Constraint-Informed Information Systems in Space Management Utilization

Declarative techniques such as Constraint Programming can be very effective in modeling and assisting management decisions. We present a method for managing university classrooms which extends the previous design of a Constraint-Informed Information System to generate the timetables while dealing with spatial resource optimization issues. We seek to maximize space utilization along two dimensions: classroom use and occupancy rates. While we want to maximize the room use rate, we still need to satisfy the soft constraints which model students’ and lecturers’ preferences. We present a constraint logic programming-based local search method which relies on an evaluation function that combines room utilization and timetable soft preferences. Based on this, we developed a tool which we applied to the improvement of classroom allocation in a University. Comparing the results to the current timetables obtained without optimizing space utilization, the initial versions of our tool manages to reach a 30% improvement in space utilization, while preserving the quality of the timetable, both for students and lecturers.

Proposal and Validation of a Scale of Composite Measure Reactivity Score to Characterize the Reactivity in Horses During Handling

This study aimed to identify the parameters related to the expression of the reactivity in horses during handling and based on that proposed and validated a scale of composite measure reactivity score to characterize horse's reactivity. To this end, the first stage (S1) proposed the scale and the second (S2) validated it. In S1, 364 Lusitano horses were evaluated, 188 were adult breeding mares (4–12 years old), and 176 were foals (males/females, aged from 2 months to 2 years). During hooves trimming, vermifuge application, palpation scores were assigned to behaviors of movement, ears and eyes position, breathing, vocalization, and urination. A response parameter called reactivity was attributed to each animal, ranging from score 1 (nonreactive/calm) to score 4 (very reactive/aggressive). The verification of the possible parameters (age, behavior), which explains the response parameter (reactivity), was taken using ordinal proportional odds model. Movement, breathing, ears and eyes position, vocalization, and age appear to explain the reactivity of horses during handling (P < .01). Therefore, based on these parameters, it was possible to propose two scales of composite measure reactivity score: one to characterize the mares and another the foals. On S2, the proposed scale was validated by the simultaneous application of Forced Human Approach Test, another commonly used test to evaluate the reactivity in horses, with a correlation of 0.97 (P < .05). The assessment of the reactivity of horses during handling by a composite measure reactivity score scale is valid, and easy to apply, without disrupting daily routine and override the impact of individual differences.

Load balancing for constraint solving with GPUs

Solving a complex Constraint Satisfaction Problem (CSP) is a computationally hard task which may require a considerable amount of time. Parallelism has been applied successfully to the job and there are already many applications capable of harnessing the parallel power of modern CPUs to speed up the solving process. Current Graphics Processing Units (GPUs), containing from a few hundred to a few thousand cores, possess a level of parallelism that surpasses that of CPUs and there are much less applications capable of solving CSPs on GPUs, leaving space for further improvement. This paper describes work in progress in the solving of CSPs on GPUs, CPUs and other devices, such as Intel Many Integrated Cores (MICs), in parallel. It presents the gains obtained when applying more devices to solve some problems and the main challenges that must be faced when using devices with as different architectures as CPUs and GPUs, with a greater focus on how to effectively achieve good load balancing between such heterogeneous devices.