations in tandem, finite intermediate buffer, andM flexible servers, we study how the servers should be assigned dynamically to stations to obtain optimal long-run average throughput. We assume that each server can work on only one job at a time, that several servers can work together on a single job, and that the travel times between stations are negligible. Under these assumptions, we completely characterize the optimal policy for systems with three servers. We also provide a conjecture for the structure of the optimal policy for systems with four or more servers that is supported by extensive numerical evidence. Finally, we develop heuristic server-assignment policies for systems with three or more servers that are easy to implement, robust with respect to the server capabilities, and generally appear to yield near-optimal long-run average throughput.

A probabilistic (max, +) approach for determining railway infrastructure capacity

Abstract We consider the problem of determining the capacity of a planned railway infrastructure layout under uncertainties. In order to address the long-term nature of the problem, in which the exact (future) demand of service is unknown, we develop a “timetable”-free approach to avoid the specification of a particular timetable. We consider a generic infra-element that allows a concise representation of many different combinations of infrastructure, safety systems and traffic regimes, such as mixed double and single track lines (e.g., a double track line including a single tunnel tube), and train operations on partly overlapping routes at station yards. We translate the capacity assessment problem for such a generic infra-element into an optimization problem and provide a solution procedure. We illustrate our approach with a capacity assessment for the newly built high-speed railway line in The Netherlands.

An approach for computing tight numerical bounds on renewal functions

This method computes tight lower and upper bounds for the renewal function. It is based on Riemann-Stieltjes integration, and provides bounds for solving certain renewal equations used in the study of availability. An error analysis is given for the numerical bounds when inter-renewal time distributions are sufficiently smooth. Three examples are explored that demonstrate the accuracy of these computed numerical bounds.

Laplace Transform and Moments of Waiting Times in Poisson Driven (max,+) Linear Systems

Abstract(Max,+) linear systems can be used to represent stochastic Petri nets belonging to the class of event graphs. This class contains various instances of queueing networks like acyclic or cyclic fork-and-join queueing networks, finite or infinite capacity tandem queueing networks with various types of blocking, synchronized queueing networks and so on. It also contains some basic manufacturing models such as kanban networks, assembly systems and so forth.In their 1997 paper, Baccelli, Hasenfuss and Schmidt provide explicit expressions for the expected value of the waiting time of the nth customer in a given subarea of a (max,+) linear system. Using similar analysis, we present explicit expressions for the moments and the Laplace transform of transient waiting times in Poisson driven (max,+) linear systems. Furthermore, starting with these closed form expressions, we also derive explicit expressions for the moments and the Laplace transform of stationary waiting times in a class of (max,+) linear systems with deterministic service times. Examples pertaining to queueing theory are given to illustrate the results.

Compensating for Failures with Flexible Servers

We consider the problem of maximizing capacity in a queueing network with flexible servers, where the classes and servers are subject to failure. We assume that the interarrival and service times are independent and identically distributed, that routing is probabilistic, and that the failure state of the system can be described by a Markov process that is independent of the other system dynamics. We find that the maximal capacity is tightly bounded by the solution of a linear programming problem and that the solution of this problem can be used to construct timed, generalized round-robin policies that approach the maximal capacity arbitrarily closely. We then give a series of structural results for our policies, including identifying when server flexibility can completely compensate for failures and when the implementation of our policies can be simplified. We conclude with a numerical example that illustrates some of the developed insights.

BIAS OPTIMALITY IN A QUEUE WITH ADMISSION CONTROL

We consider a finite capacity queueing system in which each arriving customer offers a reward. A gatekeeper decides based on the reward offered and the space remaining whether each arriving customer should be accepted or rejected. The gatekeeper only receives the offered reward if the customer is accepted. A traditional objective function is to maximize the gain, that is, the long-run average reward. It is quite possible, however, to have several different gain optimal policies that behave quite differently. Bias and Blackwell optimality are more refined objective functions that can distinguish among multiple stationary, deterministic gain optimal policies. This paper focuses on describing the structure of stationary, deterministic, optimal policies and extending this optimality to distinguish between multiple gain optimal policies. We show that these policies are of trunk reservation form and must occur consecutively. We then prove that we can distinguish among these gain optimal policies using the bias or transient reward and extend to Blackwell optimality.

ANALYTIC MODELS FOR WHEN AND HOW TO EXPEDITE IN MAKE-TO-ORDER SYSTEMS

Expediting is defined as using overtime or subcontracting to supplement regular production. This is usually done when the number of backorders has grown to be unacceptably large. In this paper, we consider analytic models for deciding when and how to expedite in a single-product make-to-order environment. We derive the structure of the optimal expediting policy in both continuous- and discrete-time cases. The continuous-time model corresponds best to subcontracting and the discrete-time model corresponds to either overtime or subcontracting. Models for performance analysis of the continuous-time case are also given.