Single machine scheduling models with deterioration and learning: handling precedence constraints via priority generation

We consider various single machine scheduling problems in which the processing time of a job depends either on its position in a processing sequence or on its start time. We focus on problems of minimizing the makespan or the sum of (weighted) completion times of the jobs. In many situations we show that the objective function is priority-generating, and therefore the corresponding scheduling problem under series-parallel precedence constraints is polynomially solvable. In other situations we provide counter-examples that show that the objective function is not priority-generating.

Two-stage no-wait scheduling models with setup and removal times separated

This paper studies two models of two-stage processing with no-wait in process. The first model is the two-machine flow shop, and the other is the assembly model. For both models we consider the problem of minimizing the makespan, provided that the setup and removal times are separated from the processing times. Each of these scheduling problems is reduced to the Traveling Salesman Problem (TSP). We show that, in general, the assembly problem is NP-hard in the strong sense. On the other hand, the two-machine flow shop problem reduces to the Gilmore-Gomory TSP, and is solvable in polynomial time. The same holds for the assembly problem under some reasonable assumptions. Using these and existing results, we provide a complete complexity classification of the relevant two-stage no-wait scheduling models.

Local models for exploratory analysis of hydrological extremes

Trend analysis is widely used for detecting changes in hydrological data. Parametric methods for this employ pre-specified models and associated tests to assess significance, whereas non-parametric methods generally apply rank tests to the data. Neither approach is suitable for exploratory analysis, because parametric models impose a particular, perhaps unsuitable, form of trend, while testing may confirm that trend is present but does not describe its form. This paper describes semi-parametric approaches to trend analysis using local likelihood fitting of annual maximum and partial duration series and illustrates their application to the exploratory analysis of changes in extremes in sea level and river flow data. Bootstrap methods are used to quantify the variability of estimates.

The emotional costs of caring incurred by men and women in the British labour market

This study investigates whether men and women in caring occupations experience more negative job-related feelings at the end of the day compared to the rest of the working population. The data are from Wave Nine of the British Household Panel Survey (1999) where respondents were asked whether, at the end of the working day, they tended to keep worrying or have trouble unwinding, and the extent to which work left them feeling exhausted or “used up.” Their responses to these questions were used to develop ordinal dependent variables. Control variables in the models include: number of children, age, hours worked per week, managerial responsibilities and job satisfaction, all of which have been shown in previous research to be significantly related to “job burnout.” The results are that those in caring occupations are more likely to feel worried, tense, drained and exhausted at the end of the working day. Women in particular appear to pay a high emotional cost for working in caring occupations. Men do not emerge unscathed, but report significantly lower levels of worry and exhaustion at the end of the day than do women.