On the Equilibrium in a Queuing System with Retrials and Strategic Arrivals (original) (raw)
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Equilibrium Play In Single-Server Queues With Endogenously Determined Arrival Times
Journal of Economic …, 2004
We study a class of queueing problems with endogenous arrival times that we formulate as non-cooperative n-person games in normal form with discrete strategy spaces, fixed starting and closing times, and complete information. With multiple equilibria in pure strategies, these queueing games give rise to problems of tacit coordination. We first describe and illustrate a Markov chain algorithm used to compute the symmetric mixed-strategy equilibrium solution. Then, we report the results of an experimental study of a large-scale (n=20) queueing game with fixed service time, FIFO queue discipline, and no balking, reneging, and early arrivals. Our results show consistent and replicable patterns of arrival that provide strong support for mixed-strategy equilibrium play on the aggregate but not individual level.
Equilibrium in a Queueing System with Retrials
Mathematics, 2022
We find an equilibrium in a single-server queueing system with retrials and strategic timing of the customers. We consider a set of customers, each of which must decide when to arrive to a queueing system during a fixed period of time. In this system, after completion of service, the server seeks a customer blocked in a virtual orbit (orbital customer) to be served next, unless a new customer captures the server. We develop, in detail, a setting with two and three customers in the set, and formulate and discuss the problem for the general case with an arbitrary number of customers. The numerical examples for the system with two and three customers included as well.
Equilibrium customer strategies in a single server Markovian queue with setup times
Queueing Systems, 2007
We consider a single server Markovian queue with setup times. Whenever this system becomes empty, the server is turned off. Whenever a customer arrives to an empty system, the server begins an exponential setup time to start service again. We assume that arriving customers decide whether to enter the system or balk based on a natural reward-cost structure, which incorporates their desire for service as well as their unwillingness to wait.
A two-dimensional Multiserver Queuing system with repeated attempts and impatience
Indian journal of science and technology, 2021
Objective: This study discusses a two-state multiserver retrial queueing system, where the customer may leave the system due to impatience. In this paper, we deal with the time dependent probabilities when all, some or none servers are busy. Method: For this model, we solved difference differential equations recursively and obtained the time dependent probabilities when all, some or none servers are busy. Findings: Time dependent probabilities of exact number of arrivals and exact number of departures at when all, some or none servers are busy are obtained. In this paper, some kind of verification and converting two state model into single state model are discussed. Some special cases of interest are also discussed. Novelty: In communication networks, multiple servers are used to reduce traffic congestion and improve system performance. The operation mode of a call center with repeated attempts provides an initial motivation for our study. Keywords: Impatience; Multiserver; Probabil...
Ticket queues with regular and strategic customers
Queueing Systems
We study a Markovian single-server ticket queue where, upon arrival, each customer can draw a number from a take-a-number machine, while the number of the customer currently being served is displayed on a panel. The difference between the above two numbers is called the 'virtual queue length'. We consider a nonhomogeneous population of customers comprised of two types: 'regular', and 'strategic'. Upon arrival a regular customer, regardless of the value of the virtual queue length, draws a number from the machine, joins the queue and waits in the system until being served. A strategic customer, depending on the virtual queue length, may either join, leave, or go to 'orbit' for a random duration. If, upon return from orbit, a strategic customer realizes that s/he missed her/his turn, s/he balks. Otherwise, s/he joins the queue and waits to be served. We analyze this intricate stochastic system, calculate its steady state probabilities, derive the sojourn time's Laplace-Stieltjes transform of a regular and of a strategic customer, and calculate the system's performance measures. Finally, an economic analysis is performed to determine the optimal mean orbiting time of strategic customers for two types of objective functions. Numerical examples are presented.
A MARKOVIAN SINGLE SERVER QUEUEING SYSTEM WITH ARRIVALS DISCOURAGED BY QUEUE LENGTH
IASET, 2013
We study the queueing system where every customer on arrival makes one of the possible decisions either to join the queue or to go away without taking service never to return. Assuming such a decision to be entirely governed by the queue size at the instant of customer arrival, the transient solution is obtained analytically using the iteration method for a state dependent birth-death queue in which potential customers.
