Automated Optimal Dispatching of Service Requests

Verma, Anubha; Desai, Narayan; Bhamidipaty, Anuradha; Jain, Anshu; Nallacherry, Jayan; Roy, Sambuddha; Barnes, Stephen

doi:10.1109/srii.2011.54

Cited by 7 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In (Verma et al 2011), a two-step mixed-integer program is formulated for the problem of dispatching SRs within service systems. While their goal is similar to ours, their formulation does not model the stochastic variations in arrivals and service times.…”

Section: Service Systemsmentioning

confidence: 99%

“…Verma et al 7 considered the problem of dispatching jobs in a SS and formulate this as a two-step mixed-integer program. Wasserkrug et al 8 considered scheduling workers in various shifts of a third-level IT support system.…”

Section: Related Workmentioning

confidence: 99%

“…Many of the earlier works 7,8 do not consider non-stationary workload arrivals, service times that are not exponentially distributed and do not factor in aggregate SLAs 10 into the formulation. On the other hand, our setting captures all of these notions.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous perturbation methods for adaptive labor staffing in service systems

Prashanth

Prasad²,

Desai

et al. 2015

SIMULATION

View full text Add to dashboard Cite

Service systems are labor intensive due to the large variation in the tasks required to address service requests from multiple customers. Aligning the staffing levels to the forecasted workloads adaptively in such systems is nontrivial because of a large number of parameters and operational variations leading to a huge search space. A challenging problem here is to optimize the staffing while maintaining the system in steady-state and compliant to aggregate service level agreement (SLA) constraints. Further, because these parameters change on a weekly basis, the optimization should not take longer than a few hours. We formulate this problem as a constrained Markov cost process parameterized by the (discrete) staffing levels. We propose novel simultaneous perturbation stochastic approximation (SPSA) based SASOC (Staff Allocation using Stochastic Optimization with Constraints) algorithms for solving the above problem. The algorithms include both first order as well as second order methods and incorporate SPSA based gradient estimates in the primal, with dual ascent for the Lagrange multipliers. Both the algorithms that we propose are online, incremental and easy to implement. Further, they involve a certain generalized smooth projection operator, which is essential to project the continuous-valued worker parameter tuned by SASOC algorithms onto the discrete set. We validated our algorithms on five real-life service systems and compared them with a state-of-the-art optimization tool-kit OptQuest. Being 25 times faster than OptQuest, our algorithms are particularly suitable for adaptive labor staffing. Also, we observe that our algorithms guarantee convergence and find better solutions than OptQuest in many cases.

show abstract

Section: Service Systemsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous perturbation methods for adaptive labor staffing in service systems

Prashanth

Prasad²,

Desai

et al. 2015

SIMULATION

View full text Add to dashboard Cite

show abstract

“…Further, we also outline the differences of our model and technique from the other approaches. Verma et al [2011] proposed a novel dispatching policy by solving a certain integer programming problem. The integer program formulated there did not involve aggregate SLA constraints.…”

Section: Literature Surveymentioning

confidence: 99%

Adaptive Smoothed Functional Algorithms for Optimal Staffing Levels in Service Systems

et al. 2013

View full text Add to dashboard Cite

Service systems are people-centric. The service providers employ a large work force to service many clients, aiming to meet the 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 (Service-Level Agreement) constraints, is non-trivial. 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 algorithms: SASOC-SF-N and SASOC-SF-C, respectively, for its solution. While SASOC-SF-N uses Gaussian based smoothed-functional, SASOC-SF-C uses the Cauchy smoothed-functional algorithm for primal descent. Further, all SASOC algorithms incorporate a generalized projection operator (L.A. et al. [2012],[Bhatnagar et al., 2012, Chapter 9]) 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 recent algorithm, SASOC-SPSA, from Prashanth et al. [2011], and a 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 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.

show abstract

“…We now review relevant literature in service systems and stochastic optimization. In [5], a two step mixed-integer program is formulated for the problem of dispatching SRs within service systems. While their goal is similar, their formulation does not model the stochastic variations of arrivals or processing times.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Optimization for Adaptive Labor Staffing in Service Systems

Prashanth

Prasad

Desai

et al. 2011

Service-Oriented Computing

View full text Add to dashboard Cite

Service systems are labor intensive. Further, the workload tends to vary greatly with time. Adapting the staffing levels to the workloads in such systems is nontrivial due to a large number of parameters and operational variations, but crucial for business objectives such as minimal labor inventory. One of the central challenges is to optimize the staffing while maintaining system steadystate and compliance to aggregate SLA constraints. We formulate this problem as a parametrized constrained Markov process and propose a novel stochastic optimization algorithm for solving it. Our algorithm is a multi-timescale stochastic approximation scheme that incorporates a SPSA based algorithm for 'primal descent' and couples it with a 'dual ascent' scheme for the Lagrange multipliers. We validate this optimization scheme on five real-life service systems and compare it with a state-of-the-art optimization tool-kit OptQuest. Being two orders of magnitude faster than OptQuest, our scheme is particularly suitable for adaptive labor staffing. Also, we observe that it guarantees convergence and finds better solutions than OptQuest in many cases.

show abstract

Automated Optimal Dispatching of Service Requests

Cited by 7 publications

References 4 publications

Simultaneous perturbation methods for adaptive labor staffing in service systems

Simultaneous perturbation methods for adaptive labor staffing in service systems

Adaptive Smoothed Functional Algorithms for Optimal Staffing Levels in Service Systems

Stochastic Optimization for Adaptive Labor Staffing in Service Systems

Contact Info

Product

Resources

About