Customer routing to different servers with complete information

1989 ◽  
Vol 21 (4) ◽  
pp. 861-882 ◽  
Author(s):  
Zvi Rosberg ◽  
Parviz Kermani
Keyword(s):  
Complete Information ◽  
Service Rate ◽  
Dependent Rate ◽  
Routing Policy ◽  
State Dependent ◽  
Long Run ◽  
Ideal Situation ◽  
Ideal System ◽  
Holding Cost ◽  
Routing Policies

In this paper we consider a queueing system having n exponential servers, each with its own queue and service rate. Customers arrive according to a Poisson process with rate λ, and upon arrival each customer must be routed to some server's queue. No jockeying amongst the queues is allowed and each server serves its queue according to a first-come-first-served discipline.Each server i, 1 ≦ i ≦ n, provides service with a state-dependent rate μ(i)(k), k = 0, 1, …. In addition, at every queue i, there is a deterministic holding cost which occurs at rate h(i)(k) while there are k customers at the queue.An admissible routing policy is a policy that assigns each arriving customer to one of the queues. A decision at time t may be randomized and dependent on the queue lengths and decisions till time t. An optimal routing policy is an admissible policy that minimizes the long-run average holding cost.In this study, we bound the optimal cost from below, by considering an ideal system, where each server optimally selects a given proportion of customers, irrespective of other servers' selections. From this ideal system we construct a class of admissible routing policies, the overflow routing class, that approximates the ideal situation for each server. Finally, we evaluate the policies and compare them to the lower bound.

Customer routing to different servers with complete information

1989 ◽  
Vol 21 (04) ◽  
pp. 861-882 ◽  
Author(s):  
Zvi Rosberg ◽  
Parviz Kermani
Keyword(s):  
Complete Information ◽  
Service Rate ◽  
Dependent Rate ◽  
Routing Policy ◽  
State Dependent ◽  
Long Run ◽  
Ideal Situation ◽  
Ideal System ◽  
Holding Cost ◽  
Routing Policies

In this paper we consider a queueing system having n exponential servers, each with its own queue and service rate. Customers arrive according to a Poisson process with rate λ, and upon arrival each customer must be routed to some server's queue. No jockeying amongst the queues is allowed and each server serves its queue according to a first-come-first-served discipline. Each server i, 1 ≦ i ≦ n, provides service with a state-dependent rate μ (i)(k), k = 0, 1, …. In addition, at every queue i, there is a deterministic holding cost which occurs at rate h (i)(k) while there are k customers at the queue. An admissible routing policy is a policy that assigns each arriving customer to one of the queues. A decision at time t may be randomized and dependent on the queue lengths and decisions till time t . An optimal routing policy is an admissible policy that minimizes the long-run average holding cost. In this study, we bound the optimal cost from below, by considering an ideal system, where each server optimally selects a given proportion of customers, irrespective of other servers' selections. From this ideal system we construct a class of admissible routing policies, the overflow routing class, that approximates the ideal situation for each server. Finally, we evaluate the policies and compare them to the lower bound.

scholarly journals A Markovian growth-collapse model

2006 ◽  
Vol 38 (1) ◽  
pp. 221-243 ◽  
Author(s):  
Onno Boxma ◽  
David Perry ◽  
Wolfgang Stadje ◽  
Shelemyahu Zacks
Keyword(s):  
General Theory ◽  
Rate Function ◽  
Current Level ◽  
Hitting Times ◽  
Dependent Rate ◽  
State Dependent ◽  
Long Run ◽  
Special Cases ◽  
Collapse Model ◽  
Markov Modulated

We consider growth-collapse processes (GCPs) that grow linearly between random partial collapse times, at which they jump down according to some distribution depending on their current level. The jump occurrences are governed by a state-dependent rate function r(x). We deal with the stationary distribution of such a GCP, (Xt)t≥0, and the distributions of the hitting times Ta = inf{t ≥ 0 : Xt = a}, a > 0. After presenting the general theory of these GCPs, several important special cases are studied. We also take a brief look at the Markov-modulated case. In particular, we present a method of computing the distribution of min[Ta, σ] in this case (where σ is the time of the first jump), and apply it to determine the long-run average cost of running a certain Markov-modulated disaster-ridden system.

