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24
Dynamic admission control in a call center with one shared and two dedicated service facilities
, 2002
"... Calls of two classes arrive at a call center according to two independent Poisson processes. The center has two dedicated stations, one for each class, and one shared station. All three stations consist of parallel servers and no waiting room. Calls of each type demand exponential service times wit ..."
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Cited by 15 (2 self)
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Calls of two classes arrive at a call center according to two independent Poisson processes. The center has two dedicated stations, one for each class, and one shared station. All three stations consist of parallel servers and no waiting room. Calls of each type demand exponential service times with different service rates and generate different rewards. Moreover, the service rates are different in the shared and dedicated stations. We assume nonpreemptive service. Our objective is to derive the structure of dynamic admission policies that maximize the total expected discounted revenue over an innite horizon as well as the longrun average revenue. We show that it is optimal to serve a customer in her dedicated station whenever it is possible. For the shared station, we derive a sufcient condition for each class under which it is always optimal to accept customers of that class to the shared station if the dedicated station is full and the shared station has available servers. Furthermore, the optimal admission policy at the shared station can be characterized as a monotonic threshold policy.
Capacity management in rental businesses with two customer bases.
 Oper. Res.
, 2005
"... Abstract We consider the allocation of capacity in a system in which rental equipment is accessed by two classes of customers. We formulate the problem as a continuoustime analogue of the oneshot allocation problems found in the more traditional literature on revenue management, and we analyze a ..."
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Cited by 12 (1 self)
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Abstract We consider the allocation of capacity in a system in which rental equipment is accessed by two classes of customers. We formulate the problem as a continuoustime analogue of the oneshot allocation problems found in the more traditional literature on revenue management, and we analyze a queueing control model that approximates its dynamics. Our investigation yields three sets of results. First, we use dynamic programming to characterize properties of optimal capacity allocation policies. We identify conditions under which "complete sharing" in which both classes of customer have unlimited access to the rental fleet is optimal. Next, we develop a computationally efficient "aggregate threshold" heuristic that is based on a fluid approximation of the original stochastic model. We obtain closedform expressions for the heuristic's control parameters and show that the heuristic performs well in numerical experiments. The closedform expressions also show that, in the context of the fluid approximation, revenues are concave and increasing in the fleet size. Finally, we consider the effect of the ability to allocate capacity on optimal fleet size. We show that the optimal fleet size under allocation policies may be lower, the same as, or higher than that under complete sharing. As capacity costs increase, allocation policies allow for larger relative fleet sizes. Numerical results show that, even in cases in which dollar profits under complete sharing may be close to those under allocation policies, the capacity reductions enabled by allocation schemes can help to lift profit margins significantly.
Admission control policies in multiservice cellular networks: optimum configuration and sensitivity
 Wireless Systems and Mobility in Next Generation Internet, Gabriele Kotsis and Otto Spaniol (eds.), Lecture Notes in Computer Science (LNCS
, 2005
"... Abstract. We evaluate different call admission control policies in various multiservice cellular scenarios. For each of the studied policies we obtain the maximum calling rate that can be offered to the system to achieve a given QoS objective defined in terms of blocking probabilities. We propose a ..."
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Cited by 7 (5 self)
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Abstract. We evaluate different call admission control policies in various multiservice cellular scenarios. For each of the studied policies we obtain the maximum calling rate that can be offered to the system to achieve a given QoS objective defined in terms of blocking probabilities. We propose an optimization methodology based on a hill climbing algorithm to find the optimum configuration for most policies. The results show that policies of the trunk reservation class outperform policies that produce a productform solution and the improvement ranges approximately between 5 and 15 % in the scenarios studied. 1
Optimal and Structured Call Admission Control Policies for ResourceSharing Systems
"... Abstract—Many communication and networking systems can be modeled as resourcesharing systems with multiple classes of calls. Call admission control (CAC) is an essential component of such systems. Markov decision process (MDP) tools can be applied to analyze and compute the optimal CAC policy that ..."
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Cited by 6 (0 self)
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Abstract—Many communication and networking systems can be modeled as resourcesharing systems with multiple classes of calls. Call admission control (CAC) is an essential component of such systems. Markov decision process (MDP) tools can be applied to analyze and compute the optimal CAC policy that optimizes certain performance metrics of the system. But for most practical systems, it is prohibitively difficult to compute the optimal CAC policy using any MDP algorithm because of the “curse of dimensionality.” We are, therefore, motivated to consider two families of structured CAC policies: reservation and threshold policies. These policies are easy to implement and have good performance in practice. However, since the number of structured policies grows exponentially with the number of call classes and the capacity of the system, finding the optimal structured policy is a complex unsolved problem. In this paper, we develop fast and efficient search algorithms to determine the parameters of the structured policies. We prove the convergence of the algorithms. Through extensive numerical experiments, we show that the search algorithms converge quickly and work for systems with large capacity and many call classes. In addition, the returned structured policies have optimal or nearoptimal performance, and outperform those structured policies with parameters chosen based on simple heuristics. Index Terms—Call admission control (CAC), combinatorial optimization, Markov decision process (MDP), reservation policy, resource sharing, threshold policy. I.
Comparative evaluation of admission control policies in cellular multiservice networks
 in Proceedings of the 16th International Conference on Wireless Communications
, 2004
"... Abstract. We evaluate different call admission control policies in various multiservice cellular scenarios. For each of the studied policies we obtain the maximum calling rate that can be offered to the system to achieve a given QoS objective defined in terms of blocking probabilities. We propose an ..."
