Smoothed functionals and constrained stochastic approximation
SIAM Journal on Numerical Analysis
Convergent activation dynamics in continuous time networks
Neural Networks
Journal of Optimization Theory and Applications
SIAM Journal on Control and Optimization
A one-measurement form of simultaneous perturbation stochastic approximation
Automatica (Journal of IFAC)
ACM Transactions on Modeling and Computer Simulation (TOMACS)
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Adaptive Newton-based multivariate smoothed functional algorithms for simulation optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Service system fundamentals: work system, value chain, and life cycle
IBM Systems Journal
A Formal Model of Service Delivery
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 2
Proceedings of the 40th Conference on Winter Simulation
A simulation based scheduling model for call centers with uncertain arrival rates
Proceedings of the 40th Conference on Winter Simulation
Staffing Multiskill Call Centers via Linear Programming and Simulation
Management Science
Simple Methods for Shift Scheduling in Multiskill Call Centers
Manufacturing & Service Operations Management
Stochastic approximation algorithms for constrained optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Automated Optimal Dispatching of Service Requests
SRII '11 Proceedings of the 2011 Annual SRII Global Conference
Stochastic optimization for adaptive labor staffing in service systems
ICSOC'11 Proceedings of the 9th international conference on Service-Oriented Computing
Comparative study of stochastic algorithms for system optimization based on gradient approximations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simulation-based evaluation of dispatching policies in service systems
Proceedings of the Winter Simulation Conference
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Service systems are people-centric. The service providers employ a large workforce to service many clients, aiming to meet the service-level agreements SLAs and deliver a satisfactory client experience. A challenge is that the volumes of service requests change dynamically and the types of such requests are unique to each client. The task of adapting the staffing levels to the workloads in such systems while complying with aggregate SLA constraints is nontrivial. We formulate this problem as a constrained parametrized Markov process with a discrete parameter and propose two multi-timescale smoothed functional SF-based stochastic optimization SASOC staff allocation using stochastic optimization with constraints algorithms---SASOC-SF-N and SASOC-SF-C---for its solution. Whereas SASOC-SF-N uses a Gaussian-based smoothed functional approach, SASOC-SF-C uses the Cauchy smoothed functional algorithm for primal descent. Further, all SASOC algorithms incorporate a generalized projection operator that extends the system to a continuous setting with suitably defined transition probabilities. We validate these optimization schemes on five real-life service systems and compare their performance with a previous SASOC algorithm and the commercial optimization software OptQuest. Our algorithms are observed to be 25 times faster than OptQuest and have proven convergence guarantees to the optimal staffing levels, whereas OptQuest fails to find feasible solutions in some cases even under a reasonably high threshold on the number of search iterations. From the optimization experiments, we observe that our algorithms find better solutions than OptQuest in many cases, and among our algorithms, SASOC-SF-C performs marginally better than SASOC-SF-N.