Work–Family Balance and Alternative Work Schedules

In 2008, the State of Utah implemented a 4-day workweek for their employees. This article examines the impact on employees using a postimplementation survey. For employees on the 4-day schedule, there were no significant differences by gender on work–family balance or on the impact of the schedule. However, women did demonstrate slightly more positive attitudes toward the 4-day schedule. Employees with children at home reported lower work–family balance and greater impact of the 4-day schedule. In contrast, no difference in attitudes toward the 4-day schedule was found by age, although work–family balance differed among age groups. There were differences in work–family balance between employees on the 4-day schedule and those on traditional schedules; however, the more substantial factor was whether an employee selected his or her schedule. The current study highlights the importance of engaging employees when making significant organizational changes, such as transitioning from traditional work schedules to alternative schedules.

Differences in Depressive Symptoms between Traditional Workers and Shiftworkers

Shiftwork causes a number of physical and psychological problems. As a result, we predicted depression to be worse for shiftworkers than for workers with more traditional schedules. In addition, we predicted that women would report more depressive symptoms than men as is the case in the general population. Questionnaire packets which included the Beck Depression Inventory were mailed to 153 workers in a small community. Analysis showed that, contrary to expectation, shiftworkers as a group suffered no more depressive symptoms than traditional workers. However, while both sexes had similar mean scores on depression among the shiftworkers, women were significantly more depressed than men among traditional workers.

Improving the Efficiency of Simulated Annealing Optimization Through Detection of Productive Search

The popularity of simulated annealing for engineering design applications has grown in recent years, increasing the need for new techniques that improve algorithm performance. Simulated annealing is a time-consuming, iteration-intensive algorithm. One area of algorithm enhancement with high potential impact is the development of methods for improving the algorithm by reducing the amount of wasted or non-productive search. This paper presents an approach to detection of productive search based on statistical process control (SPC) concepts. The proposed Detection of Productive Search (DPS) annealing schedule is compared to three other viable schedules using a 100-city traveling salesman problem. The DPS schedule produces results on par with the best from the more traditional schedules but does so with significantly fewer iterations.