An Evolutionary Squeaky Wheel Optimization Approach to Personnel Scheduling
The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyze a technique called Evolutionary Squeaky Wheel Optimization and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimization improves the original Squeaky Wheel Optimization's effectiveness and execution speed by incorporating two additional steps (Selection and Mutation) for added evolution. In the Evolutionary Squeaky Wheel Optimization, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The aim of the Analysis step is to identify below average solution components by calculating a fitness value for all components. The Selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The Mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repair is carried out by using the Prioritization step to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, improvements in the Evolutionary Squeaky Wheel Optimization is achieved by selective solution disruption mixed with iterative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.
scheduling evolutionary algorithms optimization methods squeaky wheel heuristic