scholarly journals A Markovian growth-collapse model

2006 ◽  
Vol 38 (01) ◽  
pp. 221-243 ◽  
Author(s):  
Onno Boxma ◽  
David Perry ◽  
Wolfgang Stadje ◽  
Shelemyahu Zacks
Keyword(s):  
General Theory ◽  
Rate Function ◽  
Current Level ◽  
Hitting Times ◽  
Dependent Rate ◽  
State Dependent ◽  
Long Run ◽  
Special Cases ◽  
Collapse Model ◽  
Markov Modulated

We consider growth-collapse processes (GCPs) that grow linearly between random partial collapse times, at which they jump down according to some distribution depending on their current level. The jump occurrences are governed by a state-dependent rate functionr(x). We deal with the stationary distribution of such a GCP, (Xt)t≥0, and the distributions of the hitting timesTa= inf{t≥ 0 :Xt=a},a> 0. After presenting the general theory of these GCPs, several important special cases are studied. We also take a brief look at the Markov-modulated case. In particular, we present a method of computing the distribution of min[Ta, σ] in this case (where σ is the time of the first jump), and apply it to determine the long-run average cost of running a certain Markov-modulated disaster-ridden system.

Dynamic pricing in a two-class queueing system with arrival and service rate control

2020 ◽  
Author(s):  
Shuangfeng Ma ◽  
Wei Guo
Keyword(s):  
Rate Control ◽  
Dynamic Pricing ◽  
Queueing System ◽  
Recursive Algorithm ◽  
Optimization Theory ◽  
Service Rate ◽  
Long Run ◽  
Holding Cost ◽  
Service Rates ◽  
Number Of Customers

Abstract Dynamic pricing in a two-class queueing system with adjustable arrival and service rates is considered in this paper. We initially take the adjustable rates into account to maximize the long-run average social welfare and further establish matched dynamic prices to lead two distinct types of customers’ behavior. For the rate-setting problems, we apply the sensitivity-based optimization theory and an iterative algorithm to investigate the two types of customers’ optimal arrival and service rates. Next, we apply the results obtained from rate-setting problems to acquire the expected delay time by recursive algorithm and demonstrate the optimal prices formulas for multiple customers explicitly. Finally, we carry out some numerical experiments to illustrate our consequence and the performance between two kinds of customers with different level of holding cost. It appears that under low holding cost, the optimal prices for two kinds of customers are monotonically increasing in the number of customers regardless of classes, but under high holding cost, the optimal prices for the customers who have low waiting cost may drop when the number of the other class rises.

Minimizing Mean Response Time In Batch-Arrival Non-Observable Systems With Single-Server FIFO Queues Operating In Parallel

2021 ◽  
Author(s):  
Mikhail Konovalov ◽  
Rostislav Razumchik
Keyword(s):  
Response Time ◽  
Arrival Time ◽  
Time Instant ◽  
Single Server ◽  
State Information ◽  
Mean Response Time ◽  
Routing Policy ◽  
Long Run ◽  
Service Rates ◽  
Routing Policies

Consideration is given to a dispatching system, where jobs, arriving in batches, cannot be stored and thus must be immediately routed to single-server FIFO queues operating in parallel. The dispatcher can memorize its routing decisions but at any time instant does not have any system's state information. The only information available is the batch/job size and inter-arrival time distributions, and the servers' service rates. Under these conditions, one is interested in the routing policies which minimize the job's long-run mean response time. The single-parameter routing policy is being proposed which, according to the numerical experiments, outperforms best routing rules known by now for non-observable dispatching systems: probabilistic and deterministic. Both the batch-wise and job-wise assignments are studied. Extension to systems with unreliable servers is also addressed.

Dynamic Pricing and Routing for Same-Day Delivery

2020 ◽  
Vol 54 (4) ◽  
pp. 1016-1033 ◽  
Author(s):  
Marlin W. Ulmer
Keyword(s):  
Dynamic Pricing ◽  
Function Approximation ◽  
Computational Study ◽  
Routing Problem ◽  
Value Function Approximation ◽  
Routing Policy ◽  
State Dependent ◽  
Markov Decision ◽  
Fixed Prices ◽  
Number Of Customers

