A 2-year follow-up study of work ability among college educators

The aim of this study was to evaluate work ability among college educators before and after an intervention at the workplace. An administrative restructuring in the workplace started to be implemented in 2005. The work ability index (WAI) was administered to 154 educators before the restructure in 2004 and to 60 educators following the restructure in 2006. A mest comparing the WAI score of the 60 educators who took part in both phases showed a trend of improving work ability (p = 0.06; mean WAI in 2004 was 41.7 and 43.3 in 2006). The results suggest that the intervention led to an improvement in psychosocial factors, which in turn positively influenced work ability. (c) 2008 Elsevier Ltd. All rights reserved.

Job rotation in assembly lines employing disabled workers

In this paper we consider the programming of job rotation in the assembly line worker assignment and balancing problem. The motivation for this study comes from the designing of assembly lines in sheltered work centers for the disabled, where workers have different task execution times. In this context, the well-known training aspects associated with job rotation are particularly desired. We propose a metric along with a mixed integer linear model and a heuristic decomposition method to solve this new job rotation problem. Computational results show the efficacy of the proposed heuristics. (C) 2009 Elsevier B.V. All rights reserved.

Grid job scheduling using Route with Genetic Algorithm support

In 2006 the Route load balancing algorithm was proposed and compared to other techniques aiming at optimizing the process allocation in grid environments. This algorithm schedules tasks of parallel applications considering computer neighborhoods (where the distance is defined by the network latency). Route presents good results for large environments, although there are cases where neighbors do not have an enough computational capacity nor communication system capable of serving the application. In those situations the Route migrates tasks until they stabilize in a grid area with enough resources. This migration may take long time what reduces the overall performance. In order to improve such stabilization time, this paper proposes RouteGA (Route with Genetic Algorithm support) which considers historical information on parallel application behavior and also the computer capacities and load to optimize the scheduling. This information is extracted by using monitors and summarized in a knowledge base used to quantify the occupation of tasks. Afterwards, such information is used to parameterize a genetic algorithm responsible for optimizing the task allocation. Results confirm that RouteGA outperforms the load balancing carried out by the original Route, which had previously outperformed others scheduling algorithms from literature.