To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems
International Series in Operations Research & Management Science, 2003
Preface xi 1. INTRODUCTION A non-cooperative game is defined as follows. Let N = {1,. .. , n} be a finite set of players and let A i denote a set of actions available to player i ∈ N. A pure strategy for player i is an action from A i. A mixed strategy corresponds to a probability function which prescribes a randomized rule for selecting an action from A i. Denote by S i the set of strategies available to player i. A strategy profile s = (s 1 ,. .. , s n) assigns a strategy s i ∈ S i to each player i ∈ N. Each player is associated with a real payoff function F i (s). This function specifies the payoff received by player i given that the strategy profile s is adopted by the players. Denote by s −i a profile for the set of players N \ {i}. The function F i (s) = F i (s i , s −i) is assumed to be linear in s i. This means that if s i is a mixture with 1 In case of periodicity, with period d, replace the limit by averaging the limits along d consecutive periods. Note that ∞ s=0 πs(δ) does not necessarily sum up to 1. On one hand, it can be greater than 1 (in fact, can even be unbounded) when more than one recurrent chain exists, and on the other hand it may sum up to 0. An example for the latter case is when λ > µ and δ(s) = join for all s ≥ 0. x F (x, y). We are interested in cases where x(y) is continuous and strictly monotone. Figure 1.1 illustrates a situation where a strategy corresponds to a nonnegative number. It depicts one instance where x(y) is monotone decreasing and another where it is monotone increasing. We call these situations avoid the crowd (ATC) and follow the crowd (FTC), respectively. The rationale behind this terminology is that in an FTC (respectively, ATC) case, the higher the values selected by the others, the higher (respectively, lower) is one's best response. 3 An interesting generalization to this rule is proposed by Balachandran and Radhakrishnan [19]. Suppose that waiting t time units costs Ce at for given parameters C > 0 and a ≥ 0. Then, the expected waiting cost of a customer is ∞ 0 Ce at w(t) dt where w(t) is the density function of the waiting time. In an M/M/1 system w(t) = (µ − λ)e −(µ−λ)t where λ is the arrival rate and µ is the service rate. In this case the expected cost equals C µ−a−λ. Note that the case of linear waiting costs is obtained when a = 0. 4 See Deacon and Sonstelie [43] and Png and Reitman [140] for empirical studies concerning this parameter. Examples for disciplines that are strong and work-conserving are FCFS, LCFS, random order, order which is based on customers payments, and EPS. Service requirements are assumed to be independent and identically distributed. Denote by µ −1 the (common) expected service requirement (i.e., µ is the rate of service). For stability, assume that the system's utilization factor ρ = λ µ is strictly less than 1 (sometimes, when individual optimization leads to stability, this assumption is removed). The following five results hold when the arrival process is Poisson with rate λ, the service distribution is exponential (an M/M/1 model) with rate µ, and the service discipline is strong and work-conserving. They also hold for M/G/1 models when the service discipline is either EPS or LCFS-PR. The probability that n (n ≥ 0) customers are in the system (at arbitrary times as well as at arrival times) is (1 − ρ)ρ n. (1.2) 11 When 3 5λ > 1, commuters appear at a rate so low that even when all of them use the shuttle service, the individual's best response is still to use the bus service. In other words, when λ < 3 5 , using the bus service is a dominant strategy. Chapter 2 OBSERVABLE QUEUES This chapter deals with queueing systems, where an arriving customer observes the length of the queue before making his decisions.
2004
We consider a single server queueing system with repeated attempts in which customers arrive according a Markov Arrival Process (MAP) and with a LCFS PR discipline. The service times are independent and have a common general distribution. After service completion time the server initiates his search time with an arbitrary distribution function. We consider two cases where the maximum number of repeated customers waiting in the orbit to seek service again is limited by r(r < ∞) or can be unlimited (r = ∞). We derive the steady state probabilities of the embedded Markov chain at service completion times of the process and also the steady state probabilities of the underlying Markov linear process.
Homogeneous finite-source retrial queues with search of customers from the orbit
Measuring, Modelling and …, 2008
We consider a retrial queueing system with a finite number of homogeneous sources of calls and a single server. Each source generates a request after an exponentially distributed time. An arriving customer finding the server idle enters into service immediately; otherwise the customer enters into an orbit. The service times are supposed to be exponentially distributed random variables. An orbiting customer competes for service, the inter-retrial times are exponentially distributed. Upon completion of a service, with a certain probability the server searches for an orbiting customer. Assuming the search time to be negligible, the source, service, and retrial times to be independent random variables, we perform the steady-state analysis of the model computing various steady-state performance measures and illustrative numerical examples are presented. The novelty of the investigation is the introduction of orbital search by the server for customers in finite-source retrial queues. The MOSEL-2 tool is used to formulate and solve the problem.
An M/M/1/N Queuing system with Encouraged Arrivals
2017
In this paper, we develop a single-server finite capacity Markovian queuing system with encouraged arrivals. The model is solved in steady-state recursively. Necessary measures of performance are drawn. Economic analysis of the model is presented by developing a cost model. The model is studied numerically and simulated arbitrarily. The term encouraged arrivals emerged from situation that a system experiences after release of offers and discounts by firms. Encouraged arrivals is a new addition to existing customer behaviour in queuing theory.