Resumen de Consideración de aspectos aleatorios en la planificación del tiempo de trabajo con jornada anualizada
Annualized hours is a way to irregularly distribute, through a yearly period, a certain number of working hours (established by a contract). Due to the flexibility gained by applying annualized hours, the company has the opportunity of schedule the work time in a more efficient way. With this new possibilities in work time management arise new problems of work time planning, schedule programming and task assignment. The work time planning problem, with annualized hours, consist in setting, for the whole year, the weekly working hours that each worker must perform.
This thesis complements the research in work time planning with annualized hours developed by PhDs: Amaia Lusa, Rafael Pastor and Albert Corominas. On this author's research some of the annualization problem are treated. Specifically, the work time planning problem is tackled establishing effective procedures to solve several types of problems in this field. This thesis takes into consideration uncertainty sources, or stochastic aspects, that were not analyzed previously and that, if are not considered, can invalidate results of work time planning. This aspects are: ¿ Irregularities, in time service as in instantaneous demand needs: A simulation-queuing theory based method is developed to calculate needed capacity in each time period, thus the instantaneous demand is satisfied with a service level very close to the desired one.
¿ Absenteeism, as a productive capacity lacking factor: Absenteeism causes are analyzed with the purpose of establish a representative model of this factor. This model can be based on individual or team probabilities of absence, and increases the needed capacity to work planning. This way, the possible drawbacks in planned capacity derived from absenteeism are drastically reduced.
¿ Future demand uncertainty: Demand is usually considered as a stochastic factor, because it depends on several parameters that are difficult to predict. In order to reduce the risk of planning