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Cited by 5 (2 self)
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Abstract. We evaluate different call admission control policies in various multiservice cellular scenarios. For each of the studied policies we obtain the maximum calling rate that can be offered to the system to achieve a given QoS objective defined in terms of blocking probabilities. We propose an optimization methodology based on a hill climbing algorithm to find the optimum configuration for most policies. Preliminary results show that policies of the trunk reservation class outperform policies that produce a productform solution and the improvement ranges approximately between 5 and 15%.
Threshold and reservation based call admission control policies for multiservice resourcesharing systems
 in Proc. IEEE INFOCOM
"... Abstract — Many communications and networking systems can be modelled as resourcesharing systems with multiple classes of calls. Call admission control (CAC) is an essential component of such systems. For most practical systems it is prohibitively difficult to compute the optimal CAC policy that op ..."
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Cited by 3 (1 self)
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Abstract — Many communications and networking systems can be modelled as resourcesharing systems with multiple classes of calls. Call admission control (CAC) is an essential component of such systems. For most practical systems it is prohibitively difficult to compute the optimal CAC policy that optimizes certain performance metrics because of the ‘curse of dimensionality’. In this paper we study two families of structured CAC policies: threshold and reservation policies. These policies are easy to implement and have good performance in practice. However, since the number of structured policies grows exponentially with the number of call classes and the capacity of the system, finding the optimal structured policies is a complex unsolved problem. In this paper efficient search algorithms are proposed to find the coordinate optimal structured policies among all structured policies. Through extensive numerical experiments we show that the search algorithms converge quickly and work for systems with large capacity and many call classes. In addition, the returned structured policies have optimal or nearoptimal performance, and outperform those structured policies with parameters chosen based on simple heuristics. Keywords—Resource sharing, call admission control, threshold policies, reservation policies, combinatorial optimization. I.
Structured Admission Control Policy in Heterogeneous Wireless Networks with Mesh Underlay
"... Abstract—In this paper, we investigate into optimal admission control policies for Heterogeneous Wireless Networks (HWN), considering an integration of wireless mesh networks with an overlaying cellular infrastructure. In order to characterize the overflow traffic from the underlaying mesh to the ov ..."
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Cited by 3 (3 self)
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Abstract—In this paper, we investigate into optimal admission control policies for Heterogeneous Wireless Networks (HWN), considering an integration of wireless mesh networks with an overlaying cellular infrastructure. In order to characterize the overflow traffic from the underlaying mesh to the overlay, a PartiallyObservable MarkovModulated Poisson Process (POMMPP) traffic model is developed. This model captures the burstiness of the overflow traffic under the imperfect observability of the mesh network states. Then, by modeling the overlay network as a controlled POMMPP/M/C/C queueing system and obtaining structured decision theoretic results, it is shown that the optimal control policies for this class of HWNs can be characterized as monotonic threshold curves. Further, these results are used to design a computationally efficient algorithm to determine the optimal policy in terms of thresholds. Numerical observations suggest that the proposed algorithm is efficient in terms of timecomplexity and can drastically reduce the cost of dropped and blocked calls. I.
Dynamic wavelength sharing policies for absolute QoS guarantees
 in OBS networks,” in Proc. IEEE Globecom
, 2006
"... Abstract — We consider the problem of providing absolute QoS guarantees to multiple classes of users of an OBS network in terms of the endtoend burst loss. We employ Markov decision process (MDP) theory to develop wavelength sharing policies that maximize throughput while meeting the QoS guarantee ..."
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Cited by 2 (1 self)
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Abstract — We consider the problem of providing absolute QoS guarantees to multiple classes of users of an OBS network in terms of the endtoend burst loss. We employ Markov decision process (MDP) theory to develop wavelength sharing policies that maximize throughput while meeting the QoS guarantees. The randomized threshold policies we obtain are simple to implement and operate, and make effective use of statistical multiplexing. I.
Revenue and Capacity Management for a Multiclass Service System∗
, 2008
"... We consider the revenue and capacity management for a multiclass service system. Customer arrival rate for each class is price dependent. Admitted customers leave the system at constant classdependent rates. Customers are rejected when the system is full. We start with a infinitehorizon dynamic p ..."
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We consider the revenue and capacity management for a multiclass service system. Customer arrival rate for each class is price dependent. Admitted customers leave the system at constant classdependent rates. Customers are rejected when the system is full. We start with a infinitehorizon dynamic programming formulation and study the properties of the optimal control policy. Furthermore, we consider a fluid model which is formulated as an infinite horizon optimal control problem with a state space constraint where the objective is to maximize total discounted revenue. We analyze the dynamic pricing policy in the context of the fluid model and characterize its optimal solution. Solution of the fluid model is used to construct a capacity management model where the decision is the total system capacity level. The fluid model can be viewed as a deterministic fluid approximation of a stochastic service system. Throughout, we consider an admission control problem where the prices are fixed and customer service requests are either accepted or rejected upon arrival as a special case. 1
Applied Probability Trust (19 November 2004) DYNAMIC ADMISSION CONTROL FOR LOSS SYSTEMS WITH BATCH ARRIVALS
"... We consider the problem of dynamic admission control in a Markovian loss system with two classes. Jobs arrive at the system in batches, where each admitted job requires different service rates and brings different revenues depending on its class. We introduce the definition of “preferred class ” for ..."
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We consider the problem of dynamic admission control in a Markovian loss system with two classes. Jobs arrive at the system in batches, where each admitted job requires different service rates and brings different revenues depending on its class. We introduce the definition of “preferred class ” for systems receiving mixed and oneclass batches separately, and derive sufficient conditions for each system to have a preferred class. We also establish a monotonicity property of the optimal value functions, which reduces the number of possibly optimal actions.