Abstract

Abstract
Introduction: Self-report of dietary intake could be biased by social desirability thus affecting risk estimates in epidemiological studies. The objective of this study was to assess the effect of social desirability on dietary intake estimated from a food frequency questionnaire (FFQ). Methods: A convenience sample of 483 Portuguese university students was recruited. Subjects were invited to complete a two-part self-administered questionnaire: the first part included the Marlowe-Crowne Social Desirability Scale (M-CSDS), a physical activity questionnaire and self-reported height and weight; the second part, included a semi-quantitative FFQ validated for Portuguese adults, that should be returned after fulfilment. All subjects completed the first part of the questionnaire and 40.4% returned the FFQ fairly completed. Results: In multiple regression analysis, after adjustment for energy and confounders, social desirability produced a significant positive effect in the estimates of dietary fibre, vitamin C, vitamin E, magnesium and potassium, in both genders. In multiple regression, after adjustment for energy and confounders, social desirability had a significant positive effect in the estimates of vegetable consumption, for both genders, and a negative effect in white bread and beer, for women. Conclusion: Social desirability affected nutritional and food intake estimated from a food frequency questionnaire.

Abstract
In this paper, we consider the single machine earliness/tardiness scheduling problem with no idle time. Two of the lower bounds previously developed for this problem are based on Lagrangean relaxation and the multiplier adjustment method, and require an initial sequence. We investigate the sensitivity of the lower bounds to the initial sequence, and experiment with different dispatch rules and some dominance conditions. The computational results show that it is possible to obtain improved lower bounds by using a better initial sequence. The lower bounds are also incorporated in a branch-and-bound algorithm, and the computational tests show that one of the new lower bounds has the best performance for larger instances.

Abstract
This paper presents several beam search algorithms for the single-machine earliness/tardiness scheduling problem with release dates and no unforced idle time. These algorithms include classical beam search procedures, with both priority and total cost evaluation functions, as well as the filtered and recovering variants. Both priority evaluation functions and problem-specific properties were considered for the filtering step used in the filtered and recovering procedures. The computational results show that the recovering beam search algorithms outperform their filtered counterparts, while the priority-based filtering procedure proves superior to the rules-based alternative. The beam search procedure with a total cost function provides very good results but is computationally expensive. The recovering algorithm is quite close in solution quality and is significantly faster, so it can be used to solve even large instances.

Abstract
In this paper, we consider the single machine earliness/tardiness scheduling problem with different release dates and no unforced idle time. The problem is decomposed into weighted earliness and weighted tardiness subproblems. Lower bounding procedures are proposed for each of these subproblems, and the lower bound for the original problem is the sum of the lower bounds for the two subproblems. The lower bounds and several version, of a branch-and-bound algorithm are then tested on a set of randomly generated problems, and instances with up to 30 jobs are solved to optimality. To the best of our knowledge, this is the first exact approach for the early/tardy scheduling problem with release dates and no unforced idle time.

Abstract
A dispatch rule and a greedy procedure are presented for the single machine earliness/tardiness scheduling problem with no idle time and compared with the best of the existing dispatch rules. Both dispatch rules use a lookahead parameter that had previously been set at a fixed value. We develop functions that map some instance statistics into appropriate values for that parameter. We also consider the use of dominance rules to improve the solutions obtained by the heuristics. The computational results show that the function-based versions of the heuristics outperform their fixed value counterparts and that the use of the dominance rules can indeed improve solution quality with little additional computational effort.