Markovian polling systems with mixed service disciplines and retrial customers

A Markovian polling model with a mixture of exhaustive and gated type stations is considered. The cuttomers are ofn different tppes and arrive to the system acccording to the Poisson distribution, in batches containing customers of all types (correlated batch arrivals). The customers who find upon arrival the server unavailable repeat their arrival individually after a random amount of time (retrial customers). The service timesT _i and the switchover timesV _ij are arbitrarily distributed with different distributions for the different stations. For such a model we obtain formulae for the expected number of customers in each station in a steady state. Our formulae hold also for zero switchover periods and can easily be adapted to hold for the corresponding ordinary Markovian mixed polling models with/without switchover times and correlated batch arrivals. Numerical calculations are finally used to observe system's performance.     

Limit laws for coverage in backbone-sensor networks

We study coverage in sensor networks having two types of nodes, namely, sensor nodes and backbone nodes. Each sensor is capable of transmitting information over relatively small distances. The backbone nodes collect information from the sensors. This information is processed and communicated over an ad hoc network formed by the backbone nodes, which are capable of transmitting over much larger distances. We consider two models of deployment for the sensor and backbone nodes. One is a Poisson–Poisson cluster model and the other a dependently thinned Poisson point process. We deduce limit laws for functionals of vacancy in both models using properties of association for random measures.     

Class clustering destroys delay differentiation in priority queues

This paper considers a discrete-time priority queueing model with one server and two types (classes) of customers. Class-1 customers have absolute (service) priority over class-2 customers. New customer batches enter the system at the rate of one batch per slot, according to a general independent arrival process, i.e., the batch sizes (total numbers of arrivals) during consecutive time slots are i.i.d. random variables with arbitrary distribution. All customers entering the system during the same time slot (i.e., belonging to the same arrival batch) are of the same type, but customer types may change from slot to slot, i.e., from batch to batch. Specifically, the types of consecutive customer batches are correlated in a Markovian way, i.e., the probability that any batch of customers has type 1 or 2, respectively, depends on the type of the previous customer batch that has entered the system. Such an arrival model allows to vary not only the relative loads of both customer types in the arrival stream, but also the amount of correlation between the types of consecutive arrival batches. The results reveal that the amount of delay differentiation between the two customer classes that can be achieved by the priority mechanism strongly depends on the amount of such interclass correlation (or, class clustering) in the arrival stream. We believe that this phenomenon has been largely overlooked in the priority-scheduling literature.     

Single-pass and approximate dynamic-programming algorithms for order acceptance and capacity planning

This paper investigates dynamic order acceptance and capacity planning under limited regular and non-regular resources. Our goal is to maximize the profits of the accepted projects within a finite planning horizon. The way in which the projects are planned affects their payout time and, as a consequence, the reinvestment revenues as well as the available capacity for future arriving projects. In general, project proposals arise dynamically to the organization, and their actual characteristics are only revealed upon arrival. Dynamic solution approaches are therefore most likely to obtain good results. Although the problem can theoretically be solved to optimality as a stochastic dynamic program, real-life problem instances are too difficult to be solved exactly within a reasonable amount of time. Efficient and effective heuristics are thus required that supply a response without delay. For this reason, this paper considers both ‘single-pass’ algorithms as well as approximate dynamic-programming algorithms and investigates their suitability to solve the problem. Simulation experiments compare the performance of our procedures to a first-come, first-served policy that is commonly used in practice.     

Dynamic Routing and Admission Control in High-Volume Service Systems: Asymptotic Analysis via Multi-Scale Fluid Limits

Motivated by applications in telephone call centers, we consider a service system model with m customer classes and r server pools. The model is one with doubly stochastic arrivals, which means that the m-vector λ of instantaneous arrival rates is allowed to vary both temporally and stochastically. Two levels of dynamic control are considered: customers may be either blocked or accepted at the time of their arrival, and then accepted customers of each class must be routed, either immediately upon acceptance or after some period of waiting, to a server pool that is qualified to handle that class. Customers who are made to wait before commencement of their service are liable to defect. The objective is to minimize the expected sum of blocking costs, waiting costs and defection costs over a fixed and finite planning horizon. We consider an asymptotic parameter regime in which (i) the arrival rates, service rates and defection rates are uniformly accelerated by a large factor κ, then (ii) arrival rates are increased by an additional factor g(κ), and the number of servers in each pool is increased by g(κ) as well. This produces a separation of time scales, justifying a pointwise stationary stochastic fluid approximation for our original system model. In the stochastic fluid approximation, optimal admission control and routing decisions are determined by a simple linear program that uses the current arrival rate vector λ as data. We explain how to implement the fluid model's optimal control policy in our original service system context, and prove that the proposed implementation is asymptotically optimal in the first-order sense. AMS subject classification: 60K30, 90B15, 90B36     