An increasing number of e-commerce retailers offers same-day delivery. To deliver the ordered goods, providers dynamically dispatch a fleet of vehicles transporting the goods from the warehouse to the customers. In many cases, retailers offer different delivery deadline options, from four-hour delivery up to next-hour delivery. Due to the deadlines, vehicles often only deliver a few orders per trip. The overall number of served orders within the delivery horizon is small and the revenue low. As a result, many companies currently struggle to conduct same-day delivery cost-efficiently. In this paper, we show how dynamic pricing is able to substantially increase both revenue and the number of customers we are able to serve the same day. To this end, we present an anticipatory pricing and routing policy (APRP) method that incentivizes customers to select delivery deadline options efficiently for the fleet to fulfill. This maintains the fleet’s flexibility to serve more future orders. We model the respective pricing and routing problem as a Markov decision process (MDP). To apply APRP, the state-dependent opportunity costs per customer and option are required. To this end, we use a guided offline value function approximation (VFA) based on state space aggregation. The VFA approximates the opportunity cost for every state and delivery option with respect to the fleet’s flexibility. As an offline method, APRP is able to determine suitable prices instantly when a customer orders. In an extensive computational study, we compare APRP with a policy based on fixed prices and with conventional temporal and geographical pricing policies. APRP outperforms the benchmark policies significantly, leading to both a higher revenue and more customers served the same day.

scholarly journals Large Deviations for the Single-Server Queue and the Reneging Paradox

2021 ◽  
Author(s):  
Rami Atar ◽  
Amarjit Budhiraja ◽  
Paul Dupuis ◽  
Ruoyu Wu
Keyword(s):  
Large Deviations ◽  
Decay Rate ◽  
Sample Path ◽  
Arrival Rate ◽  
Rate Function ◽  
Service Rate ◽  
Single Server ◽  
Large Deviations Principle ◽  
Long Run ◽  
The Individual

For the M/M/1+M model at the law-of-large-numbers scale, the long-run reneging count per unit time does not depend on the individual (i.e., per customer) reneging rate. This paradoxical statement has a simple proof. Less obvious is a large deviations analogue of this fact, stated as follows: the decay rate of the probability that the long-run reneging count per unit time is atypically large or atypically small does not depend on the individual reneging rate. In this paper, the sample path large deviations principle for the model is proved and the rate function is computed. Next, large time asymptotics for the reneging rate are studied for the case when the arrival rate exceeds the service rate. The key ingredient is a calculus of variations analysis of the variational problem associated with atypical reneging. A characterization of the aforementioned decay rate, given explicitly in terms of the arrival and service rate parameters of the model, is provided yielding a precise mathematical description of this paradoxical behavior.

scholarly journals A Bayesian Approach for Inferring the Impact of a Discrete Character on Rates of Continuous-Character Evolution in the Presence of Background-Rate Variation

2019 ◽  
Vol 69 (3) ◽  
pp. 530-544 ◽  
Author(s):  
Michael R May ◽  
Brian R Moore
Keyword(s):  
Bayesian Approach ◽  
Null Model ◽  
Character Evolution ◽  
Rate Variation ◽  
Background Rate ◽  
Dependent Rate ◽  
State Dependent ◽  
Discrete Character ◽  
The Impact ◽  
Continuous Character

Abstract Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; for example, does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus nonreef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a “straw-man” effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation (“background-rate variation”). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae). [Bayesian phylogenetic comparative methods; continuous-character evolution; data augmentation; discrete-character evolution.]

Throughput properties of a queueing network with distributed dynamic routing and flow control

1996 ◽  
Vol 28 (01) ◽  
pp. 285-307 ◽  
Author(s):  
Leandros Tassiulas ◽  
Anthony Ephremides
Keyword(s):  
Flow Control ◽  
Rate Control ◽  
Queueing Network ◽  
Local State ◽  
Service Rate ◽  
Maximum Throughput ◽  
State Information ◽  
Arbitrary Topology ◽  
State Dependent ◽  
Service Rates

A queueing network with arbitrary topology, state dependent routing and flow control is considered. Customers may enter the network at any queue and they are routed through it until they reach certain queues from which they may leave the system. The routing is based on local state information. The service rate of a server is controlled based on local state information as well. A distributed policy for routing and service rate control is identified that achieves maximum throughput. The policy can be implemented without knowledge of the arrival and service rates. The importance of flow control is demonstrated by showing that, in certain networks, if the servers cannot be forced to idle, then no maximum throughput policy exists when the arrival rates are not known. Also a model for exchange of state information among neighboring nodes is presented and the network is studied when the routing is based on delayed state information. A distributed policy is shown to achieve maximum throughput in the case of delayed state information. Finally, some implications for deterministic flow networks are discussed.

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