Simulation-based optimization of agent scheduling in multiskill call centers (original) (raw)
Related papers
Optimizing daily agent scheduling in a multiskill call center
European Journal of Operational Research, 2010
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.
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.
Managing trade-offs in call center agent scheduling: methodology and case study
Summer Computer Simulation Conference, 2007
This paper develops a flexible and tractable scheduling methodology that produces near-optimal call center agent schedules while taking into account the costs associated with customer waiting time, customer abandonment, and call center agents. Our methodology combines integer programming (to find a desirable staffing plan for a given total number of agents) and simulation modeling (to evaluate the weekly costs of a given staffing plan). We describe the advantages of this approach over the traditional scheduling method, and test both methods by building schedules based on actual demand and shift data from an actual call center operated by Expedia.com under a variety of cost scenarios. The new scheduling approach not only out-performs the traditional staffing approach in all scenarios examined, it reduces total weekly costs of the call center's existing agent schedule by 8-25%, depending on the scenario.
Call Center Staffing with Simulation and Cutting Plane Methods
Annals of Operations Research, 2004
We present an iterative cutting plane method for minimizing staffing costs in a service system subject to satisfying acceptable service level requirements over multiple time periods. We assume that the service level cannot be easily computed, and instead is evaluated using simulation. The simulation uses the method of common random numbers, so that the same sequence of random phenomena is
Optimizing Call Center Staffing Using Simulation and Analytic Center Cutting-Plane Methods
Management Science - Management, 2008
We consider the problem of minimizing staffing costs in an inbound call center, while maintaining an acceptable level of service in multiple time periods. The problem is complicated by the fact that staffing level in one time period can affect the service levels in subsequent periods. Moreover, staff schedules typically take the form of shifts covering several periods. Interactions between staffing levels in different time periods, as well as the impact of shift requirements on the staffing levels and cost, should be considered in the planning. Traditional staffing methods based on stationary queueing formulas do not take this into account. We present a simulation-based analytic center cutting-plane method to solve a sample average approximation of the problem. We establish convergence of the method when the service-level functions are discrete pseudoconcave. An extensive numerical study of a moderately large call center shows that the method is robust and, in most of the test cases...
Management Science, 2008
Call center managers are facing increasing pressure to reduce costs while maintaining acceptable service quality. Consequently, they often face constrained stochastic optimization problems, minimizing cost subject to service-level constraints. Complicating this problem is the fact that customer-arrival rates to call centers are often time varying. Thus, to satisfy their service goals in a cost-effective manner, call centers may employ permanent operators who always provide service, and temporary operators who provide service only when the call center is busy, i.e., when the number of customers in system increases beyond a threshold level. This provides flexibility to dynamically adjust the number of operators providing service in response to the time-varying arrival rate. The constrained dynamic operator staffing (CDOS) problem involves determining the number of permanent and temporary operators, and the threshold value(s) that minimize time-average hiring and opportunity costs subj...
Optimizing the Staffing and Routing of Small-Size Hierarchical Call Centers
Production and Operations Management, 2008
M ultiple-skill call centers propagate rapidly with the development of telecommunications. An abundance of literature has already been published on call centers. Here, we want to focus on centers that would typically occur in business-to-business environments; these are call centers that handle many types of calls but where the arrival rate for each type is low. To find an optimal configuration, the integrality of the decision variables is a much more important issue than for larger call centers. The present paper proposes an approach that uses elements of combinatorial optimization to find optimal configurations. We develop an approximation method for the evaluation of the service performance. Next, we search for the minimum-cost configuration subject to service-level constraints using a branch-and-bound algorithm. What is at stake is to find the right balance between gains resulting from the economies of scale of pooling and the higher cost or cross-trained agents. The article shows that in most cases this method significantly decreases the staffing cost compared with configurations with only cross-trained or dedicated operators.
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.
Modeling and Simulation of Call Centers
Proceedings of the Winter Simulation Conference, 2005., 2005
In this review, we introduce key notions and describe the decision problems commonly encountered in call center management. Main themes are the central role of uncertainty throughout the decision hierarchy and the many operational complexities and relationships between decisions. We make connections to analytical models in the literature, emphasizing insights gained and model limitations. The high operational complexity and the prevalent uncertainty suggest that simulation modeling and simulation-based decision-making could have a central role in the management of call centers. We formulate some common decision problems and point to recently developed simulation-based solution techniques. We review recent work that supports modeling the primitive inputs to a call center and highlight call center modeling difficulties.
A Simple Staffing Method for Multiskill Call Centers
Manufacturing & Service Operations Management, 2008
W e study a simple method for staffing in multiskill call centers. The method has short computation times and determines nearly optimal staffing levels. It is in both views competitive to other methods from the literature. Because of the fast and accurate performance of the method, many different scenarios can be analyzed, and our method can be used for both tactical and strategic capacity management decisions.