Asymptotic Analysis of a Loss Model with Trunk Reservation II: Trunks Reserved for Slow Traffic

We consider a model for a single link in a circuit switched network. The link has C circuits, and the input consists of offered calls of two types, that we call primary and secondary traffic. Of the C links R are reserved for primary traffic. We assume that both traffic types arrive as Poisson arrival streams. Assuming that C is large and   R = O (1)  , the arrival rate of secondary traffic is   O ( C )  while that of primary traffic is smaller, of the order     . The holding times of the secondary calls are assumed exponentially distributed with unit mean. Those of the primary calls are exponentially distributed with a large mean, that is     . The loads for both traffic types are thus comparable  ( O ( C ))  and we assume that the system is "critically loaded," i.e., the system's capacity is approximately equal to the total load. We analyze asymptotically the steady state probability that   n ₂  (resp.   n ₁  ) circuits are occupied by primary (resp. secondary) calls. In particular, we obtain two-term asymptotic approximations to the blocking probabilities for both traffic types. This work complements part I, where we assumed that the secondary traffic had an arrival rate that was     and a service rate that was     . Thus in part I the R trunks were reserved for the "fast traffic," whose arrival and service rates were   O ( C )  and   O (1)  .     

Prioritization of organizational capital measurement indicators using fuzzy AHP

Organizational capital is a sub-dimension of the intellectual capital which is the sum of all assets that make the creative ability of the organization possible. To control and manage such an important force, the companies must measure it first. This study aims at defining a methodology to improve the quality of prioritization of organizational capital measurement indicators under uncertain conditions. To do so, a methodology based on the extent fuzzy analytic hierarchy process (AHP) is applied. Within the model, three main attributes; deployment of the strategic values, investment to the technology and flexibility of the structure; their sub-attributes and 10 indicators are defined. To define the priority of each indicator, preferences of experts are gathered using a pair-wise comparison based questionnaire. The results of the study show that “deployment of the strategic values” is the most important attribute of the organizational capital.     

Analysis of customers’ impatience in queues with server vacations

Many models for customers impatience in queueing systems have been studied in the past; the source of impatience has always been taken to be either a long wait already experienced at a queue, or a long wait anticipated by a customer upon arrival. In this paper we consider systems with servers vacations where customers’ impatience is due to an absentee of servers upon arrival. Such a model, representing frequent behavior by waiting customers in service systems, has never been treated before in the literature. We present a comprehensive analysis of the single-server, M/M/1 and M/G/1 queues, as well as of the multi-server M/M/c queue, for both the multiple and the single-vacation cases, and obtain various closed-form results. In particular, we show that the proportion of customer abandonments under the single-vacation regime is smaller than that under the multiple-vacation discipline. This work was supported by the Euro-Ngi network of excellence.     

Permanence of dispersal population model with time delays

Permanence of a dispersal single-species population model is considered where environment is partitioned into several patches and the species requires some time to disperse between the patches. The model is described by delay differential equations. The existence of food-rich patches and small dispersions among the patches are proved to be sufficient to ensure partial permanence of the model. It is also shown that partial permanence ensures permanence if each food-poor patch is connected to at least one food-rich patch and if each pair in food-rich patches is connected. Furthermore, it is proved that partial persistence is ensured even under large dispersion among food-rich patches if the dispersion time is relatively small.     

