Stochastic optimization for adaptive labor staffing in service systems

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 steady-state 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.

Simultaneous perturbation methods for adaptive labor staffing in service systems

We consider the problem of optimizing the workforce of a service system. Adapting the staffing levels in such systems is non-trivial due to large variations in workload and the large number of system parameters do not allow for a brute force search. Further, because these parameters change on a weekly basis, the optimization should not take longer than a few hours. Our aim is to find the optimum staffing levels from a discrete high-dimensional parameter set, that minimizes the long run average of the single-stage cost function, while adhering to the constraints relating to queue stability and service-level agreement (SLA) compliance. The single-stage cost function balances the conflicting objectives of utilizing workers better and attaining the target SLAs. We formulate this problem as a constrained parameterized Markov cost process parameterized by the (discrete) staffing levels. We propose novel simultaneous perturbation stochastic approximation (SPSA)-based algorithms for solving the above problem. The algorithms include both first-order as well as second-order methods and incorporate SPSA-based gradient/Hessian estimates for primal descent, while performing dual ascent for the Lagrange multipliers. Both algorithms are online and update the staffing levels in an incremental fashion. Further, they involve a certain generalized smooth projection operator, which is essential to project the continuous-valued worker parameter tuned by our algorithms onto the discrete set. The smoothness is necessary to ensure that the underlying transition dynamics of the constrained Markov cost process is itself smooth (as a function of the continuous-valued parameter): a critical requirement to prove the convergence of both algorithms. We validate our algorithms via performance simulations based on data from five real-life service systems. For the sake of comparison, we also implement a scatter search based algorithm using state-of-the-art optimization tool-kit OptQuest. From the experiments, we observe that both our algorithms converge empirically and consistently outperform OptQuest in most of the settings considered. This finding coupled with the computational advantage of our algorithms make them amenable for adaptive labor staffing in real-life service systems.

TURBULENT PUMPING OF MAGNETIC FLUX REDUCES SOLAR CYCLE MEMORY AND THUS IMPACTS PREDICTABILITY OF THE SUN'S ACTIVITY

Prediction of the Sun's magnetic activity is important because of its effect on space environment and climate. However, recent efforts to predict the amplitude of the solar cycle have resulted in diverging forecasts with no consensus. Yeates et al. have shown that the dynamical memory of the solar dynamo mechanism governs predictability, and this memory is different for advection- and diffusion-dominated solar convection zones. By utilizing stochastically forced, kinematic dynamo simulations, we demonstrate that the inclusion of downward turbulent pumping of magnetic flux reduces the memory of both advection- and diffusion-dominated solar dynamos to only one cycle; stronger pumping degrades this memory further. Thus, our results reconcile the diverging dynamo-model-based forecasts for the amplitude of solar cycle 24. We conclude that reliable predictions for the maximum of solar activity can be made only at the preceding minimum-allowing about five years of advance planning for space weather. For more accurate predictions, sequential data assimilation would be necessary in forecasting models to account for the Sun's short memory.

Detection of glycated hemoglobin using 3-Aminophenylboronic acid modified graphene oxide

This paper presents the chemical synthesis of 3-Aminophenylboronic acid (APBA) modified graphene oxide (GO) and its application to the electrochemical detection of glycated hemoglobin (GHb). The compound (GO-APBA) was synthesized by forming an amide linkage between the amino group (-NH2) of APBA and the carboxylic group (-COOH) of GO. The compound was characterized using IR spectroscopy. Detection of GHb was carried out using Electrochemical Impedance Spectroscopic (EIS) measurements with GO-APBA modified glassy carbon electrode as the working electrode.

Constrained 3D sketching with haptics and active motion annotation

The aim of this work is to enable seamless transformation of product concepts to CAD models. This necessitates availability of 3D product sketches. The present work concerns intuitive generation of 3D strokes and intrinsic support for space sharing and articulation for the components of the product being sketched. Direct creation of 3D strokes in air lacks in precision, stability and control. The inadequacy of proprioceptive feedback for the task is complimented in this work with stereo vision and haptics. Three novel methods based on pencil-paper interaction analogy for haptic rendering of strokes have been investigated. The pen-tilt based rendering is simpler and found to be more effective. For the spatial conformity, two modes of constraints for the stylus movements, corresponding to the motions on a control surface and in a control volume have been studied using novel reactive and field based haptic rendering schemes. The field based haptics, which in effect creates an attractive force field near a surface, though non-realistic, provided highly effective support for the control-surface constraints. The efficacy of the reactive haptic rendering scheme for the constrained environments has been demonstrated using scribble strokes. This can enable distributed collaborative 3D concept development. The notion of motion constraints, defined through sketch strokes enables intuitive generation of articulated 3D sketches and direct exploration of motion annotations found in most product concepts. The work, thus, establishes that modeling of the constraints is a central issue in 3D sketching.