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We study an unrelated parallel machines scheduling problem with sequence and machine dependent setup times. A logic-based Benders decomposition approach is proposed to minimize the makespan. This approach is a hybrid model that makes use of a mixed integer programming master problem and a specialized solver for travelling salesman subproblems. The master(More)
PURPOSE To identify genes with upregulated expression at the optic nerve head (ONH) that coincides with retinal ganglion cell (RGC) axon loss in glaucomatous DBA/2J mice. To further demonstrate that the proteins encoded by these genes bind to RGC axons and influence fundamental axon physiology. METHODS In situ hybridization and cell-type-specific(More)
PURPOSE To use a laser-induced ocular hypertension (LIOH) mouse model to examine the optic nerve head (ONH) expression of EphB/ephrin-B, previously shown to be upregulated in glaucomatous DBA/2J mice. To relate ephrin-B reverse signaling with states of axonal response to disease. METHODS LIOH was induced unilaterally in CD-1 mice by laser photocoagulation(More)
We study the unrelated parallel machine scheduling problem with sequence and machine dependent setup times and the objective of makespan minimization. Two exact decomposition-based methods are proposed based on logic-based Benders decomposition and branch-and-check. These approaches are hybrid models that make use of a mixed integer programming master(More)
Dynamic scheduling problems consist of both challenging combinatorics, as found in classical scheduling problems, and stochastics due to uncertainty about the arrival times, resource requirements, and processing times of jobs. To address these two challenges, we investigate the integration of queueing theory and scheduling. The former reasons about long-run(More)
Stability analysis consists of identifying conditions under which the number of jobs in a system is guaranteed to remain bounded over time. To date, such long-run performance guarantees have not been available for periodic approaches to dynamic scheduling problems. However, stability has been extensively studied in queueing theory. In this paper, we(More)
Classically, scheduling research in artificial intelligence has concentrated on the combinatorial challenges arising in a large, static domain where the set of jobs, resource capacities, and other problem parameters are known with certainty and do not change. In contrast, queueing theory has focused primarily on the stochastic arrival and resource(More)
We investigate the use of optimization-based techniques to model and solve two real-world single robot task planning problems. In the first problem, a robot must plan a set of tasks, each with different temporal constraints. In the second problem, a socially interacting robot must plan a set of tasks while considering the schedules of multiple human users,(More)
This paper presents an algorithm for resource-aware scheduling of computational jobs in a large-scale heterogeneous data center. The algorithm aims to allocate different machine configurations to job classes to attain an efficient mapping between job resource request profiles and machine resource capacity profiles. We propose a three-stage algorithm. The(More)