N-策略休假排队系统中异质信息顾客策略分析

本文研究N-策略休假M/M/1排队系统中的异质信息顾客策略和社会最优收益。来到系统的顾客分为两类,第一类顾客有系统信息,加入系统前知道服务员的状态和系统中顾客数;第二类顾客没有系统信息,加入系统前既不知道服务员的状态,也不知道系统中顾客数。利用粒子群优化算法,分析了两类顾客的最优策略行为和最优社会收益。结果表明,最优社会收益随着转换门限N的增大而减小,随着v和系统负载p的增大而增大。并且,第一类顾客的比例越大,社会收益越大。     

Inventory control in multi-channel retail

In recent years multi-channel retail systems have received increasing interest. Partly due to growing online business that serves as a second sales channel for many firms, offering channel specific prices has become a common form of revenue management. We analyze conditions for known inventory control policies to be optimal in presence of two different sales channels. We propose a single item lost sales model with a lead time of zero, periodic review and nonlinear non-stationary cost components without rationing to realistically represent a typical web-based retail scenario. We analyze three variants of the model with different arrival processes: demand not following any particular distribution, Poisson distributed demand and a batch arrival process where demand follows a Pòlya frequency type distribution. We show that without further assumptions on the arrival process, relatively strict conditions must be imposed on the penalty cost in order to achieve optimality of the base stock policy. We also show that for a Poisson arrival process with fixed ordering costs the model with two sales channels can be transformed into the well known model with a single channel where mild conditions yield optimality of an (s, S) policy. Conditions for optimality of the base stock and (s, S) policy for the batch arrival process with and without fixed ordering costs, respectively, are presented together with a proof that the batch arrival process provides valid upper and lower bounds for the optimal value function.     

Parking buses in a depot with stochastic arrival times

Given buses of different types arriving at a depot during the evening, the bus parking problem consists of assigning these buses to parking slots in such a way that they can be dispatched adequately to the next morning routes without moving them between their arrivals and departures. In practice, the bus arrival times deviate stochastically from the planned schedule. In this paper, we introduce for this problem two solution approaches that produce solutions which are robust to variations in the arrival times. The first approach considers that each arrival can deviate from its planned arrival order (sooner or later) by at most k positions, where k is a predefined parameter. In the second approach, the objective aims at minimizing the expectation of a function positively correlated with the number of buses that make the planned solution infeasible because they arrive too late or too early. In both approaches, the problem is modeled as an integer linear program that can be solved by a commercial mip solver. Computational results obtained on instances derived from a real-world dataset are reported.     

A batch arrival retrial queue with two phases of service and Bernoulli vacation schedule

We consider an M X /G/1 queueing system with two phases of heterogeneous service and Bernoulli vacation schedule which operate under a linear retrial policy. In addition, each individual customer is subject to a control admission policy upon the arrival. This model generalizes both the classical M/G/1 retrial queue with arrivals in batches and a two phase batch arrival queue with a single vacation under Bernoulli vacation schedule. We will carry out an extensive stationary analysis of the system , including existence of the stationary regime, embedded Markov chain, steady state distribution of the server state and number of customer in the retrial group, stochastic decomposition and calculation of the first moment.     

A discrete-time retrial queueing model with one server

This paper presents a one-server queueing model with retrials in discrete-time. The number of primary jobs arriving in a time slot follows a general probability distribution and the different numbers of primary arrivals in consecutive time slots are mutually independent. Each job requires from the server a generally distributed number of slots for its service, and the service times of the different jobs are independent. Jobs arriving in a slot can start their service only at the beginning of the next slot. When upon arrival jobs find the server busy all incoming jobs are sent into orbit. When upon arrival in a slot jobs find the server idle, then one of the incoming jobs (randomly chosen) in that slot starts its service at the beginning of the next slot, whereas the other incoming jobs in that slot, if any, are sent into orbit. During each slot jobs in the orbit try to re-enter the system individually, independent of each other, with a given retrial probability.     

A two-class discrete-time queueing model with two dedicated servers and global FCFS service discipline

This paper considers a simple discrete-time queueing model with two types (classes) of customers (types 1 and 2) each having their own dedicated server (servers A and B resp.). New customers enter the system according to a general independent arrival process, i.e., the total numbers of arrivals during consecutive time slots are i.i.d. random variables with arbitrary distribution. Service times are deterministically equal to 1 slot each. The system uses a “global FCFS” service discipline, i.e., all arriving customers are accommodated in one single FCFS queue, regardless of their types. As a consequence of the “global FCFS” rule, customers of one type may be blocked by customers of the other type, in that they may be unable to reach their dedicated server even at times when this server is idle, i.e., the system is basically non-workconserving. One major aim of the paper is to estimate the negative impact of this phenomenon on the queueing performance of the system, in terms of the achievable throughput, the system occupancy, the idle probability of each server and the delay. As it is clear that customers of different types hinder each other more as they tend to arrive in the system more clustered according to class, the degree of “class clustering” in the arrival process is explicitly modeled in the paper and its very direct impact on the performance measures is revealed. The motivation of our work are systems where this kind of blocking is encountered, such as input-queueing network switches or road splits.     

