999 resultados para Problem drivers.


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In practice, parallel-machine job-shop scheduling (PMJSS) is very useful in the development of standard modelling approaches and generic solution techniques for many real-world scheduling problems. In this paper, based on the analysis of structural properties in an extended disjunctive graph model, a hybrid shifting bottleneck procedure (HSBP) algorithm combined with Tabu Search metaheuristic algorithm is developed to deal with the PMJSS problem. The original-version SBP algorithm for the job-shop scheduling (JSS) has been significantly improved to solve the PMJSS problem with four novelties: i) a topological-sequence algorithm is proposed to decompose the PMJSS problem into a set of single-machine scheduling (SMS) and/or parallel-machine scheduling (PMS) subproblems; ii) a modified Carlier algorithm based on the proposed lemmas and the proofs is developed to solve the SMS subproblem; iii) the Jackson rule is extended to solve the PMS subproblem; iv) a Tabu Search metaheuristic algorithm is embedded under the framework of SBP to optimise the JSS and PMJSS cases. The computational experiments show that the proposed HSBP is very efficient in solving the JSS and PMJSS problems.

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For the shop scheduling problems such as flow-shop, job-shop, open-shop, mixed-shop, and group-shop, most research focuses on optimizing the makespan under static conditions and does not take into consideration dynamic disturbances such as machine breakdown and new job arrivals. We regard the shop scheduling problem under static conditions as the static shop scheduling problem, while the shop scheduling problem with dynamic disturbances as the dynamic shop scheduling problem. In this paper, we analyze the characteristics of the dynamic shop scheduling problem when machine breakdown and new job arrivals occur, and present a framework to model the dynamic shop scheduling problem as a static group-shop-type scheduling problem. Using the proposed framework, we apply a metaheuristic proposed for solving the static shop scheduling problem to a number of dynamic shop scheduling benchmark problems. The results show that the metaheuristic methodology which has been successfully applied to the static shop scheduling problems can also be applied to solve the dynamic shop scheduling problem efficiently.

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Three types of shop scheduling problems, the flow shop, the job shop and the open shop scheduling problems, have been widely studied in the literature. However, very few articles address the group shop scheduling problem introduced in 1997, which is a general formulation that covers the three above mentioned shop scheduling problems and the mixed shop scheduling problem. In this paper, we apply tabu search to the group shop scheduling problem and evaluate the performance of the algorithm on a set of benchmark problems. The computational results show that our tabu search algorithm is typically more efficient and faster than the other methods proposed in the literature. Furthermore, the proposed tabu search method has found some new best solutions of the benchmark instances.

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In this paper, three metaheuristics are proposed for solving a class of job shop, open shop, and mixed shop scheduling problems. We evaluate the performance of the proposed algorithms by means of a set of Lawrence’s benchmark instances for the job shop problem, a set of randomly generated instances for the open shop problem, and a combined job shop and open shop test data for the mixed shop problem. The computational results show that the proposed algorithms perform extremely well on all these three types of shop scheduling problems. The results also reveal that the mixed shop problem is relatively easier to solve than the job shop problem due to the fact that the scheduling procedure becomes more flexible by the inclusion of more open shop jobs in the mixed shop.

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In this paper, we propose three meta-heuristic algorithms for the permutation flowshop (PFS) and the general flowshop (GFS) problems. Two different neighborhood structures are used for these two types of flowshop problem. For the PFS problem, an insertion neighborhood structure is used, while for the GFS problem, a critical-path neighborhood structure is adopted. To evaluate the performance of the proposed algorithms, two sets of problem instances are tested against the algorithms for both types of flowshop problems. The computational results show that the proposed meta-heuristic algorithms with insertion neighborhood for the PFS problem perform slightly better than the corresponding algorithms with critical-path neighborhood for the GFS problem. But in terms of computation time, the GFS algorithms are faster than the corresponding PFS algorithms.

