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We describe a custom Internet of Things (IoT) device used for tracking barcoded containers end to end in a high-throughput analysis and purification laboratory. Our IoT device fills an important gap that previously prevented us from fully tracking barcoded sample containers through manual steps in a multistep workflow, such as when samples are "parked" for temporary storage, or when using instrumentation not otherwise equipped with barcode scanners, a common occurrence found with specific centrifugal evaporation instruments. The custom device reads container barcodes and sends a small amount of data to our back-end data systems. Once data have been received and processed, users are alerted to any system responses via aural and visual feedback. Components of the IoT system include a low-cost headless IoT computer, a barcode reader, and a multicolor LED strip. We believe that the model for our device will facilitate simple and rapid deployment of IoT to the broader laboratory community. All source code and device configurations will be released into the public domain and made freely available.

A scheduler has been developed for an integrated laboratory robot system that operates in an always-on mode. The integrated system is designed for imaging plates containing protein crystallization experiments, and it allows crystallographers to enter plates at any time and request that they be imaged at multiple time points in the future. The scheduler must rearrange tasks within the time it takes to image one plate, trading off the quality of the schedule for the speed of the computation. For this reason, the scheduler was based on a simulated annealing algorithm with an objective function that makes use of a linear programming solver. To optimize the scheduler, extensive computational simulations were performed involving a difficult but representative scheduling problem. The simulations explore multiple configurations of the simulated annealing algorithm, including both geometric and adaptive annealing schedules, 3 neighborhood functions, and 20 neighborhood diameters. An optimal configuration was found that produced the best results in less than 60 seconds, well within the window necessary to dynamically reschedule imaging tasks as new plates are entered into the system., (© 2014 Society for Laboratory Automation and Screening.)

A systematic method for assembling and solving complex compound distribution problems is presented in detail. The method is based on a model problem that enumerates the mathematical equations and constraints describing a source container, liquid handler, and three types of destination containers involved in a set of compound distributions. One source container and one liquid handler are permitted in any given problem formulation, although any number of compound distributions may be specified. The relative importance of all distributions is expressed by assigning weights, which are factored into the final mathematical problem specification. A computer program was created that automatically assembles and solves a complete compound distribution problem given the parameters that describe the source container, liquid handler, and any number and type of compound distributions. Business rules are accommodated by adjusting weighting factors assigned to each distribution. An example problem, presented and explored in detail, demonstrates complex and nonintuitive solution behavior.