Multi-server retrial queue with negative customers and disasters

We consider a multi-server retrial queue with waiting places in service area and four types of arrivals, positive customers, disasters and two types of negative customers, one for deleting customers in orbit and the other for deleting customers in service area. The four types of arrivals occur according to a Markovian arrival process with marked transitions (MMAP) which may induce the dependence among the arrival processes of the four types. We derive a necessary and sufficient condition for the system to be positive recurrent by comparing sample paths of auxiliary systems whose stability conditions can be obtained. We use a generalized truncated system that is obtained by modifying the retrial rates for an approximation of stationary queue length distribution and show the convergence of approximation to the original model. An algorithmic solution for the stationary queue length distribution and some numerical results are presented.      

Customer equilibrium and optimal pricing in an M/G/1 queue with heterogeneous rewards and waiting cost rates

We consider an unobservable M/G/1 queue in which customers are allowed to join or balk upon arrival. The service provider charges the same admission fee to all joining customers. All joining customers receive a reward from completion of service and incur a waiting cost. The reward and waiting cost rate are random, however the customers know their own values upon arrival. We characterize the customer’s equilibrium strategy and the optimal prices associated with profit and social welfare maximization.     

Comparison of socially optimal and profit maximizing prices in an unobservable queue with heterogeneous waiting costs

We consider a general unobservable queueing model in which customers are allowed to join or balk upon arrival. The service provider charges the same admission fee to all joining customers. All joining customers receive the same reward and incur heterogeneous waiting cost rates. We show that the socially optimal arrival rate is greater than or equal to the profit maximizing arrival rate. Equivalently, the socially optimal admission fee is smaller than or equal to the profit maximizing admission fee.     

Public and private optimization at a service facility with approximate information on congestion

An M/GI/1 queueing model is considered, where the arrival rate to the facility is a continuous variable which depends, in the steady state, upon the average congestion at the facility. The population of customers arriving to the facility is partitioned into several classes dependent on the ratio of the value of time to the reward due to service but are served according to first-in-first-out rule. It is shown that under the privately optimal behavior of the individuals the facility will be dominated by the class with the highest net reward per value of time. The publicly optimal policy which maximizes the net reward due to service, after costs of waiting are deducted, is shown either to admit only a single class of customers to the facility, thus discriminating against the other classes or to be indifferent to the mix of classes. The class chosen for admission may not be the class which would have privately dominated the facility. When the expected delay experienced at the facility is fixed, a policy of tolls and rebates for the customers is obtained that will assure equal access to the facility for all customers irrespective of their classes. It is shown that the publicly optimal policy, under the condition of fixed aggregate arrival rate to the facility, is shown to be deversified.The optimal arrival rates desired by a single class are derived for two cases. When the proportions of arrivals from the classes are fixed, the aggregate arrival rate desired by a class is shown to be not greater than the equilibrium rate for the individuals from that class. Alternatively, when the aggregate arrival rate is fixed, conditions are obtained under which a class will prefer usage of the facility by several classes to its own domination.     

Group forecasting accuracy in hotels

Yield management helps hotels more profitably manage the capacity of their rooms. Hotels tend to have two types of business: transient and group. Yield management research and systems have been designed for transient business in which the group forecast is taken as a given. In this research, forecast data from approximately 90 hotels of a large North American hotel chain were used to determine the accuracy of group forecasts and to identify factors associated with accurate forecasts. Forecasts showed a positive bias and had a mean absolute percentage error (MAPE) of 40% at two months before arrival; 30% at one month before arrival; and 10–15% on the day of arrival. Larger hotels, hotels with a higher dependence on group business, and hotels that updated their forecasts frequently during the month before arrival had more accurate forecasts.