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Background: Trauma resulting from traffic crashes poses a significant problem in highly motorised countries. Over a million people worldwide are killed annually and 50 million are critically injured as a result of traffic collisions. In Australia, road crashes cost an average of $17 billion annually in personal loss of income and quality of life, organisational losses in productivity and workplace quality, and health care costs. Driver aggression has been identified as a key factor contributing to crashes, and many motorists report experiencing mild forms of aggression (e.g., rude gestures, horn honking). However despite this concern, driver aggression has received relatively little attention in empirical research, and existing research has been hampered by a number of methodological and conceptual shortcomings. Specifically, there has been substantial disagreement regarding what constitutes aggressive driving and a failure to examine both the situational factors and the emotional and cognitive processes underlying driver aggression. To enhance current understanding of aggressive driving, a model of driver aggression that highlights the cognitive and emotional processes at play in aggressive driving incidents is proposed. Aims: The research aims to improve current understanding of the complex nature of driver aggression by testing and refining a model of aggressive driving that incorporates the person-related and situational factors and the cognitive and emotional appraisal processes fundamental to driver aggression. In doing so, the research will assist to provide a clear definition of what constitutes aggressive driving, assist to identify on-road incidents that trigger driver aggression, and identify the emotional and cognitive appraisal processes that underlie driver aggression. Methods: The research involves three studies. Firstly, to contextualise the model and explore the cognitive and emotional aspects of driver aggression, a diary-based study using self-reports of aggressive driving events will be conducted with a general population of drivers. This data will be supplemented by in-depth follow-up interviews with a sub-sample of participants. Secondly, to test generalisability of the model, a large sample of drivers will be asked to respond to video-based scenarios depicting driving contexts derived from incidents identified in Study 1 as inciting aggression. Finally, to further operationalise and test the model an advanced driving simulator will be used with sample of drivers. These drivers will be exposed to various driving scenarios that would be expected to trigger negative emotional responses. Results: Work on the project has commenced and progress on the first study will be reported.

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This is a methodologically exemplary trial of a population based (universal) approach to preventing depression in young people. The programme used teachers in a classroom setting to deliver cognitive behavioural problem solving skills to a cohort of students. We have little knowledge about “best practice” to prevent depression in adolescence. Classroom-based universal approaches appear to offer advantages in recruitment rates and lack of stigmatisation over approaches that target specific groups of at risk students. Earlier research on a universal school-based approach to preventing depression in adolescents showed promise, but employed mental health professionals to teach cognitive behavioural coping skills in small groups.1 Using such an approach routinely would be economically unsustainable. Spence’s trial, with teachers as facilitators, therefore represents a “real world” intervention that could be routinely disseminated.

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The greatly increased risk of being killed or injured in a car crash for the young novice driver has been recognised in the road safety and injury prevention literature for decades. Risky driving behaviour has consistently been found to contribute to traffic crashes. Researchers have devised a number of instruments to measure this risky driving behaviour. One tool developed specifically to measure the risky behaviour of young novice drivers is the Behaviour of Young Novice Drivers Scale (BYNDS) (Scott-Parker et al., 2010). The BYNDS consists of 44 items comprising five subscales for transient violations, fixed violations, misjudgement, risky driving exposure, and driving in response to their mood. The factor structure of the BYNDS has not been examined since its development in a matched sample of 476 novice drivers aged 17-25 years. Method: The current research attempted to refine the BYNDS and explore its relationship with the self-reported crash and offence involvement and driving intentions of 390 drivers aged 17-25 years (M = 18.23, SD = 1.58) in Queensland, Australia, during their first six months of independent driving with a Provisional (intermediate) driver’s licence. A confirmatory factor analysis was undertaken examining the fit of the originally proposed BYNDS measurement model. Results: The model was not a good fit to the data. A number of iterations removed items with low factor loadings, resulting in a 36-item revised BYNDS which was a good fit to the data. The revised BYNDS was highly internally consistent. Crashes were associated with fixed violations, risky driving exposure, and misjudgement; offences were moderately associated with risky driving exposure and transient violations; and road-rule compliance intentions were highly associated with transient violations. Conclusions: Applications of the BYNDS in other young novice driver populations will further explore the factor structure of both the original and revised BYNDS. The relationships between BYNDS subscales and self-reported risky behaviour and attitudes can also inform countermeasure development, such as targeting young novice driver non-compliance through enforcement and education initiatives.

