Dynamic Call Center Routing Policies Using Call Waiting and Agent Idle Times (original) (raw)
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Simulation of Optimal Routing in Call Centers
Egyptian Computer Science Journal , 2018
Call centers can provide service to customers, handle queries, offer product support, carry out telemarketing, or market research. The challenge of queue at call center is a function of both waittimes resulting from lack of available severs and ineffective call resolution which is has to do with the agent's skill and experience to handle the problem. This paper studies the concept of call center and its challenges, discuses related literatures and adopted the results obtained from [14,16], indicating optimal as SSTF and SQR while [15] further hybridized both optimal rules to obtain Hybrid Heterogeneous Call Routing Rule (HHCRR). The methodology deployed was discrete event-driven simulation. In the displayed simulation result between SSTF, SQR and HHCRR, findings from the result proves that HHCRR performs better than both the optimal rule for wait-time (SSTF) and call resolution (SQR) routing rules
A Call-Routing Problem with Service-Level Constraints
Operations Research, 2003
We consider a queueing system, commonly found in inbound telephone call centers, that processes two types of work. Type-H jobs arrive at rate H , are processed at rate H , and are served first come, first served within class. A service-level constraint of the form E delay or P delay limits the delay in queue that these jobs may face. An infinite backlog of type-L jobs awaits processing at rate L , and there is no service-level constraint on this type of work. A pool of c identical servers processes all jobs, and a system controller must maximize the rate at which type-L jobs are processed, subject to the service-level constraint placed on the type-H work. We formulate the problem as a constrained, average-cost Markov decision process and determine the structure of effective routing policies. When the expected service times of the two classes are the same, these policies are globally optimal, and the computation time required to find the optimal policy is about that required to calculate the normalizing constant for a simple M/M/c system. When the expected service times of the two classes differ, the policies are optimal within the class of priority policies, and the determination of optimal policy parameters can be determined through the solution of a linear program with O(c 3) variables and O(c 2) constraints.
Staffing Multiskill Call Centers via Linear Programming and Simulation
Management Science, 2008
We study an iterative cutting-plane algorithm on an integer program, for minimizing the staffing costs of a multiskill call center subject to service-level requirements which are estimated by simulation. We solve a sample average version of the problem, where the service-levels are expressed as functions of the staffing for a fixed sequence of random numbers driving the simulation.
Improving Call Center Operations Using Performance-Based Routing Strategies
2007
The paper presents a simulation study of performance-based call routing strategies using a variety of routing rules based on historic data such as average handling time and first call resolution rate. We demonstrate the relative benefits of various performance-based call routing strategies using actual data from a financial call center. In particular, our modeling results indicate that call routing based on adjusted average handle time (AAHT) and z-scores of AAHT are two strategies that can substantially improve overall call center performance and customer satisfaction.
Online scheduling policies for multiclass call centers with impatient customers
European Journal of Operational Research, 2010
We consider a call center with two classes of impatient customers: premium and regular classes. Modeling our call center as a multiclass GI/GI/s + M queue, we focus on developing scheduling policies that satisfy a target ratio constraint on the abandonment probabilities of premium customers to regular ones. The problem is inspired by a real call center application in which we want to reach some predefined preference between customer classes for any workload condition. The motivation for this constraint comes from the difficulty of predicting in a quite satisfying way the workload. In such a case, the traditional routing problem formulation with differentiated service levels for different customer classes would be useless. For this new problem formulation, we propose two families of online scheduling policies: queue joining and call selection policies. The principle of our policies is that we adjust their routing rules by dynamically changing their parameters. We then evaluate the performance of these policies through a numerical study. The policies are characterized by simplicity and ease of implementation.
Many inbound call centers offer free calls for their clients. Routing calls has an important role in call center costs, be-cause it affects costs caused by customers and staff. Call center managers are challenged to settle how to use customer and service representatives information to decide which calls should be handled by which type of service representatives. Routing policies must provide an efficient match to customer calls to service representatives, and the policies are con-strained by performance indicators which affect customer waiting and service time. This work investigates routing policies which could reduce call costs in toll-free services without making changes in other cost components and op-erational indicators. This work uses a call center multiagent based simulation model to test call routing policies. The multiagent based model deals with customer and service rep behaviors and provides the opportunity to comprehend call center operation problems and challenges. Exp...
Analyzing Skill-Based Routing Call Centers Using Discrete-Event Simulation and Design Experiment
Proceedings of the 2004 Winter Simulation Conference, 2004., 2004
Call center customer service representatives (CSRs) or agents tend to have different skills. Some CSRs can handle one type of call, while other CSRs can handle other types of calls. Advances in automatic call distributors (ACDs) have made it possible to have skill-based routing (SBR) which is the protocol for online routing of incoming calls to the appropriate CSRs. At present, very little is known about SBR. We develop a discrete-event simulation model to analyze the performance of a M n /M n /C/K SBR environment in which incoming calls are handled in priority order and in a non-preemptive manner. We use the design of experiment framework to conduct our analysis. We show empirically that the scenario in which agents have 2 skills is almost as efficient as the scenario where agents have all skills (resource pooling). Also, we discover that no interaction exists between call rate factors when resource pooling exists.
Simulation-based optimization of agent scheduling in multiskill call centers
2008
We examine and compare simulation-based algorithms for solving the agent scheduling problem in a multiskill call center. This problem consists in minimizing the total costs of agents under constraints on the expected service level per call type, per period, and aggregated. We propose a solution approach that combines simulation with integer or linear programming, with cut generation. In our numerical experiments with realistic problem instances, this approach performs better than all other methods proposed previously for this problem. We also show that the two-step approach, which is the standard method for solving this problem, sometimes yield solutions that are highly suboptimal and inferior to those obtained by our proposed method.
Routing to Manage Resolution and Waiting Time in Call Centers with Heterogeneous Servers
Manufacturing & Service Operations Management, 2012
I n many call centers, agents are trained to handle all arriving calls but exhibit very different performance for the same call type, where we define performance by both the average call handling time and the call resolution probability. In this paper, we explore strategies for determining which calls should be handled by which agents, where these assignments are dynamically determined based on the specific attributes of the agents and/or the current state of the system. We test several routing strategies using data obtained from a medium-sized financial service firm's customer service call centers and present empirical performance results. These results allow us to characterize overall performance in terms of customer waiting time and overall resolution rate, identifying an efficient frontier of routing rules for this contact center.
Modelling and simulation of a telephone call center
Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), 2003
We consider a system with two types of traffic and two types of agents. Outbound calls are served only by blend agents, whereas inbound calls can be served by either inboundonly or blend agents. Our objective is to allocate a number of agents such that some service requirement is satisfied. We have taken two approaches in analyzing this staffing problem: We developed a simulation model of the call center, which allows us to do a what-if analysis, as well as continuous-time Markov chain (CTMC) queueing models, which provide approximations of system performance measures. We describe the simulation model in this paper.