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The graduated driver licensing (GDL) program in Queensland, Australia, was considerably enhanced in July 2007. This paper explores the compliance of young Learner and Provisional (intermediate) drivers with current GDL requirements and general road rules. Unsupervised driving, Learner logbook accuracy, and experiences of punishment avoidance were explored, along with speeding as a Provisional driver. Participants (609 females; M = 17.43 years) self-reported sociodemographic characteristics, driving behaviours and licensing experiences as Learners. A subset of participants (238 females, 105 males) completed another survey six months later exploring their Provisional behaviours and experiences. While the majority of the participants reported compliance with both the GDL requirements and general road rules such as stopping at red lights on their Learner licence; a considerable proportion reported speeding. Furthermore, they reported becoming less compliant during the Provisional phase, particularly with speed limits. Self-reported speeding was predicted by younger age at licensure, being in a relationship, driving unsupervised, submitting inaccurate Learner logbooks, and speeding as a Learner. Enforcement and education countermeasures should focus upon curtailing noncompliance, targeting speeding in particular. Novice drivers should be encouraged to comply with all road rules, including speed limits, and safe driving behaviours should be developed and reinforced during the Learner and early Provisional periods. Novice drivers have been found to model their parents’ driving, and parents are pivotal in regulating novice driving. It is vital young novice drivers and parents alike are encouraged to comply with all road rules, including GDL requirements.

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The literature supporting the notion that active, student-centered learning is superior to passive, teacher-centered instruction is encyclopedic (Bonwell & Eison, 1991; Bruning, Schraw, & Ronning, 1999; Haile, 1997a, 1997b, 1998; Johnson, Johnson, & Smith, 1999). Previous action research demonstrated that introducing a learning activity in class improved the learning outcomes of students (Mejias, 2010). People acquire knowledge and skills through practice and reflection, not by watching and listening to others telling them how to do something. In this context, this project aims to find more insights about the level of interactivity in the curriculum a class should have and its alignment with assessment so the intended learning outcomes (ILOs) are achieved. In this project, interactivity is implemented in the form of problem- based learning (PBL). I present the argument that a more continuous formative feedback when implemented with the correct amount of PBL stimulates student engagement bringing enormous benefits to student learning. Different levels of practical work (PBL) were implemented together with two different assessment approaches in two subjects. The outcomes were measured using qualitative and quantitative data to evaluate the levels of student engagement and satisfaction in the terms of ILOs.

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Purpose: Young novice drivers continue to be overrepresented in fatalities and injuries arising from crashes even with the introduction of countermeasures such as graduated driver licensing (GDL). Enhancing countermeasures requires a better understanding of the variables influencing risky driving. One of the most common risky behaviours performed by drivers of all ages is speeding, which is particularly risky for young novice drivers who, due to their driving inexperience, have difficulty in identifying and responding appropriately to road hazards. Psychosocial theory can improve our understanding of contributors to speeding, thereby informing countermeasure development and evaluation. This paper reports an application of Akers’ social learning theory (SLT), augmented by Gerrard and Gibbons’ prototype/willingness model (PWM), in addition to personal characteristics of age, gender, car ownership, and psychological traits/states of anxiety, depression, sensation seeking propensity and reward sensitivity, to examine the influences on self-reported speeding of young novice drivers with a Provisional (intermediate) licence in Queensland, Australia. Method: Young drivers (n = 378) recruited in 2010 for longitudinal research completed two surveys containing the Behaviour of Young Novice Drivers Scale, and reported their attitudes and behaviours as pre-Licence/Learner (Survey 1) and Provisional (Survey 2) drivers and their sociodemographic characteristics. Results: An Akers’ measurement model was created. Hierarchical multiple regressions revealed that (1) personal characteristics (PC) explained 20.3%; (2) the combination of PC and SLT explained 41.1%; and (3) the combination of PC, SLT and PWM explained 53.7% of variance in self-reported speeding. Whilst there appeared to be considerable shared variance, the significant predictors in the final model included gender, car ownership, reward sensitivity, depression, personal attitudes, and Learner speeding. Conclusions: These results highlight the capacity for psychosocial theory to improve our understanding of speeding by young novice drivers, revealing relationships between previous behaviour, attitudes, psychosocial characteristics and speeding. The findings suggest multi-faceted countermeasures should target the risky behaviour of Learners, and Learner supervisors should be encouraged to monitor their Learners’ driving speed. Novice drivers should be discouraged from developing risky attitudes towards speeding.

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Court costs, resource-intensive trials, booming prison populations and the obduracy of recidivism rates all present as ugly excesses of the criminal law adversarial paradigm. To combat these excesses, problem-solving courts have evolved with an edict to address the underlying issues that have caused an individual to commit a crime. When a judge seeks to help a problem-solving court participant deal with issues like addiction, mental health or poverty, they are performing a very different role to that of a judicial officer in the traditional court hierarchy. They are no longer the removed, independent arbiter — a problem-solving court judge steps into the ‘arena’ with the participant and makes active use of their judicial authority to assist in rehabilitation and positive behavioural change. Problem-solving court judges employing the principles of therapeutic jurisprudence appreciate that their interaction with participants can have therapeutic and anti-therapeutic consequences. This article will consider how the deployment of therapeutic measures (albeit with good intention) can lead to the behavioural manifestation of partiality and bias on the part of problem-solving court judges. Chapter III of the Commonwealth Constitution will then be analysed to highlight why the operation and functioning of problem solving courts may be deemed unconstitutional. Part IV of this article will explain how a problem-solving court judge who is not acting impartially or independently will potentially contravene the requirements of the Constitution. It will finally be suggested that judges who possess a high level of emotional intelligence will be the most successful in administering an independent and impartial problem solving court.

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Scientific efforts to understand and reduce the occurrence of road crashes continue to expand, particularly in the areas of vulnerable road user groups. Three groups that are receiving increasing attention within the literature are younger drivers, motorcyclists and older drivers. These three groups are at an elevated risk of being in a crash or seriously injured, and research continues to focus on the origins of this risk as well as the development of appropriate countermeasures to improve driving outcomes for these cohorts. However, it currently remains unclear what factors produce the largest contribution to crash risk or what countermeasures are likely to produce the greatest long term positive effects on road safety. This paper reviews research that has focused on the personal and environmental factors that increase crash risk for these groups as well as considers direction for future research in the respective areas. A major theme to emerge from this review is that while there is a plethora of individual and situational factors that influence the likelihood of crashes, these factors often combine in an additive manner to exacerbate the risk of both injury and fatality. Additionally, there are a number of risk factors that are pertinent for all three road user groups, particularly age and the level of driving experience. As a result, targeted interventions that address these factors are likely to maximise the flow-on benefits to a wider range of road users. Finally, there is a need for further research that aims to bridge the research-to-practice gap, in order to develop appropriate pathways to ensure that evidenced-based research is directly transferred to effective policies that improve safety outcomes.

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Using Gray and McNaughton’s (2000) revised Reinforcement Sensitivity Theory (r-RST), we examined the influence of personality on processing of words presented in gain-framed and loss-framed anti-speeding messages and how the processing biases associated with personality influenced message acceptance. The r-RST predicts that the nervous system regulates personality and that behaviour is dependent upon the activation of the Behavioural Activation System (BAS), activated by reward cues and the Fight-Flight-Freeze System (FFFS), activated by punishment cues. According to r-RST, individuals differ in the sensitivities of their BAS and FFFS (i.e., weak to strong), which in turn leads to stable patterns of behaviour in the presence of rewards and punishments, respectively. It was hypothesised that individual differences in personality (i.e., strength of the BAS and the FFFS) would influence the degree of both message processing (as measured by reaction time to previously viewed message words) and message acceptance (measured three ways by perceived message effectiveness, behavioural intentions, and attitudes). Specifically, it was anticipated that, individuals with a stronger BAS would process the words presented in the gain-frame messages faster than those with a weaker BAS and individuals with a stronger FFFS would process the words presented in the loss-frame messages faster than those with a weaker FFFS. Further, it was expected that greater processing (faster reaction times) would be associated with greater acceptance for that message. Driver licence holding students (N = 108) were recruited to view one of four anti-speeding messages (i.e., social gain-frame, social loss-frame, physical gain-frame, and physical loss-frame). A computerised lexical decision task assessed participants’ subsequent reaction times to message words, as an indicator of the extent of processing of the previously viewed message. Self-report measures assessed personality and the three message acceptance measures. As predicted, the degree of initial processing of the content of the social gain-framed message mediated the relationship between the reward sensitive trait and message effectiveness. Initial processing of the physical loss-framed message partially mediated the relationship between the punishment sensitive trait and both message effectiveness and behavioural intention ratings. These results show that reward sensitivity and punishment sensitivity traits influence cognitive processing of gain-framed and loss-framed message content, respectively, and subsequently, message effectiveness and behavioural intention ratings. Specifically, a range of road safety messages (i.e., gain-frame and loss-frame messages) could be designed which align with the processing biases associated with personality and which would target those individuals who are sensitive to rewards and those who are sensitive to punishments.